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Sample records for models predicting trophic

  1. Modelling emergent trophic strategies in plankton

    DEFF Research Database (Denmark)

    Andersen, Ken Haste; Aksnes, Dag L.; Berge, Terje;

    2015-01-01

    Plankton are typically divided into phytoplankton and zooplankton in marine ecosystem models. Yet, most protists in the photic zone engage in some degree of phagotrophy, and it has been suggested that trophic strategy is really a continuum between pure phototrophs (phytoplankton) and pure...... phagotrophs (unicellular zooplankton). Such a continuum of trophic strategies is well represented by trait-based modelling techniques. A key model ingredient is the size of individual cells, as size constrains affinities for nutrient uptake, photosynthesis and active encounter with other cells. We outline...... a general trait-based model of a unicellular planktonic organism where size is a central trait and where nutrient uptake, photosynthesis and phagotrophy are determined by investments into these functions and by the physical constraints imposed by organism size. This framework provides simple predictions...

  2. Damped trophic cascades driven by fishing in model marine ecosystems

    DEFF Research Database (Denmark)

    Andersen, Ken Haste; Pedersen, Martin

    2010-01-01

    that fishing does not change the overall slope of the size spectrum, but depletes the largest individuals and induces trophic cascades. A trophic cascade can propagate both up and down in trophic levels driven by a combination of changes in predation mortality and food limitation. The cascade is damped...... cascade triggered by the removal of top predators. Here we use a novel size- and trait-based model to explore how marine ecosystems might react to perturbations from different types of fishing pressure. The model explicitly resolves the whole life history of fish, from larvae to adults. The results show...

  3. Effects of lower trophic level biomass and water temperature on fish communities: A modelling study

    Science.gov (United States)

    Guiet, Jérôme; Aumont, Olivier; Poggiale, Jean-Christophe; Maury, Olivier

    2016-08-01

    Physical and biogeochemical changes of the oceans have complex influences on fish communities. Variations of resource and temperature affect metabolic rates at the individual level, biomass fluxes at the species level, and trophic structure as well as diversity at the community level. We use a Dynamic Energy Budget-, trait-based model of the consumers' community size-spectrum to assess the effects of lower trophic level biomass and water temperature on communities at steady state. First, we look at the stressors separately in idealized simulations, varying one while the second remains constant. A multi-domain response is observed. Linked to the number of trophic levels sustained in the consumers' community, the regimes highlighted present similar properties when lower trophic level biomass is increased or temperature decreased. These trophic-length domains correspond to different efficiencies of the transfer of biomass from small to large individuals. They are characterized by different sensitivities of fish communities to environmental changes. Moreover, differences in the scaling of individuals' metabolism and prey assimilation with temperature lead to a shrinking of fish communities with warming. In a second step, we look at the impact of simultaneous variations of stressors along a mean latitudinal gradient of lower trophic level biomass and temperature. The model explains known observed features of global marine ecosystems such as the fact that larger species compose fish communities when latitude increases. The structure, diversity and metabolic properties of fish communities obtained with the model at different latitudes are interpreted in light of the different trophic-length domains characterized in the idealized experiments. From the equator to the poles, the structure of consumers' communities is predicted to be heterogeneous, with variable sensitivities to environmental changes.

  4. A General Approach to the Modelling of Trophic Chains

    CERN Document Server

    Dilão, R; Dilao, Rui; Domingos, Tiago

    1999-01-01

    Based on the law of mass action (and its microscopic foundation) and mass conservation, we present here a method to derive consistent dynamic models for the time evolution of systems with an arbitrary number of species. Equations are derived through a mechanistic description, ensuring that all parameters have ecological meaning. After discussing the biological mechanisms associated to the logistic and Lotka-Volterra equations, we show how to derive general models for trophic chains, including the effects of internal states at fast time scales. We show that conformity with the mass action law leads to different functional forms for the Lotka-Volterra and trophic chain models. We use mass conservation to recover the concept of carrying capacity for an arbitrary food chain.

  5. Use of Landsat data to predict the trophic state of Minnesota lakes

    Science.gov (United States)

    Lillesand, T. M.; Johnson, W. L.; Deuell, R. L.; Lindstrom, O. M.; Meisner, D. E.

    1983-01-01

    Near-concurrent Landsat Multispectral Scanner (MSS) and ground data were obtained for 60 lakes distributed in two Landsat scene areas. The ground data included measurement of secchi disk depth, chlorophyll-a, total phosphorous, turbidity, color, and total nitrogen, as well as Carlson Trophic State Index (TSI) values derived from the first three parameters. The Landsat data best correlated with the TSI values. Prediction models were developed to classify some 100 'test' lakes appearing in the two analysis scenes on the basis of TSI estimates. Clouds, wind, poor image data, small lake size, and shallow lake depth caused some problems in lake TSI prediction. Overall, however, the Landsat-predicted TSI estimates were judged to be very reliable for the secchi-derived TSI estimation, moderately reliable for prediction of the chlorophyll-a TSI, and unreliable for the phosphorous value. Numerous Landsat data extraction procedures were compared, and the success of the Landsat TSI prediction models was a strong function of the procedure employed.

  6. Modeling lake trophic state: a random forest approach

    Science.gov (United States)

    Productivity of lentic ecosystems has been well studied and it is widely accepted that as nutrient inputs increase, productivity increases and lakes transition from low trophic state (e.g. oligotrophic) to higher trophic states (e.g. eutrophic). These broad trophic state classi...

  7. Trophic network models explain instability of Early Triassic terrestrial communities.

    Science.gov (United States)

    Roopnarine, Peter D; Angielczyk, Kenneth D; Wang, Steve C; Hertog, Rachel

    2007-09-07

    Studies of the end-Permian mass extinction have emphasized potential abiotic causes and their direct biotic effects. Less attention has been devoted to secondary extinctions resulting from ecological crises and the effect of community structure on such extinctions. Here we use a trophic network model that combines topological and dynamic approaches to simulate disruptions of primary productivity in palaeocommunities. We apply the model to Permian and Triassic communities of the Karoo Basin, South Africa, and show that while Permian communities bear no evidence of being especially susceptible to extinction, Early Triassic communities appear to have been inherently less stable. Much of the instability results from the faster post-extinction diversification of amphibian guilds relative to amniotes. The resulting communities differed fundamentally in structure from their Permian predecessors. Additionally, our results imply that changing community structures over time may explain long-term trends like declining rates of Phanerozoic background extinction.

  8. Assessing Lake Trophic Status: A Proportional Odds Logistic Regression Model

    Science.gov (United States)

    Lake trophic state classifications are good predictors of ecosystem condition and are indicative of both ecosystem services (e.g., recreation and aesthetics), and disservices (e.g., harmful algal blooms). Methods for classifying trophic state are based off the foundational work o...

  9. Sensitivity of secondary production and export flux to choice of trophic transfer formulation in marine ecosystem models

    Science.gov (United States)

    Anderson, Thomas R.; Hessen, Dag O.; Mitra, Aditee; Mayor, Daniel J.; Yool, Andrew

    2013-09-01

    The performance of four contemporary formulations describing trophic transfer, which have strongly contrasting assumptions as regards the way that consumer growth is calculated as a function of food C:N ratio and in the fate of non-limiting substrates, was compared in two settings: a simple steady-state ecosystem model and a 3D biogeochemical general circulation model. Considerable variation was seen in predictions for primary production, transfer to higher trophic levels and export to the ocean interior. The physiological basis of the various assumptions underpinning the chosen formulations is open to question. Assumptions include Liebig-style limitation of growth, strict homeostasis in zooplankton biomass, and whether excess C and N are released by voiding in faecal pellets or via respiration/excretion post-absorption by the gut. Deciding upon the most appropriate means of formulating trophic transfer is not straightforward because, despite advances in ecological stoichiometry, the physiological mechanisms underlying these phenomena remain incompletely understood. Nevertheless, worrying inconsistencies are evident in the way in which fundamental transfer processes are justified and parameterised in the current generation of marine ecosystem models, manifested in the resulting simulations of ocean biogeochemistry. Our work highlights the need for modellers to revisit and appraise the equations and parameter values used to describe trophic transfer in marine ecosystem models.

  10. Trophic transfer of polychlorinated biphenyls (PCB) in a boreal lake ecosystem: testing of bioaccumulation models.

    Science.gov (United States)

    Figueiredo, Kaisa; Mäenpää, Kimmo; Leppänen, Matti T; Kiljunen, Mikko; Lyytikäinen, Merja; Kukkonen, Jussi V K; Koponen, Hannu; Biasi, Christina; Martikainen, Pertti J

    2014-01-01

    Understanding the fate of persistent organic chemicals in the environment is fundamental information for the successful protection of ecosystems and humans. A common dilemma in risk assessment is that monitoring data reveals contaminant concentrations in wildlife, while the source concentrations, route of uptake and acceptable source concentrations remain unsolved. To overcome this problem, different models have been developed in order to obtain more precise risk estimates for the food webs. However, there is still an urgent need for studies combining modelled and measured data in order to verify the functionality of the models. Studies utilising field-collected data covering entire food webs are particularly scarce. This study aims to contribute to tackling this problem by determining the validity of two bioaccumulation models, BIOv1.22 and AQUAWEBv1.2, for application to a multispecies aquatic food web. A small boreal lake, Lake Kernaalanjärvi, in Finland was investigated for its food web structure and concentrations of PCBs in all trophic levels. Trophic magnification factors (TMFs) were used to measure the bioaccumulation potential of PCBs, and the site-specific environmental parameters were used to compare predicted and observed concentrations. Site-specific concentrations in sediment pore water did not affect the modelling endpoints, but accurate site-specific measurements of freely dissolved concentrations in water turned out to be crucial for obtaining realistic model-predicted concentrations in biota. Numerous parameters and snapshot values affected the model performances, bringing uncertainty into the process and results, but overall, the models worked well for a small boreal lake ecosystem. We suggest that these models can be optimised for different ecosystems and can be useful tools for estimating the bioaccumulation and environmental fate of PCBs.

  11. Coupling of an Individual-Based Model of Anchovy with Lower Trophic Level and Hydrodynamic Models

    Institute of Scientific and Technical Information of China (English)

    WANG Yuheng; WEI Hao; Michio J. Kishi

    2013-01-01

    Anchovy (Engraulisjaponicus),a small pelagic fish and food of other economic fishes,is a key species in the Yellow Sea ecosystem.Understanding the mechanisms of its recruitment and biomass variation is important for the prediction and management of fishery resources.Coupled with a hydrodynamic model (POM) and a lower trophic level ecosystem model (NEMURO),an individual-based model of anchovy is developed to study the influence of physical environment on anchovy's biomass variation.Seasonal variations of circulation,water temperature and mix-layer depth from POM are used as external forcing for NEMURO and the anchovy model.Biomasses of large zooplankton and predatory zooplankton which anchovy feeds on are output from NEMURO and are controlled by the consumption of anchovy on them.Survival fitness theory related to temperature and food is used to determine the swimming action of anchovy in the model.The simulation results agree well with observations and elucidate the influence of temperature in over-wintering migration and food in feeding migration.

  12. A trophic model of fringing coral reefs in Nanwan Bay, southern Taiwan suggests overfishing.

    Science.gov (United States)

    Liu, Pi-Jen; Shao, Kwang-Tsao; Jan, Rong-Quen; Fan, Tung-Yung; Wong, Saou-Lien; Hwang, Jiang-Shiou; Chen, Jen-Ping; Chen, Chung-Chi; Lin, Hsing-Juh

    2009-09-01

    Several coral reefs of Nanwan Bay, Taiwan have recently undergone shifts to macroalgal or sea anemone dominance. Thus, a mass-balance trophic model was constructed to analyze the structure and functioning of the food web. The fringing reef model was comprised of 18 compartments, with the highest trophic level of 3.45 for piscivorous fish. Comparative analyses with other reef models demonstrated that Nanwan Bay was similar to reefs with high fishery catches. While coral biomass was not lower, fish biomass was lower than those of reefs with high catches. Consequently, the sums of consumption and respiratory flows and total system throughput were also decreased. The Nanwan Bay model potentially suggests an overfished status in which the mean trophic level of the catch, matter cycling, and trophic transfer efficiency are extremely reduced.

  13. Trophic modeling of Eastern Boundary Current Systems: a review and prospectus for solving the “Peruvian Puzzle”

    Directory of Open Access Journals (Sweden)

    Marc H. Taylor

    2013-04-01

    Full Text Available Eastern Boundary Current systems (EBCSs are among the most productive fishing areas in the world. High primary and secondary productivity supports a large biomass of small planktivorous pelagic fish, “small pelagics”, which are important drivers of production to the entire system whereby they can influence both higher and lower trophic levels. Environmental variability causes changes in plankton (food quality and quantity, which can affect population sizes, distribution and domi-nance among small pelagics. This variability combined with impacts from the fishery complicate the development of management strategies. Consequently, much recent work has been in the development of multispecies trophic models to better understand interdependencies and system dynamics. Despite similarities in extent, structure and primary productivity between EBCSs, the Peruvian system greatly differs from the others in the magnitude of fish catches, due mainly to the incredible production of the anchovy Engraulis ringens. This paper reviews literature concerning EBCSs dynamics and the state-of-the-art in the trophic modeling of EBCSs. The objective is to critically analyze the potential of this approach for system understanding and management and to adapt existing steady-state models of the Peruvian system for use in (future dynamic simulations. A guideline for the construction of trophodynamic models is presented taking into account the important trophic and environmental interactions. In consideration of the importance of small pelagics for the system dynamics, emphasis is placed on developing appropriate model compartmentalization and spatial delineation that facilitates dynamic simulations. Methods of model validation to historical changes are presented to support hypotheses concerning EBCS dynamics and as a critical step to the development of predictive models. Finally, the identification of direct model links to easily obtainable abiotic parameters is

  14. Bridging the gap from ocean models to population dynamics of large marine predators: A model of mid-trophic functional groups

    Science.gov (United States)

    Lehodey, Patrick; Murtugudde, Raghu; Senina, Inna

    2010-01-01

    The modeling of mid-trophic organisms of the pelagic ecosystem is a critical step in linking the coupled physical-biogeochemical models to population dynamics of large pelagic predators. Here, we provide an example of a modeling approach with definitions of several pelagic mid-trophic functional groups. This application includes six different groups characterized by their vertical behavior, i.e., occurrence of diel migration between epipelagic, mesopelagic and bathypelagic layers. Parameterization of the dynamics of these components is based on a temperature-linked time development relationship. Estimated parameters of this relationship are close to those predicted by a model based on a theoretical description of the allocation of metabolic energy at the cellular level, and that predicts a species metabolic rate in terms of its body mass and temperature. Then, a simple energy transfer from primary production is used, justified by the existence of constant slopes in log-log biomass size spectrum relationships. Recruitment, ageing, mortality and passive transport with horizontal currents, taking into account vertical behavior of organisms, are modeled by a system of advection-diffusion-reaction equations. Temperature and currents averaged in each vertical layer are provided independently by an Ocean General Circulation Model and used to drive the mid-trophic level (MTL) model. Simulation outputs are presented for the tropical Pacific Ocean to illustrate how different temperature and oceanic circulation conditions result in spatial and temporal lags between regions of high primary production and regions of aggregation of mid-trophic biomass. Predicted biomasses are compared against available data. Data requirements to evaluate outputs of these types of models are discussed, as well as the prospects that they offer both for ecosystem models of lower and upper trophic levels.

  15. Quantitative model of trophic interactions in Beibu Gulf ecosystem in the northern South China Sea

    Institute of Scientific and Technical Information of China (English)

    CHEN Zuozhi; QIU Yongsong; JIA Xiaoping

    2006-01-01

    A mass-balanced model was constructed to determine the flow-energy in a community of fishes and invertebrates in the Beibu Gulf,northern South China Sea using Ecopath and Ecosim software. Input parameters were taken from the literature, except for the biomass of fish groups which was obtained from trawl surveys during October 1997 to May 1999 in the study area. The model consisted of 16 functional groups (boxes), including one marine mammal and seabirds, each representing organisms with a similar role in the food web, and only covered the main trophic flow in the Beibu Gulf ecosystem. The results showed that the food web of Beibu Gulf was dominated by the detrital path and benthic invertebrates played a significant role in transferring energy from the detritus to higher trophic levels; phytoplankton was a primary producer and most utilized as a food source. Fractional trophic levels ranged from 1.0 to 4.08 with marine mammals occupying the highest trophic level. Using network analysis, the system network was mapped into a linear food chain and six discrete trophic levels were found with a mean transfer efficiency of 16.7% from the detritus, 16.2% from the primary producer within the ecosystem. The biomass density of the commercially utilized species estimated by the model is 8.46 t/km2, only 0.48% of the net primary production.

  16. Modelling for an improved integrated multi-trophic aquaculture system for the production of highly valued marine species

    Directory of Open Access Journals (Sweden)

    Luana Granada

    2014-05-01

    Full Text Available Integrated multi-trophic aquaculture (IMTA is regarded as a suitable approach to limit aquaculture nutrients and organic matter outputs through biomitigation. Here, species from different trophic or nutritional levels are connected through water transfer. The co-cultured species are used as biofilters, and each level has its own independent commercial value, providing both economic and environmental sustainability. In order to better understand and optimize aquaculture production systems, dynamic modelling has been developed towards the use of models for analysis and simulation of aquacultures. Several models available determine the carrying capacity of farms and the environmental effects of bivalve and fish aquaculture. Also, in the last two decades, modelling strategies have been designed in order to predict the dispersion and deposition of organic fish farm waste, usually using the mean settling velocity of faeces and feed pellets. Cultured organisms growth, effects of light and temperature on algae growth, retention of suspended solids, biodegradation of nitrogen and wastewater treatment are examples of other modelled parameters in aquaculture. Most modelling equations have been developed for monocultures, despite the increasing importance of multi-species systems, such as polyculture and IMTA systems. The main reason for the development of multi-species models is to maximize the production and optimize species combinations in order to reduce the environmental impacts of aquaculture. Some multi-species system models are available, including from the polyculture of different species of bivalves with fish to more complex systems with four trophic levels. These can incorporate ecosystem models and use dynamic energy budgets for each trophic group. In the proposed IMTA system, the bioremediation potential of the marine seaweed Gracilaria vermiculophylla (nutrient removal performance and the Mediterranean filter-feeding polychaete Sabella

  17. Trophic models: What do we learn about Celtic Sea and Bay of Biscay ecosystems?

    Science.gov (United States)

    Moullec, Fabien; Gascuel, Didier; Bentorcha, Karim; Guénette, Sylvie; Robert, Marianne

    2017-08-01

    Trophic models are key tools to go beyond the single-species approaches used in stock assessments to adopt a more holistic view and implement the Ecosystem Approach to Fisheries Management (EAFM). This study aims to: (i) analyse the trophic functioning of the Celtic Sea and the Bay of Biscay, (ii) investigate ecosystem changes over the 1980-2013 period and, (iii) explore the response to management measures at the food web scale. Ecopath models were built for each ecosystem for years 1980 and 2013, and Ecosim models were fitted to time series data of biomass and catches. EcoTroph diagnosis showed that in both ecosystems, fishing pressure focuses on high trophic levels (TLs) and, to a lesser extent, on intermediate TLs. However, the interplay between local environmental conditions, species composition and ecosystem functioning could explain the different responses to fisheries management observed between these two contiguous ecosystems. Indeed, over the study period, the ecosystem's exploitation status has improved in the Bay of Biscay but not in the Celtic Sea. This improvement does not seem to be sufficient to achieve the objectives of an EAFM, as high trophic levels were still overexploited in 2013 and simulations conducted with Ecosim in the Bay of Biscay indicate that at current fishing effort the biomass will not be rebuilt by 2030. The ecosystem's response to a reduction in fishing mortality depends on which trophic levels receive protection. Reducing fishing mortality on pelagic fish, instead of on demersal fish, appears more efficient at maximising catch and total biomass and at conserving both top-predator and intermediate TLs. Such advice-oriented trophic models should be used on a regular basis to monitor the health status of marine food webs and analyse the trade-offs between multiple objectives in an ecosystem-based fisheries management context.

  18. Patterns of trophic niche divergence between invasive and native fishes in wild communities are predictable from mesocosm studies.

    Science.gov (United States)

    Tran, Thi Nhat Quyen; Jackson, Michelle C; Sheath, Danny; Verreycken, Hugo; Britton, J Robert

    2015-07-01

    Ecological theory attempts to predict how impacts for native species arise from biological invasions. A fundamental question centres on the feeding interactions of invasive and native species: whether invasion will result in increased interspecific competition, which would result in negative consequences for the competing species, or trophic niche divergence, which would facilitate the invader's integration into the community and their coexistence with native species. Here, the feeding interactions of a highly invasive fish, topmouth gudgeon Pseudorasbora parva, with three native and functionally similar fishes were studied to determine whether patterns of either niche overlap or divergence detected in mesocosm experiments were apparent between the species at larger spatial scales. Using stable isotope analysis, their feeding relationships were assessed initially in the mesocosms (1000 L) and then in small ponds (600 m(2) ). In the mesocosms, a consistent pattern of trophic niche divergence was evident between the sympatric fishes, with niches shifting further apart in isotopic space than suggested in allopatry, revealing that sharing of food resources was limited. Sympatric P. parva also had a smaller niche than their allopatric populations. In eight small ponds where P. parva had coexisted for several years with at least one of the fish species used in the mesocosms, strong patterns of niche differentiation were also apparent, with P. parva always at a lower trophic position than the other fishes, as also occurred in the mesocosms. Where these fishes were sympatric within more complex fish communities in the large ponds, similar patterns were also apparent, with strong evidence of trophic niche differentiation. Aspects of the ecological impacts of P. parva invasion for native communities in larger ponds were consistent with those in the mesocosm experiments. Their invasion resulted in divergence in trophic niches, partly due to their reduced niche widths

  19. The distribution of persistent organic pollutants in a trophically complex Antarctic ecosystem model

    Science.gov (United States)

    Bates, Michael L.; Bengtson Nash, Susan M.; Hawker, Darryl W.; Shaw, Emily C.; Cropp, Roger A.

    2017-06-01

    Despite Antarctica's isolation from human population centres, persistent organic pollutants (POPs) are transported there via long range atmospheric transport and subsequently cold-trapped. The challenging nature of working in the Antarctic environment greatly limits our ability to monitor POP concentrations and understand the processes that govern the distribution of POPs in Antarctic ecosystems. Here we couple a dynamic, trophically complex biological model with a fugacity model to investigate the distribution of hexachlorobenzene (HCB) in a near-shore Antarctic ecosystem. Using this model we examine the steady-state, and annual cycle of HCB concentration in the atmosphere, ocean, sediment, detritus, and 21 classes of biota that span from primary producers to apex predators. The scope and trophic resolution of our model allows us to examine POP pathways through the ecosystem. In our model the main pathway of HCB to upper trophic species is via pelagic communities, with relatively little via benthic communities. Using a dynamic ecosystem model also allows us to examine the seasonal and potential climate change induced changes in POP distribution. We show that there is a large annual cycle in concentration in the planktonic communities, which may have implications for biomagnification factors calculated from observations. We also examine the direct effects of increasing temperature on the redistribution of HCB in a changing climate and find that it is likely minor compared to other indirect effects, such as changes in atmospheric circulation, sea ice dynamics, and changes to the ecosystem itself.

  20. Analysis of a stochastic tri-trophic food-chain model with harvesting.

    Science.gov (United States)

    Liu, Meng; Bai, Chuanzhi

    2016-09-01

    We consider a tri-trophic stochastic food-chain model with harvesting. We first establish critical values between persistence in mean and extinction for each species. The results show that persistence and extinction of a species only depends on the demographic impacts of environmental stochasticity on the species and species at lower tropic levels; however, the mean abundance of a species depends on the impacts of environmental stochasticity at all trophic levels. Then we consider stability in distribution of the model. Finally, we provide a necessary and sufficient condition for existence of optimal harvesting strategy and give the optimal harvesting effort and maximum of sustainable yield. The results show that the optimal harvesting strategy is closely related to the stochastic noises in the model.

  1. Trophic modeling of the Northern Humboldt Current Ecosystem, Part I: Comparing trophic linkages under La Niña and El Niño conditions

    Science.gov (United States)

    Tam, Jorge; Taylor, Marc H.; Blaskovic, Verónica; Espinoza, Pepe; Michael Ballón, R.; Díaz, Erich; Wosnitza-Mendo, Claudia; Argüelles, Juan; Purca, Sara; Ayón, Patricia; Quipuzcoa, Luis; Gutiérrez, Dimitri; Goya, Elisa; Ochoa, Noemí; Wolff, Matthias

    2008-10-01

    The El Niño of 1997-98 was one of the strongest warming events of the past century; among many other effects, it impacted phytoplankton along the Peruvian coast by changing species composition and reducing biomass. While responses of the main fish resources to this natural perturbation are relatively well known, understanding the ecosystem response as a whole requires an ecotrophic multispecies approach. In this work, we construct trophic models of the Northern Humboldt Current Ecosystem (NHCE) and compare the La Niña (LN) years in 1995-96 with the El Niño (EN) years in 1997-98. The model area extends from 4°S-16°S and to 60 nm from the coast. The model consists of 32 functional groups of organisms and differs from previous trophic models of the Peruvian system through: (i) division of plankton into size classes to account for EN-associated changes and feeding preferences of small pelagic fish, (ii) increased division of demersal groups and separation of life history stages of hake, (iii) inclusion of mesopelagic fish, and (iv) incorporation of the jumbo squid ( Dosidicus gigas), which became abundant following EN. Results show that EN reduced the size and organization of energy flows of the NHCE, but the overall functioning (proportion of energy flows used for respiration, consumption by predators, detritus and export) of the ecosystem was maintained. The reduction of diatom biomass during EN forced omnivorous planktivorous fish to switch to a more zooplankton-dominated diet, raising their trophic level. Consequently, in the EN model the trophic level increased for several predatory groups (mackerel, other large pelagics, sea birds, pinnipeds) and for fishery catch. A high modeled biomass of macrozooplankton was needed to balance the consumption by planktivores, especially during EN condition when observed diatoms biomass diminished dramatically. Despite overall lower planktivorous fish catches, the higher primary production required-to-catch ratio implied a

  2. Modelling species invasions using thermal and trophic niche dynamics under climate change

    Directory of Open Access Journals (Sweden)

    Simone eLibralato

    2015-05-01

    Full Text Available Changing marine temperatures modify the distributional ranges of natural populations, but the success of invasion of new areas depends on local physical and ecological conditions. We explore the invasion by thermophilic species and their ecosystem effects by simulating a sea surface temperature increase using a trophodynamic model for the northern Adriatic Sea (NAS, in which thermal and trophic niches are explicitly represented for each thermophilic non-indigenous species and native species. The NAS acts as a cul-de-sac for local species, preventing a further poleward migration as a response to temperature rise. In this situation, model results showed that effects of warming and invasion produced complex, non-linear changes on biomasses but never resulted in a complete overturn of a group of native species and/or a bloom of invasive ones. Despite this, the diversity index stabilizes at increased values after simulating invasion, possibly indicating that in such enclosed systems the establishment of invasive species could represent enrichment in ecosystem structure. In addition, the absence of complete species substitution clearly showed the contribution of resident species towards increasing the resilience, i.e. the capability of the system to cope with invasion without changing substantially. Contrasting scenarios highlighted that changes in ecosystem primary production and species adaptation had secondary effects in ecosystem structure, while results for scenarios with different exploitation levels indicated that fishing can destabilize community structure in these change contexts, e.g. reducing community resilience. The results confirmed the importance of an ecological niche approach to analyze possible effects of invasion and highlighted the complexity of dynamics linked to temperature-driven species invasion’, in terms of both the predicted strength of impacts and the direction of biomass change.

  3. The ecological module of BOATS-1.0: a bioenergetically-constrained model of marine upper trophic levels suitable for studies of fisheries and ocean biogeochemistry

    Science.gov (United States)

    Carozza, D. A.; Bianchi, D.; Galbraith, E. D.

    2015-12-01

    Environmental change and the exploitation of marine resources have had profound impacts on marine communities, with potential implications for ocean biogeochemistry and food security. In order to study such global-scale problems, it is helpful to have computationally efficient numerical models that predict the first-order features of fish biomass production as a function of the environment, based on empirical and mechanistic understandings of marine ecosystems. Here we describe the ecological module of the BiOeconomic mArine Trophic Size-spectrum (BOATS) model, which takes an Earth-system approach to modeling fish biomass at the global scale. The ecological model is designed to be used on an Earth System model grid, and determines size spectra of fish biomass by explicitly resolving life history as a function of local temperature and net primary production. Biomass production is limited by the availability of photosynthetic energy to upper trophic levels, following empirical trophic efficiency scalings, and by well-established empirical temperature-dependent growth rates. Natural mortality is calculated using an empirical size-based relationship, while reproduction and recruitment depend on both the food availability to larvae from net primary production and the production of eggs by mature adult fish. We describe predicted biomass spectra and compare them to observations, and conduct a sensitivity study to determine how the change as a function of net primary production and temperature. The model relies on a limited number of parameters compared to similar modeling efforts, while retaining realistic representations of biological and ecological processes, and is computationally efficient, allowing extensive parameter-space analyses even when implemented globally. As such, it enables the exploration of the linkages between ocean biogeochemistry, climate, and upper trophic levels at the global scale, as well as a representation of fish biomass for idealized studies

  4. The ecological module of BOATS-1.0: a bioenergetically constrained model of marine upper trophic levels suitable for studies of fisheries and ocean biogeochemistry

    Science.gov (United States)

    Carozza, David Anthony; Bianchi, Daniele; Galbraith, Eric Douglas

    2016-04-01

    Environmental change and the exploitation of marine resources have had profound impacts on marine communities, with potential implications for ocean biogeochemistry and food security. In order to study such global-scale problems, it is helpful to have computationally efficient numerical models that predict the first-order features of fish biomass production as a function of the environment, based on empirical and mechanistic understandings of marine ecosystems. Here we describe the ecological module of the BiOeconomic mArine Trophic Size-spectrum (BOATS) model, which takes an Earth-system approach to modelling fish biomass at the global scale. The ecological model is designed to be used on an Earth-system model grid, and determines size spectra of fish biomass by explicitly resolving life history as a function of local temperature and net primary production. Biomass production is limited by the availability of photosynthetic energy to upper trophic levels, following empirical trophic efficiency scalings, and by well-established empirical temperature-dependent growth rates. Natural mortality is calculated using an empirical size-based relationship, while reproduction and recruitment depend on both the food availability to larvae from net primary production and the production of eggs by mature adult fish. We describe predicted biomass spectra and compare them to observations, and conduct a sensitivity study to determine how they change as a function of net primary production and temperature. The model relies on a limited number of parameters compared to similar modelling efforts, while retaining reasonably realistic representations of biological and ecological processes, and is computationally efficient, allowing extensive parameter-space analyses even when implemented globally. As such, it enables the exploration of the linkages between ocean biogeochemistry, climate, and upper trophic levels at the global scale, as well as a representation of fish biomass for

  5. The ecological module of BOATS-1.0: a bioenergetically-constrained model of marine upper trophic levels suitable for studies of fisheries and ocean biogeochemistry

    Directory of Open Access Journals (Sweden)

    D. A. Carozza

    2015-12-01

    Full Text Available Environmental change and the exploitation of marine resources have had profound impacts on marine communities, with potential implications for ocean biogeochemistry and food security. In order to study such global-scale problems, it is helpful to have computationally efficient numerical models that predict the first-order features of fish biomass production as a function of the environment, based on empirical and mechanistic understandings of marine ecosystems. Here we describe the ecological module of the BiOeconomic mArine Trophic Size-spectrum (BOATS model, which takes an Earth-system approach to modeling fish biomass at the global scale. The ecological model is designed to be used on an Earth System model grid, and determines size spectra of fish biomass by explicitly resolving life history as a function of local temperature and net primary production. Biomass production is limited by the availability of photosynthetic energy to upper trophic levels, following empirical trophic efficiency scalings, and by well-established empirical temperature-dependent growth rates. Natural mortality is calculated using an empirical size-based relationship, while reproduction and recruitment depend on both the food availability to larvae from net primary production and the production of eggs by mature adult fish. We describe predicted biomass spectra and compare them to observations, and conduct a sensitivity study to determine how the change as a function of net primary production and temperature. The model relies on a limited number of parameters compared to similar modeling efforts, while retaining realistic representations of biological and ecological processes, and is computationally efficient, allowing extensive parameter-space analyses even when implemented globally. As such, it enables the exploration of the linkages between ocean biogeochemistry, climate, and upper trophic levels at the global scale, as well as a representation of fish biomass for

  6. Developing Terrestrial Trophic Models for Petroleum and Natural Gas Exploration and Production Sites: The Oklahoma Tallgrass Prairie Preserve Example

    Energy Technology Data Exchange (ETDEWEB)

    Stevenson, M; Coty, J; Stewart, J; Carlsen, T; Callaham, M

    2001-01-26

    This document details procedures to be used when constructing a conceptual terrestrial trophic model for natural gas and oil exploration and production sites. A site conceptual trophic model is intended for use in evaluating ecological impacts of oil and brine releases at E&P sites from a landscape or ecosystem perspective. The terrestrial trophic model protocol was developed using an example site, the Tallgrass Prairie Preserve (TPP) in Oklahoma. The procedure focuses on developing a terrestrial trophic model using information found in the primary literature, and augmented using site-specific research where available. Although the TPP has been the subject of considerable research and public interest since the high-profile reintroduction of bison (Bison bison) in 1993, little formal work has been done to develop a food web for the plant and animal communities found at the preserve. We describe how to divide species into guilds using explicit criteria on the basis of resource use and spatial distribution. For the TPP, sixteen guilds were developed for use in the trophic model, and the relationships among these guilds were analyzed. A brief discussion of the results of this model is provided, along with considerations for its use and areas for further study.

  7. Modelling impacts of offshore wind farms on trophic web: the Courseulles-sur-Mer case study

    Science.gov (United States)

    Raoux, Aurore; Pezy, Jean-Philippe; Dauvin, Jean-Claude; Tecchio, samuele; Degraer, Steven; Wilhelmsson, Dan; Niquil, Nathalie

    2016-04-01

    The French government is planning the construction of three offshore wind farms in Normandy. These offshore wind farms will integrate into an ecosystem already subject to a growing number of anthropogenic disturbances such as transportation, fishing, sediment deposit, and sediment extraction. The possible effects of this cumulative stressors on ecosystem functioning are still unknown, but they could impact their resilience, making them susceptible to changes from one stable state to another. Understanding the behaviour of these marine coastal complex systems is essential in order to anticipate potential state changes, and to implement conservation actions in a sustainable manner. Currently, there are no global and integrated studies on the effects of construction and exploitation of offshore wind farms. Moreover, approaches are generally focused on the conservation of some species or groups of species. Here, we develop a holistic and integrated view of ecosystem impacts through the use of trophic webs modelling tools. Trophic models describe the interaction between biological compartments at different trophic levels and are based on the quantification of flow of energy and matter in ecosystems. They allow the application of numerical methods for the characterization of emergent properties of the ecosystem, also called Ecological Network Analysis (ENA). These indices have been proposed as ecosystem health indicators as they have been demonstrated to be sensitive to different impacts on marine ecosystems. We present here in detail the strategy for analysing the potential environmental impacts of the construction of the Courseulles-sur-Mer offshore wind farm (Bay of Seine) such as the reef effect through the use of the Ecopath with Ecosim software. Similar Ecopath simulations will be made in the future on the Le Tréport offshore wind farm site. Results will contribute to a better knowledge of the impacts of the offshore wind farms on ecosystems. They also allow to

  8. Description of the East Brazil Large Marine Ecosystem using a trophic model

    Directory of Open Access Journals (Sweden)

    Kátia M.F. Freire

    2008-09-01

    Full Text Available The objective of this study was to describe the marine ecosystem off northeastern Brazil. A trophic model was constructed for the 1970s using Ecopath with Ecosim. The impact of most of the forty-one functional groups was modest, probably due to the highly reticulated diet matrix. However, seagrass and macroalgae exerted a strong positive impact on manatee and herbivorous reef fishes, respectively. A high negative impact of omnivorous reef fishes on spiny lobsters and of sharks on swordfish was observed. Spiny lobsters and swordfish had the largest biomass changes for the simulation period (1978-2000; tunas, other large pelagics and sharks showed intermediate rates of biomass decline; and a slight increase in biomass was observed for toothed cetaceans, large carnivorous reef fishes, and dolphinfish. Recycling was an important feature of this ecosystem with low phytoplankton-originated primary production. The mean transfer efficiency between trophic levels was 11.4%. The gross efficiency of the fisheries was very low (0.00002, probably due to the low exploitation rate of most of the resources in the 1970s. Basic local information was missing for many groups. When information gaps are filled, this model may serve more credibly for the exploration of fishing policies for this area within an ecosystem approach.

  9. Modelling of PCB trophic transfer in the Gulf of Lions; 3D coupled model application

    Science.gov (United States)

    Alekseenko, Elena; Thouvenin, Benedicte; Tronczynsky, Jacek; Carlotti, Francois; Garreau, Pierre; Tixier, Celine; Baklouti, Melika

    2017-04-01

    This work aims at assessing the role of plankton in the transfer of PCBs to higher trophic levels in the Gulf of Lions (NW Mediterranean Sea) using a 3D modelling approach, which is coupling biogeochemical and hydrodynamical processes and taking into account the physical-chemical properties of PCBs. Transport of various PCB species were simulated during one year: total dissolved, freely dissolved, particulate, biosorbed on plankton, assimilated by zooplankton. PCB budgets and fluxes into the Gulf of Lions between various species were governed by different processes, such as: adsorption/desorption, bacteria and plankton mortality, zooplankton excretion, grazing, mineralization, volatilization and biodegradation. CB153 (2,2',4,4',5,5' hexachlorobiphényle) congener have been considered in the model, since it presents a large amount of PCB among the other congeners in the environment of the Gulf of Lions. At first, the simulated PCBs distributions within particulate matter and plankton were compared with available in-situ measurements (COSTAS and Merlumed field campaigns) performed in the Gulf of Lions. Two size classes of plankton X (60μ msuspended solids have been considered for the comparison. In general, the magnitudes of CB153 concentrations within two size classes of plankton in April are comparable to the measured ones except the eastern station C1. The magnitudes of living CB153 concentrations in January are less close to the measured ones in vicinity of the Rhone River. Then, the analyses of spatial-temporal variations of PCB within different compartments and within three different zones (coastal, intermediate and offshore zones) of GoL have been performed in order to advance in understanding the contamination pathways from air and water to plankton. For all zones CB153 concentration is raising in January and in July 2010, what is linked with two Rhone River flood events started in the middle of December 2009 and in the middle of June 2010. In all zones

  10. Couplerlib: a metadata-driven library for the integration of multiple models of higher and lower trophic level marine systems with inexact functional group matching

    Science.gov (United States)

    Beecham, Jonathan; Bruggeman, Jorn; Aldridge, John; Mackinson, Steven

    2016-03-01

    End-to-end modelling is a rapidly developing strategy for modelling in marine systems science and management. However, problems remain in the area of data matching and sub-model compatibility. A mechanism and novel interfacing system (Couplerlib) is presented whereby a physical-biogeochemical model (General Ocean Turbulence Model-European Regional Seas Ecosystem Model, GOTM-ERSEM) that predicts dynamics of the lower trophic level (LTL) organisms in marine ecosystems is coupled to a dynamic ecosystem model (Ecosim), which predicts food-web interactions among higher trophic level (HTL) organisms. Coupling is achieved by means of a bespoke interface, which handles the system incompatibilities between the models and a more generic Couplerlib library, which uses metadata descriptions in extensible mark-up language (XML) to marshal data between groups, paying attention to functional group mappings and compatibility of units between models. In addition, within Couplerlib, models can be coupled across networks by means of socket mechanisms. As a demonstration of this approach, a food-web model (Ecopath with Ecosim, EwE) and a physical-biogeochemical model (GOTM-ERSEM) representing the North Sea ecosystem were joined with Couplerlib. The output from GOTM-ERSEM varies between years, depending on oceanographic and meteorological conditions. Although inter-annual variability was clearly present, there was always the tendency for an annual cycle consisting of a peak of diatoms in spring, followed by (less nutritious) flagellates and dinoflagellates through the summer, resulting in an early summer peak in the mesozooplankton biomass. Pelagic productivity, predicted by the LTL model, was highly seasonal with little winter food for the higher trophic levels. The Ecosim model was originally based on the assumption of constant annual inputs of energy and, consequently, when coupled, pelagic species suffered population losses over the winter months. By contrast, benthic populations

  11. Monitoring and modeling water temperature and trophic status of a shallow Mediterranean lake

    Science.gov (United States)

    Giadrossich, Filippo; Bueche, Thomas; Pulina, Silvia; Marrosu, Roberto; Padedda, Bachisio Mario; Mariani, Maria Antonietta; Vetter, Mark; Cohen, Denis; Pirastru, Mario; Niedda, Marcello; Lugliè, Antonella

    2017-04-01

    Lakes are sensitive to changes in climate and human activities. Over the last few decades, Mediterranean lakes have experienced various problems due to the current climate change (drought, flood, warming, salt accumulation, water quality changes, etc.), often amplified by water use, intensification of land use activities, and pollution. The overall impact of these changes on water resources is still an open question. In this study we monitor the trophic status and the dynamics of water temperature of Lake Baratz, the only natural lake in Sardinia, Italy, characterized by high salinity and shallow depth. We extend the research carried out in the past 8 years by integrating new physical, chemical and biological data using a multidisciplinary approach that combines hydrological and biological dynamics. In particular, the lake water balance and the thermal and hydrochemical regime are studied with a lake dynamic model (the General Lake Model or GLM) which combine the energy budget method for estimating lake evaporation, and a physically-based rainfall-runoff simulator for estimating lake inflow, calibrated with measurements at the cross section of the main inlet stream. The trophic state of the lake was evaluated applying the OCDE Probability Distribution Diagrams method, which requires nutrient concentrations in the lake (total phosphorus), phytoplankton chlorophyll a and Secchi disk transparency data. We collected field data from a raft station and a land station, measuring net solar radiation, air temperature and relative humidity, precipitation, wind velocity, atmospheric pressure, and temperature from thermistors submerged in the uppermost three centimeters of water and beneath the lake surface at depths of 1, 2, 3, 4, 5, 6, and 8 m. Samples for nutrients and chlorophyll a analyses were collected at the same above mentioned depths close to the raft station using a Niskin bottle. Temperature, salinity, pH, and dissolved oxygen were measured using a multi

  12. Assessing the trophic position and ecological role of squids in marine ecosystems by means of food-web models

    Science.gov (United States)

    Coll, Marta; Navarro, Joan; Olson, Robert J.; Christensen, Villy

    2013-10-01

    We synthesized available information from ecological models at local and regional scales to obtain a global picture of the trophic position and ecological role of squids in marine ecosystems. First, static food-web models were used to analyze basic ecological parameters and indicators of squids: biomass, production, consumption, trophic level, omnivory index, predation mortality diet, and the ecological role. In addition, we developed various dynamic temporal simulations using two food-web models that included squids in their parameterization, and we investigated potential impacts of fishing pressure and environmental conditions for squid populations and, consequently, for marine food webs. Our results showed that squids occupy a large range of trophic levels in marine food webs and show a large trophic width, reflecting the versatility in their feeding behaviors and dietary habits. Models illustrated that squids are abundant organisms in marine ecosystems, and have high growth and consumption rates, but these parameters are highly variable because squids are adapted to a large variety of environmental conditions. Results also show that squids can have a large trophic impact on other elements of the food web, and top-down control from squids to their prey can be high. In addition, some squid species are important prey of apical predators and may be keystone species in marine food webs. In fact, we found strong interrelationships between neritic squids and the populations of their prey and predators in coastal and shelf areas, while the role of squids in open ocean and upwelling ecosystems appeared more constrained to a bottom-up impact on their predators. Therefore, large removals of squids will likely have large-scale effects on marine ecosystems. In addition, simulations confirm that squids are able to benefit from a general increase in fishing pressure, mainly due to predation release, and quickly respond to changes triggered by the environment. Squids may thus

  13. Numerical Bifurcation Analysis of Physiologically Structured Populations: Consumer-Resource, Cannibalistic and Trophic Models.

    Science.gov (United States)

    Sánchez Sanz, Julia; Getto, Philipp

    2016-07-01

    With the aim of applying numerical methods, we develop a formalism for physiologically structured population models in a new generality that includes consumer-resource, cannibalism and trophic models. The dynamics at the population level are formulated as a system of Volterra functional equations coupled to ODE. For this general class, we develop numerical methods to continue equilibria with respect to a parameter, detect transcritical and saddle-node bifurcations and compute curves in parameter planes along which these bifurcations occur. The methods combine curve continuation, ODE solvers and test functions. Finally, we apply the methods to the above models using existing data for Daphnia magna consuming Algae and for Perca fluviatilis feeding on Daphnia magna. In particular, we validate the methods by deriving expressions for equilibria and bifurcations with respect to which we compute errors, and by comparing the obtained curves with curves that were computed earlier with other methods. We also present new curves to show how the methods can easily be applied to derive new biological insight. Schemes of algorithms are included.

  14. Trophic State and Toxic Cyanobacteria Density in Optimization Modeling of Multi-Reservoir Water Resource Systems

    Directory of Open Access Journals (Sweden)

    Andrea Sulis

    2014-04-01

    Full Text Available The definition of a synthetic index for classifying the quality of water bodies is a key aspect in integrated planning and management of water resource systems. In previous works [1,2], a water system optimization modeling approach that requires a single quality index for stored water in reservoirs has been applied to a complex multi-reservoir system. Considering the same modeling field, this paper presents an improved quality index estimated both on the basis of the overall trophic state of the water body and on the basis of the density values of the most potentially toxic Cyanobacteria. The implementation of the index into the optimization model makes it possible to reproduce the conditions limiting water use due to excessive nutrient enrichment in the water body and to the health hazard linked to toxic blooms. The analysis of an extended limnological database (1996–2012 in four reservoirs of the Flumendosa-Campidano system (Sardinia, Italy provides useful insights into the strengths and limitations of the proposed synthetic index.

  15. Predicting Trophic Interactions and Habitat Utilization in the California Current Ecosystem

    Science.gov (United States)

    2015-09-30

    assimilation (A. Moore), forage fish ecology (K. Rose ) and pinniped ecology (D. Costa). The team also includes a postdoctoral research associate (L...task (1) led to the submission of two manuscripts to Progress in Oceanography describing the fully coupled ecosystem model framework ( Rose et al., 2015...right: spatial patterns and percent variance explained. Center: normalized amplitudes (red squares = sea lion; blue triangles = sardine). Figure

  16. Wind power prediction models

    Science.gov (United States)

    Levy, R.; Mcginness, H.

    1976-01-01

    Investigations were performed to predict the power available from the wind at the Goldstone, California, antenna site complex. The background for power prediction was derived from a statistical evaluation of available wind speed data records at this location and at nearby locations similarly situated within the Mojave desert. In addition to a model for power prediction over relatively long periods of time, an interim simulation model that produces sample wind speeds is described. The interim model furnishes uncorrelated sample speeds at hourly intervals that reproduce the statistical wind distribution at Goldstone. A stochastic simulation model to provide speed samples representative of both the statistical speed distributions and correlations is also discussed.

  17. Applying SWAT to predict ortho-phosphate loads and trophic status in four reservoirs in the upper Olifants catchment, South Africa

    CSIR Research Space (South Africa)

    Dabrowski, James M

    2014-07-01

    Full Text Available , 2629–2643, 2014 www.hydrol-earth-syst-sci.net/18/2629/2014/ doi:10.5194/hess-18-2629-2014 © Author(s) 2014. CC Attribution 3.0 License. Applying SWAT to predict ortho-phosphate loads and trophic status in four reservoirs in the upper Olifants catchment... of sewage treatment works (STWs) are operating poorly and are likely an important source of bioavailable ortho- phosphate (OP) in the catchment. The Soil Water Assessment Tool (SWAT) was used to identify important sources of OP loading in the catchment...

  18. Predictive models in urology.

    Science.gov (United States)

    Cestari, Andrea

    2013-01-01

    Predictive modeling is emerging as an important knowledge-based technology in healthcare. The interest in the use of predictive modeling reflects advances on different fronts such as the availability of health information from increasingly complex databases and electronic health records, a better understanding of causal or statistical predictors of health, disease processes and multifactorial models of ill-health and developments in nonlinear computer models using artificial intelligence or neural networks. These new computer-based forms of modeling are increasingly able to establish technical credibility in clinical contexts. The current state of knowledge is still quite young in understanding the likely future direction of how this so-called 'machine intelligence' will evolve and therefore how current relatively sophisticated predictive models will evolve in response to improvements in technology, which is advancing along a wide front. Predictive models in urology are gaining progressive popularity not only for academic and scientific purposes but also into the clinical practice with the introduction of several nomograms dealing with the main fields of onco-urology.

  19. Establishment and metabolic analysis of a model microbial community for understanding trophic and electron accepting interactions of subsurface anaerobic environments

    Directory of Open Access Journals (Sweden)

    Yang Zamin K

    2010-05-01

    Full Text Available Abstract Background Communities of microorganisms control the rates of key biogeochemical cycles, and are important for biotechnology, bioremediation, and industrial microbiological processes. For this reason, we constructed a model microbial community comprised of three species dependent on trophic interactions. The three species microbial community was comprised of Clostridium cellulolyticum, Desulfovibrio vulgaris Hildenborough, and Geobacter sulfurreducens and was grown under continuous culture conditions. Cellobiose served as the carbon and energy source for C. cellulolyticum, whereas D. vulgaris and G. sulfurreducens derived carbon and energy from the metabolic products of cellobiose fermentation and were provided with sulfate and fumarate respectively as electron acceptors. Results qPCR monitoring of the culture revealed C. cellulolyticum to be dominant as expected and confirmed the presence of D. vulgaris and G. sulfurreducens. Proposed metabolic modeling of carbon and electron flow of the three-species community indicated that the growth of C. cellulolyticum and D. vulgaris were electron donor limited whereas G. sulfurreducens was electron acceptor limited. Conclusions The results demonstrate that C. cellulolyticum, D. vulgaris, and G. sulfurreducens can be grown in coculture in a continuous culture system in which D. vulgaris and G. sulfurreducens are dependent upon the metabolic byproducts of C. cellulolyticum for nutrients. This represents a step towards developing a tractable model ecosystem comprised of members representing the functional groups of a trophic network.

  20. MODEL PREDICTIVE CONTROL FUNDAMENTALS

    African Journals Online (AJOL)

    2012-07-02

    Jul 2, 2012 ... paper, we will present an introduction to the theory and application of MPC with Matlab codes written to ... model predictive control, linear systems, discrete-time systems, ... and then compute very rapidly for this open-loop con-.

  1. Unifying ecological stoichiometry and metabolic theory to predict production and trophic transfer in a marine planktonic food web.

    Science.gov (United States)

    Moorthi, Stefanie D; Schmitt, Jennifer A; Ryabov, Alexey; Tsakalakis, Ioannis; Blasius, Bernd; Prelle, Lara; Tiedemann, Marc; Hodapp, Dorothee

    2016-05-19

    Two ecological frameworks have been used to explain multitrophic interactions, but rarely in combination: (i) ecological stoichiometry (ES), explaining consumption rates in response to consumers' demand and prey's nutrient content; and (ii) metabolic theory of ecology (MTE), proposing that temperature and body mass affect metabolic rates, growth and consumption rates. Here we combined both, ES and MTE to investigate interactive effects of phytoplankton prey stoichiometry, temperature and zooplankton consumer body mass on consumer grazing rates and production in a microcosm experiment. A simple model integrating parameters from both frameworks was used to predict interactive effects of temperature and nutrient conditions on consumer performance. Overall, model predictions reflected experimental patterns well: consumer grazing rates and production increased with temperature, as could be expected based on MTE. With decreasing algal food quality, grazing rates increased due to compensatory feeding, while consumer growth rates and final biovolume decreased. Nutrient effects on consumer biovolume increased with increasing temperature, while nutrient effects on grazing rates decreased. Highly interactive effects of temperature and nutrient supply indicate that combining the frameworks of ES and MTE is highly important to enhance our ability to predict ecosystem functioning in the context of global change.

  2. Water trophicity of Utricularia microhabitats identlfied by means of SOFM as a tool in ecological modeling

    Directory of Open Access Journals (Sweden)

    Piotr Kosiba

    2011-01-01

    Full Text Available The study objects were 48 microhabitats of five Utricularia species in Lower and Upper Silesia (POLAND. The aim of the paper was to focus on application of the Self-Organizing Feature Map in assessment of water trophicity in Utricularia microhabitats, and to describe how SOFM can be used for the study of ecological subjects. This method was compared with the hierarchical tree plot of cluster analysis to check whether this techniques give similar results. In effect, both topological map of SOFM and dendrogram of cluster analysis show differences between Utricularia species microhabitats in respect of water quality, from eutrophic for U. vulgaris to dystrophic for U. minor and U. intermedia. The used methods give similar results and constitute a validation of the SOFM method in this type of studies.

  3. Predicting lake trophic state by relating Secchi-disk transparency measurements to Landsat-satellite imagery for Michigan inland lakes, 2003-05 and 2007-08

    Science.gov (United States)

    Fuller, L.M.; Jodoin, R.S.; Minnerick, R.J.

    2011-01-01

    Inland lakes are an important economic and environmental resource for Michigan. The U.S. Geological Survey and the Michigan Department of Natural Resources and Environment have been cooperatively monitoring the quality of selected lakes in Michigan through the Lake Water Quality Assessment program. Sampling for this program began in 2001; by 2010, 730 of Michigan’s 11,000 inland lakes are expected to have been sampled once. Volunteers coordinated by the Michigan Department of Natural Resources and Environment began sampling lakes in 1974 and continue to sample (in 2010) approximately 250 inland lakes each year through the Michigan Cooperative Lakes Monitoring Program. Despite these sampling efforts, it still is impossible to physically collect measurements for all Michigan inland lakes; however, Landsat-satellite imagery has been used successfully in Minnesota, Wisconsin, Michigan, and elsewhere to predict the trophic state of unsampled inland lakes greater than 20 acres by producing regression equations relating in-place Secchi-disk measurements to Landsat bands. This study tested three alternatives to methods previously used in Michigan to improve results for predicted statewide Trophic State Index (TSI) computed from Secchi-disk transparency (TSI (SDT)). The alternative methods were used on 14 Landsat-satellite scenes with statewide TSI (SDT) for two time periods (2003– 05 and 2007–08). Specifically, the methods were (1) satellitedata processing techniques to remove areas affected by clouds, cloud shadows, haze, shoreline, and dense vegetation for inland lakes greater than 20 acres in Michigan; (2) comparison of the previous method for producing a single open-water predicted TSI (SDT) value (which was based on an area of interest (AOI) and lake-average approach) to an alternative Gethist method for identifying open-water areas in inland lakes (which follows the initial satellite-data processing and targets the darkest pixels, representing the deepest water

  4. Predicting the effects of climate change on trophic status of three morphologically varying lakes: Implications for lake restoration and management

    DEFF Research Database (Denmark)

    Trolle, Dennis; Hamilton, David P.; Pilditch, Conrad A.

    2011-01-01

    column temperature, dissolved oxygen (DO), total phosphorus (TP), total nitrogen (TN) and chlorophyll a (Chl a) concentrations. To represent a possible future climate at the end of this century, mean annual changes in air temperature by 2100, derived from the IPCC A2 scenario downscaled for these lake...... regions, were added to the daily baseline temperatures for years 2002-2007. Lake model simulations using this future climate scenario indicate differential increases in eutrophication in all three lakes, especially during summer months. The predicted effects on annual mean surface water concentrations...

  5. Nominal model predictive control

    OpenAIRE

    Grüne, Lars

    2013-01-01

    5 p., to appear in Encyclopedia of Systems and Control, Tariq Samad, John Baillieul (eds.); International audience; Model Predictive Control is a controller design method which synthesizes a sampled data feedback controller from the iterative solution of open loop optimal control problems.We describe the basic functionality of MPC controllers, their properties regarding feasibility, stability and performance and the assumptions needed in order to rigorously ensure these properties in a nomina...

  6. Nominal Model Predictive Control

    OpenAIRE

    Grüne, Lars

    2014-01-01

    5 p., to appear in Encyclopedia of Systems and Control, Tariq Samad, John Baillieul (eds.); International audience; Model Predictive Control is a controller design method which synthesizes a sampled data feedback controller from the iterative solution of open loop optimal control problems.We describe the basic functionality of MPC controllers, their properties regarding feasibility, stability and performance and the assumptions needed in order to rigorously ensure these properties in a nomina...

  7. Candidate Prediction Models and Methods

    DEFF Research Database (Denmark)

    Nielsen, Henrik Aalborg; Nielsen, Torben Skov; Madsen, Henrik

    2005-01-01

    This document lists candidate prediction models for Work Package 3 (WP3) of the PSO-project called ``Intelligent wind power prediction systems'' (FU4101). The main focus is on the models transforming numerical weather predictions into predictions of power production. The document also outlines...... the possibilities w.r.t. different numerical weather predictions actually available to the project....

  8. Predictive Surface Complexation Modeling

    Energy Technology Data Exchange (ETDEWEB)

    Sverjensky, Dimitri A. [Johns Hopkins Univ., Baltimore, MD (United States). Dept. of Earth and Planetary Sciences

    2016-11-29

    Surface complexation plays an important role in the equilibria and kinetics of processes controlling the compositions of soilwaters and groundwaters, the fate of contaminants in groundwaters, and the subsurface storage of CO2 and nuclear waste. Over the last several decades, many dozens of individual experimental studies have addressed aspects of surface complexation that have contributed to an increased understanding of its role in natural systems. However, there has been no previous attempt to develop a model of surface complexation that can be used to link all the experimental studies in order to place them on a predictive basis. Overall, my research has successfully integrated the results of the work of many experimentalists published over several decades. For the first time in studies of the geochemistry of the mineral-water interface, a practical predictive capability for modeling has become available. The predictive correlations developed in my research now enable extrapolations of experimental studies to provide estimates of surface chemistry for systems not yet studied experimentally and for natural and anthropogenically perturbed systems.

  9. Variable δ(15)N diet-tissue discrimination factors among sharks: implications for trophic position, diet and food web models.

    Science.gov (United States)

    Olin, Jill A; Hussey, Nigel E; Grgicak-Mannion, Alice; Fritts, Mark W; Wintner, Sabine P; Fisk, Aaron T

    2013-01-01

    The application of stable isotopes to characterize the complexities of a species foraging behavior and trophic relationships is dependent on assumptions of δ(15)N diet-tissue discrimination factors (∆(15)N). As ∆(15)N values have been experimentally shown to vary amongst consumers, tissues and diet composition, resolving appropriate species-specific ∆(15)N values can be complex. Given the logistical and ethical challenges of controlled feeding experiments for determining ∆(15)N values for large and/or endangered species, our objective was to conduct an assessment of a range of reported ∆(15)N values that can hypothetically serve as surrogates for describing the predator-prey relationships of four shark species that feed on prey from different trophic levels (i.e., different mean δ(15)N dietary values). Overall, the most suitable species-specific ∆(15)N values decreased with increasing dietary-δ(15)N values based on stable isotope Bayesian ellipse overlap estimates of shark and the principal prey functional groups contributing to the diet determined from stomach content analyses. Thus, a single ∆(15)N value was not supported for this speciose group of marine predatory fishes. For example, the ∆(15)N value of 3.7‰ provided the highest percent overlap between prey and predator isotope ellipses for the bonnethead shark (mean diet δ(15)N = 9‰) whereas a ∆(15)N value shark (mean diet δ(15)N = 15‰). These data corroborate the previously reported inverse ∆(15)N-dietary δ(15)N relationship when both isotope ellipses of principal prey functional groups and the broader identified diet of each species were considered supporting the adoption of different ∆(15)N values that reflect the predators' δ(15)N-dietary value. These findings are critical for refining the application of stable isotope modeling approaches as inferences regarding a species' ecological role in their community will be influenced with consequences for conservation and

  10. ERSEM 15.06: a generic model for marine biogeochemistry and the ecosystem dynamics of the lower trophic levels

    Science.gov (United States)

    Butenschön, Momme; Clark, James; Aldridge, John N.; Icarus Allen, Julian; Artioli, Yuri; Blackford, Jeremy; Bruggeman, Jorn; Cazenave, Pierre; Ciavatta, Stefano; Kay, Susan; Lessin, Gennadi; van Leeuwen, Sonja; van der Molen, Johan; de Mora, Lee; Polimene, Luca; Sailley, Sevrine; Stephens, Nicholas; Torres, Ricardo

    2016-04-01

    The European Regional Seas Ecosystem Model (ERSEM) is one of the most established ecosystem models for the lower trophic levels of the marine food web in the scientific literature. Since its original development in the early nineties it has evolved significantly from a coastal ecosystem model for the North Sea to a generic tool for ecosystem simulations from shelf seas to the global ocean. The current model release contains all essential elements for the pelagic and benthic parts of the marine ecosystem, including the microbial food web, the carbonate system, and calcification. Its distribution is accompanied by a testing framework enabling the analysis of individual parts of the model. Here we provide a detailed mathematical description of all ERSEM components along with case studies of mesocosm-type simulations, water column implementations, and a brief example of a full-scale application for the north-western European shelf. Validation against in situ data demonstrates the capability of the model to represent the marine ecosystem in contrasting environments.

  11. Candidate Prediction Models and Methods

    DEFF Research Database (Denmark)

    Nielsen, Henrik Aalborg; Nielsen, Torben Skov; Madsen, Henrik

    2005-01-01

    This document lists candidate prediction models for Work Package 3 (WP3) of the PSO-project called ``Intelligent wind power prediction systems'' (FU4101). The main focus is on the models transforming numerical weather predictions into predictions of power production. The document also outlines...

  12. Trophic hierarchies illuminated via amino acid isotopic analysis.

    Directory of Open Access Journals (Sweden)

    Shawn A Steffan

    Full Text Available Food web ecologists have long sought to characterize the trophic niches of animals using stable isotopic analysis. However, distilling trophic position from isotopic composition has been difficult, largely because of the variability associated with trophic discrimination factors (inter-trophic isotopic fractionation and routing. We circumvented much of this variability using compound-specific isotopic analysis (CSIA. We examined the (15N signatures of amino acids extracted from organisms reared in pure culture at four discrete trophic levels, across two model communities. We calculated the degree of enrichment at each trophic level and found there was a consistent trophic discrimination factor (~7.6‰. The constancy of the CSIA-derived discrimination factor permitted unprecedented accuracy in the measurement of animal trophic position. Conversely, trophic position estimates generated via bulk-(15N analysis significantly underestimated trophic position, particularly among higher-order consumers. We then examined the trophic hierarchy of a free-roaming arthropod community, revealing the highest trophic position (5.07 and longest food chain ever reported using CSIA. High accuracy in trophic position estimation brings trophic function into sharper focus, providing greater resolution to the analysis of food webs.

  13. Comprehensive Model of Jumbo Squid Dosidicus gigas Trophic Ecology in the Northern Humboldt Current System

    Science.gov (United States)

    Alegre, Ana; Ménard, Frédéric; Tafur, Ricardo; Espinoza, Pepe; Argüelles, Juan; Maehara, Víctor; Flores, Oswaldo; Simier, Monique; Bertrand, Arnaud

    2014-01-01

    The jumbo squid Dosidicus gigas plays an important role in marine food webs both as predator and prey. We investigated the ontogenetic and spatiotemporal variability of the diet composition of jumbo squid in the northern Humboldt Current system. For that purpose we applied several statistical methods to an extensive dataset of 3,618 jumbo squid non empty stomachs collected off Peru from 2004 to 2011. A total of 55 prey taxa was identified that we aggregated into eleven groups. Our results evidenced a large variability in prey composition as already observed in other systems. However, our data do not support the hypothesis that jumbo squids select the most abundant or energetic taxon in a prey assemblage, neglecting the other available prey. Indeed, multinomial model predictions showed that stomach fullness increased with the number of prey taxa, while most stomachs with low contents contained one or two prey taxa only. Our results therefore question the common hypothesis that predators seek locally dense aggregations of monospecific prey. In addition D. gigas consumes very few anchovy Engraulis ringens in Peru, whereas a tremendous biomass of anchovy is potentially available. It seems that D. gigas cannot reach the oxygen unsaturated waters very close to the coast, where the bulk of anchovy occurs. Indeed, even if jumbo squid can forage in hypoxic deep waters during the day, surface normoxic waters are then required to recover its maintenance respiration (or energy?). Oxygen concentration could thus limit the co-occurrence of both species and then preclude predator-prey interactions. Finally we propose a conceptual model illustrating the opportunistic foraging behaviour of jumbo squid impacted by ontogenetic migration and potentially constrained by oxygen saturation in surface waters. PMID:24465788

  14. Comprehensive model of Jumbo squid Dosidicus gigas trophic ecology in the Northern Humboldt current system.

    Directory of Open Access Journals (Sweden)

    Ana Alegre

    Full Text Available The jumbo squid Dosidicus gigas plays an important role in marine food webs both as predator and prey. We investigated the ontogenetic and spatiotemporal variability of the diet composition of jumbo squid in the northern Humboldt Current system. For that purpose we applied several statistical methods to an extensive dataset of 3,618 jumbo squid non empty stomachs collected off Peru from 2004 to 2011. A total of 55 prey taxa was identified that we aggregated into eleven groups. Our results evidenced a large variability in prey composition as already observed in other systems. However, our data do not support the hypothesis that jumbo squids select the most abundant or energetic taxon in a prey assemblage, neglecting the other available prey. Indeed, multinomial model predictions showed that stomach fullness increased with the number of prey taxa, while most stomachs with low contents contained one or two prey taxa only. Our results therefore question the common hypothesis that predators seek locally dense aggregations of monospecific prey. In addition D. gigas consumes very few anchovy Engraulis ringens in Peru, whereas a tremendous biomass of anchovy is potentially available. It seems that D. gigas cannot reach the oxygen unsaturated waters very close to the coast, where the bulk of anchovy occurs. Indeed, even if jumbo squid can forage in hypoxic deep waters during the day, surface normoxic waters are then required to recover its maintenance respiration (or energy?. Oxygen concentration could thus limit the co-occurrence of both species and then preclude predator-prey interactions. Finally we propose a conceptual model illustrating the opportunistic foraging behaviour of jumbo squid impacted by ontogenetic migration and potentially constrained by oxygen saturation in surface waters.

  15. Comprehensive model of Jumbo squid Dosidicus gigas trophic ecology in the Northern Humboldt current system.

    Science.gov (United States)

    Alegre, Ana; Ménard, Frédéric; Tafur, Ricardo; Espinoza, Pepe; Argüelles, Juan; Maehara, Víctor; Flores, Oswaldo; Simier, Monique; Bertrand, Arnaud

    2014-01-01

    The jumbo squid Dosidicus gigas plays an important role in marine food webs both as predator and prey. We investigated the ontogenetic and spatiotemporal variability of the diet composition of jumbo squid in the northern Humboldt Current system. For that purpose we applied several statistical methods to an extensive dataset of 3,618 jumbo squid non empty stomachs collected off Peru from 2004 to 2011. A total of 55 prey taxa was identified that we aggregated into eleven groups. Our results evidenced a large variability in prey composition as already observed in other systems. However, our data do not support the hypothesis that jumbo squids select the most abundant or energetic taxon in a prey assemblage, neglecting the other available prey. Indeed, multinomial model predictions showed that stomach fullness increased with the number of prey taxa, while most stomachs with low contents contained one or two prey taxa only. Our results therefore question the common hypothesis that predators seek locally dense aggregations of monospecific prey. In addition D. gigas consumes very few anchovy Engraulis ringens in Peru, whereas a tremendous biomass of anchovy is potentially available. It seems that D. gigas cannot reach the oxygen unsaturated waters very close to the coast, where the bulk of anchovy occurs. Indeed, even if jumbo squid can forage in hypoxic deep waters during the day, surface normoxic waters are then required to recover its maintenance respiration (or energy?). Oxygen concentration could thus limit the co-occurrence of both species and then preclude predator-prey interactions. Finally we propose a conceptual model illustrating the opportunistic foraging behaviour of jumbo squid impacted by ontogenetic migration and potentially constrained by oxygen saturation in surface waters.

  16. Melanoma risk prediction models

    Directory of Open Access Journals (Sweden)

    Nikolić Jelena

    2014-01-01

    Full Text Available Background/Aim. The lack of effective therapy for advanced stages of melanoma emphasizes the importance of preventive measures and screenings of population at risk. Identifying individuals at high risk should allow targeted screenings and follow-up involving those who would benefit most. The aim of this study was to identify most significant factors for melanoma prediction in our population and to create prognostic models for identification and differentiation of individuals at risk. Methods. This case-control study included 697 participants (341 patients and 356 controls that underwent extensive interview and skin examination in order to check risk factors for melanoma. Pairwise univariate statistical comparison was used for the coarse selection of the most significant risk factors. These factors were fed into logistic regression (LR and alternating decision trees (ADT prognostic models that were assessed for their usefulness in identification of patients at risk to develop melanoma. Validation of the LR model was done by Hosmer and Lemeshow test, whereas the ADT was validated by 10-fold cross-validation. The achieved sensitivity, specificity, accuracy and AUC for both models were calculated. The melanoma risk score (MRS based on the outcome of the LR model was presented. Results. The LR model showed that the following risk factors were associated with melanoma: sunbeds (OR = 4.018; 95% CI 1.724- 9.366 for those that sometimes used sunbeds, solar damage of the skin (OR = 8.274; 95% CI 2.661-25.730 for those with severe solar damage, hair color (OR = 3.222; 95% CI 1.984-5.231 for light brown/blond hair, the number of common naevi (over 100 naevi had OR = 3.57; 95% CI 1.427-8.931, the number of dysplastic naevi (from 1 to 10 dysplastic naevi OR was 2.672; 95% CI 1.572-4.540; for more than 10 naevi OR was 6.487; 95%; CI 1.993-21.119, Fitzpatricks phototype and the presence of congenital naevi. Red hair, phototype I and large congenital naevi were

  17. Connecting alveolate cell biology with trophic ecology in the marine plankton using the ciliate Favella as a model.

    Science.gov (United States)

    Echevarria, Michael L; Wolfe, Gordon V; Strom, Suzanne L; Taylor, Alison R

    2014-10-01

    Planktonic alveolates (ciliates and dinoflagellates), key trophic links in marine planktonic communities, exhibit complex behaviors that are underappreciated by microbiologists and ecologists. Furthermore, the physiological mechanisms underlying these behaviors are still poorly understood except in a few freshwater model ciliates, which are significantly different in cell structure and behavior than marine planktonic species. Here, we argue for an interdisciplinary research approach to connect physiological mechanisms with population-level outcomes of behaviors. Presenting the tintinnid ciliate Favella as a model alveolate, we review its population ecology, behavior, and cellular/molecular biology in the context of sensory biology and synthesize past research and current findings to construct a conceptual model describing the sensory biology of Favella. We discuss how emerging genomic information and new technical methods for integrating research across different levels of biological organization are paving the way for rapid advance. These research approaches will yield a deeper understanding of the role that planktonic alveolates may play in biogeochemical cycles, and how they may respond to future ocean conditions.

  18. Mercury cycling in aquatic ecosystems and trophic state-related variables--implications from structural equation modeling.

    Science.gov (United States)

    Pollman, Curtis D

    2014-11-15

    Structural equation modeling (SEM) provides a framework that can more properly handle complex variable interactions inherent in mercury cycling and its bioaccumulation compared to more traditional regression-based methods. SEM was applied to regional data sets for three different types of aquatic ecosystems within Florida, USA--lakes, streams, and the Everglades--to evaluate the underlying nature (i.e., indirect and direct) of the relationships between fish mercury concentrations and trophic state related variables such as nutrients, dissolved organic carbon (DOC), sulfate, and alkalinity. The modeling results indicated some differences in key variable relationships--for example, the effect of nutrients on fish mercury in lakes and streams was uniformly negative through direct and indirect pathways consistent with biodilution or eutrophication-associated effects on food web structure. Somewhat surprisingly, however, was that total phosphorus did not serve as a meaningful variable in the Everglades model, apparently because its effects were masked or secondary to the effects of DOC. What is perhaps a more important result were two key similarities across the three systems. First, the modeling clearly indicates that the dominant influence on fish tissue mercury concentrations in all three systems is related to variations in the methylmercury signal. Second, the modeling demonstrated that the effect of DOC on fish mercury concentrations was exerted through multiple and antagonistic pathways, including facilitated transport of total mercury and methylmercury, enhanced rates of methylation, and limitations imposed on bioavailability. Indeed, while the individual DOC pathways in the models were all highly significant (generally pmodel was greatly reduced or insignificant. These results can help explain contradictory results obtained previously by other researchers in other systems, and illustrate the importance of SEM as a modeling tool when studying systems with complex

  19. Variable δ(15N diet-tissue discrimination factors among sharks: implications for trophic position, diet and food web models.

    Directory of Open Access Journals (Sweden)

    Jill A Olin

    Full Text Available The application of stable isotopes to characterize the complexities of a species foraging behavior and trophic relationships is dependent on assumptions of δ(15N diet-tissue discrimination factors (∆(15N. As ∆(15N values have been experimentally shown to vary amongst consumers, tissues and diet composition, resolving appropriate species-specific ∆(15N values can be complex. Given the logistical and ethical challenges of controlled feeding experiments for determining ∆(15N values for large and/or endangered species, our objective was to conduct an assessment of a range of reported ∆(15N values that can hypothetically serve as surrogates for describing the predator-prey relationships of four shark species that feed on prey from different trophic levels (i.e., different mean δ(15N dietary values. Overall, the most suitable species-specific ∆(15N values decreased with increasing dietary-δ(15N values based on stable isotope Bayesian ellipse overlap estimates of shark and the principal prey functional groups contributing to the diet determined from stomach content analyses. Thus, a single ∆(15N value was not supported for this speciose group of marine predatory fishes. For example, the ∆(15N value of 3.7‰ provided the highest percent overlap between prey and predator isotope ellipses for the bonnethead shark (mean diet δ(15N = 9‰ whereas a ∆(15N value < 2.3‰ provided the highest percent overlap between prey and predator isotope ellipses for the white shark (mean diet δ(15N = 15‰. These data corroborate the previously reported inverse ∆(15N-dietary δ(15N relationship when both isotope ellipses of principal prey functional groups and the broader identified diet of each species were considered supporting the adoption of different ∆(15N values that reflect the predators' δ(15N-dietary value. These findings are critical for refining the application of stable isotope modeling approaches as inferences regarding a species

  20. Analysis of trophic interactions reveals highly plastic response to climate change in a tri-trophic High-Arctic ecosystem

    DEFF Research Database (Denmark)

    Mortensen, Lars O.; Schmidt, Niels Martin; Hoye, Toke T.

    2016-01-01

    -Arctic tri-trophic system of flowers, insects and waders (Charadriiformes), with latent factors representing phenology (timing of life history events) and performance (abundance or reproduction success) for each trophic level. The effects derived from the model demonstrated that the time of snowmelt directly...... from the tri-trophic community presented here emphasise that effects of climate on species in consumer-resource systems may propagate through trophic levels...

  1. Predictive Models for Music

    OpenAIRE

    Paiement, Jean-François; Grandvalet, Yves; Bengio, Samy

    2008-01-01

    Modeling long-term dependencies in time series has proved very difficult to achieve with traditional machine learning methods. This problem occurs when considering music data. In this paper, we introduce generative models for melodies. We decompose melodic modeling into two subtasks. We first propose a rhythm model based on the distributions of distances between subsequences. Then, we define a generative model for melodies given chords and rhythms based on modeling sequences of Narmour featur...

  2. Bioenergetics model output - Trophic impacts of bald eagles in the Puget Sound food web

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This project is developing models to examine the ecological roles of bald eagles in the Puget Sound region. It is primarily being done by NMFS FTEs, in collaboration...

  3. Food web model output - Trophic impacts of bald eagles in the Puget Sound food web

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This project is developing models to examine the ecological roles of bald eagles in the Puget Sound region. It is primarily being done by NMFS FTEs, in collaboration...

  4. From Eutrophic to Mesotrophic: Modelling Watershed Management Scenarios to Change the Trophic Status of a Reservoir

    Directory of Open Access Journals (Sweden)

    Marcos Mateus

    2014-03-01

    Full Text Available Management decisions related with water quality in lakes and reservoirs require a combined land-water processes study approach. This study reports on an integrated watershed-reservoir modeling methodology: the Soil and Water Assessment Tool (SWAT model to estimate the nutrient input loads from the watershed, used afterwards as boundary conditions to the reservoir model, CE-QUAL-W2. The integrated modeling system was applied to the Torrão reservoir and drainage basin. The objective of the study was to quantify the total maximum input load that allows the reservoir to be classified as mesotrophic. Torrão reservoir is located in the Tâmega River, one of the most important tributaries of the Douro River in Portugal. The watershed is characterized by a variety of land uses and urban areas, accounting for a total Waste Water Treatment Plants (WWTP discharge of ~100,000 p.e. According to the criteria defined by the National Water Institute (based on the WWTP Directive, the Torrão reservoir is classified as eutrophic. Model estimates show that a 10% reduction in nutrient loads will suffice to change the state to mesotrophic, and should target primarily WWTP effluents, but also act on diffuse sources. The method applied in this study should provide a basis for water environmental management decision-making.

  5. Exploring optimal fishing scenarios for the multispecies artisanal fisheries of Eritrea using a trophic model

    NARCIS (Netherlands)

    Tsehaye, I.W.; Nagelkerke, L.A.J.

    2008-01-01

    This study represents the first attempt to assess the potential for fisheries in the artisanal Red Sea reef fisheries of Eritrea in an ecosystem context. We used an Ecopath with Ecosim model to integrate known aspects of the ecosystem and its inhabitants into a single framework, with the aim to gain

  6. Zephyr - the prediction models

    DEFF Research Database (Denmark)

    Nielsen, Torben Skov; Madsen, Henrik; Nielsen, Henrik Aalborg

    2001-01-01

    This paper briefly describes new models and methods for predicationg the wind power output from wind farms. The system is being developed in a project which has the research organization Risø and the department of Informatics and Mathematical Modelling (IMM) as the modelling team and all the Dani...

  7. A model of trophic flows in the northern Benguela upwelling system during the 1980s

    DEFF Research Database (Denmark)

    Shannon, L.J.; Jarre, Astrid

    1999-01-01

    in the northern Benguela had both undergone a decline, yet were still heavily fished. Horse mackerel Trachurus trachurus capensis had increased over the previous decade and was the dominant pelagic species during the 1980s, with high catches. Production by some groups, such as goby Sufflogobius bibarbatus...... in the northern Benguela during the 1980s was high, comparable to that of the Peruvian system in the 1960s and almost double that of the northern Benguela during the 1970s. Horse mackerel and hake catches were both high, with fishing on hake being ecologically more expensive. Biomass of benthic producers, meio......- and macrobenthos were a quarter of the total biomass of these groups in the southern Benguela. The sensitivity of the model to parameter estimates is highlighted. Uncertainty about some of the parameters, thought to have major influences on the functioning of the model, is explored...

  8. High biomass and production but low energy transfer efficiency of Caribbean parrotfish: implications for trophic models of coral reefs

    NARCIS (Netherlands)

    Van Rooij, J.M.; Videler, J.J.; Bruggemann, J.

    1998-01-01

    Quantitative data are presented to assess the trophic role of scarids on the fringing coral reef of Bonaire (Netherlands Antilles): with particular emphasis on the stoplight parrotfish Sparisoma viride. Average herbivore biomass on the reef was 690 kg ha(-1), 22% of which was accounted for by S.

  9. High biomass and production but low energy transfer efficiency of Caribbean parrotfish : implications for trophic models of coral reefs

    NARCIS (Netherlands)

    van Rooij, J.M.; Bruggemann, J.H; Videler, J.J

    1998-01-01

    Quantitative data are presented to assess the trophic role of scarids on the fringing coral reef of Bonaire (Netherlands Antilles): with particular emphasis on the stoplight parrotfish Sparisoma viride. Average herbivore biomass on the reef was 690 kg ha(-1), 22% of which was accounted for by S.

  10. A method for improving predictive modeling by taking into account lag time: Example of selenium bioaccumulation in a flowing system

    Energy Technology Data Exchange (ETDEWEB)

    Beckon, William N., E-mail: William_Beckon@fws.gov

    2016-07-15

    Highlights: • A method for estimating response time in cause-effect relationships is demonstrated. • Predictive modeling is appreciably improved by taking into account this lag time. • Bioaccumulation lag is greater for organisms at higher trophic levels. • This methodology may be widely applicable in disparate disciplines. - Abstract: For bioaccumulative substances, efforts to predict concentrations in organisms at upper trophic levels, based on measurements of environmental exposure, have been confounded by the appreciable but hitherto unknown amount of time it may take for bioaccumulation to occur through various pathways and across several trophic transfers. The study summarized here demonstrates an objective method of estimating this lag time by testing a large array of potential lag times for selenium bioaccumulation, selecting the lag that provides the best regression between environmental exposure (concentration in ambient water) and concentration in the tissue of the target organism. Bioaccumulation lag is generally greater for organisms at higher trophic levels, reaching times of more than a year in piscivorous fish. Predictive modeling of bioaccumulation is improved appreciably by taking into account this lag. More generally, the method demonstrated here may improve the accuracy of predictive modeling in a wide variety of other cause-effect relationships in which lag time is substantial but inadequately known, in disciplines as diverse as climatology (e.g., the effect of greenhouse gases on sea levels) and economics (e.g., the effects of fiscal stimulus on employment).

  11. Trophic network model of exposed sandy coast: Linking continental and marine water ecosystems

    Science.gov (United States)

    Razinkovas-Baziukas, Artūras; Morkūnė, Rasa; Bacevičius, Egidijus; Gasiūnaitė, Zita Rasuolė

    2017-08-01

    A macroscopic food web network for the exposed sandy coastal zone of the south-eastern Baltic Sea was reconstructed using ECOPATH software to assess the matter and energy balance in the ecosystem. The model incorporated 40 living functional groups representing the Baltic Sea coastal system of Lithuania during the first decade of 21rst century. The overall pedigree index of our model was relatively high (0.66) as much of the input data originated from the study area. The results indicate net heterotrophy of the coastal zone due to strong influences from the nearby river - lagoon system (Curonian Lagoon). The majority of fish species and waterbirds were present in the coastal system on a seasonal basis and their migrations contributed to heterotrophic conditions. Among fish, the freshwater stragglers possibly contribute to the reversal of flow in biomass and energy from the coastal zone to the river-lagoon system. Top predators such as breeding and wintering piscivorous waterbirds and large pike-perch were identified as keystone species. There was a clear negative balance for the biomass of small marine pelagic fishes such as smelt, sprat and Baltic herring which represent the main prey items in this system.

  12. Assessing the contribution of marine protected areas to the trophic functioning of ecosystems: a model for the Banc d'Arguin and the Mauritanian shelf.

    Directory of Open Access Journals (Sweden)

    Sylvie Guénette

    Full Text Available Most modelling studies addressed the effectiveness of marine protected areas (MPA for fisheries sustainability through single species approach. Only a few models analysed the potential benefits of MPAs at the ecosystem level, estimating the potential export of fish biomass from the reserve or analysing the trophic relationships between organisms inside and outside the MPA. Here, we propose to use food web models to assess the contribution of a MPA to the trophic functioning of a larger ecosystem. This approach is applied to the Banc d'Arguin National Park, a large MPA located on the Mauritanian shelf. The ecosystem was modeled using Ecopath with Ecosim, a model that accounts for fisheries, food web structure, and some aspects of the spatial distribution of species, for the period 1991-2006. Gaps in knowledge and uncertainty were taken into account by building three different models. Results showed that the Banc d'Arguin contributes about 9 to 13% to the total consumption, is supporting about 23% of the total production and 18% of the total catch of the Mauritanian shelf ecosystem, and up to 50% for coastal fish. Of the 29 exploited groups, 15 depend on the Banc for more than 30% of their direct or indirect consumptions. Between 1991 and 2006, the fishing pressure increased leading to a decrease in biomass and the catch of high trophic levels, confirming their overall overexploitation. Ecosim simulations showed that adding a new fleet in the Banc d'Arguin would have large impacts on the species with a high reliance on the Banc for food, resulting in a 23% decrease in the current outside MPA catches. We conclude on the usefulness of food web models to assess MPAs contribution to larger ecosystem functioning.

  13. Assessing the contribution of marine protected areas to the trophic functioning of ecosystems: a model for the Banc d'Arguin and the Mauritanian shelf.

    Science.gov (United States)

    Guénette, Sylvie; Meissa, Beyah; Gascuel, Didier

    2014-01-01

    Most modelling studies addressed the effectiveness of marine protected areas (MPA) for fisheries sustainability through single species approach. Only a few models analysed the potential benefits of MPAs at the ecosystem level, estimating the potential export of fish biomass from the reserve or analysing the trophic relationships between organisms inside and outside the MPA. Here, we propose to use food web models to assess the contribution of a MPA to the trophic functioning of a larger ecosystem. This approach is applied to the Banc d'Arguin National Park, a large MPA located on the Mauritanian shelf. The ecosystem was modeled using Ecopath with Ecosim, a model that accounts for fisheries, food web structure, and some aspects of the spatial distribution of species, for the period 1991-2006. Gaps in knowledge and uncertainty were taken into account by building three different models. Results showed that the Banc d'Arguin contributes about 9 to 13% to the total consumption, is supporting about 23% of the total production and 18% of the total catch of the Mauritanian shelf ecosystem, and up to 50% for coastal fish. Of the 29 exploited groups, 15 depend on the Banc for more than 30% of their direct or indirect consumptions. Between 1991 and 2006, the fishing pressure increased leading to a decrease in biomass and the catch of high trophic levels, confirming their overall overexploitation. Ecosim simulations showed that adding a new fleet in the Banc d'Arguin would have large impacts on the species with a high reliance on the Banc for food, resulting in a 23% decrease in the current outside MPA catches. We conclude on the usefulness of food web models to assess MPAs contribution to larger ecosystem functioning.

  14. The effect of climate change on the growth of Japanese chum salmon ( Oncorhynchus keta) using a bioenergetics model coupled with a three-dimensional lower trophic ecosystem model (NEMURO)

    Science.gov (United States)

    Kishi, Michio J.; Kaeriyama, Masahide; Ueno, Hiromichi; Kamezawa, Yasuko

    2010-07-01

    From the 1970s to 1990s, a reduction in the body size of Japanese chum salmon (Oncorhynchus keta) was observed. To investigate this body size reduction in the North Pacific, we developed a bioenergetics model for chum salmon coupled with the results from a lower trophic ecosystem model embedded into a three-dimensional global model. In the bioenergetics model, respiration and consumption terms are assumed to be functions of water temperature and prey zooplankton density, which are the determining factors of the reduction of body size. The model reproduced the body size of the 1972 and 1991 year classes of chum salmon. The reproduced body size of the 1972 year class was larger than that of 1991 year class, and this result agrees with observations from the Bering Sea. Our model also reproduced the body size trend from l970 to 2000. The prey density, especially in the eastern North Pacific, had a greater influence on the change of body size than did the SST. This suggests that the size reduction of Japanese chum salmon in the 1990s was partly affected by changes in prey zooplankton density. In the context of the global warming scenario, we discuss changes in the migration route of chum salmon and predict that the population of Japanese chum salmon experience significant declines over this century.

  15. Confidence scores for prediction models

    DEFF Research Database (Denmark)

    Gerds, Thomas Alexander; van de Wiel, MA

    2011-01-01

    modelling strategy is applied to different training sets. For each modelling strategy we estimate a confidence score based on the same repeated bootstraps. A new decomposition of the expected Brier score is obtained, as well as the estimates of population average confidence scores. The latter can be used...... to distinguish rival prediction models with similar prediction performances. Furthermore, on the subject level a confidence score may provide useful supplementary information for new patients who want to base a medical decision on predicted risk. The ideas are illustrated and discussed using data from cancer...

  16. Modelling, controlling, predicting blackouts

    CERN Document Server

    Wang, Chengwei; Baptista, Murilo S

    2016-01-01

    The electric power system is one of the cornerstones of modern society. One of its most serious malfunctions is the blackout, a catastrophic event that may disrupt a substantial portion of the system, playing havoc to human life and causing great economic losses. Thus, understanding the mechanisms leading to blackouts and creating a reliable and resilient power grid has been a major issue, attracting the attention of scientists, engineers and stakeholders. In this paper, we study the blackout problem in power grids by considering a practical phase-oscillator model. This model allows one to simultaneously consider different types of power sources (e.g., traditional AC power plants and renewable power sources connected by DC/AC inverters) and different types of loads (e.g., consumers connected to distribution networks and consumers directly connected to power plants). We propose two new control strategies based on our model, one for traditional power grids, and another one for smart grids. The control strategie...

  17. Melanoma Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing melanoma cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  18. Green Turtle Trophic Ecology

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — SWFSC is currently conducting a study of green sea turtle (Chelonia mydas) trophic ecology in the eastern Pacific. Tissue samples and stable carbon and stable...

  19. Prediction models in complex terrain

    DEFF Research Database (Denmark)

    Marti, I.; Nielsen, Torben Skov; Madsen, Henrik

    2001-01-01

    are calculated using on-line measurements of power production as well as HIRLAM predictions as input thus taking advantage of the auto-correlation, which is present in the power production for shorter pediction horizons. Statistical models are used to discribe the relationship between observed energy production......The objective of the work is to investigatethe performance of HIRLAM in complex terrain when used as input to energy production forecasting models, and to develop a statistical model to adapt HIRLAM prediction to the wind farm. The features of the terrain, specially the topography, influence...... and HIRLAM predictions. The statistical models belong to the class of conditional parametric models. The models are estimated using local polynomial regression, but the estimation method is here extended to be adaptive in order to allow for slow changes in the system e.g. caused by the annual variations...

  20. Trophic cascades of bottom-up and top-down forcing on nutrients and plankton in the Kattegat, evaluated by modelling

    Science.gov (United States)

    Petersen, Marcell Elo; Maar, Marie; Larsen, Janus; Møller, Eva Friis; Hansen, Per Juel

    2017-05-01

    The aim of the study was to investigate the relative importance of bottom-up and top-down forcing on trophic cascades in the pelagic food-web and the implications for water quality indicators (summer phytoplankton biomass and winter nutrients) in relation to management. The 3D ecological model ERGOM was validated and applied in a local set-up of the Kattegat, Denmark, using the off-line Flexsem framework. The model scenarios were conducted by changing the forcing by ± 20% of nutrient inputs (bottom-up) and mesozooplankton mortality (top-down), and both types of forcing combined. The model results showed that cascading effects operated differently depending on the forcing type. In the single-forcing bottom-up scenarios, the cascade directions were in the same direction as the forcing. For scenarios involving top-down, there was a skipped-level-transmission in the trophic responses that was either attenuated or amplified at different trophic levels. On a seasonal scale, bottom-up forcing showed strongest response during winter-spring for DIN and Chl a concentrations, whereas top-down forcing had the highest cascade strength during summer for Chl a concentrations and microzooplankton biomass. On annual basis, the system was more bottom-up than top-down controlled. Microzooplankton was found to play an important role in the pelagic food web as mediator of nutrient and energy fluxes. This study demonstrated that the best scenario for improved water quality was a combined reduction in nutrient input and mesozooplankton mortality calling for the need of an integrated management of marine areas exploited by human activities.

  1. Prediction models in complex terrain

    DEFF Research Database (Denmark)

    Marti, I.; Nielsen, Torben Skov; Madsen, Henrik

    2001-01-01

    The objective of the work is to investigatethe performance of HIRLAM in complex terrain when used as input to energy production forecasting models, and to develop a statistical model to adapt HIRLAM prediction to the wind farm. The features of the terrain, specially the topography, influence...

  2. Environmental factors which affect growth of Japanese common squid, Todarodes pacificus, analyzed by a bioenergetics model coupled with a lower trophic ecosystem model

    Science.gov (United States)

    Kishi, Michio J.; Nakajima, Kazuto; Fujii, Masahiko; Hashioka, Taketo

    2009-09-01

    Bioenergetics model is applied to Japanese common squid, Todarodes pacificus. The temporal change of wet weight of common squid, which migrates in the Sea of Japan, is simulated. The time dependent horizontal distribution of prey is calculated a priori by 3-D coupled physical-biological model. The biological model NEMURO (North Pacific Ecosystem Model for Understanding Regional Oceanography) is used to simulate the lower-trophic ecosystem including three kinds of zooplankton biomass two of which is used as prey of common squid. A bioenergetics model reproduced appropriate growth curve of common squid, migrating in the North Pacific and the Sea of Japan. The results show that the wet weight of common squid in the northern Sea of Japan is heavier than that migrating in the central Sea of Japan, because prey density of the northern Sea of Japan is higher than that of the central Sea of Japan. We also investigate the wet weight anomaly for a global warming scenario. In this case, wet weight of common squid decreases because water temperature exceeds the optimum temperature for common squid. This result indicates that migration route and spawning area of common squid might change with global warming.

  3. Trophic niche-space imaging, using resource and consumer traits

    NARCIS (Netherlands)

    Nagelkerke, L.A.J.; Rossberg, A.G.

    2014-01-01

    The strength of trophic (feeding) links between two species depends on the traits of both the consumer and the resource. But which traits of consumer and resource have to be measured to predict link strengths, and how many? A novel theoretical framework for systematically determining trophic traits

  4. Trophic niche-space imaging, using resource and consumer traits

    NARCIS (Netherlands)

    Nagelkerke, L.A.J.; Rossberg, A.G.

    2014-01-01

    The strength of trophic (feeding) links between two species depends on the traits of both the consumer and the resource. But which traits of consumer and resource have to be measured to predict link strengths, and how many? A novel theoretical framework for systematically determining trophic traits

  5. Predictive models of forest dynamics.

    Science.gov (United States)

    Purves, Drew; Pacala, Stephen

    2008-06-13

    Dynamic global vegetation models (DGVMs) have shown that forest dynamics could dramatically alter the response of the global climate system to increased atmospheric carbon dioxide over the next century. But there is little agreement between different DGVMs, making forest dynamics one of the greatest sources of uncertainty in predicting future climate. DGVM predictions could be strengthened by integrating the ecological realities of biodiversity and height-structured competition for light, facilitated by recent advances in the mathematics of forest modeling, ecological understanding of diverse forest communities, and the availability of forest inventory data.

  6. Impact of the 3 °C temperature rise on bacterial growth and carbon transfer towards higher trophic levels: Empirical models for the Adriatic Sea

    Science.gov (United States)

    Šolić, Mladen; Krstulović, Nada; Šantić, Danijela; Šestanović, Stefanija; Kušpilić, Grozdan; Bojanić, Natalia; Ordulj, Marin; Jozić, Slaven; Vrdoljak, Ana

    2017-09-01

    The Mediterranean Sea (including the Adriatic Sea) has been identified as a 'hotspot' for climate change, with the prediction of the increase in water temperature of 2-4 °C over the next few decades. Being mainly oligotrophic, and strongly phosphorus limited, the Adriatic Sea is characterized by the important role of the microbial food web in production and transfer of biomass and energy towards higher trophic levels. We hypothesized that predicted 3 °C temperature rise in the near future might cause an increase of bacterial production and bacterial losses to grazers, which could significantly enlarge the trophic base for metazoans. This empirical study is based on a combined 'space-for-time substitution' analysis (which is performed on 3583 data sets) and on an experimental approach (36 in situ grazing experiments performed at different temperatures). It showed that the predicted 3 °C temperature increase (which is a result of global warming) in the near future could cause a significant increase in bacterial growth at temperatures lower than 16 °C (during the colder winter-spring period, as well as in the deeper layers). The effect of temperature on bacterial growth could be additionally doubled in conditions without phosphorus limitation. Furthermore, a 3 °C increase in temperature could double the grazing on bacteria by heterotrophic nanoflagellate (HNF) and ciliate predators and it could increase the proportion of bacterial production transferred to the metazoan food web by 42%. Therefore, it is expected that global warming may further strengthen the role of the microbial food web in a carbon cycle in the Adriatic Sea.

  7. Trophic cascades of bottom-up and top-down forcing on nutrients and plankton in the Kattegat, evaluated by modelling

    DEFF Research Database (Denmark)

    Petersen, Marcell Elo; Maar, Marie; Larsen, Janus

    2017-01-01

    The aim of the study was to investigate the relative importance of bottom-up and top-down forcing on trophic cascades in the pelagic food-web and the implications for water quality indicators (summer phytoplankton biomass and winter nutrients) in relation to management. The 3D ecological model....... On annual basis, the system was more bottom-up than top-down controlled. Microzooplankton was found to play an important role in the pelagic food web as mediator of nutrient and energy fluxes. This study demonstrated that the best scenario for improved water quality was a combined reduction in nutrient...... ERGOM was validated and applied in a local set-up of the Kattegat, Denmark, using the off-line Flexsem framework. The model scenarios were conducted by changing the forcing by ±20% of nutrient inputs (bottom-up) and mesozooplankton mortality (top-down), and both types of forcing combined. The model...

  8. Piscivores, Trophic Cascades, and Lake Management

    Directory of Open Access Journals (Sweden)

    Ray W. Drenner

    2002-01-01

    Full Text Available The concept of cascading trophic interactions predicts that an increase in piscivore biomass in lakes will result in decreased planktivorous fish biomass, increased herbivorous zooplankton biomass, and decreased phytoplankton biomass. Though often accepted as a paradigm in the ecological literature and adopted by lake managers as a basis for lake management strategies, the trophic cascading interactions hypothesis has not received the unequivocal support (in the form of rigorous experimental testing that might be expected of a paradigm. Here we review field experiments and surveys, testing the hypothesis that effects of increasing piscivore biomass will cascade down through the food web yielding a decline in phytoplankton biomass. We found 39 studies in the scientific literature examining piscivore effects on phytoplankton biomass. Of the studies, 22 were confounded by supplemental manipulations (e.g., simultaneous reduction of nutrients or removal of planktivores and could not be used to assess piscivore effects. Of the 17 nonconfounded studies, most did not find piscivore effects on phytoplankton biomass and therefore did not support the trophic cascading interactions hypothesis. However, the trophic cascading interactions hypothesis also predicts that lake systems containing piscivores will have lower phytoplankton biomass for any given phosphorus concentration. Based on regression analyses of chlorophyll�total phosphorus relationships in the 17 nonconfounded piscivore studies, this aspect of the trophic cascading interactions hypothesis was supported. The slope of the chlorophyll vs. total phosphorus regression was lower in lakes with planktivores and piscivores compared with lakes containing only planktivores but no piscivores. We hypothesize that this slope can be used as an indicator of “functional piscivory” and that communities with extremes of functional piscivory (zero and very high represent classical 3- and 4-trophic level

  9. Impacts of Intensive Logging on the Trophic Organisation of Ant Communities in a Biodiversity Hotspot

    Science.gov (United States)

    Woodcock, Paul; Edwards, David P.; Newton, Rob J.; Vun Khen, Chey; Bottrell, Simon H.; Hamer, Keith C.

    2013-01-01

    Trophic organisation defines the flow of energy through ecosystems and is a key component of community structure. Widespread and intensifying anthropogenic disturbance threatens to disrupt trophic organisation by altering species composition and relative abundances and by driving shifts in the trophic ecology of species that persist in disturbed ecosystems. We examined how intensive disturbance caused by selective logging affects trophic organisation in the biodiversity hotspot of Sabah, Borneo. Using stable nitrogen isotopes, we quantified the positions in the food web of 159 leaf-litter ant species in unlogged and logged rainforest and tested four predictions: (i) there is a negative relationship between the trophic position of a species in unlogged forest and its change in abundance following logging, (ii) the trophic positions of species are altered by logging, (iii) disturbance alters the frequency distribution of trophic positions within the ant assemblage, and (iv) disturbance reduces food chain length. We found that ant abundance was 30% lower in logged forest than in unlogged forest but changes in abundance of individual species were not related to trophic position, providing no support for prediction (i). However, trophic positions of individual species were significantly higher in logged forest, supporting prediction (ii). Consequently, the frequency distribution of trophic positions differed significantly between unlogged and logged forest, supporting prediction (iii), and food chains were 0.2 trophic levels longer in logged forest, the opposite of prediction (iv). Our results demonstrate that disturbance can alter trophic organisation even without trophically-biased changes in community composition. Nonetheless, the absence of any reduction in food chain length in logged forest suggests that species-rich arthropod food webs do not experience trophic downgrading or a related collapse in trophic organisation despite the disturbance caused by logging

  10. Impacts of intensive logging on the trophic organisation of ant communities in a biodiversity hotspot.

    Directory of Open Access Journals (Sweden)

    Paul Woodcock

    Full Text Available Trophic organisation defines the flow of energy through ecosystems and is a key component of community structure. Widespread and intensifying anthropogenic disturbance threatens to disrupt trophic organisation by altering species composition and relative abundances and by driving shifts in the trophic ecology of species that persist in disturbed ecosystems. We examined how intensive disturbance caused by selective logging affects trophic organisation in the biodiversity hotspot of Sabah, Borneo. Using stable nitrogen isotopes, we quantified the positions in the food web of 159 leaf-litter ant species in unlogged and logged rainforest and tested four predictions: (i there is a negative relationship between the trophic position of a species in unlogged forest and its change in abundance following logging, (ii the trophic positions of species are altered by logging, (iii disturbance alters the frequency distribution of trophic positions within the ant assemblage, and (iv disturbance reduces food chain length. We found that ant abundance was 30% lower in logged forest than in unlogged forest but changes in abundance of individual species were not related to trophic position, providing no support for prediction (i. However, trophic positions of individual species were significantly higher in logged forest, supporting prediction (ii. Consequently, the frequency distribution of trophic positions differed significantly between unlogged and logged forest, supporting prediction (iii, and food chains were 0.2 trophic levels longer in logged forest, the opposite of prediction (iv. Our results demonstrate that disturbance can alter trophic organisation even without trophically-biased changes in community composition. Nonetheless, the absence of any reduction in food chain length in logged forest suggests that species-rich arthropod food webs do not experience trophic downgrading or a related collapse in trophic organisation despite the disturbance caused by

  11. Trophic designation and live coral cover predict changes in reef-fish community structure along a shallow to mesophotic gradient in Hawaii

    Science.gov (United States)

    Kane, Corinne N.; Tissot, Brian N.

    2017-09-01

    Reef-fish community structure and habitat associations are well documented for shallow coral reefs (reefs (mesophotic reefs; >30 m). We documented the community structure of fishes and seafloor habitat composition through visual observations at depth intervals from 3 to 50 m in West Hawaii. Community structure changed gradually with depth, with more than 78% of fish species observed at mesophotic depths also found in shallow reef habitats. Depth explained 17% of the variation in reef-fish community structure; live coral cover explained 10% and prevalence of sand accounted for 7% of the fitted variation indicating that depth-related factors and coral habitat play a predominant role in structuring these communities. Differences in community structure also appear to be linked closely with feeding behavior. Trophic designation accounted for 31% of the fitted variation, with changes in herbivore abundance accounting for 10% of the variation. These findings suggest that changes in reef-fish community composition from shallow to mesophotic environments are largely influenced by trophic position, coral habitat and indirect effects of depth itself.

  12. Multispecies QSAR modeling for predicting the aquatic toxicity of diverse organic chemicals for regulatory toxicology.

    Science.gov (United States)

    Singh, Kunwar P; Gupta, Shikha; Kumar, Anuj; Mohan, Dinesh

    2014-05-19

    The research aims to develop multispecies quantitative structure-activity relationships (QSARs) modeling tools capable of predicting the acute toxicity of diverse chemicals in various Organization for Economic Co-operation and Development (OECD) recommended test species of different trophic levels for regulatory toxicology. Accordingly, the ensemble learning (EL) approach based classification and regression QSAR models, such as decision treeboost (DTB) and decision tree forest (DTF) implementing stochastic gradient boosting and bagging algorithms were developed using the algae (P. subcapitata) experimental toxicity data for chemicals. The EL-QSAR models were successfully applied to predict toxicities of wide groups of chemicals in other test species including algae (S. obliguue), daphnia, fish, and bacteria. Structural diversity of the selected chemicals and those of the end-point toxicity data of five different test species were tested using the Tanimoto similarity index and Kruskal-Wallis (K-W) statistics. Predictive and generalization abilities of the constructed QSAR models were compared using statistical parameters. The developed QSAR models (DTB and DTF) yielded a considerably high classification accuracy in complete data of model building (algae) species (97.82%, 99.01%) and ranged between 92.50%-94.26% and 92.14%-94.12% in four test species, respectively, whereas regression QSAR models (DTB and DTF) rendered high correlation (R(2)) between the measured and model predicted toxicity end-point values and low mean-squared error in model building (algae) species (0.918, 0.15; 0.905, 0.21) and ranged between 0.575 and 0.672, 0.18-0.51 and 0.605-0.689 and 0.20-0.45 in four different test species. The developed QSAR models exhibited good predictive and generalization abilities in different test species of varied trophic levels and can be used for predicting the toxicities of new chemicals for screening and prioritization of chemicals for regulation.

  13. Trophic cascades of bottom-up and top-down forcing on nutrients and plankton in the Kattegat, evaluated by modelling

    DEFF Research Database (Denmark)

    Petersen, Marcell Elo; Maar, Marie; Larsen, Janus

    2017-01-01

    The aim of the study was to investigate the relative importance of bottom-up and top-down forcing on trophic cascades in the pelagic food-web and the implications for water quality indicators (summer phytoplankton biomass and winter nutrients) in relation to management. The 3D ecological model....... On annual basis, the system was more bottom-up than top-down controlled. Microzooplankton was found to play an important role in the pelagic food web as mediator of nutrient and energy fluxes. This study demonstrated that the best scenario for improved water quality was a combined reduction in nutrient...... input and mesozooplankton mortality calling for the need of an integrated management of marine areas exploited by human activities....

  14. [Trophic chains in soil].

    Science.gov (United States)

    Goncharov, A A; Tiunov, A V

    2013-01-01

    Trophic links of soil animals are extensively diverse but also flexible. Moreover, feeding activity of large soil saprotrophs often cascades into a range of ecosystem-level consequences via the ecological engineering. Better knowledge on the main sources of energy utilized by soil animals is needed for understanding functional structure of soil animal communities and their participation in the global carbon cycling. Using published and original data, we consider the relative importance of dead organic matter and saprotrophic microorganisms as a basal energy source in the detritus-based food chains, the feeding of endogeic macrofauna on the stabilized soil organic matter, and the role of recent photosynthate in the energy budget of soil communities. Soil food webs are spatially and functionally compartmentalized, though the separation of food chains into bacteria- and fungi-based channels seems to be an over-simplification. The regulation of the litter decomposition rates via top-down trophic interactions across more than one trophic level is only partly supported by experimental data, but mobile litter-dwelling predators play a crucial role in integrating local food webs within and across neighboring ecosystems.

  15. New insights into the Middle Pleistocene paleoecology and paleoenvironment of the Northern Iberian Peninsula (Punta Lucero Quarry site, Biscay): A combined approach using mammalian stable isotope analysis and trophic resource availability modeling

    Science.gov (United States)

    Domingo, Laura; Rodríguez-Gómez, Guillermo; Libano, Iñaki; Gómez-Olivencia, Asier

    2017-08-01

    The northern coastal area of the Iberian Peninsula shows an excellent archaeo-paleontological record with a unique representation of Pleistocene mammalian fossils. While the Late Pleistocene is better recorded, the Middle Pleistocene record remains more fragmentary. The Punta Lucero site (Biscay) has yielded the most important fossil assemblage of the middle Middle Pleistocene for the northern Iberian Peninsula in both, number of identified specimens and taxonomic diversity. Punta Lucero constitutes a unique opportunity to evaluate Middle Pleistocene mammalian resource and habitat use, and trophic dynamics employing a combined approach: biogeochemical analysis and mathematical modeling. Stable isotope analysis points to resource partitioning between Punta Lucero cervids and bovids. Stable isotope analysis and trophic modeling evidence resource overlap and interspecific competition among predators, especially between the scimitar-toothed cat Homotherium latidens and the European jaguar Panthera gombaszoegensis. The trophic resource availability modeling assumes that Canis mosbachensis consumed a 20% of preys of more than 10 kg, mainly as carrion. Thus, while there would be a taxonomic overlap with those preys consumed by the large felids, the different strategy would have facilitated the coexistence of these canids with larger carnivores. Trophic modeling indicates a high competition among the predator guild. The potential presence of hominins in the area would have reached to an unsustainable situation. However, the potential presence of other prey species, such as Equus sp., would have made the ecosystem more sustainable. The methodology followed in this study highlights the potential of multidisciplinary approaches in the assessment of Pleistocene faunal dynamics.

  16. Shifts in the trophic base of intermittent stream food webs

    Science.gov (United States)

    Dekar, Matthew P.; Magoulick, Daniel D.; Huxel, G.R.

    2009-01-01

    Understanding spatial and temporal variation in the trophic base of stream food webs is critical for predicting population and community stability, and ecosystem function. We used stable isotope ratios (13C/12C, and 15N/14N) to characterize the trophic base of two streams in the Ozark Mountains of northwest Arkansas, U.S.A. We predicted that autochthonous resources would be more important during the spring and summer and allochthonous resources would be more important in the winter due to increased detritus inputs from the riparian zone during autumn leaf drop. We predicted that stream communities would demonstrate increased reliance on autochthonous resources at sites with larger watersheds and greater canopy openness. The study was conducted at three low-order sites in the Mulberry River Drainage (watershed area range: 81-232 km2) seasonally in 2006 and 2007. We used circular statistics to examine community-wide shifts in isotope space among fish and invertebrate consumers in relation to basal resources, including detritus and periphyton. Mixing models were used to quantify the relative contribution of autochthonous and allochthonous energy sources to individual invertebrate consumers. Significant isotopic shifts occurred but results varied by season and site indicating substantial variation in the trophic base of stream food webs. In terms of temporal variation, consumers shifted toward periphyton in the summer during periods of low discharge, but results varied during the interval between summer and winter. Our results did not demonstrate increased reliance on periphyton with increasing watershed area or canopy openness, and detritus was important at all the sites. In our study, riffle-pool geomorphology likely disrupted the expected spatial pattern and stream drying likely impacted the availability and distribution of basal resources.

  17. New insights into the trophic and cytoprotective effects of creatine in in vitro and in vivo models of cell maturation.

    Science.gov (United States)

    Sestili, Piero; Ambrogini, Patrizia; Barbieri, Elena; Sartini, Stefano; Fimognari, Carmela; Calcabrini, Cinzia; Diaz, Anna Rita; Guescini, Michele; Polidori, Emanuela; Luchetti, Francesca; Canonico, Barbara; Lattanzi, Davide; Cuppini, Riccardo; Papa, Stefano; Stocchi, Vilberto

    2016-08-01

    A growing body of scientific reports indicates that the role of creatine (Cr) in cellular biochemistry and physiology goes beyond its contribution to cell energy. Indeed Cr has been shown to exert multiple effects promoting a wide range of physiological responses in vitro as well as in vivo. Included in these, Cr promotes in vitro neuron and muscle cell differentiation, viability and survival under normal or adverse conditions; anabolic, protective and pro-differentiative effects have also been observed in vivo. For example Cr has been shown to accelerate in vitro differentiation of cultured C2C12 myoblasts into myotubes, where it also induces a slight but significant hypertrophic effect as compared to unsupplemented cultures; Cr also prevents the anti-differentiation effects caused by oxidative stress in the same cells. In trained adults, Cr increases the mRNA expression of relevant myogemic factors, protein synthesis, muscle strength and size, in cooperation with physical exercise. As to neurons and central nervous system, Cr favors the electrophysiological maturation of chick neuroblasts in vitro and protects them from oxidative stress-caused killing; similarly, Cr promotes the survival and differentiation of GABA-ergic neurons in fetal spinal cord cultures in vitro; in vivo, maternal Cr supplementation promotes the morpho-functional development of hippocampal neurons in rat offsprings. This article, which presents also some new experimental data, focuses on the trophic, pro-survival and pro-differentiation effects of Cr and examines the ensuing preventive and therapeutic potential in pathological muscle and brain conditions.

  18. The relative influence of competition and prey defences on the trophic structure of animalivorous bat ensembles.

    Science.gov (United States)

    Schoeman, M Corrie; Jacobs, David S

    2011-06-01

    Deterministic filters such as competition and prey defences should have a strong influence on the community structure of animals like animalivorous bats which have life histories characterized by low fecundity, low predation risk, long life expectancy and stable populations. We investigated the relative influence of these two deterministic filters on the trophic structure of animalivorous bat assemblages in South Africa. We used null models to test if patterns of dietary overlap were significantly different from patterns expected by chance and multivariate analyses to test the correlations between diet and phenotype (body size, wing morphology and echolocation). We found little evidence that competition structured the trophic niche of coexisting bats. Contrary to predictions from competition, dietary overlap between bats of ensembles and functional groups (open-air, clutter-edge, and clutter foragers) were significantly higher than expected by chance. Instead, we found support for the predictions of the allotonic frequency hypothesis: there were significant relationships between peak echolocation frequency and the proportion of moths in the diets of bats at local and regional scales, and peak echolocation frequency was the best predictor of diet even after we controlled for the influence of body size and phylogeny. These results suggest that echolocation frequency and prey hearing exert more influence on the trophic structure of sympatric animalivorous bats than competition. Nonetheless, differential habitat use and sensory bias may also be major determinants of trophic structure because these are also correlated with frequencies of bat calls.

  19. PREDICT : model for prediction of survival in localized prostate cancer

    NARCIS (Netherlands)

    Kerkmeijer, Linda G W; Monninkhof, Evelyn M.; van Oort, Inge M.; van der Poel, Henk G.; de Meerleer, Gert; van Vulpen, Marco

    2016-01-01

    Purpose: Current models for prediction of prostate cancer-specific survival do not incorporate all present-day interventions. In the present study, a pre-treatment prediction model for patients with localized prostate cancer was developed.Methods: From 1989 to 2008, 3383 patients were treated with I

  20. Mercury pathways and trophic interactions in New Brunswick lakes

    Energy Technology Data Exchange (ETDEWEB)

    Barry, E.; Curry, A. [New Brunswick Univ., Cooperative Fish and Wildlife Research Unit, Fredericton, NB (Canada); Burgess, N. [Environment Canada, Canadian Wildlife Service, Sackville, NB (Canada); Bielak, A. [Environment Canada, Environmental Conservation Branch, Dartmouth, NS (Canada)

    1998-11-01

    This study was designed to determine the pathway taken by mercury from primary to top consumer in lakes of southern New Brunswick. The study was part of a development of new models to predict the fate of mercury in natural systems. Fish communities in 18 lakes were surveyed in 1997 and analyzed for mercury, stomach content and stable isotope ratios. Adult and tadpole bullfrogs, plankton and benthic invertebrates from the littoral zone were also collected and examined for total mercury and methylmercury concentrations. Stable isotope analysis was also conducted for each species of organism to elucidate mercury pathways based on the trophic position and mercury concentration ratios. Since this study is the first in-depth look at mercury concentrations in lake ecosystems in this region, it is expected that the trophic interactions within these lakes will lead to a greater understanding of the route mercury takes within lakes. It is also expected to lead to further development of predictive models of fish and loon mercury levels in New Brunswick and in Atlantic Canada as a whole.

  1. A method for improving predictive modeling by taking into account lag time: Example of selenium bioaccumulation in a flowing system.

    Science.gov (United States)

    Beckon, William N

    2016-07-01

    For bioaccumulative substances, efforts to predict concentrations in organisms at upper trophic levels, based on measurements of environmental exposure, have been confounded by the appreciable but hitherto unknown amount of time it may take for bioaccumulation to occur through various pathways and across several trophic transfers. The study summarized here demonstrates an objective method of estimating this lag time by testing a large array of potential lag times for selenium bioaccumulation, selecting the lag that provides the best regression between environmental exposure (concentration in ambient water) and concentration in the tissue of the target organism. Bioaccumulation lag is generally greater for organisms at higher trophic levels, reaching times of more than a year in piscivorous fish. Predictive modeling of bioaccumulation is improved appreciably by taking into account this lag. More generally, the method demonstrated here may improve the accuracy of predictive modeling in a wide variety of other cause-effect relationships in which lag time is substantial but inadequately known, in disciplines as diverse as climatology (e.g., the effect of greenhouse gases on sea levels) and economics (e.g., the effects of fiscal stimulus on employment).

  2. Predictive Modeling of Cardiac Ischemia

    Science.gov (United States)

    Anderson, Gary T.

    1996-01-01

    The goal of the Contextual Alarms Management System (CALMS) project is to develop sophisticated models to predict the onset of clinical cardiac ischemia before it occurs. The system will continuously monitor cardiac patients and set off an alarm when they appear about to suffer an ischemic episode. The models take as inputs information from patient history and combine it with continuously updated information extracted from blood pressure, oxygen saturation and ECG lines. Expert system, statistical, neural network and rough set methodologies are then used to forecast the onset of clinical ischemia before it transpires, thus allowing early intervention aimed at preventing morbid complications from occurring. The models will differ from previous attempts by including combinations of continuous and discrete inputs. A commercial medical instrumentation and software company has invested funds in the project with a goal of commercialization of the technology. The end product will be a system that analyzes physiologic parameters and produces an alarm when myocardial ischemia is present. If proven feasible, a CALMS-based system will be added to existing heart monitoring hardware.

  3. Trigeminal trophic syndrome

    Directory of Open Access Journals (Sweden)

    Parimalam Kumar

    2014-01-01

    Full Text Available Trigeminal trophic syndrome (TTS is a rare cause of facial ulceration, consequent to damage to the trigeminal nerve or its central sensory connections. We reporta case of TTS in a 48-year-old woman with Bell′s palsy following herpes zoster infection. The patient was treated and counseled. There hasnot been any recurrence for 1 year and the patient is being followed-up. The diagnosis of TTS should be suspected when there is unilateral facial ulceration, especially involving the ala nasi associated with sensory impairment.

  4. Does morphology predict trophic niche differentiation? Relationship between feeding habits and body shape in four co-occurring juvenile species (Pisces: Perciformes, Sparidae)

    Science.gov (United States)

    Ventura, Daniele; Bonhomme, Vincent; Colangelo, Paolo; Bonifazi, Andrea; Jona Lasinio, Giovanna; Ardizzone, Giandomenico

    2017-05-01

    Feeding habits, diet overlap and morphological correlates of four juvenile species of the genus Diplodus were investigated during their settlement periods, along the Tyrrhenian coast. Stomach content analysis showed that the diets of D. sargus and D. puntazzo mainly comprised benthic prey such as harpacticoid copepods, amphipods and polychaetes. On the other hand, D. vulgaris and D. annularis fed mainly on planktonic prey such as ciclopoids, calanoids copepods and fish larvae. A biologically significant diet overlap, calculated using the Schoener index, was recorded between D. sargus and D. puntazzo and between D. vulgaris and D. annularis. Morphological characters related to feeding such as gape height and gut length with their relative growth patterns suggested that different trophic preferences have led to a morphological diversification of feeding structures. Therefore, a geometric morphometric outline method, namely Elliptic Fourier Analysis (EFA) was used to examine shape modification of the head and body regions. The multivariate analyses performed on shape descriptors demonstrated that the four species were morphologically distinct due to different feeding habits: the two species which feed mainly on benthic prey presented a discoidal shape, with broad profiles and rounded head; by contrast, the other two species which relied mostly on planktonic prey, presented a streamlined and more elongated body shape.

  5. Numerical weather prediction model tuning via ensemble prediction system

    Science.gov (United States)

    Jarvinen, H.; Laine, M.; Ollinaho, P.; Solonen, A.; Haario, H.

    2011-12-01

    This paper discusses a novel approach to tune predictive skill of numerical weather prediction (NWP) models. NWP models contain tunable parameters which appear in parameterizations schemes of sub-grid scale physical processes. Currently, numerical values of these parameters are specified manually. In a recent dual manuscript (QJRMS, revised) we developed a new concept and method for on-line estimation of the NWP model parameters. The EPPES ("Ensemble prediction and parameter estimation system") method requires only minimal changes to the existing operational ensemble prediction infra-structure and it seems very cost-effective because practically no new computations are introduced. The approach provides an algorithmic decision making tool for model parameter optimization in operational NWP. In EPPES, statistical inference about the NWP model tunable parameters is made by (i) generating each member of the ensemble of predictions using different model parameter values, drawn from a proposal distribution, and (ii) feeding-back the relative merits of the parameter values to the proposal distribution, based on evaluation of a suitable likelihood function against verifying observations. In the presentation, the method is first illustrated in low-order numerical tests using a stochastic version of the Lorenz-95 model which effectively emulates the principal features of ensemble prediction systems. The EPPES method correctly detects the unknown and wrongly specified parameters values, and leads to an improved forecast skill. Second, results with an atmospheric general circulation model based ensemble prediction system show that the NWP model tuning capacity of EPPES scales up to realistic models and ensemble prediction systems. Finally, a global top-end NWP model tuning exercise with preliminary results is published.

  6. When should we expect early bursts of trait evolution in comparative data? Predictions from an evolutionary food web model.

    Science.gov (United States)

    Ingram, T; Harmon, L J; Shurin, J B

    2012-09-01

    Conceptual models of adaptive radiation predict that competitive interactions among species will result in an early burst of speciation and trait evolution followed by a slowdown in diversification rates. Empirical studies often show early accumulation of lineages in phylogenetic trees, but usually fail to detect early bursts of phenotypic evolution. We use an evolutionary simulation model to assemble food webs through adaptive radiation, and examine patterns in the resulting phylogenetic trees and species' traits (body size and trophic position). We find that when foraging trade-offs result in food webs where all species occupy integer trophic levels, lineage diversity and trait disparity are concentrated early in the tree, consistent with the early burst model. In contrast, in food webs in which many omnivorous species feed at multiple trophic levels, high levels of turnover of species' identities and traits tend to eliminate the early burst signal. These results suggest testable predictions about how the niche structure of ecological communities may be reflected by macroevolutionary patterns.

  7. Return Predictability, Model Uncertainty, and Robust Investment

    DEFF Research Database (Denmark)

    Lukas, Manuel

    Stock return predictability is subject to great uncertainty. In this paper we use the model confidence set approach to quantify uncertainty about expected utility from investment, accounting for potential return predictability. For monthly US data and six representative return prediction models, we...

  8. Diet compositions and trophic guild structure of the eastern Chukchi Sea demersal fish community

    Science.gov (United States)

    Whitehouse, George A.; Buckley, Troy W.; Danielson, Seth L.

    2017-01-01

    Fishes are an important link in Arctic marine food webs, connecting production of lower trophic levels to apex predators. We analyzed 1773 stomach samples from 39 fish species collected during a bottom trawl survey of the eastern Chukchi Sea in the summer of 2012. We used hierarchical cluster analysis of diet dissimilarities on 21 of the most well sampled species to identify four distinct trophic guilds: gammarid amphipod consumers, benthic invertebrate generalists, fish and shrimp consumers, and zooplankton consumers. The trophic guilds reflect dominant prey types in predator diets. We used constrained analysis of principal coordinates (CAP) to determine if variation within the composite guild diets could be explained by a suite of non-diet variables. All CAP models explained a significant proportion of the variance in the diet matrices, ranging from 7% to 25% of the total variation. Explanatory variables tested included latitude, longitude, predator length, depth, and water mass. These results indicate a trophic guild structure is present amongst the demersal fish community during summer in the eastern Chukchi Sea. Regular monitoring of the food habits of the demersal fish community will be required to improve our understanding of the spatial, temporal, and interannual variation in diet composition, and to improve our ability to identify and predict the impacts of climate change and commercial development on the structure and functioning of the Chukchi Sea ecosystem.

  9. Analysis and prediction of agricultural pest dynamics with Tiko'n, a generic tool to develop agroecological food web models

    Science.gov (United States)

    Malard, J. J.; Rojas, M.; Adamowski, J. F.; Anandaraja, N.; Tuy, H.; Melgar-Quiñonez, H.

    2016-12-01

    While several well-validated crop growth models are currently widely used, very few crop pest models of the same caliber have been developed or applied, and pest models that take trophic interactions into account are even rarer. This may be due to several factors, including 1) the difficulty of representing complex agroecological food webs in a quantifiable model, and 2) the general belief that pesticides effectively remove insect pests from immediate concern. However, pests currently claim a substantial amount of harvests every year (and account for additional control costs), and the impact of insects and of their trophic interactions on agricultural crops cannot be ignored, especially in the context of changing climates and increasing pressures on crops across the globe. Unfortunately, most integrated pest management frameworks rely on very simple models (if at all), and most examples of successful agroecological management remain more anecdotal than scientifically replicable. In light of this, there is a need for validated and robust agroecological food web models that allow users to predict the response of these webs to changes in management, crops or climate, both in order to predict future pest problems under a changing climate as well as to develop effective integrated management plans. Here we present Tiko'n, a Python-based software whose API allows users to rapidly build and validate trophic web agroecological models that predict pest dynamics in the field. The programme uses a Bayesian inference approach to calibrate the models according to field data, allowing for the reuse of literature data from various sources and reducing the need for extensive field data collection. We apply the model to the cononut black-headed caterpillar (Opisina arenosella) and associated parasitoid data from Sri Lanka, showing how the modeling framework can be used to rapidly develop, calibrate and validate models that elucidate how the internal structures of food webs

  10. Predictive Model Assessment for Count Data

    Science.gov (United States)

    2007-09-05

    critique count regression models for patent data, and assess the predictive performance of Bayesian age-period-cohort models for larynx cancer counts...the predictive performance of Bayesian age-period-cohort models for larynx cancer counts in Germany. We consider a recent suggestion by Baker and...Figure 5. Boxplots for various scores for patent data count regressions. 11 Table 1 Four predictive models for larynx cancer counts in Germany, 1998–2002

  11. From neurons to epidemics: How trophic coherence affects spreading processes

    CERN Document Server

    Klaise, Janis

    2016-01-01

    Trophic coherence, a measure of the extent to which the nodes of a directed network are organised in levels, has recently been shown to be closely related to many structural and dynamical aspects of complex systems, including graph eigenspectra, the prevalence or absence of feed-back cycles, and linear stability. Furthermore, non-trivial trophic structures have been observed in networks of neurons, species, genes, metabolites, cellular signalling, concatenated words, P2P users, and world trade. Here we consider two simple yet apparently quite different dynamical models -- one a Susceptible-Infected-Susceptible (SIS) epidemic model adapted to include complex contagion, the other an Amari-Hopfield neural network -- and show that in both cases the related spreading processes are modulated in similar ways by the trophic coherence of the underlying networks. To do this, we propose a network assembly model which can generate structures with tunable trophic coherence, limiting in either perfectly stratified networks...

  12. Nonlinear chaotic model for predicting storm surges

    Directory of Open Access Journals (Sweden)

    M. Siek

    2010-09-01

    Full Text Available This paper addresses the use of the methods of nonlinear dynamics and chaos theory for building a predictive chaotic model from time series. The chaotic model predictions are made by the adaptive local models based on the dynamical neighbors found in the reconstructed phase space of the observables. We implemented the univariate and multivariate chaotic models with direct and multi-steps prediction techniques and optimized these models using an exhaustive search method. The built models were tested for predicting storm surge dynamics for different stormy conditions in the North Sea, and are compared to neural network models. The results show that the chaotic models can generally provide reliable and accurate short-term storm surge predictions.

  13. Nonlinear chaotic model for predicting storm surges

    NARCIS (Netherlands)

    Siek, M.; Solomatine, D.P.

    This paper addresses the use of the methods of nonlinear dynamics and chaos theory for building a predictive chaotic model from time series. The chaotic model predictions are made by the adaptive local models based on the dynamical neighbors found in the reconstructed phase space of the observables.

  14. EFFICIENT PREDICTIVE MODELLING FOR ARCHAEOLOGICAL RESEARCH

    OpenAIRE

    Balla, A.; Pavlogeorgatos, G.; Tsiafakis, D.; Pavlidis, G.

    2014-01-01

    The study presents a general methodology for designing, developing and implementing predictive modelling for identifying areas of archaeological interest. The methodology is based on documented archaeological data and geographical factors, geospatial analysis and predictive modelling, and has been applied to the identification of possible Macedonian tombs’ locations in Northern Greece. The model was tested extensively and the results were validated using a commonly used predictive gain,...

  15. How to Establish Clinical Prediction Models

    Directory of Open Access Journals (Sweden)

    Yong-ho Lee

    2016-03-01

    Full Text Available A clinical prediction model can be applied to several challenging clinical scenarios: screening high-risk individuals for asymptomatic disease, predicting future events such as disease or death, and assisting medical decision-making and health education. Despite the impact of clinical prediction models on practice, prediction modeling is a complex process requiring careful statistical analyses and sound clinical judgement. Although there is no definite consensus on the best methodology for model development and validation, a few recommendations and checklists have been proposed. In this review, we summarize five steps for developing and validating a clinical prediction model: preparation for establishing clinical prediction models; dataset selection; handling variables; model generation; and model evaluation and validation. We also review several studies that detail methods for developing clinical prediction models with comparable examples from real practice. After model development and vigorous validation in relevant settings, possibly with evaluation of utility/usability and fine-tuning, good models can be ready for the use in practice. We anticipate that this framework will revitalize the use of predictive or prognostic research in endocrinology, leading to active applications in real clinical practice.

  16. Comparison of Prediction-Error-Modelling Criteria

    DEFF Research Database (Denmark)

    Jørgensen, John Bagterp; Jørgensen, Sten Bay

    2007-01-01

    is a realization of a continuous-discrete multivariate stochastic transfer function model. The proposed prediction error-methods are demonstrated for a SISO system parameterized by the transfer functions with time delays of a continuous-discrete-time linear stochastic system. The simulations for this case suggest......Single and multi-step prediction-error-methods based on the maximum likelihood and least squares criteria are compared. The prediction-error methods studied are based on predictions using the Kalman filter and Kalman predictors for a linear discrete-time stochastic state space model, which...... computational resources. The identification method is suitable for predictive control....

  17. Dominant predators mediate the impact of habitat size on trophic structure in bromeliad invertebrate communities.

    Science.gov (United States)

    Petermann, Jana S; Farjalla, Vinicius F; Jocque, Merlijn; Kratina, Pavel; MacDonald, A Andrew M; Marino, Nicholas A C; De Omena, Paula M; Piccoli, Gustavo C O; Richardson, Barbara A; Richardson, Michael J; Romero, Gustavo Q; Videla, Martin; Srivastava, Diane S

    2015-02-01

    Local habitat size has been shown to influence colonization and extinction processes of species in patchy environments. However, species differ in body size, mobility, and trophic level, and may not respond in the same way to habitat size. Thus far, we have a limited understanding of how habitat size influences the structure of multitrophic communities and to what extent the effects may be generalizable over a broad geographic range. Here, we used water-filled bromeliads of different sizes as a natural model system to examine the effects of habitat size on the trophic structure of their inhabiting invertebrate communities. We collected composition and biomass data from 651 bromeliad communities from eight sites across Central and South America differing in environmental conditions, species pools, and the presence of large-bodied odonate predators. We found that trophic structure in the communities changed dramatically with changes in habitat (bromeliad) size. Detritivore : resource ratios showed a consistent negative relationship with habitat size across sites. In contrast, changes in predator: detritivore (prey) ratios depended on the presence of odonates as dominant predators in the regional pool. At sites without odonates, predator: detritivore biomass ratios decreased with increasing habitat size. At sites with odonates, we found odonates to be more frequently present in large than in small bromeliads, and predator: detritivore biomass ratios increased with increasing habitat size to the point where some trophic pyramids became inverted. Our results show that the distribution of biomass amongst food-web levels depends strongly on habitat size, largely irrespective of geographic differences in environmental conditions or detritivore species compositions. However, the presence of large-bodied predators in the regional species pool may fundamentally alter this relationship between habitat size and trophic structure. We conclude that taking into account the

  18. Bioenergetics, Trophic Ecology, and Niche Separation of Tunas.

    Science.gov (United States)

    Olson, R J; Young, J W; Ménard, F; Potier, M; Allain, V; Goñi, N; Logan, J M; Galván-Magaña, F

    Tunas are highly specialized predators that have evolved numerous adaptations for a lifestyle that requires large amounts of energy consumption. Here we review our understanding of the bioenergetics and feeding dynamics of tunas on a global scale, with an emphasis on yellowfin, bigeye, skipjack, albacore, and Atlantic bluefin tunas. Food consumption balances bioenergetics expenditures for respiration, growth (including gonad production), specific dynamic action, egestion, and excretion. Tunas feed across the micronekton and some large zooplankton. Some tunas appear to time their life history to take advantage of ephemeral aggregations of crustacean, fish, and molluscan prey. Ontogenetic and spatial diet differences are substantial, and significant interdecadal changes in prey composition have been observed. Diet shifts from larger to smaller prey taxa highlight ecosystem-wide changes in prey availability and diversity and provide implications for changing bioenergetics requirements into the future. Where tunas overlap, we show evidence of niche separation between them; resources are divided largely by differences in diet percentages and size ranges of prey taxa. The lack of long-term data limits the ability to predict impacts of climate change on tuna feeding behaviour. We note the need for systematic collection of feeding data as part of routine monitoring of these species, and we highlight the advantages of using biochemical techniques for broad-scale analyses of trophic relations. We support the continued development of ecosystem models, which all too often lack the regional-specific trophic data needed to adequately investigate climate and fishing impacts. © 2016 Elsevier Ltd. All rights reserved.

  19. Infectious Agents Trigger Trophic Cascades.

    Science.gov (United States)

    Buck, Julia C; Ripple, William J

    2017-09-01

    Most demonstrated trophic cascades originate with predators, but infectious agents can also cause top-down indirect effects in ecosystems. Here we synthesize the literature on trophic cascades initiated by infectious agents including parasitoids, pathogens, parasitic castrators, macroparasites, and trophically transmitted parasites. Like predators, infectious agents can cause density-mediated and trait-mediated indirect effects through their direct consumptive and nonconsumptive effects respectively. Unlike most predators, however, infectious agents are not fully and immediately lethal to their victims, so their consumptive effects can also trigger trait-mediated indirect effects. We find that the frequency of trophic cascades reported for different consumer types scales with consumer lethality. Furthermore, we emphasize the value of uniting predator-prey and parasite-host theory under a general consumer-resource framework. Copyright © 2017 Elsevier Ltd. All rights reserved.

  20. Case studies in archaeological predictive modelling

    NARCIS (Netherlands)

    Verhagen, Jacobus Wilhelmus Hermanus Philippus

    2007-01-01

    In this thesis, a collection of papers is put together dealing with various quantitative aspects of predictive modelling and archaeological prospection. Among the issues covered are the effects of survey bias on the archaeological data used for predictive modelling, and the complexities of testing p

  1. Childhood asthma prediction models: a systematic review.

    Science.gov (United States)

    Smit, Henriette A; Pinart, Mariona; Antó, Josep M; Keil, Thomas; Bousquet, Jean; Carlsen, Kai H; Moons, Karel G M; Hooft, Lotty; Carlsen, Karin C Lødrup

    2015-12-01

    Early identification of children at risk of developing asthma at school age is crucial, but the usefulness of childhood asthma prediction models in clinical practice is still unclear. We systematically reviewed all existing prediction models to identify preschool children with asthma-like symptoms at risk of developing asthma at school age. Studies were included if they developed a new prediction model or updated an existing model in children aged 4 years or younger with asthma-like symptoms, with assessment of asthma done between 6 and 12 years of age. 12 prediction models were identified in four types of cohorts of preschool children: those with health-care visits, those with parent-reported symptoms, those at high risk of asthma, or children in the general population. Four basic models included non-invasive, easy-to-obtain predictors only, notably family history, allergic disease comorbidities or precursors of asthma, and severity of early symptoms. Eight extended models included additional clinical tests, mostly specific IgE determination. Some models could better predict asthma development and other models could better rule out asthma development, but the predictive performance of no single model stood out in both aspects simultaneously. This finding suggests that there is a large proportion of preschool children with wheeze for which prediction of asthma development is difficult.

  2. Reef Fishes at All Trophic Levels Respond Positively to Effective Marine Protected Areas.

    Directory of Open Access Journals (Sweden)

    German A Soler

    Full Text Available Marine Protected Areas (MPAs offer a unique opportunity to test the assumption that fishing pressure affects some trophic groups more than others. Removal of larger predators through fishing is often suggested to have positive flow-on effects for some lower trophic groups, in which case protection from fishing should result in suppression of lower trophic groups as predator populations recover. We tested this by assessing differences in the trophic structure of reef fish communities associated with 79 MPAs and open-access sites worldwide, using a standardised quantitative dataset on reef fish community structure. The biomass of all major trophic groups (higher carnivores, benthic carnivores, planktivores and herbivores was significantly greater (by 40% - 200% in effective no-take MPAs relative to fished open-access areas. This effect was most pronounced for individuals in large size classes, but with no size class of any trophic group showing signs of depressed biomass in MPAs, as predicted from higher predator abundance. Thus, greater biomass in effective MPAs implies that exploitation on shallow rocky and coral reefs negatively affects biomass of all fish trophic groups and size classes. These direct effects of fishing on trophic structure appear stronger than any top down effects on lower trophic levels that would be imposed by intact predator populations. We propose that exploitation affects fish assemblages at all trophic levels, and that local ecosystem function is generally modified by fishing.

  3. Chronically administered retinoic acid has trophic effects in the rat small intestine and promotes adaptation in a resection model of short bowel syndrome.

    Science.gov (United States)

    Wang, Lihua; Tang, Yuzhu; Rubin, Deborah C; Levin, Marc S

    2007-06-01

    Following the loss of functional small bowel surface area, the intestine undergoes a compensatory adaptive response. The observation that adaptation is inhibited in vitamin A-deficient rats following submassive intestinal resection suggested that vitamin A is required for this response and raised the possibility that exogenous vitamin A could augment adaptation. Therefore, to directly assess whether chronically administered retinoic acid could stimulate gut adaptation in a model of short bowel syndrome and to address the mechanisms of any such effects, Sprague-Dawley rats were implanted with controlled release retinoic acid or control pellets and then subjected to mid-small bowel or sham resections. At 2 wk postoperation, changes in gut morphology, crypt cell proliferation and apoptosis, enterocyte migration, the extracellular matrix, and gene expression were assessed. Retinoic acid had significant trophic effects in resected and sham-resected rats. Retinoic acid markedly inhibited apoptosis and stimulated crypt cell proliferation and enterocyte migration postresection. Data presented indicate that these proadaptive effects of retinoic acid may be mediated via changes in the extracellular matrix (e.g., by increasing collagen IV synthesis, decreasing E-cadherin expression, and reducing integrin beta(3) levels), via affects on Hedgehog signaling (e.g., by reducing expression of the Hedgehog receptors Ptch and Ptch2 and the Gli1 transcription factor), by increasing expression of Reg1 and Pap1, and by modulation of retinoid and peroxisome proliferator-activated receptor signaling pathways. These studies are the first to demonstrate that retinoic acid can significantly enhance intestinal adaptation and suggest it may be beneficial in patients with short bowel syndrome.

  4. Long term patterns in the late summer trophic niche of the invasive pumpkinseed sunfish Lepomis gibbosus

    Directory of Open Access Journals (Sweden)

    Gkenas C.

    2016-01-01

    Full Text Available Quantifying the trophic dynamics of invasive species in novel habitats is important for predicting the success of potential invaders and evaluating their ecological effects. The North American pumpkinseed sunfish Lepomis gibbosus is a successful invader in Europe, where it has caused negative ecological effects primarily through trophic interactions. Here, we quantified variations in the late summer trophic niche of pumpkinseed during establishment and integration in the mainstem of the Guadiana river, using stomach content analyses over a period of 40 years. Pumpkinseed showed a shift from trophic specialization during establishment to trophic generalism during integration. These results were concomitant with an increase in diet breadth that was accompanied by higher individual diet specialization particularly in large individuals. Irrespective of their drivers, these changes in trophic niche suggest that the potential ecological effects of pumpkinseed on recipient ecosystems can vary temporally along the invasion process.

  5. Model predictive control classical, robust and stochastic

    CERN Document Server

    Kouvaritakis, Basil

    2016-01-01

    For the first time, a textbook that brings together classical predictive control with treatment of up-to-date robust and stochastic techniques. Model Predictive Control describes the development of tractable algorithms for uncertain, stochastic, constrained systems. The starting point is classical predictive control and the appropriate formulation of performance objectives and constraints to provide guarantees of closed-loop stability and performance. Moving on to robust predictive control, the text explains how similar guarantees may be obtained for cases in which the model describing the system dynamics is subject to additive disturbances and parametric uncertainties. Open- and closed-loop optimization are considered and the state of the art in computationally tractable methods based on uncertainty tubes presented for systems with additive model uncertainty. Finally, the tube framework is also applied to model predictive control problems involving hard or probabilistic constraints for the cases of multiplic...

  6. Human mesenchymal stem cells prolong survival and ameliorate motor deficit through trophic support in Huntington's disease mouse models.

    Directory of Open Access Journals (Sweden)

    Yuan-Ta Lin

    Full Text Available We investigated the therapeutic potential of human bone marrow-derived mesenchymal stem cells (hBM-MSCs in Huntington's disease (HD mouse models. Ten weeks after intrastriatal injection of quinolinic acid (QA, mice that received hBM-MSC transplantation showed a significant reduction in motor function impairment and increased survival rate. Transplanted hBM-MSCs were capable of survival, and inducing neural proliferation and differentiation in the QA-lesioned striatum. In addition, the transplanted hBM-MSCs induced microglia, neuroblasts and bone marrow-derived cells to migrate into the QA-lesioned region. Similar results were obtained in R6/2-J2, a genetically-modified animal model of HD, except for the improvement of motor function. After hBM-MSC transplantation, the transplanted hBM-MSCs may integrate with the host cells and increase the levels of laminin, Von Willebrand Factor (VWF, stromal cell-derived factor-1 (SDF-1, and the SDF-1 receptor Cxcr4. The p-Erk1/2 expression was increased while Bax and caspase-3 levels were decreased after hBM-MSC transplantation suggesting that the reduced level of apoptosis after hBM-MSC transplantation was of benefit to the QA-lesioned mice. Our data suggest that hBM-MSCs have neural differentiation improvement potential, neurotrophic support capability and an anti-apoptotic effect, and may be a feasible candidate for HD therapy.

  7. Fatty acid biomarkers: validation of food web and trophic markers using C-13-labelled fatty acids in juvenile sandeel ( Ammodytes tobianus )

    DEFF Research Database (Denmark)

    Dalsgaard, Anne Johanne Tang; St. John, Michael

    2004-01-01

    A key issue in marine science is parameterizing trophic interactions in marine food webs, thereby developing an understanding of the importance of top-down and bottom-up controls on populations of key trophic players. This study validates the utility of fatty acid food web and trophic markers using...... C-13-labelled fatty acids to verify the conservative incorporation of fatty acid tracers by juvenile sandeel (Ammodytes tobianus) and assess their uptake, clearance, and metabolic turnover rates. Juvenile sandeel were fed for 16 days in the laboratory on a formulated diet enriched in (13)C16......:0 followed by 14 days on a formulated diet enriched in (13)C18:3(n - 3). An exponential model was employed to estimate the uptake and clearance rates of recovered labelled fatty acids as a function of growth and fatty acid metabolism. The model predicted a faster uptake of (13)C18:3(n - 3) than (13)C16:0 (0...

  8. Energy based prediction models for building acoustics

    DEFF Research Database (Denmark)

    Brunskog, Jonas

    2012-01-01

    In order to reach robust and simplified yet accurate prediction models, energy based principle are commonly used in many fields of acoustics, especially in building acoustics. This includes simple energy flow models, the framework of statistical energy analysis (SEA) as well as more elaborated...... principles as, e.g., wave intensity analysis (WIA). The European standards for building acoustic predictions, the EN 12354 series, are based on energy flow and SEA principles. In the present paper, different energy based prediction models are discussed and critically reviewed. Special attention is placed...

  9. Massive Predictive Modeling using Oracle R Enterprise

    CERN Document Server

    CERN. Geneva

    2014-01-01

    R is fast becoming the lingua franca for analyzing data via statistics, visualization, and predictive analytics. For enterprise-scale data, R users have three main concerns: scalability, performance, and production deployment. Oracle's R-based technologies - Oracle R Distribution, Oracle R Enterprise, Oracle R Connector for Hadoop, and the R package ROracle - address these concerns. In this talk, we introduce Oracle's R technologies, highlighting how each enables R users to achieve scalability and performance while making production deployment of R results a natural outcome of the data analyst/scientist efforts. The focus then turns to Oracle R Enterprise with code examples using the transparency layer and embedded R execution, targeting massive predictive modeling. One goal behind massive predictive modeling is to build models per entity, such as customers, zip codes, simulations, in an effort to understand behavior and tailor predictions at the entity level. Predictions...

  10. Liver Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing liver cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  11. Colorectal Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing colorectal cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  12. Cervical Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing cervical cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  13. Prostate Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing prostate cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  14. Pancreatic Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing pancreatic cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  15. Colorectal Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing colorectal cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  16. Bladder Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing bladder cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  17. Esophageal Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing esophageal cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  18. Lung Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing lung cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  19. Breast Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing breast cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  20. Ovarian Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing ovarian cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  1. Testicular Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of testicular cervical cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  2. Posterior Predictive Model Checking in Bayesian Networks

    Science.gov (United States)

    Crawford, Aaron

    2014-01-01

    This simulation study compared the utility of various discrepancy measures within a posterior predictive model checking (PPMC) framework for detecting different types of data-model misfit in multidimensional Bayesian network (BN) models. The investigated conditions were motivated by an applied research program utilizing an operational complex…

  3. A Course in... Model Predictive Control.

    Science.gov (United States)

    Arkun, Yaman; And Others

    1988-01-01

    Describes a graduate engineering course which specializes in model predictive control. Lists course outline and scope. Discusses some specific topics and teaching methods. Suggests final projects for the students. (MVL)

  4. Equivalency and unbiasedness of grey prediction models

    Institute of Scientific and Technical Information of China (English)

    Bo Zeng; Chuan Li; Guo Chen; Xianjun Long

    2015-01-01

    In order to deeply research the structure discrepancy and modeling mechanism among different grey prediction mo-dels, the equivalence and unbiasedness of grey prediction mo-dels are analyzed and verified. The results show that al the grey prediction models that are strictly derived from x(0)(k) +az(1)(k) = b have the identical model structure and simulation precision. Moreover, the unbiased simulation for the homoge-neous exponential sequence can be accomplished. However, the models derived from dx(1)/dt+ax(1) =b are only close to those derived from x(0)(k)+az(1)(k)=b provided that|a|has to satisfy|a| < 0.1; neither could the unbiased simulation for the homoge-neous exponential sequence be achieved. The above conclusions are proved and verified through some theorems and examples.

  5. Predictability of extreme values in geophysical models

    Directory of Open Access Journals (Sweden)

    A. E. Sterk

    2012-09-01

    Full Text Available Extreme value theory in deterministic systems is concerned with unlikely large (or small values of an observable evaluated along evolutions of the system. In this paper we study the finite-time predictability of extreme values, such as convection, energy, and wind speeds, in three geophysical models. We study whether finite-time Lyapunov exponents are larger or smaller for initial conditions leading to extremes. General statements on whether extreme values are better or less predictable are not possible: the predictability of extreme values depends on the observable, the attractor of the system, and the prediction lead time.

  6. Hybrid modeling and prediction of dynamical systems

    Science.gov (United States)

    Lloyd, Alun L.; Flores, Kevin B.

    2017-01-01

    Scientific analysis often relies on the ability to make accurate predictions of a system’s dynamics. Mechanistic models, parameterized by a number of unknown parameters, are often used for this purpose. Accurate estimation of the model state and parameters prior to prediction is necessary, but may be complicated by issues such as noisy data and uncertainty in parameters and initial conditions. At the other end of the spectrum exist nonparametric methods, which rely solely on data to build their predictions. While these nonparametric methods do not require a model of the system, their performance is strongly influenced by the amount and noisiness of the data. In this article, we consider a hybrid approach to modeling and prediction which merges recent advancements in nonparametric analysis with standard parametric methods. The general idea is to replace a subset of a mechanistic model’s equations with their corresponding nonparametric representations, resulting in a hybrid modeling and prediction scheme. Overall, we find that this hybrid approach allows for more robust parameter estimation and improved short-term prediction in situations where there is a large uncertainty in model parameters. We demonstrate these advantages in the classical Lorenz-63 chaotic system and in networks of Hindmarsh-Rose neurons before application to experimentally collected structured population data. PMID:28692642

  7. Risk terrain modeling predicts child maltreatment.

    Science.gov (United States)

    Daley, Dyann; Bachmann, Michael; Bachmann, Brittany A; Pedigo, Christian; Bui, Minh-Thuy; Coffman, Jamye

    2016-12-01

    As indicated by research on the long-term effects of adverse childhood experiences (ACEs), maltreatment has far-reaching consequences for affected children. Effective prevention measures have been elusive, partly due to difficulty in identifying vulnerable children before they are harmed. This study employs Risk Terrain Modeling (RTM), an analysis of the cumulative effect of environmental factors thought to be conducive for child maltreatment, to create a highly accurate prediction model for future substantiated child maltreatment cases in the City of Fort Worth, Texas. The model is superior to commonly used hotspot predictions and more beneficial in aiding prevention efforts in a number of ways: 1) it identifies the highest risk areas for future instances of child maltreatment with improved precision and accuracy; 2) it aids the prioritization of risk-mitigating efforts by informing about the relative importance of the most significant contributing risk factors; 3) since predictions are modeled as a function of easily obtainable data, practitioners do not have to undergo the difficult process of obtaining official child maltreatment data to apply it; 4) the inclusion of a multitude of environmental risk factors creates a more robust model with higher predictive validity; and, 5) the model does not rely on a retrospective examination of past instances of child maltreatment, but adapts predictions to changing environmental conditions. The present study introduces and examines the predictive power of this new tool to aid prevention efforts seeking to improve the safety, health, and wellbeing of vulnerable children.

  8. Property predictions using microstructural modeling

    Energy Technology Data Exchange (ETDEWEB)

    Wang, K.G. [Department of Materials Science and Engineering, Rensselaer Polytechnic Institute, CII 9219, 110 8th Street, Troy, NY 12180-3590 (United States)]. E-mail: wangk2@rpi.edu; Guo, Z. [Sente Software Ltd., Surrey Technology Centre, 40 Occam Road, Guildford GU2 7YG (United Kingdom); Sha, W. [Metals Research Group, School of Civil Engineering, Architecture and Planning, The Queen' s University of Belfast, Belfast BT7 1NN (United Kingdom); Glicksman, M.E. [Department of Materials Science and Engineering, Rensselaer Polytechnic Institute, CII 9219, 110 8th Street, Troy, NY 12180-3590 (United States); Rajan, K. [Department of Materials Science and Engineering, Rensselaer Polytechnic Institute, CII 9219, 110 8th Street, Troy, NY 12180-3590 (United States)

    2005-07-15

    Precipitation hardening in an Fe-12Ni-6Mn maraging steel during overaging is quantified. First, applying our recent kinetic model of coarsening [Phys. Rev. E, 69 (2004) 061507], and incorporating the Ashby-Orowan relationship, we link quantifiable aspects of the microstructures of these steels to their mechanical properties, including especially the hardness. Specifically, hardness measurements allow calculation of the precipitate size as a function of time and temperature through the Ashby-Orowan relationship. Second, calculated precipitate sizes and thermodynamic data determined with Thermo-Calc[copyright] are used with our recent kinetic coarsening model to extract diffusion coefficients during overaging from hardness measurements. Finally, employing more accurate diffusion parameters, we determined the hardness of these alloys independently from theory, and found agreement with experimental hardness data. Diffusion coefficients determined during overaging of these steels are notably higher than those found during the aging - an observation suggesting that precipitate growth during aging and precipitate coarsening during overaging are not controlled by the same diffusion mechanism.

  9. Dynamic modeling predicts continued bioaccumulation of polybrominated diphenyl ethers (PBDEs) in smallmouth bass (Micropterus dolomiu) post phase-out due to invasive prey and shifts in predation.

    Science.gov (United States)

    Wallace, Joshua S; Blersch, David M

    2015-11-01

    Unprecedented food chain links between benthic and pelagic organisms are often thought to disrupt traditional contaminant transport and uptake due to changes in predation and mobilization of otherwise sequestered pollutants. A bioaccumulation model for polybrominated diphenyl ethers (PBDEs) is developed to simulate increases in biotic congener loads based upon trophic transfer through diet and gill uptake for a Lake Erie food chain including two invasive species as a benthic-pelagic link. The model utilizes species-specific bioenergetic parameters in a four-level food chain including the green alga Scenedesmus quadricauda, zebra mussels (Dreissena polymorpha), round goby (Appollonia melanostoma), and the smallmouth bass (Micropterus dolomiu). The model was calibrated to current biotic concentrations and predicts an increase in contaminant load by almost 48% in the upper trophic level in two years. Validation to archival data resulted in <2% error from reported values following a two-year simulation.

  10. Spatial Economics Model Predicting Transport Volume

    Directory of Open Access Journals (Sweden)

    Lu Bo

    2016-10-01

    Full Text Available It is extremely important to predict the logistics requirements in a scientific and rational way. However, in recent years, the improvement effect on the prediction method is not very significant and the traditional statistical prediction method has the defects of low precision and poor interpretation of the prediction model, which cannot only guarantee the generalization ability of the prediction model theoretically, but also cannot explain the models effectively. Therefore, in combination with the theories of the spatial economics, industrial economics, and neo-classical economics, taking city of Zhuanghe as the research object, the study identifies the leading industry that can produce a large number of cargoes, and further predicts the static logistics generation of the Zhuanghe and hinterlands. By integrating various factors that can affect the regional logistics requirements, this study established a logistics requirements potential model from the aspect of spatial economic principles, and expanded the way of logistics requirements prediction from the single statistical principles to an new area of special and regional economics.

  11. Toward a Global Ocean Ecosystem Mid-trophic Automatic Acoustic Sampler (MAAS)

    OpenAIRE

    Handegard, Nils Olav; Demer, David A; Kloser, Rudy; Lehodey, Patrick; Maury, Olivier; Simard, Yvard

    2009-01-01

    Despite their huge biomass and pivotal role, the mid-trophic levels of marine ecosystems are not generally subject to systematic monitoring. Data from such monitoring is crucial for parameterizing, validating, and constraining numerical models of mid-trophic communities. In recent years, acoustic sampling technology has matured, and we argue that acoustic sampling technology, due to long-range propagation in water, is the only means to efficiently observe the large biomass of the mid-trophic ...

  12. Modeling and Prediction Using Stochastic Differential Equations

    DEFF Research Database (Denmark)

    Juhl, Rune; Møller, Jan Kloppenborg; Jørgensen, John Bagterp

    2016-01-01

    Pharmacokinetic/pharmakodynamic (PK/PD) modeling for a single subject is most often performed using nonlinear models based on deterministic ordinary differential equations (ODEs), and the variation between subjects in a population of subjects is described using a population (mixed effects) setup...... that describes the variation between subjects. The ODE setup implies that the variation for a single subject is described by a single parameter (or vector), namely the variance (covariance) of the residuals. Furthermore the prediction of the states is given as the solution to the ODEs and hence assumed...... deterministic and can predict the future perfectly. A more realistic approach would be to allow for randomness in the model due to e.g., the model be too simple or errors in input. We describe a modeling and prediction setup which better reflects reality and suggests stochastic differential equations (SDEs...

  13. Precision Plate Plan View Pattern Predictive Model

    Institute of Scientific and Technical Information of China (English)

    ZHAO Yang; YANG Quan; HE An-rui; WANG Xiao-chen; ZHANG Yun

    2011-01-01

    According to the rolling features of plate mill, a 3D elastic-plastic FEM (finite element model) based on full restart method of ANSYS/LS-DYNA was established to study the inhomogeneous plastic deformation of multipass plate rolling. By analyzing the simulation results, the difference of head and tail ends predictive models was found and modified. According to the numerical simulation results of 120 different kinds of conditions, precision plate plan view pattern predictive model was established. Based on these models, the sizing MAS (mizushima automatic plan view pattern control system) method was designed and used on a 2 800 mm plate mill. Comparing the rolled plates with and without PVPP (plan view pattern predictive) model, the reduced width deviation indicates that the olate !olan view Dattern predictive model is preeise.

  14. NBC Hazard Prediction Model Capability Analysis

    Science.gov (United States)

    1999-09-01

    Puff( SCIPUFF ) Model Verification and Evaluation Study, Air Resources Laboratory, NOAA, May 1998. Based on the NOAA review, the VLSTRACK developers...TO SUBSTANTIAL DIFFERENCES IN PREDICTIONS HPAC uses a transport and dispersion (T&D) model called SCIPUFF and an associated mean wind field model... SCIPUFF is a model for atmospheric dispersion that uses the Gaussian puff method - an arbitrary time-dependent concentration field is represented

  15. Corporate prediction models, ratios or regression analysis?

    NARCIS (Netherlands)

    Bijnen, E.J.; Wijn, M.F.C.M.

    1994-01-01

    The models developed in the literature with respect to the prediction of a company s failure are based on ratios. It has been shown before that these models should be rejected on theoretical grounds. Our study of industrial companies in the Netherlands shows that the ratios which are used in

  16. Modelling Chemical Reasoning to Predict Reactions

    CERN Document Server

    Segler, Marwin H S

    2016-01-01

    The ability to reason beyond established knowledge allows Organic Chemists to solve synthetic problems and to invent novel transformations. Here, we propose a model which mimics chemical reasoning and formalises reaction prediction as finding missing links in a knowledge graph. We have constructed a knowledge graph containing 14.4 million molecules and 8.2 million binary reactions, which represents the bulk of all chemical reactions ever published in the scientific literature. Our model outperforms a rule-based expert system in the reaction prediction task for 180,000 randomly selected binary reactions. We show that our data-driven model generalises even beyond known reaction types, and is thus capable of effectively (re-) discovering novel transformations (even including transition-metal catalysed reactions). Our model enables computers to infer hypotheses about reactivity and reactions by only considering the intrinsic local structure of the graph, and because each single reaction prediction is typically ac...

  17. Evaluation of CASP8 model quality predictions

    KAUST Repository

    Cozzetto, Domenico

    2009-01-01

    The model quality assessment problem consists in the a priori estimation of the overall and per-residue accuracy of protein structure predictions. Over the past years, a number of methods have been developed to address this issue and CASP established a prediction category to evaluate their performance in 2006. In 2008 the experiment was repeated and its results are reported here. Participants were invited to infer the correctness of the protein models submitted by the registered automatic servers. Estimates could apply to both whole models and individual amino acids. Groups involved in the tertiary structure prediction categories were also asked to assign local error estimates to each predicted residue in their own models and their results are also discussed here. The correlation between the predicted and observed correctness measures was the basis of the assessment of the results. We observe that consensus-based methods still perform significantly better than those accepting single models, similarly to what was concluded in the previous edition of the experiment. © 2009 WILEY-LISS, INC.

  18. Inducible defences and trophic structure

    NARCIS (Netherlands)

    Vos, M.; Verschoor, A.M.; Kooi, B.W.; Wäckers, F.L.; DeAngelis, D.L.; Mooij, W.M.

    2004-01-01

    Resource edibility is a crucial factor in ecological theory on the relative importance of bottom-up and top-down control. Current theory explains trophic structure in terms of the relative abundance and succession of edible and inedible species across gradients of primary productivity. We argue that

  19. Inducible defences and trophic structure.

    NARCIS (Netherlands)

    Vos, M.; Verschoor, A.M.; Kooi, B.W.; Wackers, F.L.; DeAngelis, D.L.; Mooij, W.M.

    2004-01-01

    Resource edibility is a crucial factor in ecological theory on the relative importance of bottom-up and top-down control. Current theory explains trophic structure in terms of the relative abundance and succession of edible and inedible species across gradients of primary productivity. We argue that

  20. Genetic models of homosexuality: generating testable predictions

    OpenAIRE

    Gavrilets, Sergey; Rice, William R.

    2006-01-01

    Homosexuality is a common occurrence in humans and other species, yet its genetic and evolutionary basis is poorly understood. Here, we formulate and study a series of simple mathematical models for the purpose of predicting empirical patterns that can be used to determine the form of selection that leads to polymorphism of genes influencing homosexuality. Specifically, we develop theory to make contrasting predictions about the genetic characteristics of genes influencing homosexuality inclu...

  1. Wind farm production prediction - The Zephyr model

    Energy Technology Data Exchange (ETDEWEB)

    Landberg, L. [Risoe National Lab., Wind Energy Dept., Roskilde (Denmark); Giebel, G. [Risoe National Lab., Wind Energy Dept., Roskilde (Denmark); Madsen, H. [IMM (DTU), Kgs. Lyngby (Denmark); Nielsen, T.S. [IMM (DTU), Kgs. Lyngby (Denmark); Joergensen, J.U. [Danish Meteorologisk Inst., Copenhagen (Denmark); Lauersen, L. [Danish Meteorologisk Inst., Copenhagen (Denmark); Toefting, J. [Elsam, Fredericia (DK); Christensen, H.S. [Eltra, Fredericia (Denmark); Bjerge, C. [SEAS, Haslev (Denmark)

    2002-06-01

    This report describes a project - funded by the Danish Ministry of Energy and the Environment - which developed a next generation prediction system called Zephyr. The Zephyr system is a merging between two state-of-the-art prediction systems: Prediktor of Risoe National Laboratory and WPPT of IMM at the Danish Technical University. The numerical weather predictions were generated by DMI's HIRLAM model. Due to technical difficulties programming the system, only the computational core and a very simple version of the originally very complex system were developed. The project partners were: Risoe, DMU, DMI, Elsam, Eltra, Elkraft System, SEAS and E2. (au)

  2. Cryptic population dynamics: rapid evolution masks trophic interactions.

    Directory of Open Access Journals (Sweden)

    Takehito Yoshida

    2007-09-01

    Full Text Available Trophic relationships, such as those between predator and prey or between pathogen and host, are key interactions linking species in ecological food webs. The structure of these links and their strengths have major consequences for the dynamics and stability of food webs. The existence and strength of particular trophic links has often been assessed using observational data on changes in species abundance through time. Here we show that very strong links can be completely missed by these kinds of analyses when changes in population abundance are accompanied by contemporaneous rapid evolution in the prey or host species. Experimental observations, in rotifer-alga and phage-bacteria chemostats, show that the predator or pathogen can exhibit large-amplitude cycles while the abundance of the prey or host remains essentially constant. We know that the species are tightly linked in these experimental microcosms, but without this knowledge, we would infer from observed patterns in abundance that the species are weakly or not at all linked. Mathematical modeling shows that this kind of cryptic dynamics occurs when there is rapid prey or host evolution for traits conferring defense against attack, and the cost of defense (in terms of tradeoffs with other fitness components is low. Several predictions of the theory that we developed to explain the rotifer-alga experiments are confirmed in the phage-bacteria experiments, where bacterial evolution could be tracked. Modeling suggests that rapid evolution may also confound experimental approaches to measuring interaction strength, but it identifies certain experimental designs as being more robust against potential confounding by rapid evolution.

  3. Predictive model for segmented poly(urea

    Directory of Open Access Journals (Sweden)

    Frankl P.

    2012-08-01

    Full Text Available Segmented poly(urea has been shown to be of significant benefit in protecting vehicles from blast and impact and there have been several experimental studies to determine the mechanisms by which this protective function might occur. One suggested route is by mechanical activation of the glass transition. In order to enable design of protective structures using this material a constitutive model and equation of state are needed for numerical simulation hydrocodes. Determination of such a predictive model may also help elucidate the beneficial mechanisms that occur in polyurea during high rate loading. The tool deployed to do this has been Group Interaction Modelling (GIM – a mean field technique that has been shown to predict the mechanical and physical properties of polymers from their structure alone. The structure of polyurea has been used to characterise the parameters in the GIM scheme without recourse to experimental data and the equation of state and constitutive model predicts response over a wide range of temperatures and strain rates. The shock Hugoniot has been predicted and validated against existing data. Mechanical response in tensile tests has also been predicted and validated.

  4. From neurons to epidemics: How trophic coherence affects spreading processes

    Science.gov (United States)

    Klaise, Janis; Johnson, Samuel

    2016-06-01

    Trophic coherence, a measure of the extent to which the nodes of a directed network are organised in levels, has recently been shown to be closely related to many structural and dynamical aspects of complex systems, including graph eigenspectra, the prevalence or absence of feedback cycles, and linear stability. Furthermore, non-trivial trophic structures have been observed in networks of neurons, species, genes, metabolites, cellular signalling, concatenated words, P2P users, and world trade. Here, we consider two simple yet apparently quite different dynamical models—one a susceptible-infected-susceptible epidemic model adapted to include complex contagion and the other an Amari-Hopfield neural network—and show that in both cases the related spreading processes are modulated in similar ways by the trophic coherence of the underlying networks. To do this, we propose a network assembly model which can generate structures with tunable trophic coherence, limiting in either perfectly stratified networks or random graphs. We find that trophic coherence can exert a qualitative change in spreading behaviour, determining whether a pulse of activity will percolate through the entire network or remain confined to a subset of nodes, and whether such activity will quickly die out or endure indefinitely. These results could be important for our understanding of phenomena such as epidemics, rumours, shocks to ecosystems, neuronal avalanches, and many other spreading processes.

  5. PREDICTIVE CAPACITY OF ARCH FAMILY MODELS

    Directory of Open Access Journals (Sweden)

    Raphael Silveira Amaro

    2016-03-01

    Full Text Available In the last decades, a remarkable number of models, variants from the Autoregressive Conditional Heteroscedastic family, have been developed and empirically tested, making extremely complex the process of choosing a particular model. This research aim to compare the predictive capacity, using the Model Confidence Set procedure, than five conditional heteroskedasticity models, considering eight different statistical probability distributions. The financial series which were used refers to the log-return series of the Bovespa index and the Dow Jones Industrial Index in the period between 27 October 2008 and 30 December 2014. The empirical evidences showed that, in general, competing models have a great homogeneity to make predictions, either for a stock market of a developed country or for a stock market of a developing country. An equivalent result can be inferred for the statistical probability distributions that were used.

  6. Predictive QSAR modeling of phosphodiesterase 4 inhibitors.

    Science.gov (United States)

    Kovalishyn, Vasyl; Tanchuk, Vsevolod; Charochkina, Larisa; Semenuta, Ivan; Prokopenko, Volodymyr

    2012-02-01

    A series of diverse organic compounds, phosphodiesterase type 4 (PDE-4) inhibitors, have been modeled using a QSAR-based approach. 48 QSAR models were compared by following the same procedure with different combinations of descriptors and machine learning methods. QSAR methodologies used random forests and associative neural networks. The predictive ability of the models was tested through leave-one-out cross-validation, giving a Q² = 0.66-0.78 for regression models and total accuracies Ac=0.85-0.91 for classification models. Predictions for the external evaluation sets obtained accuracies in the range of 0.82-0.88 (for active/inactive classifications) and Q² = 0.62-0.76 for regressions. The method showed itself to be a potential tool for estimation of IC₅₀ of new drug-like candidates at early stages of drug development. Copyright © 2011 Elsevier Inc. All rights reserved.

  7. Enhanced understanding of ectoparasite–host trophic linkages on coral reefs through stable isotope analysis

    Directory of Open Access Journals (Sweden)

    Amanda W.J. Demopoulos

    2015-04-01

    Full Text Available Parasitism, although the most common type of ecological interaction, is usually ignored in food web models and studies of trophic connectivity. Stable isotope analysis is widely used in assessing the flow of energy in ecological communities and thus is a potentially valuable tool in understanding the cryptic trophic relationships mediated by parasites. In an effort to assess the utility of stable isotope analysis in understanding the role of parasites in complex coral-reef trophic systems, we performed stable isotope analysis on three common Caribbean reef fish hosts and two kinds of ectoparasitic isopods: temporarily parasitic gnathiids (Gnathia marleyi and permanently parasitic cymothoids (Anilocra. To further track the transfer of fish-derived carbon (energy from parasites to parasite consumers, gnathiids from host fish were also fed to captive Pederson shrimp (Ancylomenes pedersoni for at least 1 month. Parasitic isopods had δ13C and δ15N values similar to their host, comparable with results from the small number of other host–parasite studies that have employed stable isotopes. Adult gnathiids were enriched in 15N and depleted in 13C relative to juvenile gnathiids, providing insights into the potential isotopic fractionation associated with blood-meal assimilation and subsequent metamorphosis. Gnathiid-fed Pedersen shrimp also had δ13C values consistent with their food source and enriched in 15N as predicted due to trophic fractionation. These results further indicate that stable isotopes can be an effective tool in deciphering cryptic feeding relationships involving parasites and their consumers, and the role of parasites and cleaners in carbon transfer in coral-reef ecosystems specifically.

  8. Enhanced understanding of ectoparasite: host trophic linkages on coral reefs through stable isotope analysis

    Science.gov (United States)

    Demopoulos, Amanda W. J.; Sikkel, Paul C.

    2015-01-01

    Parasitism, although the most common type of ecological interaction, is usually ignored in food web models and studies of trophic connectivity. Stable isotope analysis is widely used in assessing the flow of energy in ecological communities and thus is a potentially valuable tool in understanding the cryptic trophic relationships mediated by parasites. In an effort to assess the utility of stable isotope analysis in understanding the role of parasites in complex coral-reef trophic systems, we performed stable isotope analysis on three common Caribbean reef fish hosts and two kinds of ectoparasitic isopods: temporarily parasitic gnathiids (Gnathia marleyi) and permanently parasitic cymothoids (Anilocra). To further track the transfer of fish-derived carbon (energy) from parasites to parasite consumers, gnathiids from host fish were also fed to captive Pederson shrimp (Ancylomenes pedersoni) for at least 1 month. Parasitic isopods had δ13C and δ15N values similar to their host, comparable with results from the small number of other host–parasite studies that have employed stable isotopes. Adult gnathiids were enriched in 15N and depleted in13C relative to juvenile gnathiids, providing insights into the potential isotopic fractionation associated with blood-meal assimilation and subsequent metamorphosis. Gnathiid-fed Pedersen shrimp also had δ13C values consistent with their food source and enriched in 15N as predicted due to trophic fractionation. These results further indicate that stable isotopes can be an effective tool in deciphering cryptic feeding relationships involving parasites and their consumers, and the role of parasites and cleaners in carbon transfer in coral-reef ecosystems specifically.

  9. The trophic responses of two different rodent–vector–plague systems to climate change

    Science.gov (United States)

    Xu, Lei; Schmid, Boris V.; Liu, Jun; Si, Xiaoyan; Stenseth, Nils Chr.; Zhang, Zhibin

    2015-01-01

    Plague, the causative agent of three devastating pandemics in history, is currently a re-emerging disease, probably due to climate change and other anthropogenic changes. Without understanding the response of plague systems to anthropogenic or climate changes in their trophic web, it is unfeasible to effectively predict years with high risks of plague outbreak, hampering our ability for effective prevention and control of the disease. Here, by using surveillance data, we apply structural equation modelling to reveal the drivers of plague prevalence in two very different rodent systems: those of the solitary Daurian ground squirrel and the social Mongolian gerbil. We show that plague prevalence in the Daurian ground squirrel is not detectably related to its trophic web, and that therefore surveillance efforts should focus on detecting plague directly in this ecosystem. On the other hand, plague in the Mongolian gerbil is strongly embedded in a complex, yet understandable trophic web of climate, vegetation, and rodent and flea densities, making the ecosystem suitable for more sophisticated low-cost surveillance practices, such as remote sensing. As for the trophic webs of the two rodent species, we find that increased vegetation is positively associated with higher temperatures and precipitation for both ecosystems. We furthermore find a positive association between vegetation and ground squirrel density, yet a negative association between vegetation and gerbil density. Our study thus shows how past surveillance records can be used to design and improve existing plague prevention and control measures, by tailoring them to individual plague foci. Such measures are indeed highly needed under present conditions with prevailing climate change. PMID:25540277

  10. The trophic responses of two different rodent-vector-plague systems to climate change.

    Science.gov (United States)

    Xu, Lei; Schmid, Boris V; Liu, Jun; Si, Xiaoyan; Stenseth, Nils Chr; Zhang, Zhibin

    2015-02-07

    Plague, the causative agent of three devastating pandemics in history, is currently a re-emerging disease, probably due to climate change and other anthropogenic changes. Without understanding the response of plague systems to anthropogenic or climate changes in their trophic web, it is unfeasible to effectively predict years with high risks of plague outbreak, hampering our ability for effective prevention and control of the disease. Here, by using surveillance data, we apply structural equation modelling to reveal the drivers of plague prevalence in two very different rodent systems: those of the solitary Daurian ground squirrel and the social Mongolian gerbil. We show that plague prevalence in the Daurian ground squirrel is not detectably related to its trophic web, and that therefore surveillance efforts should focus on detecting plague directly in this ecosystem. On the other hand, plague in the Mongolian gerbil is strongly embedded in a complex, yet understandable trophic web of climate, vegetation, and rodent and flea densities, making the ecosystem suitable for more sophisticated low-cost surveillance practices, such as remote sensing. As for the trophic webs of the two rodent species, we find that increased vegetation is positively associated with higher temperatures and precipitation for both ecosystems. We furthermore find a positive association between vegetation and ground squirrel density, yet a negative association between vegetation and gerbil density. Our study thus shows how past surveillance records can be used to design and improve existing plague prevention and control measures, by tailoring them to individual plague foci. Such measures are indeed highly needed under present conditions with prevailing climate change. © 2014 The Author(s) Published by the Royal Society. All rights reserved.

  11. Evolution of complex life cycles in trophically transmitted helminths. I. Host incorporation and trophic ascent.

    Science.gov (United States)

    Parker, G A; Ball, M A; Chubb, J C

    2015-02-01

    Links between parasites and food webs are evolutionarily ancient but dynamic: life history theory provides insights into helminth complex life cycle origins. Most adult helminths benefit by sexual reproduction in vertebrates, often high up food chains, but direct infection is commonly constrained by a trophic vacuum between free-living propagules and definitive hosts. Intermediate hosts fill this vacuum, facilitating transmission to definitive hosts. The central question concerns why sexual reproduction, and sometimes even larval growth, is suppressed in intermediate hosts, favouring growth arrest at larval maturity in intermediate hosts and reproductive suppression until transmission to definitive hosts? Increased longevity and higher growth in definitive hosts can generate selection for larger parasite body size and higher fecundity at sexual maturity. Life cycle length is increased by two evolutionary mechanisms, upward and downward incorporation, allowing simple (one-host) cycles to become complex (multihost). In downward incorporation, an intermediate host is added below the definitive host: models suggest that downward incorporation probably evolves only after ecological or evolutionary perturbations create a trophic vacuum. In upward incorporation, a new definitive host is added above the original definitive host, which subsequently becomes an intermediate host, again maintained by the trophic vacuum: theory suggests that this is plausible even under constant ecological/evolutionary conditions. The final cycle is similar irrespective of its origin (upward or downward). Insights about host incorporation are best gained by linking comparative phylogenetic analyses (describing evolutionary history) with evolutionary models (examining selective forces). Ascent of host trophic levels and evolution of optimal host taxa ranges are discussed. © 2015 European Society For Evolutionary Biology. Journal of Evolutionary Biology © 2015 European Society For Evolutionary

  12. Modelling the predictive performance of credit scoring

    Directory of Open Access Journals (Sweden)

    Shi-Wei Shen

    2013-02-01

    Full Text Available Orientation: The article discussed the importance of rigour in credit risk assessment.Research purpose: The purpose of this empirical paper was to examine the predictive performance of credit scoring systems in Taiwan.Motivation for the study: Corporate lending remains a major business line for financial institutions. However, in light of the recent global financial crises, it has become extremely important for financial institutions to implement rigorous means of assessing clients seeking access to credit facilities.Research design, approach and method: Using a data sample of 10 349 observations drawn between 1992 and 2010, logistic regression models were utilised to examine the predictive performance of credit scoring systems.Main findings: A test of Goodness of fit demonstrated that credit scoring models that incorporated the Taiwan Corporate Credit Risk Index (TCRI, micro- and also macroeconomic variables possessed greater predictive power. This suggests that macroeconomic variables do have explanatory power for default credit risk.Practical/managerial implications: The originality in the study was that three models were developed to predict corporate firms’ defaults based on different microeconomic and macroeconomic factors such as the TCRI, asset growth rates, stock index and gross domestic product.Contribution/value-add: The study utilises different goodness of fits and receiver operator characteristics during the examination of the robustness of the predictive power of these factors.

  13. Calibrated predictions for multivariate competing risks models.

    Science.gov (United States)

    Gorfine, Malka; Hsu, Li; Zucker, David M; Parmigiani, Giovanni

    2014-04-01

    Prediction models for time-to-event data play a prominent role in assessing the individual risk of a disease, such as cancer. Accurate disease prediction models provide an efficient tool for identifying individuals at high risk, and provide the groundwork for estimating the population burden and cost of disease and for developing patient care guidelines. We focus on risk prediction of a disease in which family history is an important risk factor that reflects inherited genetic susceptibility, shared environment, and common behavior patterns. In this work family history is accommodated using frailty models, with the main novel feature being allowing for competing risks, such as other diseases or mortality. We show through a simulation study that naively treating competing risks as independent right censoring events results in non-calibrated predictions, with the expected number of events overestimated. Discrimination performance is not affected by ignoring competing risks. Our proposed prediction methodologies correctly account for competing events, are very well calibrated, and easy to implement.

  14. Modelling language evolution: Examples and predictions.

    Science.gov (United States)

    Gong, Tao; Shuai, Lan; Zhang, Menghan

    2014-06-01

    We survey recent computer modelling research of language evolution, focusing on a rule-based model simulating the lexicon-syntax coevolution and an equation-based model quantifying the language competition dynamics. We discuss four predictions of these models: (a) correlation between domain-general abilities (e.g. sequential learning) and language-specific mechanisms (e.g. word order processing); (b) coevolution of language and relevant competences (e.g. joint attention); (c) effects of cultural transmission and social structure on linguistic understandability; and (d) commonalities between linguistic, biological, and physical phenomena. All these contribute significantly to our understanding of the evolutions of language structures, individual learning mechanisms, and relevant biological and socio-cultural factors. We conclude the survey by highlighting three future directions of modelling studies of language evolution: (a) adopting experimental approaches for model evaluation; (b) consolidating empirical foundations of models; and (c) multi-disciplinary collaboration among modelling, linguistics, and other relevant disciplines.

  15. Modelling language evolution: Examples and predictions

    Science.gov (United States)

    Gong, Tao; Shuai, Lan; Zhang, Menghan

    2014-06-01

    We survey recent computer modelling research of language evolution, focusing on a rule-based model simulating the lexicon-syntax coevolution and an equation-based model quantifying the language competition dynamics. We discuss four predictions of these models: (a) correlation between domain-general abilities (e.g. sequential learning) and language-specific mechanisms (e.g. word order processing); (b) coevolution of language and relevant competences (e.g. joint attention); (c) effects of cultural transmission and social structure on linguistic understandability; and (d) commonalities between linguistic, biological, and physical phenomena. All these contribute significantly to our understanding of the evolutions of language structures, individual learning mechanisms, and relevant biological and socio-cultural factors. We conclude the survey by highlighting three future directions of modelling studies of language evolution: (a) adopting experimental approaches for model evaluation; (b) consolidating empirical foundations of models; and (c) multi-disciplinary collaboration among modelling, linguistics, and other relevant disciplines.

  16. The effects of spatial scale on trophic interactions

    NARCIS (Netherlands)

    Koppel, J. van de; Bardgett, R.D.; Bengtsson, J.; Rodriguez-Barrueco, C.; Rietkerk, M.G.; Wassen, M.J.; Wolters, V.

    2005-01-01

    Food chain models have dominated empirical studies of trophic interactions in the past decades, and have lead to important insights into the factors that control ecological communities. Despite the importance of food chain models in instigating ecological investigations, many empirical studies still

  17. Global Solar Dynamo Models: Simulations and Predictions

    Indian Academy of Sciences (India)

    Mausumi Dikpati; Peter A. Gilman

    2008-03-01

    Flux-transport type solar dynamos have achieved considerable success in correctly simulating many solar cycle features, and are now being used for prediction of solar cycle timing and amplitude.We first define flux-transport dynamos and demonstrate how they work. The essential added ingredient in this class of models is meridional circulation, which governs the dynamo period and also plays a crucial role in determining the Sun’s memory about its past magnetic fields.We show that flux-transport dynamo models can explain many key features of solar cycles. Then we show that a predictive tool can be built from this class of dynamo that can be used to predict mean solar cycle features by assimilating magnetic field data from previous cycles.

  18. Model Predictive Control of Sewer Networks

    Science.gov (United States)

    Pedersen, Einar B.; Herbertsson, Hannes R.; Niemann, Henrik; Poulsen, Niels K.; Falk, Anne K. V.

    2017-01-01

    The developments in solutions for management of urban drainage are of vital importance, as the amount of sewer water from urban areas continues to increase due to the increase of the world’s population and the change in the climate conditions. How a sewer network is structured, monitored and controlled have thus become essential factors for effcient performance of waste water treatment plants. This paper examines methods for simplified modelling and controlling a sewer network. A practical approach to the problem is used by analysing simplified design model, which is based on the Barcelona benchmark model. Due to the inherent constraints the applied approach is based on Model Predictive Control.

  19. DKIST Polarization Modeling and Performance Predictions

    Science.gov (United States)

    Harrington, David

    2016-05-01

    Calibrating the Mueller matrices of large aperture telescopes and associated coude instrumentation requires astronomical sources and several modeling assumptions to predict the behavior of the system polarization with field of view, altitude, azimuth and wavelength. The Daniel K Inouye Solar Telescope (DKIST) polarimetric instrumentation requires very high accuracy calibration of a complex coude path with an off-axis f/2 primary mirror, time dependent optical configurations and substantial field of view. Polarization predictions across a diversity of optical configurations, tracking scenarios, slit geometries and vendor coating formulations are critical to both construction and contined operations efforts. Recent daytime sky based polarization calibrations of the 4m AEOS telescope and HiVIS spectropolarimeter on Haleakala have provided system Mueller matrices over full telescope articulation for a 15-reflection coude system. AEOS and HiVIS are a DKIST analog with a many-fold coude optical feed and similar mirror coatings creating 100% polarization cross-talk with altitude, azimuth and wavelength. Polarization modeling predictions using Zemax have successfully matched the altitude-azimuth-wavelength dependence on HiVIS with the few percent amplitude limitations of several instrument artifacts. Polarization predictions for coude beam paths depend greatly on modeling the angle-of-incidence dependences in powered optics and the mirror coating formulations. A 6 month HiVIS daytime sky calibration plan has been analyzed for accuracy under a wide range of sky conditions and data analysis algorithms. Predictions of polarimetric performance for the DKIST first-light instrumentation suite have been created under a range of configurations. These new modeling tools and polarization predictions have substantial impact for the design, fabrication and calibration process in the presence of manufacturing issues, science use-case requirements and ultimate system calibration

  20. Modelling Chemical Reasoning to Predict Reactions

    OpenAIRE

    Segler, Marwin H. S.; Waller, Mark P.

    2016-01-01

    The ability to reason beyond established knowledge allows Organic Chemists to solve synthetic problems and to invent novel transformations. Here, we propose a model which mimics chemical reasoning and formalises reaction prediction as finding missing links in a knowledge graph. We have constructed a knowledge graph containing 14.4 million molecules and 8.2 million binary reactions, which represents the bulk of all chemical reactions ever published in the scientific literature. Our model outpe...

  1. Predictive Modeling of the CDRA 4BMS

    Science.gov (United States)

    Coker, Robert; Knox, James

    2016-01-01

    Fully predictive models of the Four Bed Molecular Sieve of the Carbon Dioxide Removal Assembly on the International Space Station are being developed. This virtual laboratory will be used to help reduce mass, power, and volume requirements for future missions. In this paper we describe current and planned modeling developments in the area of carbon dioxide removal to support future crewed Mars missions as well as the resolution of anomalies observed in the ISS CDRA.

  2. Raman Model Predicting Hardness of Covalent Crystals

    OpenAIRE

    Zhou, Xiang-Feng; Qian, Quang-Rui; Sun, Jian; Tian, Yongjun; Wang, Hui-Tian

    2009-01-01

    Based on the fact that both hardness and vibrational Raman spectrum depend on the intrinsic property of chemical bonds, we propose a new theoretical model for predicting hardness of a covalent crystal. The quantitative relationship between hardness and vibrational Raman frequencies deduced from the typical zincblende covalent crystals is validated to be also applicable for the complex multicomponent crystals. This model enables us to nondestructively and indirectly characterize the hardness o...

  3. Predictive Modelling of Mycotoxins in Cereals

    NARCIS (Netherlands)

    Fels, van der H.J.; Liu, C.

    2015-01-01

    In dit artikel worden de samenvattingen van de presentaties tijdens de 30e bijeenkomst van de Werkgroep Fusarium weergegeven. De onderwerpen zijn: Predictive Modelling of Mycotoxins in Cereals.; Microbial degradation of DON.; Exposure to green leaf volatiles primes wheat against FHB but boosts

  4. Unreachable Setpoints in Model Predictive Control

    DEFF Research Database (Denmark)

    Rawlings, James B.; Bonné, Dennis; Jørgensen, John Bagterp

    2008-01-01

    steady state is established for terminal constraint model predictive control (MPC). The region of attraction is the steerable set. Existing analysis methods for closed-loop properties of MPC are not applicable to this new formulation, and a new analysis method is developed. It is shown how to extend...

  5. Predictive Modelling of Mycotoxins in Cereals

    NARCIS (Netherlands)

    Fels, van der H.J.; Liu, C.

    2015-01-01

    In dit artikel worden de samenvattingen van de presentaties tijdens de 30e bijeenkomst van de Werkgroep Fusarium weergegeven. De onderwerpen zijn: Predictive Modelling of Mycotoxins in Cereals.; Microbial degradation of DON.; Exposure to green leaf volatiles primes wheat against FHB but boosts produ

  6. Prediction modelling for population conviction data

    NARCIS (Netherlands)

    Tollenaar, N.

    2017-01-01

    In this thesis, the possibilities of using prediction models for judicial penal case data are investigated. The development and refinement of a risk taxation scale based on these data is discussed. When false positives are weighted equally severe as false negatives, 70% can be classified correctly.

  7. A Predictive Model for MSSW Student Success

    Science.gov (United States)

    Napier, Angela Michele

    2011-01-01

    This study tested a hypothetical model for predicting both graduate GPA and graduation of University of Louisville Kent School of Social Work Master of Science in Social Work (MSSW) students entering the program during the 2001-2005 school years. The preexisting characteristics of demographics, academic preparedness and culture shock along with…

  8. Predictability of extreme values in geophysical models

    NARCIS (Netherlands)

    Sterk, A.E.; Holland, M.P.; Rabassa, P.; Broer, H.W.; Vitolo, R.

    2012-01-01

    Extreme value theory in deterministic systems is concerned with unlikely large (or small) values of an observable evaluated along evolutions of the system. In this paper we study the finite-time predictability of extreme values, such as convection, energy, and wind speeds, in three geophysical model

  9. A revised prediction model for natural conception

    NARCIS (Netherlands)

    Bensdorp, A.J.; Steeg, J.W. van der; Steures, P.; Habbema, J.D.; Hompes, P.G.; Bossuyt, P.M.; Veen, F. van der; Mol, B.W.; Eijkemans, M.J.; Kremer, J.A.M.; et al.,

    2017-01-01

    One of the aims in reproductive medicine is to differentiate between couples that have favourable chances of conceiving naturally and those that do not. Since the development of the prediction model of Hunault, characteristics of the subfertile population have changed. The objective of this analysis

  10. Distributed Model Predictive Control via Dual Decomposition

    DEFF Research Database (Denmark)

    Biegel, Benjamin; Stoustrup, Jakob; Andersen, Palle

    2014-01-01

    This chapter presents dual decomposition as a means to coordinate a number of subsystems coupled by state and input constraints. Each subsystem is equipped with a local model predictive controller while a centralized entity manages the subsystems via prices associated with the coupling constraints...

  11. Predictive Modelling of Mycotoxins in Cereals

    NARCIS (Netherlands)

    Fels, van der H.J.; Liu, C.

    2015-01-01

    In dit artikel worden de samenvattingen van de presentaties tijdens de 30e bijeenkomst van de Werkgroep Fusarium weergegeven. De onderwerpen zijn: Predictive Modelling of Mycotoxins in Cereals.; Microbial degradation of DON.; Exposure to green leaf volatiles primes wheat against FHB but boosts produ

  12. Leptogenesis in minimal predictive seesaw models

    CERN Document Server

    Björkeroth, Fredrik; Varzielas, Ivo de Medeiros; King, Stephen F

    2015-01-01

    We estimate the Baryon Asymmetry of the Universe (BAU) arising from leptogenesis within a class of minimal predictive seesaw models involving two right-handed neutrinos and simple Yukawa structures with one texture zero. The two right-handed neutrinos are dominantly responsible for the "atmospheric" and "solar" neutrino masses with Yukawa couplings to $(\

  13. Trophic structure of pelagic species in the northwestern Mediterranean Sea

    Science.gov (United States)

    Albo-Puigserver, Marta; Navarro, Joan; Coll, Marta; Layman, Craig A.; Palomera, Isabel

    2016-11-01

    Ecological knowledge of food web interactions within pelagic marine communities is often limited, impairing our capabilities to manage these ecologically and economically important marine fish species. Here we used stable isotope analyses to investigate trophic interactions in the pelagic ecosystem of the northwestern Mediterranean Sea during 2012 and 2013. Our results suggest that European sardine, Sardina pilchardus, and anchovy, Engraulis encrasicolus, are consumers located at relatively low levels of the pelagic food web. Unexpectedly, the round sardinella, Sardinella aurita, appeared to be located at a higher trophic level than the other small pelagic fish species, although previous studies found similarity in their diets. Isotope data suggested that trophic niches of species within the genera Trachurus spp. and Scomber spp., were distinct. Atlantic bonito Sarda sarda, European hake Merluccius merluccius and European squid Loligo vulgaris, appeared to feed at higher trophic levels than other species. Despite some intraspecific seasonal variability for some species, community trophic structure appeared relatively stable through the year. These data provide an important step for developing models of food web dynamics in the northwestern Mediterranean Sea.

  14. Analysis and prediction of pest dynamics in an agroforestry context using Tiko'n, a generic tool to develop food web models

    Science.gov (United States)

    Rojas, Marcela; Malard, Julien; Adamowski, Jan; Carrera, Jaime Luis; Maas, Raúl

    2017-04-01

    While it is known that climate change will impact future plant-pest population dynamics, potentially affecting crop damage, agroforestry with its enhanced biodiversity is said to reduce the outbreaks of pest insects by providing natural enemies for the control of pest populations. This premise is known in the literature as the natural enemy hypothesis and has been widely studied qualitatively. However, disagreement still exists on whether biodiversity enhancement reduces pest outbreaks, showing the need of quantitatively understanding the mechanisms behind the interactions between pests and natural enemies, also known as trophic interactions. Crop pest models that study insect population dynamics in agroforestry contexts are very rare, and pest models that take trophic interactions into account are even rarer. This may be due to the difficulty of representing complex food webs in a quantifiable model. There is therefore a need for validated food web models that allow users to predict the response of these webs to changes in climate in agroforestry systems. In this study we present Tiko'n, a Python-based software whose API allows users to rapidly build and validate trophic web models; the program uses a Bayesian inference approach to calibrate the models according to field data, allowing for the reuse of literature data from various sources and reducing the need for extensive field data collection. Tiko'n was run using coffee leaf miner (Leucoptera coffeella) and associated parasitoid data from a shaded coffee plantation, showing the mechanisms of insect population dynamics within a tri-trophic food web in an agroforestry system.

  15. Specialized Language Models using Dialogue Predictions

    CERN Document Server

    Popovici, C; Popovici, Cosmin; Baggia, Paolo

    1996-01-01

    This paper analyses language modeling in spoken dialogue systems for accessing a database. The use of several language models obtained by exploiting dialogue predictions gives better results than the use of a single model for the whole dialogue interaction. For this reason several models have been created, each one for a specific system question, such as the request or the confirmation of a parameter. The use of dialogue-dependent language models increases the performance both at the recognition and at the understanding level, especially on answers to system requests. Moreover other methods to increase performance, like automatic clustering of vocabulary words or the use of better acoustic models during recognition, does not affect the improvements given by dialogue-dependent language models. The system used in our experiments is Dialogos, the Italian spoken dialogue system used for accessing railway timetable information over the telephone. The experiments were carried out on a large corpus of dialogues coll...

  16. Caries risk assessment models in caries prediction

    Directory of Open Access Journals (Sweden)

    Amila Zukanović

    2013-11-01

    Full Text Available Objective. The aim of this research was to assess the efficiency of different multifactor models in caries prediction. Material and methods. Data from the questionnaire and objective examination of 109 examinees was entered into the Cariogram, Previser and Caries-Risk Assessment Tool (CAT multifactor risk assessment models. Caries risk was assessed with the help of all three models for each patient, classifying them as low, medium or high-risk patients. The development of new caries lesions over a period of three years [Decay Missing Filled Tooth (DMFT increment = difference between Decay Missing Filled Tooth Surface (DMFTS index at baseline and follow up], provided for examination of the predictive capacity concerning different multifactor models. Results. The data gathered showed that different multifactor risk assessment models give significantly different results (Friedman test: Chi square = 100.073, p=0.000. Cariogram is the model which identified the majority of examinees as medium risk patients (70%. The other two models were more radical in risk assessment, giving more unfavorable risk –profiles for patients. In only 12% of the patients did the three multifactor models assess the risk in the same way. Previser and CAT gave the same results in 63% of cases – the Wilcoxon test showed that there is no statistically significant difference in caries risk assessment between these two models (Z = -1.805, p=0.071. Conclusions. Evaluation of three different multifactor caries risk assessment models (Cariogram, PreViser and CAT showed that only the Cariogram can successfully predict new caries development in 12-year-old Bosnian children.

  17. Disease prediction models and operational readiness.

    Directory of Open Access Journals (Sweden)

    Courtney D Corley

    Full Text Available The objective of this manuscript is to present a systematic review of biosurveillance models that operate on select agents and can forecast the occurrence of a disease event. We define a disease event to be a biological event with focus on the One Health paradigm. These events are characterized by evidence of infection and or disease condition. We reviewed models that attempted to predict a disease event, not merely its transmission dynamics and we considered models involving pathogens of concern as determined by the US National Select Agent Registry (as of June 2011. We searched commercial and government databases and harvested Google search results for eligible models, using terms and phrases provided by public health analysts relating to biosurveillance, remote sensing, risk assessments, spatial epidemiology, and ecological niche modeling. After removal of duplications and extraneous material, a core collection of 6,524 items was established, and these publications along with their abstracts are presented in a semantic wiki at http://BioCat.pnnl.gov. As a result, we systematically reviewed 44 papers, and the results are presented in this analysis. We identified 44 models, classified as one or more of the following: event prediction (4, spatial (26, ecological niche (28, diagnostic or clinical (6, spread or response (9, and reviews (3. The model parameters (e.g., etiology, climatic, spatial, cultural and data sources (e.g., remote sensing, non-governmental organizations, expert opinion, epidemiological were recorded and reviewed. A component of this review is the identification of verification and validation (V&V methods applied to each model, if any V&V method was reported. All models were classified as either having undergone Some Verification or Validation method, or No Verification or Validation. We close by outlining an initial set of operational readiness level guidelines for disease prediction models based upon established Technology

  18. Model Predictive Control based on Finite Impulse Response Models

    DEFF Research Database (Denmark)

    Prasath, Guru; Jørgensen, John Bagterp

    2008-01-01

    We develop a regularized l2 finite impulse response (FIR) predictive controller with input and input-rate constraints. Feedback is based on a simple constant output disturbance filter. The performance of the predictive controller in the face of plant-model mismatch is investigated by simulations...

  19. ENSO Prediction using Vector Autoregressive Models

    Science.gov (United States)

    Chapman, D. R.; Cane, M. A.; Henderson, N.; Lee, D.; Chen, C.

    2013-12-01

    A recent comparison (Barnston et al, 2012 BAMS) shows the ENSO forecasting skill of dynamical models now exceeds that of statistical models, but the best statistical models are comparable to all but the very best dynamical models. In this comparison the leading statistical model is the one based on the Empirical Model Reduction (EMR) method. Here we report on experiments with multilevel Vector Autoregressive models using only sea surface temperatures (SSTs) as predictors. VAR(L) models generalizes Linear Inverse Models (LIM), which are a VAR(1) method, as well as multilevel univariate autoregressive models. Optimal forecast skill is achieved using 12 to 14 months of prior state information (i.e 12-14 levels), which allows SSTs alone to capture the effects of other variables such as heat content as well as seasonality. The use of multiple levels allows the model advancing one month at a time to perform at least as well for a 6 month forecast as a model constructed to explicitly forecast 6 months ahead. We infer that the multilevel model has fully captured the linear dynamics (cf. Penland and Magorian, 1993 J. Climate). Finally, while VAR(L) is equivalent to L-level EMR, we show in a 150 year cross validated assessment that we can increase forecast skill by improving on the EMR initialization procedure. The greatest benefit of this change is in allowing the prediction to make effective use of information over many more months.

  20. Electrostatic ion thrusters - towards predictive modeling

    Energy Technology Data Exchange (ETDEWEB)

    Kalentev, O.; Matyash, K.; Duras, J.; Lueskow, K.F.; Schneider, R. [Ernst-Moritz-Arndt Universitaet Greifswald, D-17489 (Germany); Koch, N. [Technische Hochschule Nuernberg Georg Simon Ohm, Kesslerplatz 12, D-90489 Nuernberg (Germany); Schirra, M. [Thales Electronic Systems GmbH, Soeflinger Strasse 100, D-89077 Ulm (Germany)

    2014-02-15

    The development of electrostatic ion thrusters so far has mainly been based on empirical and qualitative know-how, and on evolutionary iteration steps. This resulted in considerable effort regarding prototype design, construction and testing and therefore in significant development and qualification costs and high time demands. For future developments it is anticipated to implement simulation tools which allow for quantitative prediction of ion thruster performance, long-term behavior and space craft interaction prior to hardware design and construction. Based on integrated numerical models combining self-consistent kinetic plasma models with plasma-wall interaction modules a new quality in the description of electrostatic thrusters can be reached. These open the perspective for predictive modeling in this field. This paper reviews the application of a set of predictive numerical modeling tools on an ion thruster model of the HEMP-T (High Efficiency Multi-stage Plasma Thruster) type patented by Thales Electron Devices GmbH. (copyright 2014 WILEY-VCH Verlag GmbH and Co. KGaA, Weinheim) (orig.)

  1. Gas explosion prediction using CFD models

    Energy Technology Data Exchange (ETDEWEB)

    Niemann-Delius, C.; Okafor, E. [RWTH Aachen Univ. (Germany); Buhrow, C. [TU Bergakademie Freiberg Univ. (Germany)

    2006-07-15

    A number of CFD models are currently available to model gaseous explosions in complex geometries. Some of these tools allow the representation of complex environments within hydrocarbon production plants. In certain explosion scenarios, a correction is usually made for the presence of buildings and other complexities by using crude approximations to obtain realistic estimates of explosion behaviour as can be found when predicting the strength of blast waves resulting from initial explosions. With the advance of computational technology, and greater availability of computing power, computational fluid dynamics (CFD) tools are becoming increasingly available for solving such a wide range of explosion problems. A CFD-based explosion code - FLACS can, for instance, be confidently used to understand the impact of blast overpressures in a plant environment consisting of obstacles such as buildings, structures, and pipes. With its porosity concept representing geometry details smaller than the grid, FLACS can represent geometry well, even when using coarse grid resolutions. The performance of FLACS has been evaluated using a wide range of field data. In the present paper, the concept of computational fluid dynamics (CFD) and its application to gas explosion prediction is presented. Furthermore, the predictive capabilities of CFD-based gaseous explosion simulators are demonstrated using FLACS. Details about the FLACS-code, some extensions made to FLACS, model validation exercises, application, and some results from blast load prediction within an industrial facility are presented. (orig.)

  2. Genetic models of homosexuality: generating testable predictions.

    Science.gov (United States)

    Gavrilets, Sergey; Rice, William R

    2006-12-22

    Homosexuality is a common occurrence in humans and other species, yet its genetic and evolutionary basis is poorly understood. Here, we formulate and study a series of simple mathematical models for the purpose of predicting empirical patterns that can be used to determine the form of selection that leads to polymorphism of genes influencing homosexuality. Specifically, we develop theory to make contrasting predictions about the genetic characteristics of genes influencing homosexuality including: (i) chromosomal location, (ii) dominance among segregating alleles and (iii) effect sizes that distinguish between the two major models for their polymorphism: the overdominance and sexual antagonism models. We conclude that the measurement of the genetic characteristics of quantitative trait loci (QTLs) found in genomic screens for genes influencing homosexuality can be highly informative in resolving the form of natural selection maintaining their polymorphism.

  3. Characterizing Attention with Predictive Network Models.

    Science.gov (United States)

    Rosenberg, M D; Finn, E S; Scheinost, D; Constable, R T; Chun, M M

    2017-04-01

    Recent work shows that models based on functional connectivity in large-scale brain networks can predict individuals' attentional abilities. While being some of the first generalizable neuromarkers of cognitive function, these models also inform our basic understanding of attention, providing empirical evidence that: (i) attention is a network property of brain computation; (ii) the functional architecture that underlies attention can be measured while people are not engaged in any explicit task; and (iii) this architecture supports a general attentional ability that is common to several laboratory-based tasks and is impaired in attention deficit hyperactivity disorder (ADHD). Looking ahead, connectivity-based predictive models of attention and other cognitive abilities and behaviors may potentially improve the assessment, diagnosis, and treatment of clinical dysfunction. Copyright © 2017 Elsevier Ltd. All rights reserved.

  4. A Study On Distributed Model Predictive Consensus

    CERN Document Server

    Keviczky, Tamas

    2008-01-01

    We investigate convergence properties of a proposed distributed model predictive control (DMPC) scheme, where agents negotiate to compute an optimal consensus point using an incremental subgradient method based on primal decomposition as described in Johansson et al. [2006, 2007]. The objective of the distributed control strategy is to agree upon and achieve an optimal common output value for a group of agents in the presence of constraints on the agent dynamics using local predictive controllers. Stability analysis using a receding horizon implementation of the distributed optimal consensus scheme is performed. Conditions are given under which convergence can be obtained even if the negotiations do not reach full consensus.

  5. NONLINEAR MODEL PREDICTIVE CONTROL OF CHEMICAL PROCESSES

    Directory of Open Access Journals (Sweden)

    R. G. SILVA

    1999-03-01

    Full Text Available A new algorithm for model predictive control is presented. The algorithm utilizes a simultaneous solution and optimization strategy to solve the model's differential equations. The equations are discretized by equidistant collocation, and along with the algebraic model equations are included as constraints in a nonlinear programming (NLP problem. This algorithm is compared with the algorithm that uses orthogonal collocation on finite elements. The equidistant collocation algorithm results in simpler equations, providing a decrease in computation time for the control moves. Simulation results are presented and show a satisfactory performance of this algorithm.

  6. Performance model to predict overall defect density

    Directory of Open Access Journals (Sweden)

    J Venkatesh

    2012-08-01

    Full Text Available Management by metrics is the expectation from the IT service providers to stay as a differentiator. Given a project, the associated parameters and dynamics, the behaviour and outcome need to be predicted. There is lot of focus on the end state and in minimizing defect leakage as much as possible. In most of the cases, the actions taken are re-active. It is too late in the life cycle. Root cause analysis and corrective actions can be implemented only to the benefit of the next project. The focus has to shift left, towards the execution phase than waiting for lessons to be learnt post the implementation. How do we pro-actively predict defect metrics and have a preventive action plan in place. This paper illustrates the process performance model to predict overall defect density based on data from projects in an organization.

  7. Neuro-fuzzy modeling in bankruptcy prediction

    Directory of Open Access Journals (Sweden)

    Vlachos D.

    2003-01-01

    Full Text Available For the past 30 years the problem of bankruptcy prediction had been thoroughly studied. From the paper of Altman in 1968 to the recent papers in the '90s, the progress of prediction accuracy was not satisfactory. This paper investigates an alternative modeling of the system (firm, combining neural networks and fuzzy controllers, i.e. using neuro-fuzzy models. Classical modeling is based on mathematical models that describe the behavior of the firm under consideration. The main idea of fuzzy control, on the other hand, is to build a model of a human control expert who is capable of controlling the process without thinking in a mathematical model. This control expert specifies his control action in the form of linguistic rules. These control rules are translated into the framework of fuzzy set theory providing a calculus, which can stimulate the behavior of the control expert and enhance its performance. The accuracy of the model is studied using datasets from previous research papers.

  8. Pressure prediction model for compression garment design.

    Science.gov (United States)

    Leung, W Y; Yuen, D W; Ng, Sun Pui; Shi, S Q

    2010-01-01

    Based on the application of Laplace's law to compression garments, an equation for predicting garment pressure, incorporating the body circumference, the cross-sectional area of fabric, applied strain (as a function of reduction factor), and its corresponding Young's modulus, is developed. Design procedures are presented to predict garment pressure using the aforementioned parameters for clinical applications. Compression garments have been widely used in treating burning scars. Fabricating a compression garment with a required pressure is important in the healing process. A systematic and scientific design method can enable the occupational therapist and compression garments' manufacturer to custom-make a compression garment with a specific pressure. The objectives of this study are 1) to develop a pressure prediction model incorporating different design factors to estimate the pressure exerted by the compression garments before fabrication; and 2) to propose more design procedures in clinical applications. Three kinds of fabrics cut at different bias angles were tested under uniaxial tension, as were samples made in a double-layered structure. Sets of nonlinear force-extension data were obtained for calculating the predicted pressure. Using the value at 0° bias angle as reference, the Young's modulus can vary by as much as 29% for fabric type P11117, 43% for fabric type PN2170, and even 360% for fabric type AP85120 at a reduction factor of 20%. When comparing the predicted pressure calculated from the single-layered and double-layered fabrics, the double-layered construction provides a larger range of target pressure at a particular strain. The anisotropic and nonlinear behaviors of the fabrics have thus been determined. Compression garments can be methodically designed by the proposed analytical pressure prediction model.

  9. Statistical assessment of predictive modeling uncertainty

    Science.gov (United States)

    Barzaghi, Riccardo; Marotta, Anna Maria

    2017-04-01

    When the results of geophysical models are compared with data, the uncertainties of the model are typically disregarded. We propose a method for defining the uncertainty of a geophysical model based on a numerical procedure that estimates the empirical auto and cross-covariances of model-estimated quantities. These empirical values are then fitted by proper covariance functions and used to compute the covariance matrix associated with the model predictions. The method is tested using a geophysical finite element model in the Mediterranean region. Using a novel χ2 analysis in which both data and model uncertainties are taken into account, the model's estimated tectonic strain pattern due to the Africa-Eurasia convergence in the area that extends from the Calabrian Arc to the Alpine domain is compared with that estimated from GPS velocities while taking into account the model uncertainty through its covariance structure and the covariance of the GPS estimates. The results indicate that including the estimated model covariance in the testing procedure leads to lower observed χ2 values that have better statistical significance and might help a sharper identification of the best-fitting geophysical models.

  10. Seasonal Predictability in a Model Atmosphere.

    Science.gov (United States)

    Lin, Hai

    2001-07-01

    The predictability of atmospheric mean-seasonal conditions in the absence of externally varying forcing is examined. A perfect-model approach is adopted, in which a global T21 three-level quasigeostrophic atmospheric model is integrated over 21 000 days to obtain a reference atmospheric orbit. The model is driven by a time-independent forcing, so that the only source of time variability is the internal dynamics. The forcing is set to perpetual winter conditions in the Northern Hemisphere (NH) and perpetual summer in the Southern Hemisphere.A significant temporal variability in the NH 90-day mean states is observed. The component of that variability associated with the higher-frequency motions, or climate noise, is estimated using a method developed by Madden. In the polar region, and to a lesser extent in the midlatitudes, the temporal variance of the winter means is significantly greater than the climate noise, suggesting some potential predictability in those regions.Forecast experiments are performed to see whether the presence of variance in the 90-day mean states that is in excess of the climate noise leads to some skill in the prediction of these states. Ensemble forecast experiments with nine members starting from slightly different initial conditions are performed for 200 different 90-day means along the reference atmospheric orbit. The serial correlation between the ensemble means and the reference orbit shows that there is skill in the 90-day mean predictions. The skill is concentrated in those regions of the NH that have the largest variance in excess of the climate noise. An EOF analysis shows that nearly all the predictive skill in the seasonal means is associated with one mode of variability with a strong axisymmetric component.

  11. Trophic niche of squids: Insights from isotopic data in marine systems worldwide

    Science.gov (United States)

    Navarro, Joan; Coll, Marta; Somes, Christoper J.; Olson, Robert J.

    2013-10-01

    Cephalopods are an important prey resource for fishes, seabirds, and marine mammals, and are also voracious predators on crustaceans, fishes, squid and zooplankton. Because of their high feeding rates and abundance, squids have the potential to exert control on the recruitment of commercially important fishes. In this review, we synthesize the available information for two intrinsic markers (δ15N and δ13C isotopic values) in squids for all oceans and several types of ecosystems to obtain a global view of the trophic niches of squids in marine ecosystems. In particular, we aimed to examine whether the trophic positions and trophic widths of squid species vary among oceans and ecosystem types. To correctly compare across systems, we adjusted squid δ15N values for the isotopic variability of phytoplankton at the base of the food web provided by an ocean circulation-biogeochemistry-isotope model. Studies that focused on the trophic ecology of squids using isotopic techniques were few, and most of the information on squids was from studies on their predators. Our results showed that squids occupy a large range of trophic positions and exploit a large range of trophic resources, reflecting the versatility of their feeding behavior and confirming conclusions from food-web models. Clear differences in both trophic position and trophic width were found among oceans and ecosystem types. The study also reinforces the importance of considering the natural variation in isotopic values when comparing the isotopic values of consumers inhabiting different ecosystems.

  12. A kinetic model for predicting biodegradation.

    Science.gov (United States)

    Dimitrov, S; Pavlov, T; Nedelcheva, D; Reuschenbach, P; Silvani, M; Bias, R; Comber, M; Low, L; Lee, C; Parkerton, T; Mekenyan, O

    2007-01-01

    Biodegradation plays a key role in the environmental risk assessment of organic chemicals. The need to assess biodegradability of a chemical for regulatory purposes supports the development of a model for predicting the extent of biodegradation at different time frames, in particular the extent of ultimate biodegradation within a '10 day window' criterion as well as estimating biodegradation half-lives. Conceptually this implies expressing the rate of catabolic transformations as a function of time. An attempt to correlate the kinetics of biodegradation with molecular structure of chemicals is presented. A simplified biodegradation kinetic model was formulated by combining the probabilistic approach of the original formulation of the CATABOL model with the assumption of first order kinetics of catabolic transformations. Nonlinear regression analysis was used to fit the model parameters to OECD 301F biodegradation kinetic data for a set of 208 chemicals. The new model allows the prediction of biodegradation multi-pathways, primary and ultimate half-lives and simulation of related kinetic biodegradation parameters such as biological oxygen demand (BOD), carbon dioxide production, and the nature and amount of metabolites as a function of time. The model may also be used for evaluating the OECD ready biodegradability potential of a chemical within the '10-day window' criterion.

  13. Disease Prediction Models and Operational Readiness

    Energy Technology Data Exchange (ETDEWEB)

    Corley, Courtney D.; Pullum, Laura L.; Hartley, David M.; Benedum, Corey M.; Noonan, Christine F.; Rabinowitz, Peter M.; Lancaster, Mary J.

    2014-03-19

    INTRODUCTION: The objective of this manuscript is to present a systematic review of biosurveillance models that operate on select agents and can forecast the occurrence of a disease event. One of the primary goals of this research was to characterize the viability of biosurveillance models to provide operationally relevant information for decision makers to identify areas for future research. Two critical characteristics differentiate this work from other infectious disease modeling reviews. First, we reviewed models that attempted to predict the disease event, not merely its transmission dynamics. Second, we considered models involving pathogens of concern as determined by the US National Select Agent Registry (as of June 2011). Methods: We searched dozens of commercial and government databases and harvested Google search results for eligible models utilizing terms and phrases provided by public health analysts relating to biosurveillance, remote sensing, risk assessments, spatial epidemiology, and ecological niche-modeling, The publication date of search results returned are bound by the dates of coverage of each database and the date in which the search was performed, however all searching was completed by December 31, 2010. This returned 13,767 webpages and 12,152 citations. After de-duplication and removal of extraneous material, a core collection of 6,503 items was established and these publications along with their abstracts are presented in a semantic wiki at http://BioCat.pnnl.gov. Next, PNNL’s IN-SPIRE visual analytics software was used to cross-correlate these publications with the definition for a biosurveillance model resulting in the selection of 54 documents that matched the criteria resulting Ten of these documents, However, dealt purely with disease spread models, inactivation of bacteria, or the modeling of human immune system responses to pathogens rather than predicting disease events. As a result, we systematically reviewed 44 papers and the

  14. Nonlinear model predictive control theory and algorithms

    CERN Document Server

    Grüne, Lars

    2017-01-01

    This book offers readers a thorough and rigorous introduction to nonlinear model predictive control (NMPC) for discrete-time and sampled-data systems. NMPC schemes with and without stabilizing terminal constraints are detailed, and intuitive examples illustrate the performance of different NMPC variants. NMPC is interpreted as an approximation of infinite-horizon optimal control so that important properties like closed-loop stability, inverse optimality and suboptimality can be derived in a uniform manner. These results are complemented by discussions of feasibility and robustness. An introduction to nonlinear optimal control algorithms yields essential insights into how the nonlinear optimization routine—the core of any nonlinear model predictive controller—works. Accompanying software in MATLAB® and C++ (downloadable from extras.springer.com/), together with an explanatory appendix in the book itself, enables readers to perform computer experiments exploring the possibilities and limitations of NMPC. T...

  15. Predictive Modeling in Actinide Chemistry and Catalysis

    Energy Technology Data Exchange (ETDEWEB)

    Yang, Ping [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

    2016-05-16

    These are slides from a presentation on predictive modeling in actinide chemistry and catalysis. The following topics are covered in these slides: Structures, bonding, and reactivity (bonding can be quantified by optical probes and theory, and electronic structures and reaction mechanisms of actinide complexes); Magnetic resonance properties (transition metal catalysts with multi-nuclear centers, and NMR/EPR parameters); Moving to more complex systems (surface chemistry of nanomaterials, and interactions of ligands with nanoparticles); Path forward and conclusions.

  16. Estimate of the trophic status of subarctic Imandra Lake

    Directory of Open Access Journals (Sweden)

    Terent'eva I. A.

    2017-03-01

    Full Text Available The object of study is Imandra Lake – the largest reservoir of the Murmansk region. The lake is being influenced by the long-term and multi-factorial activities of mining and ore processing industries, air pollution and energetics. Moreover, the drain of municipal sewage from the large settlements situated on the lake's watershed makes a serious contribution to water pollution. As a result the lake has accumulated a significant amount of pollutants and nutrients that resulted currently in an increase of the toxic load on the lake system. One of the main ecological problems also is the intensification of the anthropogenic eutrophication processes. The aim of this study is to evaluate the current trophic status of Imandra Lake using the trophic index (TSI with the average annual values of the parameters: chlorophyll, total nitrogen, total phosphorus, and total organic carbon and to find the dynamics of these parameters' changes during more than 20-year period. The study of the trophic status of Imandra Lake has been performed for the period 1991–2013 yrs. using the trophic state index developed by Carlson, Kratzer and Bresonik, Dunalska. According to the calculated values of the indexes Bolshaja Imandra Lake corresponds to eutrophic-mesotrophic trophic status, Yokostrovskaya Imandra Lake could be described as mesotrophic. Babinskaja Imandra Lake that subjected to essential nutrient loading is close to the oligotrophic trophic status. However, some parts of Babinskaja Imandra Lake refer to the mesotrophic state due to influence of industrial, household and heated water of the Kola atomic power station. Thus, this part of Imadra Lake could be considered as a meso-oligotrophic status. It has been established that currently nitrogen is a limiting factor for development of algae in Imandra Lake. Based on the mathematical Vollenweider model the critical phosphorus loading values to control such an important nutrient element as phosphorus have been

  17. Probabilistic prediction models for aggregate quarry siting

    Science.gov (United States)

    Robinson, G.R.; Larkins, P.M.

    2007-01-01

    Weights-of-evidence (WofE) and logistic regression techniques were used in a GIS framework to predict the spatial likelihood (prospectivity) of crushed-stone aggregate quarry development. The joint conditional probability models, based on geology, transportation network, and population density variables, were defined using quarry location and time of development data for the New England States, North Carolina, and South Carolina, USA. The Quarry Operation models describe the distribution of active aggregate quarries, independent of the date of opening. The New Quarry models describe the distribution of aggregate quarries when they open. Because of the small number of new quarries developed in the study areas during the last decade, independent New Quarry models have low parameter estimate reliability. The performance of parameter estimates derived for Quarry Operation models, defined by a larger number of active quarries in the study areas, were tested and evaluated to predict the spatial likelihood of new quarry development. Population density conditions at the time of new quarry development were used to modify the population density variable in the Quarry Operation models to apply to new quarry development sites. The Quarry Operation parameters derived for the New England study area, Carolina study area, and the combined New England and Carolina study areas were all similar in magnitude and relative strength. The Quarry Operation model parameters, using the modified population density variables, were found to be a good predictor of new quarry locations. Both the aggregate industry and the land management community can use the model approach to target areas for more detailed site evaluation for quarry location. The models can be revised easily to reflect actual or anticipated changes in transportation and population features. ?? International Association for Mathematical Geology 2007.

  18. Predicting Footbridge Response using Stochastic Load Models

    DEFF Research Database (Denmark)

    Pedersen, Lars; Frier, Christian

    2013-01-01

    Walking parameters such as step frequency, pedestrian mass, dynamic load factor, etc. are basically stochastic, although it is quite common to adapt deterministic models for these parameters. The present paper considers a stochastic approach to modeling the action of pedestrians, but when doing s...... as it pinpoints which decisions to be concerned about when the goal is to predict footbridge response. The studies involve estimating footbridge responses using Monte-Carlo simulations and focus is on estimating vertical structural response to single person loading....

  19. Nonconvex Model Predictive Control for Commercial Refrigeration

    DEFF Research Database (Denmark)

    Hovgaard, Tobias Gybel; Larsen, Lars F.S.; Jørgensen, John Bagterp

    2013-01-01

    is to minimize the total energy cost, using real-time electricity prices, while obeying temperature constraints on the zones. We propose a variation on model predictive control to achieve this goal. When the right variables are used, the dynamics of the system are linear, and the constraints are convex. The cost...... the iterations, which is more than fast enough to run in real-time. We demonstrate our method on a realistic model, with a full year simulation and 15 minute time periods, using historical electricity prices and weather data, as well as random variations in thermal load. These simulations show substantial cost...

  20. Predictive In Vivo Models for Oncology.

    Science.gov (United States)

    Behrens, Diana; Rolff, Jana; Hoffmann, Jens

    2016-01-01

    Experimental oncology research and preclinical drug development both substantially require specific, clinically relevant in vitro and in vivo tumor models. The increasing knowledge about the heterogeneity of cancer requested a substantial restructuring of the test systems for the different stages of development. To be able to cope with the complexity of the disease, larger panels of patient-derived tumor models have to be implemented and extensively characterized. Together with individual genetically engineered tumor models and supported by core functions for expression profiling and data analysis, an integrated discovery process has been generated for predictive and personalized drug development.Improved “humanized” mouse models should help to overcome current limitations given by xenogeneic barrier between humans and mice. Establishment of a functional human immune system and a corresponding human microenvironment in laboratory animals will strongly support further research.Drug discovery, systems biology, and translational research are moving closer together to address all the new hallmarks of cancer, increase the success rate of drug development, and increase the predictive value of preclinical models.

  1. Constructing predictive models of human running.

    Science.gov (United States)

    Maus, Horst-Moritz; Revzen, Shai; Guckenheimer, John; Ludwig, Christian; Reger, Johann; Seyfarth, Andre

    2015-02-06

    Running is an essential mode of human locomotion, during which ballistic aerial phases alternate with phases when a single foot contacts the ground. The spring-loaded inverted pendulum (SLIP) provides a starting point for modelling running, and generates ground reaction forces that resemble those of the centre of mass (CoM) of a human runner. Here, we show that while SLIP reproduces within-step kinematics of the CoM in three dimensions, it fails to reproduce stability and predict future motions. We construct SLIP control models using data-driven Floquet analysis, and show how these models may be used to obtain predictive models of human running with six additional states comprising the position and velocity of the swing-leg ankle. Our methods are general, and may be applied to any rhythmic physical system. We provide an approach for identifying an event-driven linear controller that approximates an observed stabilization strategy, and for producing a reduced-state model which closely recovers the observed dynamics. © 2014 The Author(s) Published by the Royal Society. All rights reserved.

  2. Statistical Seasonal Sea Surface based Prediction Model

    Science.gov (United States)

    Suarez, Roberto; Rodriguez-Fonseca, Belen; Diouf, Ibrahima

    2014-05-01

    The interannual variability of the sea surface temperature (SST) plays a key role in the strongly seasonal rainfall regime on the West African region. The predictability of the seasonal cycle of rainfall is a field widely discussed by the scientific community, with results that fail to be satisfactory due to the difficulty of dynamical models to reproduce the behavior of the Inter Tropical Convergence Zone (ITCZ). To tackle this problem, a statistical model based on oceanic predictors has been developed at the Universidad Complutense of Madrid (UCM) with the aim to complement and enhance the predictability of the West African Monsoon (WAM) as an alternative to the coupled models. The model, called S4CAST (SST-based Statistical Seasonal Forecast) is based on discriminant analysis techniques, specifically the Maximum Covariance Analysis (MCA) and Canonical Correlation Analysis (CCA). Beyond the application of the model to the prediciton of rainfall in West Africa, its use extends to a range of different oceanic, atmospheric and helth related parameters influenced by the temperature of the sea surface as a defining factor of variability.

  3. Predictive modeling by the cerebellum improves proprioception.

    Science.gov (United States)

    Bhanpuri, Nasir H; Okamura, Allison M; Bastian, Amy J

    2013-09-04

    Because sensation is delayed, real-time movement control requires not just sensing, but also predicting limb position, a function hypothesized for the cerebellum. Such cerebellar predictions could contribute to perception of limb position (i.e., proprioception), particularly when a person actively moves the limb. Here we show that human cerebellar patients have proprioceptive deficits compared with controls during active movement, but not when the arm is moved passively. Furthermore, when healthy subjects move in a force field with unpredictable dynamics, they have active proprioceptive deficits similar to cerebellar patients. Therefore, muscle activity alone is likely insufficient to enhance proprioception and predictability (i.e., an internal model of the body and environment) is important for active movement to benefit proprioception. We conclude that cerebellar patients have an active proprioceptive deficit consistent with disrupted movement prediction rather than an inability to generally enhance peripheral proprioceptive signals during action and suggest that active proprioceptive deficits should be considered a fundamental cerebellar impairment of clinical importance.

  4. Trait-mediated trophic interactions: is foraging theory keeping up?

    Science.gov (United States)

    Railsback, Steven F; Harvey, Bret C

    2013-02-01

    Many ecologists believe that there is a lack of foraging theory that works in community contexts, for populations of unique individuals each making trade-offs between food and risk that are subject to feedbacks from behavior of others. Such theory is necessary to reproduce the trait-mediated trophic interactions now recognized as widespread and strong. Game theory can address feedbacks but does not provide foraging theory for unique individuals in variable environments. 'State- and prediction-based theory' (SPT) is a new approach that combines existing trade-off methods with routine updating: individuals regularly predict future food availability and risk from current conditions to optimize a fitness measure. SPT can reproduce a variety of realistic foraging behaviors and trait-mediated trophic interactions with feedbacks, even when the environment is unpredictable.

  5. A prediction model for Clostridium difficile recurrence

    Directory of Open Access Journals (Sweden)

    Francis D. LaBarbera

    2015-02-01

    Full Text Available Background: Clostridium difficile infection (CDI is a growing problem in the community and hospital setting. Its incidence has been on the rise over the past two decades, and it is quickly becoming a major concern for the health care system. High rate of recurrence is one of the major hurdles in the successful treatment of C. difficile infection. There have been few studies that have looked at patterns of recurrence. The studies currently available have shown a number of risk factors associated with C. difficile recurrence (CDR; however, there is little consensus on the impact of most of the identified risk factors. Methods: Our study was a retrospective chart review of 198 patients diagnosed with CDI via Polymerase Chain Reaction (PCR from February 2009 to Jun 2013. In our study, we decided to use a machine learning algorithm called the Random Forest (RF to analyze all of the factors proposed to be associated with CDR. This model is capable of making predictions based on a large number of variables, and has outperformed numerous other models and statistical methods. Results: We came up with a model that was able to accurately predict the CDR with a sensitivity of 83.3%, specificity of 63.1%, and area under curve of 82.6%. Like other similar studies that have used the RF model, we also had very impressive results. Conclusions: We hope that in the future, machine learning algorithms, such as the RF, will see a wider application.

  6. Gamma-Ray Pulsars Models and Predictions

    CERN Document Server

    Harding, A K

    2001-01-01

    Pulsed emission from gamma-ray pulsars originates inside the magnetosphere, from radiation by charged particles accelerated near the magnetic poles or in the outer gaps. In polar cap models, the high energy spectrum is cut off by magnetic pair production above an energy that is dependent on the local magnetic field strength. While most young pulsars with surface fields in the range B = 10^{12} - 10^{13} G are expected to have high energy cutoffs around several GeV, the gamma-ray spectra of old pulsars having lower surface fields may extend to 50 GeV. Although the gamma-ray emission of older pulsars is weaker, detecting pulsed emission at high energies from nearby sources would be an important confirmation of polar cap models. Outer gap models predict more gradual high-energy turnovers at around 10 GeV, but also predict an inverse Compton component extending to TeV energies. Detection of pulsed TeV emission, which would not survive attenuation at the polar caps, is thus an important test of outer gap models. N...

  7. Artificial Neural Network Model for Predicting Compressive

    Directory of Open Access Journals (Sweden)

    Salim T. Yousif

    2013-05-01

    Full Text Available   Compressive strength of concrete is a commonly used criterion in evaluating concrete. Although testing of the compressive strength of concrete specimens is done routinely, it is performed on the 28th day after concrete placement. Therefore, strength estimation of concrete at early time is highly desirable. This study presents the effort in applying neural network-based system identification techniques to predict the compressive strength of concrete based on concrete mix proportions, maximum aggregate size (MAS, and slump of fresh concrete. Back-propagation neural networks model is successively developed, trained, and tested using actual data sets of concrete mix proportions gathered from literature.    The test of the model by un-used data within the range of input parameters shows that the maximum absolute error for model is about 20% and 88% of the output results has absolute errors less than 10%. The parametric study shows that water/cement ratio (w/c is the most significant factor  affecting the output of the model.     The results showed that neural networks has strong potential as a feasible tool for predicting compressive strength of concrete.

  8. Ground Motion Prediction Models for Caucasus Region

    Science.gov (United States)

    Jorjiashvili, Nato; Godoladze, Tea; Tvaradze, Nino; Tumanova, Nino

    2016-04-01

    Ground motion prediction models (GMPMs) relate ground motion intensity measures to variables describing earthquake source, path, and site effects. Estimation of expected ground motion is a fundamental earthquake hazard assessment. The most commonly used parameter for attenuation relation is peak ground acceleration or spectral acceleration because this parameter gives useful information for Seismic Hazard Assessment. Since 2003 development of Georgian Digital Seismic Network has started. In this study new GMP models are obtained based on new data from Georgian seismic network and also from neighboring countries. Estimation of models is obtained by classical, statistical way, regression analysis. In this study site ground conditions are additionally considered because the same earthquake recorded at the same distance may cause different damage according to ground conditions. Empirical ground-motion prediction models (GMPMs) require adjustment to make them appropriate for site-specific scenarios. However, the process of making such adjustments remains a challenge. This work presents a holistic framework for the development of a peak ground acceleration (PGA) or spectral acceleration (SA) GMPE that is easily adjustable to different seismological conditions and does not suffer from the practical problems associated with adjustments in the response spectral domain.

  9. Modeling and Prediction of Krueger Device Noise

    Science.gov (United States)

    Guo, Yueping; Burley, Casey L.; Thomas, Russell H.

    2016-01-01

    This paper presents the development of a noise prediction model for aircraft Krueger flap devices that are considered as alternatives to leading edge slotted slats. The prediction model decomposes the total Krueger noise into four components, generated by the unsteady flows, respectively, in the cove under the pressure side surface of the Krueger, in the gap between the Krueger trailing edge and the main wing, around the brackets supporting the Krueger device, and around the cavity on the lower side of the main wing. For each noise component, the modeling follows a physics-based approach that aims at capturing the dominant noise-generating features in the flow and developing correlations between the noise and the flow parameters that control the noise generation processes. The far field noise is modeled using each of the four noise component's respective spectral functions, far field directivities, Mach number dependencies, component amplitudes, and other parametric trends. Preliminary validations are carried out by using small scale experimental data, and two applications are discussed; one for conventional aircraft and the other for advanced configurations. The former focuses on the parametric trends of Krueger noise on design parameters, while the latter reveals its importance in relation to other airframe noise components.

  10. A generative model for predicting terrorist incidents

    Science.gov (United States)

    Verma, Dinesh C.; Verma, Archit; Felmlee, Diane; Pearson, Gavin; Whitaker, Roger

    2017-05-01

    A major concern in coalition peace-support operations is the incidence of terrorist activity. In this paper, we propose a generative model for the occurrence of the terrorist incidents, and illustrate that an increase in diversity, as measured by the number of different social groups to which that an individual belongs, is inversely correlated with the likelihood of a terrorist incident in the society. A generative model is one that can predict the likelihood of events in new contexts, as opposed to statistical models which are used to predict the future incidents based on the history of the incidents in an existing context. Generative models can be useful in planning for persistent Information Surveillance and Reconnaissance (ISR) since they allow an estimation of regions in the theater of operation where terrorist incidents may arise, and thus can be used to better allocate the assignment and deployment of ISR assets. In this paper, we present a taxonomy of terrorist incidents, identify factors related to occurrence of terrorist incidents, and provide a mathematical analysis calculating the likelihood of occurrence of terrorist incidents in three common real-life scenarios arising in peace-keeping operations

  11. Optimal feedback scheduling of model predictive controllers

    Institute of Scientific and Technical Information of China (English)

    Pingfang ZHOU; Jianying XIE; Xiaolong DENG

    2006-01-01

    Model predictive control (MPC) could not be reliably applied to real-time control systems because its computation time is not well defined. Implemented as anytime algorithm, MPC task allows computation time to be traded for control performance, thus obtaining the predictability in time. Optimal feedback scheduling (FS-CBS) of a set of MPC tasks is presented to maximize the global control performance subject to limited processor time. Each MPC task is assigned with a constant bandwidth server (CBS), whose reserved processor time is adjusted dynamically. The constraints in the FSCBS guarantee scheduler of the total task set and stability of each component. The FS-CBS is shown robust against the variation of execution time of MPC tasks at runtime. Simulation results illustrate its effectiveness.

  12. Coupling age-structured stock assessment and fish bioenergetics models: a system of time-varying models for quantifying piscivory patterns during the rapid trophic shift in the main basin of Lake Huron

    Science.gov (United States)

    He, Ji X.; Bence, James R.; Madenjian, Charles P.; Pothoven, Steven A.; Dobiesz, Norine E.; Fielder, David G.; Johnson, James E.; Ebener, Mark P.; Cottrill, Adam R.; Mohr, Lloyd C.; Koproski, Scott R.

    2015-01-01

    We quantified piscivory patterns in the main basin of Lake Huron during 1984–2010 and found that the biomass transfer from prey fish to piscivores remained consistently high despite the rapid major trophic shift in the food webs. We coupled age-structured stock assessment models and fish bioenergetics models for lake trout (Salvelinus namaycush), Chinook salmon (Oncorhynchus tshawytscha), walleye (Sander vitreus), and lake whitefish (Coregonus clupeaformis). The model system also included time-varying parameters or variables of growth, length–mass relations, maturity schedules, energy density, and diets. These time-varying models reflected the dynamic connections that a fish cohort responded to year-to-year ecosystem changes at different ages and body sizes. We found that the ratio of annual predation by lake trout, Chinook salmon, and walleye combined with the biomass indices of age-1 and older alewives (Alosa pseudoharengus) and rainbow smelt (Osmerus mordax) increased more than tenfold during 1987–2010, and such increases in predation pressure were structured by relatively stable biomass of the three piscivores and stepwise declines in the biomass of alewives and rainbow smelt. The piscivore stability was supported by the use of alternative energy pathways and changes in relative composition of the three piscivores. In addition, lake whitefish became a new piscivore by feeding on round goby (Neogobius melanostomus). Their total fish consumption rivaled that of the other piscivores combined, although fish were still a modest proportion of their diet. Overall, the use of alternative energy pathways by piscivores allowed the increases in predation pressure on dominant diet species.

  13. Objective calibration of numerical weather prediction models

    Science.gov (United States)

    Voudouri, A.; Khain, P.; Carmona, I.; Bellprat, O.; Grazzini, F.; Avgoustoglou, E.; Bettems, J. M.; Kaufmann, P.

    2017-07-01

    Numerical weather prediction (NWP) and climate models use parameterization schemes for physical processes, which often include free or poorly confined parameters. Model developers normally calibrate the values of these parameters subjectively to improve the agreement of forecasts with available observations, a procedure referred as expert tuning. A practicable objective multi-variate calibration method build on a quadratic meta-model (MM), that has been applied for a regional climate model (RCM) has shown to be at least as good as expert tuning. Based on these results, an approach to implement the methodology to an NWP model is presented in this study. Challenges in transferring the methodology from RCM to NWP are not only restricted to the use of higher resolution and different time scales. The sensitivity of the NWP model quality with respect to the model parameter space has to be clarified, as well as optimize the overall procedure, in terms of required amount of computing resources for the calibration of an NWP model. Three free model parameters affecting mainly turbulence parameterization schemes were originally selected with respect to their influence on the variables associated to daily forecasts such as daily minimum and maximum 2 m temperature as well as 24 h accumulated precipitation. Preliminary results indicate that it is both affordable in terms of computer resources and meaningful in terms of improved forecast quality. In addition, the proposed methodology has the advantage of being a replicable procedure that can be applied when an updated model version is launched and/or customize the same model implementation over different climatological areas.

  14. Prediction models from CAD models of 3D objects

    Science.gov (United States)

    Camps, Octavia I.

    1992-11-01

    In this paper we present a probabilistic prediction based approach for CAD-based object recognition. Given a CAD model of an object, the PREMIO system combines techniques of analytic graphics and physical models of lights and sensors to predict how features of the object will appear in images. In nearly 4,000 experiments on analytically-generated and real images, we show that in a semi-controlled environment, predicting the detectability of features of the image can successfully guide a search procedure to make informed choices of model and image features in its search for correspondences that can be used to hypothesize the pose of the object. Furthermore, we provide a rigorous experimental protocol that can be used to determine the optimal number of correspondences to seek so that the probability of failing to find a pose and of finding an inaccurate pose are minimized.

  15. Model predictive control of MSMPR crystallizers

    Science.gov (United States)

    Moldoványi, Nóra; Lakatos, Béla G.; Szeifert, Ferenc

    2005-02-01

    A multi-input-multi-output (MIMO) control problem of isothermal continuous crystallizers is addressed in order to create an adequate model-based control system. The moment equation model of mixed suspension, mixed product removal (MSMPR) crystallizers that forms a dynamical system is used, the state of which is represented by the vector of six variables: the first four leading moments of the crystal size, solute concentration and solvent concentration. Hence, the time evolution of the system occurs in a bounded region of the six-dimensional phase space. The controlled variables are the mean size of the grain; the crystal size-distribution and the manipulated variables are the input concentration of the solute and the flow rate. The controllability and observability as well as the coupling between the inputs and the outputs was analyzed by simulation using the linearized model. It is shown that the crystallizer is a nonlinear MIMO system with strong coupling between the state variables. Considering the possibilities of the model reduction, a third-order model was found quite adequate for the model estimation in model predictive control (MPC). The mean crystal size and the variance of the size distribution can be nearly separately controlled by the residence time and the inlet solute concentration, respectively. By seeding, the controllability of the crystallizer increases significantly, and the overshoots and the oscillations become smaller. The results of the controlling study have shown that the linear MPC is an adaptable and feasible controller of continuous crystallizers.

  16. An Anisotropic Hardening Model for Springback Prediction

    Science.gov (United States)

    Zeng, Danielle; Xia, Z. Cedric

    2005-08-01

    As more Advanced High-Strength Steels (AHSS) are heavily used for automotive body structures and closures panels, accurate springback prediction for these components becomes more challenging because of their rapid hardening characteristics and ability to sustain even higher stresses. In this paper, a modified Mroz hardening model is proposed to capture realistic Bauschinger effect at reverse loading, such as when material passes through die radii or drawbead during sheet metal forming process. This model accounts for material anisotropic yield surface and nonlinear isotropic/kinematic hardening behavior. Material tension/compression test data are used to accurately represent Bauschinger effect. The effectiveness of the model is demonstrated by comparison of numerical and experimental springback results for a DP600 straight U-channel test.

  17. Predator-prey dynamics driven by feedback between functionally diverse trophic levels.

    Directory of Open Access Journals (Sweden)

    Katrin Tirok

    Full Text Available Neglecting the naturally existing functional diversity of communities and the resulting potential to respond to altered conditions may strongly reduce the realism and predictive power of ecological models. We therefore propose and study a predator-prey model that describes mutual feedback via species shifts in both predator and prey, using a dynamic trait approach. Species compositions of the two trophic levels were described by mean functional traits--prey edibility and predator food-selectivity--and functional diversities by the variances. Altered edibility triggered shifts in food-selectivity so that consumers continuously respond to the present prey composition, and vice versa. This trait-mediated feedback mechanism resulted in a complex dynamic behavior with ongoing oscillations in the mean trait values, reflecting continuous reorganization of the trophic levels. The feedback was only possible if sufficient functional diversity was present in both trophic levels. Functional diversity was internally maintained on the prey level as no niche existed in our system, which was ideal under any composition of the predator level due to the trade-offs between edibility, growth and carrying capacity. The predators were only subject to one trade-off between food-selectivity and grazing ability and in the absence of immigration, one predator type became abundant, i.e., functional diversity declined to zero. In the lack of functional diversity the system showed the same dynamics as conventional models of predator-prey interactions ignoring the potential for shifts in species composition. This way, our study identified the crucial role of trade-offs and their shape in physiological and ecological traits for preserving diversity.

  18. Predator-prey dynamics driven by feedback between functionally diverse trophic levels.

    Science.gov (United States)

    Tirok, Katrin; Bauer, Barbara; Wirtz, Kai; Gaedke, Ursula

    2011-01-01

    Neglecting the naturally existing functional diversity of communities and the resulting potential to respond to altered conditions may strongly reduce the realism and predictive power of ecological models. We therefore propose and study a predator-prey model that describes mutual feedback via species shifts in both predator and prey, using a dynamic trait approach. Species compositions of the two trophic levels were described by mean functional traits--prey edibility and predator food-selectivity--and functional diversities by the variances. Altered edibility triggered shifts in food-selectivity so that consumers continuously respond to the present prey composition, and vice versa. This trait-mediated feedback mechanism resulted in a complex dynamic behavior with ongoing oscillations in the mean trait values, reflecting continuous reorganization of the trophic levels. The feedback was only possible if sufficient functional diversity was present in both trophic levels. Functional diversity was internally maintained on the prey level as no niche existed in our system, which was ideal under any composition of the predator level due to the trade-offs between edibility, growth and carrying capacity. The predators were only subject to one trade-off between food-selectivity and grazing ability and in the absence of immigration, one predator type became abundant, i.e., functional diversity declined to zero. In the lack of functional diversity the system showed the same dynamics as conventional models of predator-prey interactions ignoring the potential for shifts in species composition. This way, our study identified the crucial role of trade-offs and their shape in physiological and ecological traits for preserving diversity.

  19. Trophic magnification of PCBs and Its relationship to the octanol-water partition coefficient.

    Science.gov (United States)

    Walters, David M; Mills, Marc A; Cade, Brian S; Burkard, Lawrence P

    2011-05-01

    We investigated polychlorinated biphenyl (PCB) bioaccumulation relative to octanol-water partition coefficient (K(OW)) and organism trophic position (TP) at the Lake Hartwell Superfund site (South Carolina). We measured PCBs (127 congeners) and stable isotopes (δ¹⁵N) in sediment, organic matter, phytoplankton, zooplankton, macroinvertebrates, and fish. TP, as calculated from δ¹⁵N, was significantly, positively related to PCB concentrations, and food web trophic magnification factors (TMFs) ranged from 1.5-6.6 among congeners. TMFs of individual congeners increased strongly with log K(OW), as did the predictive power (r²) of individual TP-PCB regression models used to calculate TMFs. We developed log K(OW)-TMF models for eight food webs with vastly different environments (freshwater, marine, arctic, temperate) and species composition (cold- vs warmblooded consumers). The effect of K(OW) on congener TMFs varied strongly across food webs (model slopes 0.0-15.0) because the range of TMFs among studies was also highly variable. We standardized TMFs within studies to mean = 0, standard deviation (SD) = 1 to normalize for scale differences and found a remarkably consistent K(OW) effect on TMFs (no difference in model slopes among food webs). Our findings underscore the importance of hydrophobicity (as characterized by K(OW)) in regulating bioaccumulation of recalcitrant compounds in aquatic systems, and demonstrate that relationships between chemical K(OW) and bioaccumulation from field studies are more generalized than previously recognized.

  20. Assessment, modelization and analysis of {sup 106} Ru experimental transfers through a freshwater trophic system; Evaluation, modelisation et analyse des transferts experimentaux du {sup 106}Ru au sein d`un reseau trophique d`eau douce

    Energy Technology Data Exchange (ETDEWEB)

    Vray, F.

    1994-11-24

    Experiments are carried out in order to study {sup 106} RU transfers through a freshwater ecosystem including 2 abiotic compartments (water and sediment) and 3 trophic levels (10 species). Experimental results are expressed mathematically so as they can be included into a global model which is then tested in two different situations. The comparison of the available data concerning the in situ measured concentrations to the corresponding calculated ones validates the whole procedure. Analysis of the so validated results lightens ruthenium distribution process in the environment. The rare detection of this radionuclide in organisms living in areas contaminated by known meaningful releases can be explained by a relativity high detection limit and by a slight role of the sediment as a secondary contamination source. (author). 78 figs., 18 tabs.

  1. Predictive modelling of ferroelectric tunnel junctions

    Science.gov (United States)

    Velev, Julian P.; Burton, John D.; Zhuravlev, Mikhail Ye; Tsymbal, Evgeny Y.

    2016-05-01

    Ferroelectric tunnel junctions combine the phenomena of quantum-mechanical tunnelling and switchable spontaneous polarisation of a nanometre-thick ferroelectric film into novel device functionality. Switching the ferroelectric barrier polarisation direction produces a sizable change in resistance of the junction—a phenomenon known as the tunnelling electroresistance effect. From a fundamental perspective, ferroelectric tunnel junctions and their version with ferromagnetic electrodes, i.e., multiferroic tunnel junctions, are testbeds for studying the underlying mechanisms of tunnelling electroresistance as well as the interplay between electric and magnetic degrees of freedom and their effect on transport. From a practical perspective, ferroelectric tunnel junctions hold promise for disruptive device applications. In a very short time, they have traversed the path from basic model predictions to prototypes for novel non-volatile ferroelectric random access memories with non-destructive readout. This remarkable progress is to a large extent driven by a productive cycle of predictive modelling and innovative experimental effort. In this review article, we outline the development of the ferroelectric tunnel junction concept and the role of theoretical modelling in guiding experimental work. We discuss a wide range of physical phenomena that control the functional properties of ferroelectric tunnel junctions and summarise the state-of-the-art achievements in the field.

  2. Simple predictions from multifield inflationary models.

    Science.gov (United States)

    Easther, Richard; Frazer, Jonathan; Peiris, Hiranya V; Price, Layne C

    2014-04-25

    We explore whether multifield inflationary models make unambiguous predictions for fundamental cosmological observables. Focusing on N-quadratic inflation, we numerically evaluate the full perturbation equations for models with 2, 3, and O(100) fields, using several distinct methods for specifying the initial values of the background fields. All scenarios are highly predictive, with the probability distribution functions of the cosmological observables becoming more sharply peaked as N increases. For N=100 fields, 95% of our Monte Carlo samples fall in the ranges ns∈(0.9455,0.9534), α∈(-9.741,-7.047)×10-4, r∈(0.1445,0.1449), and riso∈(0.02137,3.510)×10-3 for the spectral index, running, tensor-to-scalar ratio, and isocurvature-to-adiabatic ratio, respectively. The expected amplitude of isocurvature perturbations grows with N, raising the possibility that many-field models may be sensitive to postinflationary physics and suggesting new avenues for testing these scenarios.

  3. Trophic ecomorphology of Siluriformes (Pisces, Osteichthyes from a tropical stream

    Directory of Open Access Journals (Sweden)

    JPA Pagotto

    Full Text Available The present study analysed the relationship between morphology and trophic structure of Siluriformes (Pisces, Osteichthyes from the Caracu Stream (22º 45' S and 53º 15' W, a tributary of the Paraná River (Brazil. Sampling was carried out at three sites using electrofishing, and two species of Loricariidae and four of Heptapteridae were obtained. A cluster analysis revealed the presence of three trophic guilds (detritivores, insectivores and omnivores. Principal components analysis demonstrated the segregation of two ecomorphotypes: at one extreme there were the detritivores (Loricariidae with morphological structures that are fundamental in allowing them to fix themselves to substrates characterised by rushing torrents, thus permitting them to graze on the detritus and organic materials encrusted on the substrate; at the other extreme of the gradient there were the insectivores and omnivores (Heptapteridae, with morphological characteristics that promote superior performance in the exploitation of structurally complex habitats with low current velocity, colonised by insects and plants. Canonical discriminant analysis revealed an ecomorphological divergence between insectivores, which have morphological structures that permit them to capture prey in small spaces among rocks, and omnivores, which have a more compressed body and tend to explore food items deposited in marginal backwater zones. Mantel tests showed that trophic structure was significantly related to the body shape of a species, independently of the phylogenetic history, indicating that, in this case, there was an ecomorphotype for each trophic guild. Therefore, the present study demonstrated that the Siluriformes of the Caracu Stream were ecomorphologically structured and that morphology can be applied as an additional tool in predicting the trophic structure of this group.

  4. Predictions of models for environmental radiological assessment

    Energy Technology Data Exchange (ETDEWEB)

    Peres, Sueli da Silva; Lauria, Dejanira da Costa, E-mail: suelip@ird.gov.br, E-mail: dejanira@irg.gov.br [Instituto de Radioprotecao e Dosimetria (IRD/CNEN-RJ), Servico de Avaliacao de Impacto Ambiental, Rio de Janeiro, RJ (Brazil); Mahler, Claudio Fernando [Coppe. Instituto Alberto Luiz Coimbra de Pos-Graduacao e Pesquisa de Engenharia, Universidade Federal do Rio de Janeiro (UFRJ) - Programa de Engenharia Civil, RJ (Brazil)

    2011-07-01

    In the field of environmental impact assessment, models are used for estimating source term, environmental dispersion and transfer of radionuclides, exposure pathway, radiation dose and the risk for human beings Although it is recognized that the specific information of local data are important to improve the quality of the dose assessment results, in fact obtaining it can be very difficult and expensive. Sources of uncertainties are numerous, among which we can cite: the subjectivity of modelers, exposure scenarios and pathways, used codes and general parameters. The various models available utilize different mathematical approaches with different complexities that can result in different predictions. Thus, for the same inputs different models can produce very different outputs. This paper presents briefly the main advances in the field of environmental radiological assessment that aim to improve the reliability of the models used in the assessment of environmental radiological impact. The intercomparison exercise of model supplied incompatible results for {sup 137}Cs and {sup 60}Co, enhancing the need for developing reference methodologies for environmental radiological assessment that allow to confront dose estimations in a common comparison base. The results of the intercomparison exercise are present briefly. (author)

  5. Predicting Protein Secondary Structure with Markov Models

    DEFF Research Database (Denmark)

    Fischer, Paul; Larsen, Simon; Thomsen, Claus

    2004-01-01

    we are considering here, is to predict the secondary structure from the primary one. To this end we train a Markov model on training data and then use it to classify parts of unknown protein sequences as sheets, helices or coils. We show how to exploit the directional information contained......The primary structure of a protein is the sequence of its amino acids. The secondary structure describes structural properties of the molecule such as which parts of it form sheets, helices or coils. Spacial and other properties are described by the higher order structures. The classification task...

  6. A Modified Model Predictive Control Scheme

    Institute of Scientific and Technical Information of China (English)

    Xiao-Bing Hu; Wen-Hua Chen

    2005-01-01

    In implementations of MPC (Model Predictive Control) schemes, two issues need to be addressed. One is how to enlarge the stability region as much as possible. The other is how to guarantee stability when a computational time limitation exists. In this paper, a modified MPC scheme for constrained linear systems is described. An offline LMI-based iteration process is introduced to expand the stability region. At the same time, a database of feasible control sequences is generated offline so that stability can still be guaranteed in the case of computational time limitations. Simulation results illustrate the effectiveness of this new approach.

  7. Hierarchical Model Predictive Control for Resource Distribution

    DEFF Research Database (Denmark)

    Bendtsen, Jan Dimon; Trangbæk, K; Stoustrup, Jakob

    2010-01-01

    This paper deals with hierarchichal model predictive control (MPC) of distributed systems. A three level hierachical approach is proposed, consisting of a high level MPC controller, a second level of so-called aggregators, controlled by an online MPC-like algorithm, and a lower level of autonomous...... facilitates plug-and-play addition of subsystems without redesign of any controllers. The method is supported by a number of simulations featuring a three-level smart-grid power control system for a small isolated power grid....

  8. Explicit model predictive control accuracy analysis

    OpenAIRE

    Knyazev, Andrew; Zhu, Peizhen; Di Cairano, Stefano

    2015-01-01

    Model Predictive Control (MPC) can efficiently control constrained systems in real-time applications. MPC feedback law for a linear system with linear inequality constraints can be explicitly computed off-line, which results in an off-line partition of the state space into non-overlapped convex regions, with affine control laws associated to each region of the partition. An actual implementation of this explicit MPC in low cost micro-controllers requires the data to be "quantized", i.e. repre...

  9. Critical conceptualism in environmental modeling and prediction.

    Science.gov (United States)

    Christakos, G

    2003-10-15

    Many important problems in environmental science and engineering are of a conceptual nature. Research and development, however, often becomes so preoccupied with technical issues, which are themselves fascinating, that it neglects essential methodological elements of conceptual reasoning and theoretical inquiry. This work suggests that valuable insight into environmental modeling can be gained by means of critical conceptualism which focuses on the software of human reason and, in practical terms, leads to a powerful methodological framework of space-time modeling and prediction. A knowledge synthesis system develops the rational means for the epistemic integration of various physical knowledge bases relevant to the natural system of interest in order to obtain a realistic representation of the system, provide a rigorous assessment of the uncertainty sources, generate meaningful predictions of environmental processes in space-time, and produce science-based decisions. No restriction is imposed on the shape of the distribution model or the form of the predictor (non-Gaussian distributions, multiple-point statistics, and nonlinear models are automatically incorporated). The scientific reasoning structure underlying knowledge synthesis involves teleologic criteria and stochastic logic principles which have important advantages over the reasoning method of conventional space-time techniques. Insight is gained in terms of real world applications, including the following: the study of global ozone patterns in the atmosphere using data sets generated by instruments on board the Nimbus 7 satellite and secondary information in terms of total ozone-tropopause pressure models; the mapping of arsenic concentrations in the Bangladesh drinking water by assimilating hard and soft data from an extensive network of monitoring wells; and the dynamic imaging of probability distributions of pollutants across the Kalamazoo river.

  10. Predictive Capability Maturity Model for computational modeling and simulation.

    Energy Technology Data Exchange (ETDEWEB)

    Oberkampf, William Louis; Trucano, Timothy Guy; Pilch, Martin M.

    2007-10-01

    The Predictive Capability Maturity Model (PCMM) is a new model that can be used to assess the level of maturity of computational modeling and simulation (M&S) efforts. The development of the model is based on both the authors experience and their analysis of similar investigations in the past. The perspective taken in this report is one of judging the usefulness of a predictive capability that relies on the numerical solution to partial differential equations to better inform and improve decision making. The review of past investigations, such as the Software Engineering Institute's Capability Maturity Model Integration and the National Aeronautics and Space Administration and Department of Defense Technology Readiness Levels, indicates that a more restricted, more interpretable method is needed to assess the maturity of an M&S effort. The PCMM addresses six contributing elements to M&S: (1) representation and geometric fidelity, (2) physics and material model fidelity, (3) code verification, (4) solution verification, (5) model validation, and (6) uncertainty quantification and sensitivity analysis. For each of these elements, attributes are identified that characterize four increasing levels of maturity. Importantly, the PCMM is a structured method for assessing the maturity of an M&S effort that is directed toward an engineering application of interest. The PCMM does not assess whether the M&S effort, the accuracy of the predictions, or the performance of the engineering system satisfies or does not satisfy specified application requirements.

  11. Assessment of agglomeration, co-sedimentation and trophic transfer of titanium dioxide nanoparticles in a laboratory-scale predator-prey model system

    Science.gov (United States)

    Gupta, Govind Sharan; Kumar, Ashutosh; Shanker, Rishi; Dhawan, Alok

    2016-08-01

    Nano titanium dioxide (nTiO2) is the most abundantly released engineered nanomaterial (ENM) in aquatic environments. Therefore, it is prudent to assess its fate and its effects on lower trophic-level organisms in the aquatic food chain. A predator-and-prey-based laboratory microcosm was established using Paramecium caudatum and Escherichia coli to evaluate the effects of nTiO2. The surface interaction of nTiO2 with E. coli significantly increased after the addition of Paramecium into the microcosm. This interaction favoured the hetero-agglomeration and co-sedimentation of nTiO2. The extent of nTiO2 agglomeration under experimental conditions was as follows: combined E. coli and Paramecium > Paramecium only > E. coli only > without E. coli or Paramecium. An increase in nTiO2 internalisation in Paramecium cells was also observed in the presence or absence of E. coli cells. These interactions and nTiO2 internalisation in Paramecium cells induced statistically significant (p < 0.05) effects on growth and the bacterial ingestion rate at 24 h. These findings provide new insights into the fate of nTiO2 in the presence of bacterial-ciliate interactions in the aquatic environment.

  12. A Predictive Maintenance Model for Railway Tracks

    DEFF Research Database (Denmark)

    Li, Rui; Wen, Min; Salling, Kim Bang

    2015-01-01

    For the modern railways, maintenance is critical for ensuring safety, train punctuality and overall capacity utilization. The cost of railway maintenance in Europe is high, on average between 30,000 – 100,000 Euro per km per year [1]. Aiming to reduce such maintenance expenditure, this paper...... presents a mathematical model based on Mixed Integer Programming (MIP) which is designed to optimize the predictive railway tamping activities for ballasted track for the time horizon up to four years. The objective function is setup to minimize the actual costs for the tamping machine (measured by time...... recovery on the track quality after tamping operation and (5) Tamping machine operation factors. A Danish railway track between Odense and Fredericia with 57.2 km of length is applied for a time period of two to four years in the proposed maintenance model. The total cost can be reduced with up to 50...

  13. Evaluation of European diatom trophic indices.

    NARCIS (Netherlands)

    Lototskaya, A.A.; Verdonschot, P.F.M.; Coste, M.; Vijver, van de B.

    2011-01-01

    Freshwater diatoms are considered to be reliable indicators of the trophic status of rivers and lakes. In the past 30 years, a number of indicator indices have been developed and used for the assessment of trophic conditions all over Europe. It is however still not clear whether the ecologic signatu

  14. A predictive fitness model for influenza

    Science.gov (United States)

    Łuksza, Marta; Lässig, Michael

    2014-03-01

    The seasonal human influenza A/H3N2 virus undergoes rapid evolution, which produces significant year-to-year sequence turnover in the population of circulating strains. Adaptive mutations respond to human immune challenge and occur primarily in antigenic epitopes, the antibody-binding domains of the viral surface protein haemagglutinin. Here we develop a fitness model for haemagglutinin that predicts the evolution of the viral population from one year to the next. Two factors are shown to determine the fitness of a strain: adaptive epitope changes and deleterious mutations outside the epitopes. We infer both fitness components for the strains circulating in a given year, using population-genetic data of all previous strains. From fitness and frequency of each strain, we predict the frequency of its descendent strains in the following year. This fitness model maps the adaptive history of influenza A and suggests a principled method for vaccine selection. Our results call for a more comprehensive epidemiology of influenza and other fast-evolving pathogens that integrates antigenic phenotypes with other viral functions coupled by genetic linkage.

  15. Predictive Model of Radiative Neutrino Masses

    CERN Document Server

    Babu, K S

    2013-01-01

    We present a simple and predictive model of radiative neutrino masses. It is a special case of the Zee model which introduces two Higgs doublets and a charged singlet. We impose a family-dependent Z_4 symmetry acting on the leptons, which reduces the number of parameters describing neutrino oscillations to four. A variety of predictions follow: The hierarchy of neutrino masses must be inverted; the lightest neutrino mass is extremely small and calculable; one of the neutrino mixing angles is determined in terms of the other two; the phase parameters take CP-conserving values with \\delta_{CP} = \\pi; and the effective mass in neutrinoless double beta decay lies in a narrow range, m_{\\beta \\beta} = (17.6 - 18.5) meV. The ratio of vacuum expectation values of the two Higgs doublets, tan\\beta, is determined to be either 1.9 or 0.19 from neutrino oscillation data. Flavor-conserving and flavor-changing couplings of the Higgs doublets are also determined from neutrino data. The non-standard neutral Higgs bosons, if t...

  16. A predictive model for dimensional errors in fused deposition modeling

    DEFF Research Database (Denmark)

    Stolfi, A.

    2015-01-01

    This work concerns the effect of deposition angle (a) and layer thickness (L) on the dimensional performance of FDM parts using a predictive model based on the geometrical description of the FDM filament profile. An experimental validation over the whole a range from 0° to 177° at 3° steps and two...

  17. Effect on Prediction when Modeling Covariates in Bayesian Nonparametric Models.

    Science.gov (United States)

    Cruz-Marcelo, Alejandro; Rosner, Gary L; Müller, Peter; Stewart, Clinton F

    2013-04-01

    In biomedical research, it is often of interest to characterize biologic processes giving rise to observations and to make predictions of future observations. Bayesian nonparametric methods provide a means for carrying out Bayesian inference making as few assumptions about restrictive parametric models as possible. There are several proposals in the literature for extending Bayesian nonparametric models to include dependence on covariates. Limited attention, however, has been directed to the following two aspects. In this article, we examine the effect on fitting and predictive performance of incorporating covariates in a class of Bayesian nonparametric models by one of two primary ways: either in the weights or in the locations of a discrete random probability measure. We show that different strategies for incorporating continuous covariates in Bayesian nonparametric models can result in big differences when used for prediction, even though they lead to otherwise similar posterior inferences. When one needs the predictive density, as in optimal design, and this density is a mixture, it is better to make the weights depend on the covariates. We demonstrate these points via a simulated data example and in an application in which one wants to determine the optimal dose of an anticancer drug used in pediatric oncology.

  18. Continuous-Discrete Time Prediction-Error Identification Relevant for Linear Model Predictive Control

    DEFF Research Database (Denmark)

    Jørgensen, John Bagterp; Jørgensen, Sten Bay

    2007-01-01

    model is realized from a continuous-discrete-time linear stochastic system specified using transfer functions with time-delays. It is argued that the prediction-error criterion should be selected such that it is compatible with the objective function of the predictive controller in which the model......A Prediction-error-method tailored for model based predictive control is presented. The prediction-error method studied are based on predictions using the Kalman filter and Kalman predictors for a linear discrete-time stochastic state space model. The linear discrete-time stochastic state space...

  19. Intersexual trophic niche partitioning in an ant-eating spider (Araneae: Zodariidae.

    Directory of Open Access Journals (Sweden)

    Stano Pekár

    Full Text Available BACKGROUND: Divergence in trophic niche between the sexes may function to reduce competition between the sexes ("intersexual niche partitioning hypothesis", or may be result from differential selection among the sexes on maximizing reproductive output ("sexual selection hypothesis". The latter may lead to higher energy demands in females driven by fecundity selection, while males invest in mate searching. We tested predictions of the two hypotheses underlying intersexual trophic niche partitioning in a natural population of spiders. Zodarion jozefienae spiders specialize on Messor barbarus ants that are polymorphic in body size and hence comprise potential trophic niches for the spider, making this system well-suited to study intersexual trophic niche partitioning. METHODOLOGY/PRINCIPAL FINDINGS: Comparative analysis of trophic morphology (the chelicerae and body size of males, females and juveniles demonstrated highly female biased SSD (Sexual Size Dimorphism in body size, body weight, and in the size of chelicerae, the latter arising from sex-specific growth patterns in trophic morphology. In the field, female spiders actively selected ant sub-castes that were larger than the average prey size, and larger than ants captured by juveniles and males. Female fecundity was highly positively correlated with female body mass, which reflects foraging success during the adult stage. Females in laboratory experiments preferred the large ant sub-castes and displayed higher capture efficiency. In contrast, males occupied a different trophic niche and showed reduced foraging effort and reduced prey capture and feeding efficiency compared with females and juveniles. CONCLUSIONS/SIGNIFICANCE: Our data indicate that female-biased dimorphism in trophic morphology and body size correlate with sex-specific reproductive strategies. We propose that intersexual trophic niche partitioning is shaped primarily by fecundity selection in females, and results from sex

  20. Two criteria for evaluating risk prediction models.

    Science.gov (United States)

    Pfeiffer, R M; Gail, M H

    2011-09-01

    We propose and study two criteria to assess the usefulness of models that predict risk of disease incidence for screening and prevention, or the usefulness of prognostic models for management following disease diagnosis. The first criterion, the proportion of cases followed PCF (q), is the proportion of individuals who will develop disease who are included in the proportion q of individuals in the population at highest risk. The second criterion is the proportion needed to follow-up, PNF (p), namely the proportion of the general population at highest risk that one needs to follow in order that a proportion p of those destined to become cases will be followed. PCF (q) assesses the effectiveness of a program that follows 100q% of the population at highest risk. PNF (p) assess the feasibility of covering 100p% of cases by indicating how much of the population at highest risk must be followed. We show the relationship of those two criteria to the Lorenz curve and its inverse, and present distribution theory for estimates of PCF and PNF. We develop new methods, based on influence functions, for inference for a single risk model, and also for comparing the PCFs and PNFs of two risk models, both of which were evaluated in the same validation data.

  1. Methods for Handling Missing Variables in Risk Prediction Models

    NARCIS (Netherlands)

    Held, Ulrike; Kessels, Alfons; Aymerich, Judith Garcia; Basagana, Xavier; ter Riet, Gerben; Moons, Karel G. M.; Puhan, Milo A.

    2016-01-01

    Prediction models should be externally validated before being used in clinical practice. Many published prediction models have never been validated. Uncollected predictor variables in otherwise suitable validation cohorts are the main factor precluding external validation.We used individual patient

  2. Trophic fluxes in a beach seine fishery of the Campeche Bank

    National Research Council Canada - National Science Library

    Vega-Cendejas, M.E; Arreguin-Sanches, F; Hernandez, M

    1990-01-01

    The principal trophic interactions that occur between various species from a beach seine fishery of the Campeche Bank were determined through the ECOPATH II model using stomach contents data and biomass estimates...

  3. Estimating the magnitude of prediction uncertainties for the APLE model

    Science.gov (United States)

    Models are often used to predict phosphorus (P) loss from agricultural fields. While it is commonly recognized that model predictions are inherently uncertain, few studies have addressed prediction uncertainties using P loss models. In this study, we conduct an uncertainty analysis for the Annual P ...

  4. Prediction of Catastrophes: an experimental model

    CERN Document Server

    Peters, Randall D; Pomeau, Yves

    2012-01-01

    Catastrophes of all kinds can be roughly defined as short duration-large amplitude events following and followed by long periods of "ripening". Major earthquakes surely belong to the class of 'catastrophic' events. Because of the space-time scales involved, an experimental approach is often difficult, not to say impossible, however desirable it could be. Described in this article is a "laboratory" setup that yields data of a type that is amenable to theoretical methods of prediction. Observations are made of a critical slowing down in the noisy signal of a solder wire creeping under constant stress. This effect is shown to be a fair signal of the forthcoming catastrophe in both of two dynamical models. The first is an "abstract" model in which a time dependent quantity drifts slowly but makes quick jumps from time to time. The second is a realistic physical model for the collective motion of dislocations (the Ananthakrishna set of equations for creep). Hope thus exists that similar changes in the response to ...

  5. Predictive modeling of low solubility semiconductor alloys

    Science.gov (United States)

    Rodriguez, Garrett V.; Millunchick, Joanna M.

    2016-09-01

    GaAsBi is of great interest for applications in high efficiency optoelectronic devices due to its highly tunable bandgap. However, the experimental growth of high Bi content films has proven difficult. Here, we model GaAsBi film growth using a kinetic Monte Carlo simulation that explicitly takes cation and anion reactions into account. The unique behavior of Bi droplets is explored, and a sharp decrease in Bi content upon Bi droplet formation is demonstrated. The high mobility of simulated Bi droplets on GaAsBi surfaces is shown to produce phase separated Ga-Bi droplets as well as depressions on the film surface. A phase diagram for a range of growth rates that predicts both Bi content and droplet formation is presented to guide the experimental growth of high Bi content GaAsBi films.

  6. Distributed model predictive control made easy

    CERN Document Server

    Negenborn, Rudy

    2014-01-01

    The rapid evolution of computer science, communication, and information technology has enabled the application of control techniques to systems beyond the possibilities of control theory just a decade ago. Critical infrastructures such as electricity, water, traffic and intermodal transport networks are now in the scope of control engineers. The sheer size of such large-scale systems requires the adoption of advanced distributed control approaches. Distributed model predictive control (MPC) is one of the promising control methodologies for control of such systems.   This book provides a state-of-the-art overview of distributed MPC approaches, while at the same time making clear directions of research that deserve more attention. The core and rationale of 35 approaches are carefully explained. Moreover, detailed step-by-step algorithmic descriptions of each approach are provided. These features make the book a comprehensive guide both for those seeking an introduction to distributed MPC as well as for those ...

  7. Leptogenesis in minimal predictive seesaw models

    Science.gov (United States)

    Björkeroth, Fredrik; de Anda, Francisco J.; de Medeiros Varzielas, Ivo; King, Stephen F.

    2015-10-01

    We estimate the Baryon Asymmetry of the Universe (BAU) arising from leptogenesis within a class of minimal predictive seesaw models involving two right-handed neutrinos and simple Yukawa structures with one texture zero. The two right-handed neutrinos are dominantly responsible for the "atmospheric" and "solar" neutrino masses with Yukawa couplings to ( ν e , ν μ , ν τ ) proportional to (0, 1, 1) and (1, n, n - 2), respectively, where n is a positive integer. The neutrino Yukawa matrix is therefore characterised by two proportionality constants with their relative phase providing a leptogenesis-PMNS link, enabling the lightest right-handed neutrino mass to be determined from neutrino data and the observed BAU. We discuss an SU(5) SUSY GUT example, where A 4 vacuum alignment provides the required Yukawa structures with n = 3, while a {{Z}}_9 symmetry fixes the relatives phase to be a ninth root of unity.

  8. Predicting what helminth parasites a fish species should have using Parasite Co-occurrence Modeler (PaCo)

    Science.gov (United States)

    Strona, Giovanni; Lafferty, Kevin D.

    2013-01-01

    Fish pathologists are often interested in which parasites would likely be present in a particular host. Parasite Co-occurrence Modeler (PaCo) is a tool for identifying a list of parasites known from fish species that are similar ecologically, phylogenetically, and geographically to the host of interest. PaCo uses data from FishBase (maximum length, growth rate, life span, age at maturity, trophic level, phylogeny, and biogeography) to estimate compatibility between a target host and parasite species–genera from the major helminth groups (Acanthocephala, Cestoda, Monogenea, Nematoda, and Trematoda). Users can include any combination of host attributes in a model. These unique features make PaCo an innovative tool for addressing both theoretical and applied questions in parasitology. In addition to predicting the occurrence of parasites, PaCo can be used to investigate how host characteristics shape parasite communities. To test the performance of the PaCo algorithm, we created 12,400 parasite lists by applying any possible combination of model parameters (248) to 50 fish hosts. We then measured the relative importance of each parameter by assessing their frequency in the best models for each host. Host phylogeny and host geography were identified as the most important factors, with both present in 88% of the best models. Habitat (64%) was identified in more than half of the best models. Among ecological parameters, trophic level (41%) was the most relevant while life span (34%), growth rate (32%), maximum length (28%), and age at maturity (20%) were less commonly linked to best models. PaCo is free to use at www.purl.oclc.org/fishpest.

  9. Predicting what helminth parasites a fish species should have using Parasite Co-occurrence Modeler (PaCo).

    Science.gov (United States)

    Strona, Giovanni; Lafferty, Kevin D

    2013-02-01

    Fish pathologists are often interested in which parasites would likely be present in a particular host. Parasite Co-occurrence Modeler (PaCo) is a tool for identifying a list of parasites known from fish species that are similar ecologically, phylogenetically, and geographically to the host of interest. PaCo uses data from FishBase (maximum length, growth rate, life span, age at maturity, trophic level, phylogeny, and biogeography) to estimate compatibility between a target host and parasite species-genera from the major helminth groups (Acanthocephala, Cestoda, Monogenea, Nematoda, and Trematoda). Users can include any combination of host attributes in a model. These unique features make PaCo an innovative tool for addressing both theoretical and applied questions in parasitology. In addition to predicting the occurrence of parasites, PaCo can be used to investigate how host characteristics shape parasite communities. To test the performance of the PaCo algorithm, we created 12,400 parasite lists by applying any possible combination of model parameters (248) to 50 fish hosts. We then measured the relative importance of each parameter by assessing their frequency in the best models for each host. Host phylogeny and host geography were identified as the most important factors, with both present in 88% of the best models. Habitat (64%) was identified in more than half of the best models. Among ecological parameters, trophic level (41%) was the most relevant while life span (34%), growth rate (32%), maximum length (28%), and age at maturity (20%) were less commonly linked to best models. PaCo is free to use at www.purl.oclc.org/fishpest.

  10. Trophic factors as modulators of motor neuron physiology and survival: implications for ALS therapy

    Directory of Open Access Journals (Sweden)

    Luis B Tovar-y-Romo

    2014-02-01

    Full Text Available Motor neuron physiology and development depend on a continuous and tightly regulated trophic support from a variety of cellular sources. Trophic factors guide the generation and positioning of motor neurons during every stage of the developmental process. As well, they are involved in axon guidance and synapse formation. Even in the adult spinal cord an uninterrupted trophic input is required to maintain neuronal functioning and protection from noxious stimuli. Among the trophic factors that have been demonstrated to participate in motor neuron physiology are vascular endothelial growth factor (VEGF, glial-derived neurotrophic factor (GDNF, ciliary neurotrophic factor (CNTF and insulin-like growth factor 1 (IGF-1. Upon binding to membrane receptors expressed in motor neurons or neighboring glia, these trophic factors activate intracellular signaling pathways that promote cell survival and have protective action on motor neurons, in both in vivo and in vitro models of neuronal degeneration. For these reasons these factors have been considered a promising therapeutic method for amyotrophic lateral sclerosis (ALS and other neurodegenerative diseases, although their efficacy in human clinical trials have not yet shown the expected protection. In this review we summarize experimental data on the role of these trophic factors in motor neuron function and survival, as well as their mechanisms of action. We also briefly discuss the potential therapeutic use of the trophic factors and why these therapies may have not been yet successful in the clinical use.

  11. Comparing model predictions for ecosystem-based management

    DEFF Research Database (Denmark)

    Jacobsen, Nis Sand; Essington, Timothy E.; Andersen, Ken Haste

    2016-01-01

    Ecosystem modeling is becoming an integral part of fisheries management, but there is a need to identify differences between predictions derived from models employed for scientific and management purposes. Here, we compared two models: a biomass-based food-web model (Ecopath with Ecosim (Ew......E)) and a size-structured fish community model. The models were compared with respect to predicted ecological consequences of fishing to identify commonalities and differences in model predictions for the California Current fish community. We compared the models regarding direct and indirect responses to fishing...... on one or more species. The size-based model predicted a higher fishing mortality needed to reach maximum sustainable yield than EwE for most species. The size-based model also predicted stronger top-down effects of predator removals than EwE. In contrast, EwE predicted stronger bottom-up effects...

  12. Remaining Useful Lifetime (RUL - Probabilistic Predictive Model

    Directory of Open Access Journals (Sweden)

    Ephraim Suhir

    2011-01-01

    Full Text Available Reliability evaluations and assurances cannot be delayed until the device (system is fabricated and put into operation. Reliability of an electronic product should be conceived at the early stages of its design; implemented during manufacturing; evaluated (considering customer requirements and the existing specifications, by electrical, optical and mechanical measurements and testing; checked (screened during manufacturing (fabrication; and, if necessary and appropriate, maintained in the field during the product’s operation Simple and physically meaningful probabilistic predictive model is suggested for the evaluation of the remaining useful lifetime (RUL of an electronic device (system after an appreciable deviation from its normal operation conditions has been detected, and the increase in the failure rate and the change in the configuration of the wear-out portion of the bathtub has been assessed. The general concepts are illustrated by numerical examples. The model can be employed, along with other PHM forecasting and interfering tools and means, to evaluate and to maintain the high level of the reliability (probability of non-failure of a device (system at the operation stage of its lifetime.

  13. A Predictive Model of Geosynchronous Magnetopause Crossings

    CERN Document Server

    Dmitriev, A; Chao, J -K

    2013-01-01

    We have developed a model predicting whether or not the magnetopause crosses geosynchronous orbit at given location for given solar wind pressure Psw, Bz component of interplanetary magnetic field (IMF) and geomagnetic conditions characterized by 1-min SYM-H index. The model is based on more than 300 geosynchronous magnetopause crossings (GMCs) and about 6000 minutes when geosynchronous satellites of GOES and LANL series are located in the magnetosheath (so-called MSh intervals) in 1994 to 2001. Minimizing of the Psw required for GMCs and MSh intervals at various locations, Bz and SYM-H allows describing both an effect of magnetopause dawn-dusk asymmetry and saturation of Bz influence for very large southward IMF. The asymmetry is strong for large negative Bz and almost disappears when Bz is positive. We found that the larger amplitude of negative SYM-H the lower solar wind pressure is required for GMCs. We attribute this effect to a depletion of the dayside magnetic field by a storm-time intensification of t...

  14. Predictive modeling for EBPC in EBDW

    Science.gov (United States)

    Zimmermann, Rainer; Schulz, Martin; Hoppe, Wolfgang; Stock, Hans-Jürgen; Demmerle, Wolfgang; Zepka, Alex; Isoyan, Artak; Bomholt, Lars; Manakli, Serdar; Pain, Laurent

    2009-10-01

    We demonstrate a flow for e-beam proximity correction (EBPC) to e-beam direct write (EBDW) wafer manufacturing processes, demonstrating a solution that covers all steps from the generation of a test pattern for (experimental or virtual) measurement data creation, over e-beam model fitting, proximity effect correction (PEC), and verification of the results. We base our approach on a predictive, physical e-beam simulation tool, with the possibility to complement this with experimental data, and the goal of preparing the EBPC methods for the advent of high-volume EBDW tools. As an example, we apply and compare dose correction and geometric correction for low and high electron energies on 1D and 2D test patterns. In particular, we show some results of model-based geometric correction as it is typical for the optical case, but enhanced for the particularities of e-beam technology. The results are used to discuss PEC strategies, with respect to short and long range effects.

  15. Model for predicting mountain wave field uncertainties

    Science.gov (United States)

    Damiens, Florentin; Lott, François; Millet, Christophe; Plougonven, Riwal

    2017-04-01

    Studying the propagation of acoustic waves throughout troposphere requires knowledge of wind speed and temperature gradients from the ground up to about 10-20 km. Typical planetary boundary layers flows are known to present vertical low level shears that can interact with mountain waves, thereby triggering small-scale disturbances. Resolving these fluctuations for long-range propagation problems is, however, not feasible because of computer memory/time restrictions and thus, they need to be parameterized. When the disturbances are small enough, these fluctuations can be described by linear equations. Previous works by co-authors have shown that the critical layer dynamics that occur near the ground produces large horizontal flows and buoyancy disturbances that result in intense downslope winds and gravity wave breaking. While these phenomena manifest almost systematically for high Richardson numbers and when the boundary layer depth is relatively small compare to the mountain height, the process by which static stability affects downslope winds remains unclear. In the present work, new linear mountain gravity wave solutions are tested against numerical predictions obtained with the Weather Research and Forecasting (WRF) model. For Richardson numbers typically larger than unity, the mesoscale model is used to quantify the effect of neglected nonlinear terms on downslope winds and mountain wave patterns. At these regimes, the large downslope winds transport warm air, a so called "Foehn" effect than can impact sound propagation properties. The sensitivity of small-scale disturbances to Richardson number is quantified using two-dimensional spectral analysis. It is shown through a pilot study of subgrid scale fluctuations of boundary layer flows over realistic mountains that the cross-spectrum of mountain wave field is made up of the same components found in WRF simulations. The impact of each individual component on acoustic wave propagation is discussed in terms of

  16. Neural Fuzzy Inference System-Based Weather Prediction Model and Its Precipitation Predicting Experiment

    Directory of Open Access Journals (Sweden)

    Jing Lu

    2014-11-01

    Full Text Available We propose a weather prediction model in this article based on neural network and fuzzy inference system (NFIS-WPM, and then apply it to predict daily fuzzy precipitation given meteorological premises for testing. The model consists of two parts: the first part is the “fuzzy rule-based neural network”, which simulates sequential relations among fuzzy sets using artificial neural network; and the second part is the “neural fuzzy inference system”, which is based on the first part, but could learn new fuzzy rules from the previous ones according to the algorithm we proposed. NFIS-WPM (High Pro and NFIS-WPM (Ave are improved versions of this model. It is well known that the need for accurate weather prediction is apparent when considering the benefits. However, the excessive pursuit of accuracy in weather prediction makes some of the “accurate” prediction results meaningless and the numerical prediction model is often complex and time-consuming. By adapting this novel model to a precipitation prediction problem, we make the predicted outcomes of precipitation more accurate and the prediction methods simpler than by using the complex numerical forecasting model that would occupy large computation resources, be time-consuming and which has a low predictive accuracy rate. Accordingly, we achieve more accurate predictive precipitation results than by using traditional artificial neural networks that have low predictive accuracy.

  17. RFI modeling and prediction approach for SATOP applications: RFI prediction models

    Science.gov (United States)

    Nguyen, Tien M.; Tran, Hien T.; Wang, Zhonghai; Coons, Amanda; Nguyen, Charles C.; Lane, Steven A.; Pham, Khanh D.; Chen, Genshe; Wang, Gang

    2016-05-01

    This paper describes a technical approach for the development of RFI prediction models using carrier synchronization loop when calculating Bit or Carrier SNR degradation due to interferences for (i) detecting narrow-band and wideband RFI signals, and (ii) estimating and predicting the behavior of the RFI signals. The paper presents analytical and simulation models and provides both analytical and simulation results on the performance of USB (Unified S-Band) waveforms in the presence of narrow-band and wideband RFI signals. The models presented in this paper will allow the future USB command systems to detect the RFI presence, estimate the RFI characteristics and predict the RFI behavior in real-time for accurate assessment of the impacts of RFI on the command Bit Error Rate (BER) performance. The command BER degradation model presented in this paper also allows the ground system operator to estimate the optimum transmitted SNR to maintain a required command BER level in the presence of both friendly and un-friendly RFI sources.

  18. Prediction models : the right tool for the right problem

    NARCIS (Netherlands)

    Kappen, Teus H.; Peelen, Linda M.

    2016-01-01

    PURPOSE OF REVIEW: Perioperative prediction models can help to improve personalized patient care by providing individual risk predictions to both patients and providers. However, the scientific literature on prediction model development and validation can be quite technical and challenging to unders

  19. Foundation Settlement Prediction Based on a Novel NGM Model

    Directory of Open Access Journals (Sweden)

    Peng-Yu Chen

    2014-01-01

    Full Text Available Prediction of foundation or subgrade settlement is very important during engineering construction. According to the fact that there are lots of settlement-time sequences with a nonhomogeneous index trend, a novel grey forecasting model called NGM (1,1,k,c model is proposed in this paper. With an optimized whitenization differential equation, the proposed NGM (1,1,k,c model has the property of white exponential law coincidence and can predict a pure nonhomogeneous index sequence precisely. We used two case studies to verify the predictive effect of NGM (1,1,k,c model for settlement prediction. The results show that this model can achieve excellent prediction accuracy; thus, the model is quite suitable for simulation and prediction of approximate nonhomogeneous index sequence and has excellent application value in settlement prediction.

  20. Predictability of the Indian Ocean Dipole in the coupled models

    Science.gov (United States)

    Liu, Huafeng; Tang, Youmin; Chen, Dake; Lian, Tao

    2017-03-01

    In this study, the Indian Ocean Dipole (IOD) predictability, measured by the Indian Dipole Mode Index (DMI), is comprehensively examined at the seasonal time scale, including its actual prediction skill and potential predictability, using the ENSEMBLES multiple model ensembles and the recently developed information-based theoretical framework of predictability. It was found that all model predictions have useful skill, which is normally defined by the anomaly correlation coefficient larger than 0.5, only at around 2-3 month leads. This is mainly because there are more false alarms in predictions as leading time increases. The DMI predictability has significant seasonal variation, and the predictions whose target seasons are boreal summer (JJA) and autumn (SON) are more reliable than that for other seasons. All of models fail to predict the IOD onset before May and suffer from the winter (DJF) predictability barrier. The potential predictability study indicates that, with the model development and initialization improvement, the prediction of IOD onset is likely to be improved but the winter barrier cannot be overcome. The IOD predictability also has decadal variation, with a high skill during the 1960s and the early 1990s, and a low skill during the early 1970s and early 1980s, which is very consistent with the potential predictability. The main factors controlling the IOD predictability, including its seasonal and decadal variations, are also analyzed in this study.

  1. Not all jellyfish are equal: isotopic evidence for inter- and intraspecific variation in jellyfish trophic ecology

    Directory of Open Access Journals (Sweden)

    Nicholas E.C. Fleming

    2015-07-01

    Full Text Available Jellyfish are highly topical within studies of pelagic food-webs and there is a growing realisation that their role is more complex than once thought. Efforts being made to include jellyfish within fisheries and ecosystem models are an important step forward, but our present understanding of their underlying trophic ecology can lead to their oversimplification in these models. Gelatinous zooplankton represent a polyphyletic assemblage spanning >2,000 species that inhabit coastal seas to the deep-ocean and employ a wide variety of foraging strategies. Despite this diversity, many contemporary modelling approaches include jellyfish as a single functional group feeding at one or two trophic levels at most. Recent reviews have drawn attention to this issue and highlighted the need for improved communication between biologists and theoreticians if this problem is to be overcome. We used stable isotopes to investigate the trophic ecology of three co-occurring scyphozoan jellyfish species (Aurelia aurita, Cyanea lamarckii and C. capillata within a temperate, coastal food-web in the NE Atlantic. Using information on individual size, time of year and δ13C and δ15N stable isotope values, we examined: (1 whether all jellyfish could be considered as a single functional group, or showed distinct inter-specific differences in trophic ecology; (2 Were size-based shifts in trophic position, found previously in A. aurita, a common trait across species?; (3 When considered collectively, did the trophic position of three sympatric species remain constant over time? Differences in δ15N (trophic position were evident between all three species, with size-based and temporal shifts in δ15N apparent in A. aurita and C. capillata. The isotopic niche width for all species combined increased throughout the season, reflecting temporal shifts in trophic position and seasonal succession in these gelatinous species. Taken together, these findings support previous

  2. Trophic and individual efficiencies of size-structured communities

    DEFF Research Database (Denmark)

    Andersen, Ken Haste; Beyer, Jan; Lundberg, P.

    2009-01-01

    Individual and trophic efficiencies of size-structured communities are derived from mechanistically based principles at the individual level. The derivations are relevant for communities with a size-based trophic structure, i.e. where trophic level is strongly correlated with individual size...... as in many aquatic systems. The derivations are used to link Lindeman's trophic theory and trophic theory based on average individuals with explicit individual-level size spectrum theory. The trophic efficiency based on the transfer of mass between trophic levels through predator-prey interactions...

  3. Nonconvex model predictive control for commercial refrigeration

    Science.gov (United States)

    Gybel Hovgaard, Tobias; Boyd, Stephen; Larsen, Lars F. S.; Bagterp Jørgensen, John

    2013-08-01

    We consider the control of a commercial multi-zone refrigeration system, consisting of several cooling units that share a common compressor, and is used to cool multiple areas or rooms. In each time period we choose cooling capacity to each unit and a common evaporation temperature. The goal is to minimise the total energy cost, using real-time electricity prices, while obeying temperature constraints on the zones. We propose a variation on model predictive control to achieve this goal. When the right variables are used, the dynamics of the system are linear, and the constraints are convex. The cost function, however, is nonconvex due to the temperature dependence of thermodynamic efficiency. To handle this nonconvexity we propose a sequential convex optimisation method, which typically converges in fewer than 5 or so iterations. We employ a fast convex quadratic programming solver to carry out the iterations, which is more than fast enough to run in real time. We demonstrate our method on a realistic model, with a full year simulation and 15-minute time periods, using historical electricity prices and weather data, as well as random variations in thermal load. These simulations show substantial cost savings, on the order of 30%, compared to a standard thermostat-based control system. Perhaps more important, we see that the method exhibits sophisticated response to real-time variations in electricity prices. This demand response is critical to help balance real-time uncertainties in generation capacity associated with large penetration of intermittent renewable energy sources in a future smart grid.

  4. Leptogenesis in minimal predictive seesaw models

    Energy Technology Data Exchange (ETDEWEB)

    Björkeroth, Fredrik [School of Physics and Astronomy, University of Southampton,Southampton, SO17 1BJ (United Kingdom); Anda, Francisco J. de [Departamento de Física, CUCEI, Universidad de Guadalajara,Guadalajara (Mexico); Varzielas, Ivo de Medeiros; King, Stephen F. [School of Physics and Astronomy, University of Southampton,Southampton, SO17 1BJ (United Kingdom)

    2015-10-15

    We estimate the Baryon Asymmetry of the Universe (BAU) arising from leptogenesis within a class of minimal predictive seesaw models involving two right-handed neutrinos and simple Yukawa structures with one texture zero. The two right-handed neutrinos are dominantly responsible for the “atmospheric” and “solar” neutrino masses with Yukawa couplings to (ν{sub e},ν{sub μ},ν{sub τ}) proportional to (0,1,1) and (1,n,n−2), respectively, where n is a positive integer. The neutrino Yukawa matrix is therefore characterised by two proportionality constants with their relative phase providing a leptogenesis-PMNS link, enabling the lightest right-handed neutrino mass to be determined from neutrino data and the observed BAU. We discuss an SU(5) SUSY GUT example, where A{sub 4} vacuum alignment provides the required Yukawa structures with n=3, while a ℤ{sub 9} symmetry fixes the relatives phase to be a ninth root of unity.

  5. QSPR Models for Octane Number Prediction

    Directory of Open Access Journals (Sweden)

    Jabir H. Al-Fahemi

    2014-01-01

    Full Text Available Quantitative structure-property relationship (QSPR is performed as a means to predict octane number of hydrocarbons via correlating properties to parameters calculated from molecular structure; such parameters are molecular mass M, hydration energy EH, boiling point BP, octanol/water distribution coefficient logP, molar refractivity MR, critical pressure CP, critical volume CV, and critical temperature CT. Principal component analysis (PCA and multiple linear regression technique (MLR were performed to examine the relationship between multiple variables of the above parameters and the octane number of hydrocarbons. The results of PCA explain the interrelationships between octane number and different variables. Correlation coefficients were calculated using M.S. Excel to examine the relationship between multiple variables of the above parameters and the octane number of hydrocarbons. The data set was split into training of 40 hydrocarbons and validation set of 25 hydrocarbons. The linear relationship between the selected descriptors and the octane number has coefficient of determination (R2=0.932, statistical significance (F=53.21, and standard errors (s =7.7. The obtained QSPR model was applied on the validation set of octane number for hydrocarbons giving RCV2=0.942 and s=6.328.

  6. Trophic ulcers in the carpal tunnel syndrome

    Directory of Open Access Journals (Sweden)

    Abelardo Q.-C. Araújo

    1993-09-01

    Full Text Available A patient with carpal tunnel syndrome (CTS and trophic ulcers is described. Despite the healing of the ulcers after surgery for CTS, the severe sensory deficit and the electrophysiological tests have not shown any significant improvement. We think these findings argue against the hypothesis of the sensory deficit being responsible for the trophic ulcers. We favor a major role for the sympathetic disturbances as the main cause for those lesions.

  7. Biomass changes and trophic amplification of plankton in a warmer ocean.

    Science.gov (United States)

    Chust, Guillem; Allen, J Icarus; Bopp, Laurent; Schrum, Corinna; Holt, Jason; Tsiaras, Kostas; Zavatarelli, Marco; Chifflet, Marina; Cannaby, Heather; Dadou, Isabelle; Daewel, Ute; Wakelin, Sarah L; Machu, Eric; Pushpadas, Dhanya; Butenschon, Momme; Artioli, Yuri; Petihakis, George; Smith, Chris; Garçon, Veronique; Goubanova, Katerina; Le Vu, Briac; Fach, Bettina A; Salihoglu, Baris; Clementi, Emanuela; Irigoien, Xabier

    2014-07-01

    Ocean warming can modify the ecophysiology and distribution of marine organisms, and relationships between species, with nonlinear interactions between ecosystem components potentially resulting in trophic amplification. Trophic amplification (or attenuation) describe the propagation of a hydroclimatic signal up the food web, causing magnification (or depression) of biomass values along one or more trophic pathways. We have employed 3-D coupled physical-biogeochemical models to explore ecosystem responses to climate change with a focus on trophic amplification. The response of phytoplankton and zooplankton to global climate-change projections, carried out with the IPSL Earth System Model by the end of the century, is analysed at global and regional basis, including European seas (NE Atlantic, Barents Sea, Baltic Sea, Black Sea, Bay of Biscay, Adriatic Sea, Aegean Sea) and the Eastern Boundary Upwelling System (Benguela). Results indicate that globally and in Atlantic Margin and North Sea, increased ocean stratification causes primary production and zooplankton biomass to decrease in response to a warming climate, whilst in the Barents, Baltic and Black Seas, primary production and zooplankton biomass increase. Projected warming characterized by an increase in sea surface temperature of 2.29 ± 0.05 °C leads to a reduction in zooplankton and phytoplankton biomasses of 11% and 6%, respectively. This suggests negative amplification of climate driven modifications of trophic level biomass through bottom-up control, leading to a reduced capacity of oceans to regulate climate through the biological carbon pump. Simulations suggest negative amplification is the dominant response across 47% of the ocean surface and prevails in the tropical oceans; whilst positive trophic amplification prevails in the Arctic and Antarctic oceans. Trophic attenuation is projected in temperate seas. Uncertainties in ocean plankton projections, associated to the use of single global and

  8. Biomass changes and trophic amplification of plankton in a warmer ocean

    KAUST Repository

    Chust, Guillem

    2014-05-07

    Ocean warming can modify the ecophysiology and distribution of marine organisms, and relationships between species, with nonlinear interactions between ecosystem components potentially resulting in trophic amplification. Trophic amplification (or attenuation) describe the propagation of a hydroclimatic signal up the food web, causing magnification (or depression) of biomass values along one or more trophic pathways. We have employed 3-D coupled physical-biogeochemical models to explore ecosystem responses to climate change with a focus on trophic amplification. The response of phytoplankton and zooplankton to global climate-change projections, carried out with the IPSL Earth System Model by the end of the century, is analysed at global and regional basis, including European seas (NE Atlantic, Barents Sea, Baltic Sea, Black Sea, Bay of Biscay, Adriatic Sea, Aegean Sea) and the Eastern Boundary Upwelling System (Benguela). Results indicate that globally and in Atlantic Margin and North Sea, increased ocean stratification causes primary production and zooplankton biomass to decrease in response to a warming climate, whilst in the Barents, Baltic and Black Seas, primary production and zooplankton biomass increase. Projected warming characterized by an increase in sea surface temperature of 2.29 ± 0.05 °C leads to a reduction in zooplankton and phytoplankton biomasses of 11% and 6%, respectively. This suggests negative amplification of climate driven modifications of trophic level biomass through bottom-up control, leading to a reduced capacity of oceans to regulate climate through the biological carbon pump. Simulations suggest negative amplification is the dominant response across 47% of the ocean surface and prevails in the tropical oceans; whilst positive trophic amplification prevails in the Arctic and Antarctic oceans. Trophic attenuation is projected in temperate seas. Uncertainties in ocean plankton projections, associated to the use of single global and

  9. Predictability in models of the atmospheric circulation.

    NARCIS (Netherlands)

    Houtekamer, P.L.

    1992-01-01

    It will be clear from the above discussions that skill forecasts are still in their infancy. Operational skill predictions do not exist. One is still struggling to prove that skill predictions, at any range, have any quality at all. It is not clear what the statistics of the analysis error are. The

  10. Seasonal succession in zooplankton feeding traits reveals trophic trait coupling

    DEFF Research Database (Denmark)

    Kenitz, Kasia; Visser, Andre; Mariani, Patrizio

    2017-01-01

    succession and shows how the physical environment controls the vertical structure of plankton communities, where ambush feeders exhibit a preference for greater depths during summer. We characterize the seasonal succession as trophic trait coupling and conjecture that this coupling leads to a trophic trait......The seasonal forcing of pelagic communities invokes a succession of the dominant phytoplankton and zooplankton species. Here, we characterize the seasonal succession of the plankton traits and their interactions using observations and model simulations of the plankton community in the western...... English Channel. We focus on activity traits that characterize the defensive and feeding abilities of zooplankton and distinguish between low risk, low return ambush feeders and high risk, high return feeding-current feeders. While the phytoplankton succession depends on traits related to nutrient...

  11. Trophic magnification of organic chemicals: A global synthesis

    Science.gov (United States)

    Walters, David; Jardine, T.D.; Cade, Brian S.; Kidd, K.A.; Muir, D.C.G.; Leipzig-Scott, Peter C.

    2016-01-01

    Production of organic chemicals (OCs) is increasing exponentially, and some OCs biomagnify through food webs to potentially toxic levels. Biomagnification under field conditions is best described by trophic magnification factors (TMFs; per trophic level change in log-concentration of a chemical) which have been measured for more than two decades. Syntheses of TMF behavior relative to chemical traits and ecosystem properties are lacking. We analyzed >1500 TMFs to identify OCs predisposed to biomagnify and to assess ecosystem vulnerability. The highest TMFs were for OCs that are slowly metabolized by animals (metabolic rate kM  0.2 day–1). This probabilistic model provides a new global tool for screening existing and new OCs for their biomagnification potential.

  12. Food-web structure in low- and high-dimensional trophic niche spaces

    Science.gov (United States)

    Rossberg, Axel G.; Brännström, Åke; Dieckmann, Ulf

    2010-01-01

    A question central to modelling and, ultimately, managing food webs concerns the dimensionality of trophic niche space, that is, the number of independent traits relevant for determining consumer–resource links. Food-web topologies can often be interpreted by assuming resource traits to be specified by points along a line and each consumer's diet to be given by resources contained in an interval on this line. This phenomenon, called intervality, has been known for 30 years and is widely acknowledged to indicate that trophic niche space is close to one-dimensional. We show that the degrees of intervality observed in nature can be reproduced in arbitrary-dimensional trophic niche spaces, provided that the processes of evolutionary diversification and adaptation are taken into account. Contrary to expectations, intervality is least pronounced at intermediate dimensions and steadily improves towards lower- and higher-dimensional trophic niche spaces. PMID:20462875

  13. Allostasis: a model of predictive regulation.

    Science.gov (United States)

    Sterling, Peter

    2012-04-12

    The premise of the standard regulatory model, "homeostasis", is flawed: the goal of regulation is not to preserve constancy of the internal milieu. Rather, it is to continually adjust the milieu to promote survival and reproduction. Regulatory mechanisms need to be efficient, but homeostasis (error-correction by feedback) is inherently inefficient. Thus, although feedbacks are certainly ubiquitous, they could not possibly serve as the primary regulatory mechanism. A newer model, "allostasis", proposes that efficient regulation requires anticipating needs and preparing to satisfy them before they arise. The advantages: (i) errors are reduced in magnitude and frequency; (ii) response capacities of different components are matched -- to prevent bottlenecks and reduce safety factors; (iii) resources are shared between systems to minimize reserve capacities; (iv) errors are remembered and used to reduce future errors. This regulatory strategy requires a dedicated organ, the brain. The brain tracks multitudinous variables and integrates their values with prior knowledge to predict needs and set priorities. The brain coordinates effectors to mobilize resources from modest bodily stores and enforces a system of flexible trade-offs: from each organ according to its ability, to each organ according to its need. The brain also helps regulate the internal milieu by governing anticipatory behavior. Thus, an animal conserves energy by moving to a warmer place - before it cools, and it conserves salt and water by moving to a cooler one before it sweats. The behavioral strategy requires continuously updating a set of specific "shopping lists" that document the growing need for each key component (warmth, food, salt, water). These appetites funnel into a common pathway that employs a "stick" to drive the organism toward filling the need, plus a "carrot" to relax the organism when the need is satisfied. The stick corresponds broadly to the sense of anxiety, and the carrot broadly to

  14. Trophic niche shifts driven by phytoplankton in sandy beach ecosystems

    Science.gov (United States)

    Bergamino, Leandro; Martínez, Ana; Han, Eunah; Lercari, Diego; Defeo, Omar

    2016-10-01

    Stable isotopes (δ13C and δ15N) together with chlorophyll a and densities of surf diatoms were used to analyze changes in trophic niches of species in two sandy beaches of Uruguay with contrasting morphodynamics (i.e. dissipative vs. reflective). Consumers and food sources were collected over four seasons, including sediment organic matter (SOM), suspended particulate organic matter (POM) and the surf zone diatom Asterionellopsis guyunusae. Circular statistics and a Bayesian isotope mixing model were used to quantify food web differences between beaches. Consumers changed their trophic niche between beaches in the same direction of the food web space towards higher reliance on surf diatoms in the dissipative beach. Mixing models indicated that A. guyunusae was the primary nutrition source for suspension feeders in the dissipative beach, explaining their change in dietary niche compared to the reflective beach where the proportional contribution of surf diatoms was low. The high C/N ratios in A. guyunusae indicated its high nutritional value and N content, and may help to explain the high assimilation by suspension feeders at the dissipative beach. Furthermore, density of A. guyunusae was higher in the dissipative than in the reflective beach, and cell density was positively correlated with chlorophyll a only in the dissipative beach. Therefore, surf diatoms are important drivers in the dynamics of sandy beach food webs, determining the trophic niche space and productivity. Our study provides valuable insights on shifting foraging behavior by beach fauna in response to changes in resource availability.

  15. Required Collaborative Work in Online Courses: A Predictive Modeling Approach

    Science.gov (United States)

    Smith, Marlene A.; Kellogg, Deborah L.

    2015-01-01

    This article describes a predictive model that assesses whether a student will have greater perceived learning in group assignments or in individual work. The model produces correct classifications 87.5% of the time. The research is notable in that it is the first in the education literature to adopt a predictive modeling methodology using data…

  16. A prediction model for assessing residential radon concentration in Switzerland

    NARCIS (Netherlands)

    Hauri, D.D.; Huss, A.; Zimmermann, F.; Kuehni, C.E.; Roosli, M.

    2012-01-01

    Indoor radon is regularly measured in Switzerland. However, a nationwide model to predict residential radon levels has not been developed. The aim of this study was to develop a prediction model to assess indoor radon concentrations in Switzerland. The model was based on 44,631 measurements from the

  17. Distributional Analysis for Model Predictive Deferrable Load Control

    OpenAIRE

    Chen, Niangjun; Gan, Lingwen; Low, Steven H.; Wierman, Adam

    2014-01-01

    Deferrable load control is essential for handling the uncertainties associated with the increasing penetration of renewable generation. Model predictive control has emerged as an effective approach for deferrable load control, and has received considerable attention. In particular, previous work has analyzed the average-case performance of model predictive deferrable load control. However, to this point, distributional analysis of model predictive deferrable load control has been elusive. In ...

  18. Trophic relationships in an estuarine environment: A quantitative fatty acid analysis signature approach

    Science.gov (United States)

    Magnone, Larisa; Bessonart, Martin; Gadea, Juan; Salhi, María

    2015-12-01

    In order to better understand the functioning of aquatic environments, it is necessary to obtain accurate diet estimations in food webs. Their description should incorporate information about energy flow and the relative importance of trophic pathways. Fatty acids have been extensively used in qualitative studies on trophic relationships in food webs. Recently a new method to estimate quantitatively single predator diet has been developed. In this study, a model of aquatic food web through quantitative fatty acid signature analysis was generated to identify the trophic interactions among the species in the Rocha Lagoon. The biological sampling over two consecutive annual periods was comprehensive enough to identify all functional groups in the aquatic food web (except birds and mammals). Heleobia australis seemed to play a central role in this estuarine ecosystem. As both, a grazer and a prey to several other species, probably H. australis is transferring a great amount of energy to upper trophic levels. Most of the species at Rocha Lagoon have a wide range of prey items in their diet reflecting a complex food web, which is characteristic of extremely dynamic environment as estuarine ecosystems. QFASA is a model in tracing and quantitative estimate trophic pathways among species in an estuarine food web. The results obtained in the present work are a valuable contribution in the understanding of trophic relationships in Rocha Lagoon.

  19. The importance of predator–prey overlap: predicting North Sea cod recovery with a multispecies assessment model

    DEFF Research Database (Denmark)

    Kempf, Alexander; Dingsør, Gjert Endre; Huse, Geir

    2010-01-01

    of time-invariant and year- and quarter-specific overlap estimates on the historical (1991–2007) and predicted trophic interactions, as well as the development of predator and prey stocks, was investigated. The focus was set on a general comparison between single-species and multispecies forecasts...

  20. Prediction for Major Adverse Outcomes in Cardiac Surgery: Comparison of Three Prediction Models

    Directory of Open Access Journals (Sweden)

    Cheng-Hung Hsieh

    2007-09-01

    Conclusion: The Parsonnet score performed as well as the logistic regression models in predicting major adverse outcomes. The Parsonnet score appears to be a very suitable model for clinicians to use in risk stratification of cardiac surgery.

  1. On hydrological model complexity, its geometrical interpretations and prediction uncertainty

    NARCIS (Netherlands)

    Arkesteijn, E.C.M.M.; Pande, S.

    2013-01-01

    Knowledge of hydrological model complexity can aid selection of an optimal prediction model out of a set of available models. Optimal model selection is formalized as selection of the least complex model out of a subset of models that have lower empirical risk. This may be considered equivalent to

  2. Probabilistic Modeling and Visualization for Bankruptcy Prediction

    DEFF Research Database (Denmark)

    Antunes, Francisco; Ribeiro, Bernardete; Pereira, Francisco Camara

    2017-01-01

    In accounting and finance domains, bankruptcy prediction is of great utility for all of the economic stakeholders. The challenge of accurate assessment of business failure prediction, specially under scenarios of financial crisis, is known to be complicated. Although there have been many successful...... studies on bankruptcy detection, seldom probabilistic approaches were carried out. In this paper we assume a probabilistic point-of-view by applying Gaussian Processes (GP) in the context of bankruptcy prediction, comparing it against the Support Vector Machines (SVM) and the Logistic Regression (LR......). Using real-world bankruptcy data, an in-depth analysis is conducted showing that, in addition to a probabilistic interpretation, the GP can effectively improve the bankruptcy prediction performance with high accuracy when compared to the other approaches. We additionally generate a complete graphical...

  3. Predictive modeling of dental pain using neural network.

    Science.gov (United States)

    Kim, Eun Yeob; Lim, Kun Ok; Rhee, Hyun Sill

    2009-01-01

    The mouth is a part of the body for ingesting food that is the most basic foundation and important part. The dental pain predicted by the neural network model. As a result of making a predictive modeling, the fitness of the predictive modeling of dental pain factors was 80.0%. As for the people who are likely to experience dental pain predicted by the neural network model, preventive measures including proper eating habits, education on oral hygiene, and stress release must precede any dental treatment.

  4. Prediction of peptide bonding affinity: kernel methods for nonlinear modeling

    CERN Document Server

    Bergeron, Charles; Sundling, C Matthew; Krein, Michael; Katt, Bill; Sukumar, Nagamani; Breneman, Curt M; Bennett, Kristin P

    2011-01-01

    This paper presents regression models obtained from a process of blind prediction of peptide binding affinity from provided descriptors for several distinct datasets as part of the 2006 Comparative Evaluation of Prediction Algorithms (COEPRA) contest. This paper finds that kernel partial least squares, a nonlinear partial least squares (PLS) algorithm, outperforms PLS, and that the incorporation of transferable atom equivalent features improves predictive capability.

  5. Comparisons of Faulting-Based Pavement Performance Prediction Models

    Directory of Open Access Journals (Sweden)

    Weina Wang

    2017-01-01

    Full Text Available Faulting prediction is the core of concrete pavement maintenance and design. Highway agencies are always faced with the problem of lower accuracy for the prediction which causes costly maintenance. Although many researchers have developed some performance prediction models, the accuracy of prediction has remained a challenge. This paper reviews performance prediction models and JPCP faulting models that have been used in past research. Then three models including multivariate nonlinear regression (MNLR model, artificial neural network (ANN model, and Markov Chain (MC model are tested and compared using a set of actual pavement survey data taken on interstate highway with varying design features, traffic, and climate data. It is found that MNLR model needs further recalibration, while the ANN model needs more data for training the network. MC model seems a good tool for pavement performance prediction when the data is limited, but it is based on visual inspections and not explicitly related to quantitative physical parameters. This paper then suggests that the further direction for developing the performance prediction model is incorporating the advantages and disadvantages of different models to obtain better accuracy.

  6. Prediction using patient comparison vs. modeling: a case study for mortality prediction.

    Science.gov (United States)

    Hoogendoorn, Mark; El Hassouni, Ali; Mok, Kwongyen; Ghassemi, Marzyeh; Szolovits, Peter

    2016-08-01

    Information in Electronic Medical Records (EMRs) can be used to generate accurate predictions for the occurrence of a variety of health states, which can contribute to more pro-active interventions. The very nature of EMRs does make the application of off-the-shelf machine learning techniques difficult. In this paper, we study two approaches to making predictions that have hardly been compared in the past: (1) extracting high-level (temporal) features from EMRs and building a predictive model, and (2) defining a patient similarity metric and predicting based on the outcome observed for similar patients. We analyze and compare both approaches on the MIMIC-II ICU dataset to predict patient mortality and find that the patient similarity approach does not scale well and results in a less accurate model (AUC of 0.68) compared to the modeling approach (0.84). We also show that mortality can be predicted within a median of 72 hours.

  7. Fuzzy predictive filtering in nonlinear economic model predictive control for demand response

    DEFF Research Database (Denmark)

    Santos, Rui Mirra; Zong, Yi; Sousa, Joao M. C.;

    2016-01-01

    The performance of a model predictive controller (MPC) is highly correlated with the model's accuracy. This paper introduces an economic model predictive control (EMPC) scheme based on a nonlinear model, which uses a branch-and-bound tree search for solving the inherent non-convex optimization...... problem. Moreover, to reduce the computation time and improve the controller's performance, a fuzzy predictive filter is introduced. With the purpose of testing the developed EMPC, a simulation controlling the temperature levels of an intelligent office building (PowerFlexHouse), with and without fuzzy...

  8. Predictive modeling and reducing cyclic variability in autoignition engines

    Energy Technology Data Exchange (ETDEWEB)

    Hellstrom, Erik; Stefanopoulou, Anna; Jiang, Li; Larimore, Jacob

    2016-08-30

    Methods and systems are provided for controlling a vehicle engine to reduce cycle-to-cycle combustion variation. A predictive model is applied to predict cycle-to-cycle combustion behavior of an engine based on observed engine performance variables. Conditions are identified, based on the predicted cycle-to-cycle combustion behavior, that indicate high cycle-to-cycle combustion variation. Corrective measures are then applied to prevent the predicted high cycle-to-cycle combustion variation.

  9. Effect of Cultivation on Spatial Distribution of Nematode Trophic Groups in Black Soil

    Institute of Scientific and Technical Information of China (English)

    LIANG WENJU; LI QI; JIANG YONG; CHEN WENBO; WEN DAZHONG

    2003-01-01

    Geostatistics combined with GIS was applied to assess the spatial distribution of nematode trophic groups following two contrasting soil uses in the black soil region of Northeast China. Two plots, one with fallow for 12 years and the other cultivated, were marked on regular square grids with 2-m spacing. Soil samples were collected from each sampling point, nematodes were extracted from these samples and classified into four trophic groups: bacterivores, fungivores, plant parasites, and omnivores/predators. The numbers of total nematodes and trophic groups analyzed had normal distributions on both fallow and cultivated plots. The absolute abundances of total nematodes and trophic groups were observed to be much more homogeneous on cultivated plot than on fallow one. Geostatistical analysis showed that the densities of total nematodes and trophic groups on both fallow and cultivated plots exhibited spatial dependence at the sampled scale and their experimental semivariograms were adjusted to a spherical or exponential model, except those of bacterivores and fungivores on cultivated plot. The spatial distribution of nematode trophic groups was found to be different for the two land uses, indicating that cultivation changed the native condition for soil nematode activities.

  10. Trophic transfer of microplastics in aquatic ecosystems: Identifying critical research needs.

    Science.gov (United States)

    Au, Sarah Y; Lee, Cindy M; Weinstein, John E; van den Hurk, Peter; Klaine, Stephen J

    2017-05-01

    To evaluate the process of trophic transfer of microplastics, it is important to consider various abiotic and biotic factors involved in their ingestion, egestion, bioaccumulation, and biomagnification. Toward this end, a review of the literature on microplastics has been conducted to identify factors influencing their uptake and absorption; their residence times in organisms and bioaccumulation; the physical effects of their aggregation in gastrointestinal tracts; and their potential to act as vectors for the transfer of other contaminants. Limited field evidence from higher trophic level organisms in a variety of habitats suggests that trophic transfer of microplastics may be a common phenomenon and occurs concurrently with direct ingestion. Critical research needs include standardizing methods of field characterization of microplastics, quantifying uptake and depuration rates in organisms at different trophic levels, quantifying the influence that microplastics have on the uptake and/or depuration of environmental contaminants among different trophic levels, and investigating the potential for biomagnification of microplastic-associated chemicals. More integrated approaches involving computational modeling are required to fully assess trophic transfer of microplastics. Integr Environ Assess Manag 2017;13:505-509. © 2017 SETAC. © 2017 SETAC.

  11. Persistence of trophic hotspots and relation to human impacts within an upwelling marine ecosystem.

    Science.gov (United States)

    Santora, Jarrod A; Sydeman, William J; Schroeder, Isaac D; Field, John C; Miller, Rebecca R; Wells, Brian K

    2017-03-01

    Human impacts (e.g., fishing, pollution, and shipping) on pelagic ecosystems are increasing, causing concerns about stresses on marine food webs. Maintaining predator-prey relationships through protection of pelagic hotspots is crucial for conservation and management of living marine resources. Biotic components of pelagic, plankton-based, ecosystems exhibit high variability in abundance in time and space (i.e., extreme patchiness), requiring investigation of persistence of abundance across trophic levels to resolve trophic hotspots. Using a 26-yr record of indicators for primary production, secondary (zooplankton and larval fish), and tertiary (seabirds) consumers, we show distributions of trophic hotspots in the southern California Current Ecosystem result from interactions between a strong upwelling center and a productive retention zone with enhanced nutrients, which concentrate prey and predators across multiple trophic levels. Trophic hotspots also overlap with human impacts, including fisheries extraction of coastal pelagic and groundfish species, as well as intense commercial shipping traffic. Spatial overlap of trophic hotspots with fisheries and shipping increases vulnerability of the ecosystem to localized depletion of forage fish, ship strikes on marine mammals, and pollution. This study represents a critical step toward resolving pelagic areas of high conservation interest for planktonic ecosystems and may serve as a model for other ocean regions where ecosystem-based management and marine spatial planning of pelagic ecosystems is warranted. © 2016 by the Ecological Society of America.

  12. Trophic implications and faunal resilience following one-off and successive disturbances to an Amphibolis griffithii seagrass system.

    Science.gov (United States)

    Gartner, Adam; Lavery, Paul S; Lonzano-Montes, Hector

    2015-05-15

    Disturbances in seagrass systems often lead to considerable loss of seagrass fauna. We examined the capacity for seagrass fauna, across multiple trophic levels, to recover from disturbances, using empirical and modelling techniques. Model outputs, using Ecosim with Ecopath (EwE), were consistent with the results of field investigations, highlighting the models robustness. Modelled outcomes suggest second and third order consumers are likely to be negatively effected by disturbances in the seagrass canopy. Particularly piscivores, which once disturbed, appear unlikely to recover following severe declines in primary productivity. EwE also revealed the complex interaction between the duration and intensity of disturbances on seagrass fauna, which may differentially affect higher order consumers. Further, modelling predicted a variable capacity of higher order consumers to recover from successive disturbances, suggesting taxa with comparatively fast reproductive cycles and short generation terms would be more resilient than taxa with comparatively long generation terms and slow reproductive cycles.

  13. Intelligent predictive model of ventilating capacity of imperial smelt furnace

    Institute of Scientific and Technical Information of China (English)

    唐朝晖; 胡燕瑜; 桂卫华; 吴敏

    2003-01-01

    In order to know the ventilating capacity of imperial smelt furnace (ISF), and increase the output of plumbum, an intelligent modeling method based on gray theory and artificial neural networks(ANN) is proposed, in which the weight values in the integrated model can be adjusted automatically. An intelligent predictive model of the ventilating capacity of the ISF is established and analyzed by the method. The simulation results and industrial applications demonstrate that the predictive model is close to the real plant, the relative predictive error is 0.72%, which is 50% less than the single model, leading to a notable increase of the output of plumbum.

  14. A Prediction Model of the Capillary Pressure J-Function

    Science.gov (United States)

    Xu, W. S.; Luo, P. Y.; Sun, L.; Lin, N.

    2016-01-01

    The capillary pressure J-function is a dimensionless measure of the capillary pressure of a fluid in a porous medium. The function was derived based on a capillary bundle model. However, the dependence of the J-function on the saturation Sw is not well understood. A prediction model for it is presented based on capillary pressure model, and the J-function prediction model is a power function instead of an exponential or polynomial function. Relative permeability is calculated with the J-function prediction model, resulting in an easier calculation and results that are more representative. PMID:27603701

  15. Adaptation of Predictive Models to PDA Hand-Held Devices

    Directory of Open Access Journals (Sweden)

    Lin, Edward J

    2008-01-01

    Full Text Available Prediction models using multiple logistic regression are appearing with increasing frequency in the medical literature. Problems associated with these models include the complexity of computations when applied in their pure form, and lack of availability at the bedside. Personal digital assistant (PDA hand-held devices equipped with spreadsheet software offer the clinician a readily available and easily applied means of applying predictive models at the bedside. The purposes of this article are to briefly review regression as a means of creating predictive models and to describe a method of choosing and adapting logistic regression models to emergency department (ED clinical practice.

  16. A model to predict the power output from wind farms

    Energy Technology Data Exchange (ETDEWEB)

    Landberg, L. [Riso National Lab., Roskilde (Denmark)

    1997-12-31

    This paper will describe a model that can predict the power output from wind farms. To give examples of input the model is applied to a wind farm in Texas. The predictions are generated from forecasts from the NGM model of NCEP. These predictions are made valid at individual sites (wind farms) by applying a matrix calculated by the sub-models of WASP (Wind Atlas Application and Analysis Program). The actual wind farm production is calculated using the Riso PARK model. Because of the preliminary nature of the results, they will not be given. However, similar results from Europe will be given.

  17. Modelling microbial interactions and food structure in predictive microbiology

    NARCIS (Netherlands)

    Malakar, P.K.

    2002-01-01

    Keywords: modelling, dynamic models, microbial interactions, diffusion, microgradients, colony growth, predictive microbiology.

    Growth response of microorganisms in foods is a complex process. Innovations in food production and preservation techniques have resulted in adoption of

  18. Modelling microbial interactions and food structure in predictive microbiology

    NARCIS (Netherlands)

    Malakar, P.K.

    2002-01-01

    Keywords: modelling, dynamic models, microbial interactions, diffusion, microgradients, colony growth, predictive microbiology.    Growth response of microorganisms in foods is a complex process. Innovations in food production and preservation techniques have resulted in adoption of new technologies

  19. Estimating Model Prediction Error: Should You Treat Predictions as Fixed or Random?

    Science.gov (United States)

    Wallach, Daniel; Thorburn, Peter; Asseng, Senthold; Challinor, Andrew J.; Ewert, Frank; Jones, James W.; Rotter, Reimund; Ruane, Alexander

    2016-01-01

    Crop models are important tools for impact assessment of climate change, as well as for exploring management options under current climate. It is essential to evaluate the uncertainty associated with predictions of these models. We compare two criteria of prediction error; MSEP fixed, which evaluates mean squared error of prediction for a model with fixed structure, parameters and inputs, and MSEP uncertain( X), which evaluates mean squared error averaged over the distributions of model structure, inputs and parameters. Comparison of model outputs with data can be used to estimate the former. The latter has a squared bias term, which can be estimated using hindcasts, and a model variance term, which can be estimated from a simulation experiment. The separate contributions to MSEP uncertain (X) can be estimated using a random effects ANOVA. It is argued that MSEP uncertain (X) is the more informative uncertainty criterion, because it is specific to each prediction situation.

  20. Predicting Career Advancement with Structural Equation Modelling

    Science.gov (United States)

    Heimler, Ronald; Rosenberg, Stuart; Morote, Elsa-Sofia

    2012-01-01

    Purpose: The purpose of this paper is to use the authors' prior findings concerning basic employability skills in order to determine which skills best predict career advancement potential. Design/methodology/approach: Utilizing survey responses of human resource managers, the employability skills showing the largest relationships to career…

  1. Predicting Career Advancement with Structural Equation Modelling

    Science.gov (United States)

    Heimler, Ronald; Rosenberg, Stuart; Morote, Elsa-Sofia

    2012-01-01

    Purpose: The purpose of this paper is to use the authors' prior findings concerning basic employability skills in order to determine which skills best predict career advancement potential. Design/methodology/approach: Utilizing survey responses of human resource managers, the employability skills showing the largest relationships to career…

  2. Modeling and prediction of surgical procedure times

    NARCIS (Netherlands)

    P.S. Stepaniak (Pieter); C. Heij (Christiaan); G. de Vries (Guus)

    2009-01-01

    textabstractAccurate prediction of medical operation times is of crucial importance for cost efficient operation room planning in hospitals. This paper investigates the possible dependence of procedure times on surgeon factors like age, experience, gender, and team composition. The effect of these f

  3. Prediction Model of Sewing Technical Condition by Grey Neural Network

    Institute of Scientific and Technical Information of China (English)

    DONG Ying; FANG Fang; ZHANG Wei-yuan

    2007-01-01

    The grey system theory and the artificial neural network technology were applied to predict the sewing technical condition. The representative parameters, such as needle, stitch, were selected. Prediction model was established based on the different fabrics' mechanical properties that measured by KES instrument. Grey relevant degree analysis was applied to choose the input parameters of the neural network. The result showed that prediction model has good precision. The average relative error was 4.08% for needle and 4.25% for stitch.

  4. Active diagnosis of hybrid systems - A model predictive approach

    OpenAIRE

    2009-01-01

    A method for active diagnosis of hybrid systems is proposed. The main idea is to predict the future output of both normal and faulty model of the system; then at each time step an optimization problem is solved with the objective of maximizing the difference between the predicted normal and faulty outputs constrained by tolerable performance requirements. As in standard model predictive control, the first element of the optimal input is applied to the system and the whole procedure is repeate...

  5. Evaluation of Fast-Time Wake Vortex Prediction Models

    Science.gov (United States)

    Proctor, Fred H.; Hamilton, David W.

    2009-01-01

    Current fast-time wake models are reviewed and three basic types are defined. Predictions from several of the fast-time models are compared. Previous statistical evaluations of the APA-Sarpkaya and D2P fast-time models are discussed. Root Mean Square errors between fast-time model predictions and Lidar wake measurements are examined for a 24 hr period at Denver International Airport. Shortcomings in current methodology for evaluating wake errors are also discussed.

  6. Comparison of Simple Versus Performance-Based Fall Prediction Models

    Directory of Open Access Journals (Sweden)

    Shekhar K. Gadkaree BS

    2015-05-01

    Full Text Available Objective: To compare the predictive ability of standard falls prediction models based on physical performance assessments with more parsimonious prediction models based on self-reported data. Design: We developed a series of fall prediction models progressing in complexity and compared area under the receiver operating characteristic curve (AUC across models. Setting: National Health and Aging Trends Study (NHATS, which surveyed a nationally representative sample of Medicare enrollees (age ≥65 at baseline (Round 1: 2011-2012 and 1-year follow-up (Round 2: 2012-2013. Participants: In all, 6,056 community-dwelling individuals participated in Rounds 1 and 2 of NHATS. Measurements: Primary outcomes were 1-year incidence of “any fall” and “recurrent falls.” Prediction models were compared and validated in development and validation sets, respectively. Results: A prediction model that included demographic information, self-reported problems with balance and coordination, and previous fall history was the most parsimonious model that optimized AUC for both any fall (AUC = 0.69, 95% confidence interval [CI] = [0.67, 0.71] and recurrent falls (AUC = 0.77, 95% CI = [0.74, 0.79] in the development set. Physical performance testing provided a marginal additional predictive value. Conclusion: A simple clinical prediction model that does not include physical performance testing could facilitate routine, widespread falls risk screening in the ambulatory care setting.

  7. Testing and analysis of internal hardwood log defect prediction models

    Science.gov (United States)

    R. Edward. Thomas

    2011-01-01

    The severity and location of internal defects determine the quality and value of lumber sawn from hardwood logs. Models have been developed to predict the size and position of internal defects based on external defect indicator measurements. These models were shown to predict approximately 80% of all internal knots based on external knot indicators. However, the size...

  8. Comparison of Simple Versus Performance-Based Fall Prediction Models

    Directory of Open Access Journals (Sweden)

    Shekhar K. Gadkaree BS

    2015-05-01

    Full Text Available Objective: To compare the predictive ability of standard falls prediction models based on physical performance assessments with more parsimonious prediction models based on self-reported data. Design: We developed a series of fall prediction models progressing in complexity and compared area under the receiver operating characteristic curve (AUC across models. Setting: National Health and Aging Trends Study (NHATS, which surveyed a nationally representative sample of Medicare enrollees (age ≥65 at baseline (Round 1: 2011-2012 and 1-year follow-up (Round 2: 2012-2013. Participants: In all, 6,056 community-dwelling individuals participated in Rounds 1 and 2 of NHATS. Measurements: Primary outcomes were 1-year incidence of “ any fall ” and “ recurrent falls .” Prediction models were compared and validated in development and validation sets, respectively. Results: A prediction model that included demographic information, self-reported problems with balance and coordination, and previous fall history was the most parsimonious model that optimized AUC for both any fall (AUC = 0.69, 95% confidence interval [CI] = [0.67, 0.71] and recurrent falls (AUC = 0.77, 95% CI = [0.74, 0.79] in the development set. Physical performance testing provided a marginal additional predictive value. Conclusion: A simple clinical prediction model that does not include physical performance testing could facilitate routine, widespread falls risk screening in the ambulatory care setting.

  9. Refining the Committee Approach and Uncertainty Prediction in Hydrological Modelling

    NARCIS (Netherlands)

    Kayastha, N.

    2014-01-01

    Due to the complexity of hydrological systems a single model may be unable to capture the full range of a catchment response and accurately predict the streamflows. The multi modelling approach opens up possibilities for handling such difficulties and allows improve the predictive capability of mode

  10. Refining the committee approach and uncertainty prediction in hydrological modelling

    NARCIS (Netherlands)

    Kayastha, N.

    2014-01-01

    Due to the complexity of hydrological systems a single model may be unable to capture the full range of a catchment response and accurately predict the streamflows. The multi modelling approach opens up possibilities for handling such difficulties and allows improve the predictive capability of mode

  11. Refining the Committee Approach and Uncertainty Prediction in Hydrological Modelling

    NARCIS (Netherlands)

    Kayastha, N.

    2014-01-01

    Due to the complexity of hydrological systems a single model may be unable to capture the full range of a catchment response and accurately predict the streamflows. The multi modelling approach opens up possibilities for handling such difficulties and allows improve the predictive capability of mode

  12. Refining the committee approach and uncertainty prediction in hydrological modelling

    NARCIS (Netherlands)

    Kayastha, N.

    2014-01-01

    Due to the complexity of hydrological systems a single model may be unable to capture the full range of a catchment response and accurately predict the streamflows. The multi modelling approach opens up possibilities for handling such difficulties and allows improve the predictive capability of mode

  13. Adding propensity scores to pure prediction models fails to improve predictive performance

    Directory of Open Access Journals (Sweden)

    Amy S. Nowacki

    2013-08-01

    Full Text Available Background. Propensity score usage seems to be growing in popularity leading researchers to question the possible role of propensity scores in prediction modeling, despite the lack of a theoretical rationale. It is suspected that such requests are due to the lack of differentiation regarding the goals of predictive modeling versus causal inference modeling. Therefore, the purpose of this study is to formally examine the effect of propensity scores on predictive performance. Our hypothesis is that a multivariable regression model that adjusts for all covariates will perform as well as or better than those models utilizing propensity scores with respect to model discrimination and calibration.Methods. The most commonly encountered statistical scenarios for medical prediction (logistic and proportional hazards regression were used to investigate this research question. Random cross-validation was performed 500 times to correct for optimism. The multivariable regression models adjusting for all covariates were compared with models that included adjustment for or weighting with the propensity scores. The methods were compared based on three predictive performance measures: (1 concordance indices; (2 Brier scores; and (3 calibration curves.Results. Multivariable models adjusting for all covariates had the highest average concordance index, the lowest average Brier score, and the best calibration. Propensity score adjustment and inverse probability weighting models without adjustment for all covariates performed worse than full models and failed to improve predictive performance with full covariate adjustment.Conclusion. Propensity score techniques did not improve prediction performance measures beyond multivariable adjustment. Propensity scores are not recommended if the analytical goal is pure prediction modeling.

  14. Impact of modellers' decisions on hydrological a priori predictions

    Science.gov (United States)

    Holländer, H. M.; Bormann, H.; Blume, T.; Buytaert, W.; Chirico, G. B.; Exbrayat, J.-F.; Gustafsson, D.; Hölzel, H.; Krauße, T.; Kraft, P.; Stoll, S.; Blöschl, G.; Flühler, H.

    2014-06-01

    In practice, the catchment hydrologist is often confronted with the task of predicting discharge without having the needed records for calibration. Here, we report the discharge predictions of 10 modellers - using the model of their choice - for the man-made Chicken Creek catchment (6 ha, northeast Germany, Gerwin et al., 2009b) and we analyse how well they improved their prediction in three steps based on adding information prior to each following step. The modellers predicted the catchment's hydrological response in its initial phase without having access to the observed records. They used conceptually different physically based models and their modelling experience differed largely. Hence, they encountered two problems: (i) to simulate discharge for an ungauged catchment and (ii) using models that were developed for catchments, which are not in a state of landscape transformation. The prediction exercise was organized in three steps: (1) for the first prediction the modellers received a basic data set describing the catchment to a degree somewhat more complete than usually available for a priori predictions of ungauged catchments; they did not obtain information on stream flow, soil moisture, nor groundwater response and had therefore to guess the initial conditions; (2) before the second prediction they inspected the catchment on-site and discussed their first prediction attempt; (3) for their third prediction they were offered additional data by charging them pro forma with the costs for obtaining this additional information. Holländer et al. (2009) discussed the range of predictions obtained in step (1). Here, we detail the modeller's assumptions and decisions in accounting for the various processes. We document the prediction progress as well as the learning process resulting from the availability of added information. For the second and third steps, the progress in prediction quality is evaluated in relation to individual modelling experience and costs of

  15. Econometric models for predicting confusion crop ratios

    Science.gov (United States)

    Umberger, D. E.; Proctor, M. H.; Clark, J. E.; Eisgruber, L. M.; Braschler, C. B. (Principal Investigator)

    1979-01-01

    Results for both the United States and Canada show that econometric models can provide estimates of confusion crop ratios that are more accurate than historical ratios. Whether these models can support the LACIE 90/90 accuracy criterion is uncertain. In the United States, experimenting with additional model formulations could provide improved methods models in some CRD's, particularly in winter wheat. Improved models may also be possible for the Canadian CD's. The more aggressive province/state models outperformed individual CD/CRD models. This result was expected partly because acreage statistics are based on sampling procedures, and the sampling precision declines from the province/state to the CD/CRD level. Declining sampling precision and the need to substitute province/state data for the CD/CRD data introduced measurement error into the CD/CRD models.

  16. PEEX Modelling Platform for Seamless Environmental Prediction

    Science.gov (United States)

    Baklanov, Alexander; Mahura, Alexander; Arnold, Stephen; Makkonen, Risto; Petäjä, Tuukka; Kerminen, Veli-Matti; Lappalainen, Hanna K.; Ezau, Igor; Nuterman, Roman; Zhang, Wen; Penenko, Alexey; Gordov, Evgeny; Zilitinkevich, Sergej; Kulmala, Markku

    2017-04-01

    The Pan-Eurasian EXperiment (PEEX) is a multidisciplinary, multi-scale research programme stared in 2012 and aimed at resolving the major uncertainties in Earth System Science and global sustainability issues concerning the Arctic and boreal Northern Eurasian regions and in China. Such challenges include climate change, air quality, biodiversity loss, chemicalization, food supply, and the use of natural resources by mining, industry, energy production and transport. The research infrastructure introduces the current state of the art modeling platform and observation systems in the Pan-Eurasian region and presents the future baselines for the coherent and coordinated research infrastructures in the PEEX domain. The PEEX modeling Platform is characterized by a complex seamless integrated Earth System Modeling (ESM) approach, in combination with specific models of different processes and elements of the system, acting on different temporal and spatial scales. The ensemble approach is taken to the integration of modeling results from different models, participants and countries. PEEX utilizes the full potential of a hierarchy of models: scenario analysis, inverse modeling, and modeling based on measurement needs and processes. The models are validated and constrained by available in-situ and remote sensing data of various spatial and temporal scales using data assimilation and top-down modeling. The analyses of the anticipated large volumes of data produced by available models and sensors will be supported by a dedicated virtual research environment developed for these purposes.

  17. Models Predicting Success of Infertility Treatment: A Systematic Review

    Science.gov (United States)

    Zarinara, Alireza; Zeraati, Hojjat; Kamali, Koorosh; Mohammad, Kazem; Shahnazari, Parisa; Akhondi, Mohammad Mehdi

    2016-01-01

    Background: Infertile couples are faced with problems that affect their marital life. Infertility treatment is expensive and time consuming and occasionally isn’t simply possible. Prediction models for infertility treatment have been proposed and prediction of treatment success is a new field in infertility treatment. Because prediction of treatment success is a new need for infertile couples, this paper reviewed previous studies for catching a general concept in applicability of the models. Methods: This study was conducted as a systematic review at Avicenna Research Institute in 2015. Six data bases were searched based on WHO definitions and MESH key words. Papers about prediction models in infertility were evaluated. Results: Eighty one papers were eligible for the study. Papers covered years after 1986 and studies were designed retrospectively and prospectively. IVF prediction models have more shares in papers. Most common predictors were age, duration of infertility, ovarian and tubal problems. Conclusion: Prediction model can be clinically applied if the model can be statistically evaluated and has a good validation for treatment success. To achieve better results, the physician and the couples’ needs estimation for treatment success rate were based on history, the examination and clinical tests. Models must be checked for theoretical approach and appropriate validation. The privileges for applying the prediction models are the decrease in the cost and time, avoiding painful treatment of patients, assessment of treatment approach for physicians and decision making for health managers. The selection of the approach for designing and using these models is inevitable. PMID:27141461

  18. MULTI MODEL DATA MINING APPROACH FOR HEART FAILURE PREDICTION

    Directory of Open Access Journals (Sweden)

    Priyanka H U

    2016-09-01

    Full Text Available Developing predictive modelling solutions for risk estimation is extremely challenging in health-care informatics. Risk estimation involves integration of heterogeneous clinical sources having different representation from different health-care provider making the task increasingly complex. Such sources are typically voluminous, diverse, and significantly change over the time. Therefore, distributed and parallel computing tools collectively termed big data tools are in need which can synthesize and assist the physician to make right clinical decisions. In this work we propose multi-model predictive architecture, a novel approach for combining the predictive ability of multiple models for better prediction accuracy. We demonstrate the effectiveness and efficiency of the proposed work on data from Framingham Heart study. Results show that the proposed multi-model predictive architecture is able to provide better accuracy than best model approach. By modelling the error of predictive models we are able to choose sub set of models which yields accurate results. More information was modelled into system by multi-level mining which has resulted in enhanced predictive accuracy.

  19. The regional prediction model of PM10 concentrations for Turkey

    Science.gov (United States)

    Güler, Nevin; Güneri İşçi, Öznur

    2016-11-01

    This study is aimed to predict a regional model for weekly PM10 concentrations measured air pollution monitoring stations in Turkey. There are seven geographical regions in Turkey and numerous monitoring stations at each region. Predicting a model conventionally for each monitoring station requires a lot of labor and time and it may lead to degradation in quality of prediction when the number of measurements obtained from any õmonitoring station is small. Besides, prediction models obtained by this way only reflect the air pollutant behavior of a small area. This study uses Fuzzy C-Auto Regressive Model (FCARM) in order to find a prediction model to be reflected the regional behavior of weekly PM10 concentrations. The superiority of FCARM is to have the ability of considering simultaneously PM10 concentrations measured monitoring stations in the specified region. Besides, it also works even if the number of measurements obtained from the monitoring stations is different or small. In order to evaluate the performance of FCARM, FCARM is executed for all regions in Turkey and prediction results are compared to statistical Autoregressive (AR) Models predicted for each station separately. According to Mean Absolute Percentage Error (MAPE) criteria, it is observed that FCARM provides the better predictions with a less number of models.

  20. Gaussian mixture models as flux prediction method for central receivers

    Science.gov (United States)

    Grobler, Annemarie; Gauché, Paul; Smit, Willie

    2016-05-01

    Flux prediction methods are crucial to the design and operation of central receiver systems. Current methods such as the circular and elliptical (bivariate) Gaussian prediction methods are often used in field layout design and aiming strategies. For experimental or small central receiver systems, the flux profile of a single heliostat often deviates significantly from the circular and elliptical Gaussian models. Therefore a novel method of flux prediction was developed by incorporating the fitting of Gaussian mixture models onto flux profiles produced by flux measurement or ray tracing. A method was also developed to predict the Gaussian mixture model parameters of a single heliostat for a given time using image processing. Recording the predicted parameters in a database ensures that more accurate predictions are made in a shorter time frame.

  1. Using Species Distribution Models to Predict Potential Landscape Restoration Effects on Puma Conservation.

    Science.gov (United States)

    Angelieri, Cintia Camila Silva; Adams-Hosking, Christine; Ferraz, Katia Maria Paschoaletto Micchi de Barros; de Souza, Marcelo Pereira; McAlpine, Clive Alexander

    2016-01-01

    A mosaic of intact native and human-modified vegetation use can provide important habitat for top predators such as the puma (Puma concolor), avoiding negative effects on other species and ecological processes due to cascade trophic interactions. This study investigates the effects of restoration scenarios on the puma's habitat suitability in the most developed Brazilian region (São Paulo State). Species Distribution Models incorporating restoration scenarios were developed using the species' occurrence information to (1) map habitat suitability of pumas in São Paulo State, Southeast, Brazil; (2) test the relative contribution of environmental variables ecologically relevant to the species habitat suitability and (3) project the predicted habitat suitability to future native vegetation restoration scenarios. The Maximum Entropy algorithm was used (Test AUC of 0.84 ± 0.0228) based on seven environmental non-correlated variables and non-autocorrelated presence-only records (n = 342). The percentage of native vegetation (positive influence), elevation (positive influence) and density of roads (negative influence) were considered the most important environmental variables to the model. Model projections to restoration scenarios reflected the high positive relationship between pumas and native vegetation. These projections identified new high suitability areas for pumas (probability of presence >0.5) in highly deforested regions. High suitability areas were increased from 5.3% to 8.5% of the total State extension when the landscapes were restored for ≥ the minimum native vegetation cover rule (20%) established by the Brazilian Forest Code in private lands. This study highlights the importance of a landscape planning approach to improve the conservation outlook for pumas and other species, including not only the establishment and management of protected areas, but also the habitat restoration on private lands. Importantly, the results may inform environmental

  2. Using social network analysis tools in ecology : Markov process transition models applied to the seasonal trophic network dynamics of the Chesapeake Bay

    NARCIS (Netherlands)

    Johnson, Jeffrey C.; Luczkovich, Joseph J.; Borgatti, Stephen P.; Snijders, Tom A. B.; Luczkovich, S.P.

    2009-01-01

    Ecosystem components interact in complex ways and change over time due to a variety of both internal and external influences (climate change, season cycles, human impacts). Such processes need to be modeled dynamically using appropriate statistical methods for assessing change in network structure.

  3. The influence of nutrients, biliary-pancreatic secretions, and systemic trophic hormones on intestinal adaptation in a Roux-en-Y bypass model

    DEFF Research Database (Denmark)

    Taqi, Esmaeel; Wallace, Laurie E; de Heuvel, Elaine

    2010-01-01

    The signals that govern the upregulation of nutrient absorption (adaptation) after intestinal resection are not well understood. A Gastric Roux-en-Y bypass (GRYB) model was used to isolate the relative contributions of direct mucosal stimulation by nutrients, biliary-pancreatic secretions, and sy...

  4. Using social network analysis tools in ecology : Markov process transition models applied to the seasonal trophic network dynamics of the Chesapeake Bay

    NARCIS (Netherlands)

    Johnson, Jeffrey C.; Luczkovich, Joseph J.; Borgatti, Stephen P.; Snijders, Tom A. B.; Luczkovich, S.P.

    2009-01-01

    Ecosystem components interact in complex ways and change over time due to a variety of both internal and external influences (climate change, season cycles, human impacts). Such processes need to be modeled dynamically using appropriate statistical methods for assessing change in network structure.

  5. Qualitative models to predict impacts of human interventions in a wetland ecosystem

    Directory of Open Access Journals (Sweden)

    S. Loiselle

    2002-07-01

    Full Text Available The large shallow wetlands that dominate much of the South American continent are rich in biodiversity and complexity. Many of these undamaged ecosystems are presently being examined for their potential economic utility, putting pressure on local authorities and the conservation community to find ways of correctly utilising the available natural resources without compromising the ecosystem functioning and overall integrity. Contrary to many northern hemisphere ecosystems, there have been little long term ecological studies of these systems, leading to a lack of quantitative data on which to construct ecological or resource use models. As a result, decision makers, even well meaning ones, have difficulty in determining if particular economic activities can potentially cause significant damage to the ecosystem and how one should go about monitoring the impacts of such activities. While the direct impact of many activities is often known, the secondary indirect impacts are usually less clear and can depend on local ecological conditions.

    The use of qualitative models is a helpful tool to highlight potential feedback mechanisms and secondary effects of management action on ecosystem integrity. The harvesting of a single, apparently abundant, species can have indirect secondary effects on key trophic and abiotic compartments. In this paper, loop model analysis is used to qualitatively examine secondary effects of potential economic activities in a large wetland area in northeast Argentina, the Esteros del Ibera. Based on interaction with local actors together with observed ecological information, loop models were constructed to reflect relationships between biotic and abiotic compartments. A series of analyses were made to study the effect of different economic scenarios on key ecosystem compartments. Important impacts on key biotic compartments (phytoplankton, zooplankton, ichthyofauna, aquatic macrophytes and on the abiotic environment

  6. Nonlinear model predictive control of a packed distillation column

    Energy Technology Data Exchange (ETDEWEB)

    Patwardhan, A.A.; Edgar, T.F. (Univ. of Texas, Austin, TX (United States). Dept. of Chemical Engineering)

    1993-10-01

    A rigorous dynamic model based on fundamental chemical engineering principles was formulated for a packed distillation column separating a mixture of cyclohexane and n-heptane. This model was simplified to a form suitable for use in on-line model predictive control calculations. A packed distillation column was operated at several operating conditions to estimate two unknown model parameters in the rigorous and simplified models. The actual column response to step changes in the feed rate, distillate rate, and reboiler duty agreed well with dynamic model predictions. One unusual characteristic observed was that the packed column exhibited gain-sign changes, which are very difficult to treat using conventional linear feedback control. Nonlinear model predictive control was used to control the distillation column at an operating condition where the process gain changed sign. An on-line, nonlinear model-based scheme was used to estimate unknown/time-varying model parameters.

  7. Application of Nonlinear Predictive Control Based on RBF Network Predictive Model in MCFC Plant

    Institute of Scientific and Technical Information of China (English)

    CHEN Yue-hua; CAO Guang-yi; ZHU Xin-jian

    2007-01-01

    This paper described a nonlinear model predictive controller for regulating a molten carbonate fuel cell (MCFC). A detailed mechanism model of output voltage of a MCFC was presented at first. However, this model was too complicated to be used in a control system. Consequently, an off line radial basis function (RBF) network was introduced to build a nonlinear predictive model. And then, the optimal control sequences were obtained by applying golden mean method. The models and controller have been realized in the MATLAB environment. Simulation results indicate the proposed algorithm exhibits satisfying control effect even when the current densities vary largely.

  8. A burnout prediction model based around char morphology

    Energy Technology Data Exchange (ETDEWEB)

    T. Wu; E. Lester; M. Cloke [University of Nottingham, Nottingham (United Kingdom). Nottingham Energy and Fuel Centre

    2005-07-01

    Poor burnout in a coal-fired power plant has marked penalties in the form of reduced energy efficiency and elevated waste material that can not be utilized. The prediction of coal combustion behaviour in a furnace is of great significance in providing valuable information not only for process optimization but also for coal buyers in the international market. Coal combustion models have been developed that can make predictions about burnout behaviour and burnout potential. Most of these kinetic models require standard parameters such as volatile content, particle size and assumed char porosity in order to make a burnout prediction. This paper presents a new model called the Char Burnout Model (ChB) that also uses detailed information about char morphology in its prediction. The model can use data input from one of two sources. Both sources are derived from image analysis techniques. The first from individual analysis and characterization of real char types using an automated program. The second from predicted char types based on data collected during the automated image analysis of coal particles. Modelling results were compared with a different carbon burnout kinetic model and burnout data from re-firing the chars in a drop tube furnace operating at 1300{sup o}C, 5% oxygen across several residence times. An improved agreement between ChB model and DTF experimental data proved that the inclusion of char morphology in combustion models can improve model predictions. 27 refs., 4 figs., 4 tabs.

  9. Model-based uncertainty in species range prediction

    DEFF Research Database (Denmark)

    Pearson, R. G.; Thuiller, Wilfried; Bastos Araujo, Miguel;

    2006-01-01

    Aim Many attempts to predict the potential range of species rely on environmental niche (or 'bioclimate envelope') modelling, yet the effects of using different niche-based methodologies require further investigation. Here we investigate the impact that the choice of model can have on predictions...... day (using the area under the receiver operating characteristic curve (AUC) and kappa statistics) and by assessing consistency in predictions of range size changes under future climate (using cluster analysis). Results Our analyses show significant differences between predictions from different models......, with predicted changes in range size by 2030 differing in both magnitude and direction (e.g. from 92% loss to 322% gain). We explain differences with reference to two characteristics of the modelling techniques: data input requirements (presence/absence vs. presence-only approaches) and assumptions made by each...

  10. A new ensemble model for short term wind power prediction

    DEFF Research Database (Denmark)

    Madsen, Henrik; Albu, Razvan-Daniel; Felea, Ioan;

    2012-01-01

    As the objective of this study, a non-linear ensemble system is used to develop a new model for predicting wind speed in short-term time scale. Short-term wind power prediction becomes an extremely important field of research for the energy sector. Regardless of the recent advancements in the re......-search of prediction models, it was observed that different models have different capabilities and also no single model is suitable under all situations. The idea behind EPS (ensemble prediction systems) is to take advantage of the unique features of each subsystem to detain diverse patterns that exist in the dataset....... The conferred results show that the prediction errors can be decreased, while the computation time is reduced....

  11. Improving Environmental Model Calibration and Prediction

    Science.gov (United States)

    2011-01-18

    groundwater model calibration. Adv. Water Resour., 29(4):605–623, 2006. [9] B.E. Skahill, J.S. Baggett, S. Frankenstein , and C.W. Downer. More efficient...of Hydrology, Environmental Modelling & Software, or Water Resources Research). Skahill, B., Baggett, J., Frankenstein , S., and Downer, C.W. (2009

  12. Model Predictive Control for Smart Energy Systems

    DEFF Research Database (Denmark)

    Halvgaard, Rasmus

    load shifting capabilities of the units that adapts to the given price predictions. We furthermore evaluated control performance in terms of economic savings for different control strategies and forecasts. Chapter 5 describes and compares the proposed large-scale Aggregator control strategies....... Aggregators are assumed to play an important role in the future Smart Grid and coordinate a large portfolio of units. The developed economic MPC controllers interfaces each unit directly to an Aggregator. We developed several MPC-based aggregation strategies that coordinates the global behavior of a portfolio...

  13. Combining logistic regression and neural networks to create predictive models.

    OpenAIRE

    Spackman, K. A.

    1992-01-01

    Neural networks are being used widely in medicine and other areas to create predictive models from data. The statistical method that most closely parallels neural networks is logistic regression. This paper outlines some ways in which neural networks and logistic regression are similar, shows how a small modification of logistic regression can be used in the training of neural network models, and illustrates the use of this modification for variable selection and predictive model building wit...

  14. Assessment of performance of survival prediction models for cancer prognosis

    Directory of Open Access Journals (Sweden)

    Chen Hung-Chia

    2012-07-01

    Full Text Available Abstract Background Cancer survival studies are commonly analyzed using survival-time prediction models for cancer prognosis. A number of different performance metrics are used to ascertain the concordance between the predicted risk score of each patient and the actual survival time, but these metrics can sometimes conflict. Alternatively, patients are sometimes divided into two classes according to a survival-time threshold, and binary classifiers are applied to predict each patient’s class. Although this approach has several drawbacks, it does provide natural performance metrics such as positive and negative predictive values to enable unambiguous assessments. Methods We compare the survival-time prediction and survival-time threshold approaches to analyzing cancer survival studies. We review and compare common performance metrics for the two approaches. We present new randomization tests and cross-validation methods to enable unambiguous statistical inferences for several performance metrics used with the survival-time prediction approach. We consider five survival prediction models consisting of one clinical model, two gene expression models, and two models from combinations of clinical and gene expression models. Results A public breast cancer dataset was used to compare several performance metrics using five prediction models. 1 For some prediction models, the hazard ratio from fitting a Cox proportional hazards model was significant, but the two-group comparison was insignificant, and vice versa. 2 The randomization test and cross-validation were generally consistent with the p-values obtained from the standard performance metrics. 3 Binary classifiers highly depended on how the risk groups were defined; a slight change of the survival threshold for assignment of classes led to very different prediction results. Conclusions 1 Different performance metrics for evaluation of a survival prediction model may give different conclusions in

  15. A thermodynamic model to predict wax formation in petroleum fluids

    Energy Technology Data Exchange (ETDEWEB)

    Coutinho, J.A.P. [Universidade de Aveiro (Portugal). Dept. de Quimica. Centro de Investigacao em Quimica]. E-mail: jcoutinho@dq.ua.pt; Pauly, J.; Daridon, J.L. [Universite de Pau et des Pays de l' Adour, Pau (France). Lab. des Fluides Complexes

    2001-12-01

    Some years ago the authors proposed a model for the non-ideality of the solid phase, based on the Predictive Local Composition concept. This was first applied to the Wilson equation and latter extended to NRTL and UNIQUAC models. Predictive UNIQUAC proved to be extraordinarily successful in predicting the behaviour of both model and real hydrocarbon fluids at low temperatures. This work illustrates the ability of Predictive UNIQUAC in the description of the low temperature behaviour of petroleum fluids. It will be shown that using Predictive UNIQUAC in the description of the solid phase non-ideality a complete prediction of the low temperature behaviour of synthetic paraffin solutions, fuels and crude oils is achieved. The composition of both liquid and solid phases, the amount of crystals formed and the cloud points are predicted within the accuracy of the experimental data. The extension of Predictive UNIQUAC to high pressures, by coupling it with an EOS/G{sup E} model based on the SRK EOS used with the LCVM mixing rule, is proposed and predictions of phase envelopes for live oils are compared with experimental data. (author)

  16. A THERMODYNAMIC MODEL TO PREDICT WAX FORMATION IN PETROLEUM FLUIDS

    Directory of Open Access Journals (Sweden)

    J.A.P. Coutinho

    2001-12-01

    Full Text Available Some years ago the authors proposed a model for the non-ideality of the solid phase, based on the Predictive Local Composition concept. This was first applied to the Wilson equation and latter extended to NRTL and UNIQUAC models. Predictive UNIQUAC proved to be extraordinarily successful in predicting the behaviour of both model and real hydrocarbon fluids at low temperatures. This work illustrates the ability of Predictive UNIQUAC in the description of the low temperature behaviour of petroleum fluids. It will be shown that using Predictive UNIQUAC in the description of the solid phase non-ideality a complete prediction of the low temperature behaviour of synthetic paraffin solutions, fuels and crude oils is achieved. The composition of both liquid and solid phases, the amount of crystals formed and the cloud points are predicted within the accuracy of the experimental data. The extension of Predictive UNIQUAC to high pressures, by coupling it with an EOS/G E model based on the SRK EOS used with the LCVM mixing rule, is proposed and predictions of phase envelopes for live oils are compared with experimental data.

  17. A systematic review of predictive modeling for bronchiolitis.

    Science.gov (United States)

    Luo, Gang; Nkoy, Flory L; Gesteland, Per H; Glasgow, Tiffany S; Stone, Bryan L

    2014-10-01

    Bronchiolitis is the most common cause of illness leading to hospitalization in young children. At present, many bronchiolitis management decisions are made subjectively, leading to significant practice variation among hospitals and physicians caring for children with bronchiolitis. To standardize care for bronchiolitis, researchers have proposed various models to predict the disease course to help determine a proper management plan. This paper reviews the existing state of the art of predictive modeling for bronchiolitis. Predictive modeling for respiratory syncytial virus (RSV) infection is covered whenever appropriate, as RSV accounts for about 70% of bronchiolitis cases. A systematic review was conducted through a PubMed search up to April 25, 2014. The literature on predictive modeling for bronchiolitis was retrieved using a comprehensive search query, which was developed through an iterative process. Search results were limited to human subjects, the English language, and children (birth to 18 years). The literature search returned 2312 references in total. After manual review, 168 of these references were determined to be relevant and are discussed in this paper. We identify several limitations and open problems in predictive modeling for bronchiolitis, and provide some preliminary thoughts on how to address them, with the hope to stimulate future research in this domain. Many problems remain open in predictive modeling for bronchiolitis. Future studies will need to address them to achieve optimal predictive models. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

  18. Climate predictability and prediction skill on seasonal time scales over South America from CHFP models

    Science.gov (United States)

    Osman, Marisol; Vera, C. S.

    2016-11-01

    This work presents an assessment of the predictability and skill of climate anomalies over South America. The study was made considering a multi-model ensemble of seasonal forecasts for surface air temperature, precipitation and regional circulation, from coupled global circulation models included in the Climate Historical Forecast Project. Predictability was evaluated through the estimation of the signal-to-total variance ratio while prediction skill was assessed computing anomaly correlation coefficients. Both indicators present over the continent higher values at the tropics than at the extratropics for both, surface air temperature and precipitation. Moreover, predictability and prediction skill for temperature are slightly higher in DJF than in JJA while for precipitation they exhibit similar levels in both seasons. The largest values of predictability and skill for both variables and seasons are found over northwestern South America while modest but still significant values for extratropical precipitation at southeastern South America and the extratropical Andes. The predictability levels in ENSO years of both variables are slightly higher, although with the same spatial distribution, than that obtained considering all years. Nevertheless, predictability at the tropics for both variables and seasons diminishes in both warm and cold ENSO years respect to that in all years. The latter can be attributed to changes in signal rather than in the noise. Predictability and prediction skill for low-level winds and upper-level zonal winds over South America was also assessed. Maximum levels of predictability for low-level winds were found were maximum mean values are observed, i.e. the regions associated with the equatorial trade winds, the midlatitudes westerlies and the South American Low-Level Jet. Predictability maxima for upper-level zonal winds locate where the subtropical jet peaks. Seasonal changes in wind predictability are observed that seem to be related to

  19. Predicting and Modelling of Survival Data when Cox's Regression Model does not hold

    DEFF Research Database (Denmark)

    Scheike, Thomas H.; Zhang, Mei-Jie

    2002-01-01

    Aalen model; additive risk model; counting processes; competing risk; Cox regression; flexible modeling; goodness of fit; prediction of survival; survival analysis; time-varying effects......Aalen model; additive risk model; counting processes; competing risk; Cox regression; flexible modeling; goodness of fit; prediction of survival; survival analysis; time-varying effects...

  20. Intersexual Trophic Niche Partitioning in an Ant-Eating spider (Araneae: Zodariidae)

    DEFF Research Database (Denmark)

    Pekár, Stanislav; Martisová, Martina; Bilde, T.

    2011-01-01

    Background Divergence in trophic niche between the sexes may function to reduce competition between the sexes (“intersexual niche partitioning hypothesis”), or may be result from differential selection among the sexes on maximizing reproductive output (“sexual selection hypothesis”). The latter may...... lead to higher energy demands in females driven by fecundity selection, while males invest in mate searching. We tested predictions of the two hypotheses underlying intersexual trophic niche partitioning in a natural population of spiders. Zodarion jozefienae spiders specialize on Messor barbarus ants...... demonstrated highly female biased SSD (Sexual Size Dimorphism) in body size, body weight, and in the size of chelicerae, the latter arising from sex-specific growth patterns in trophic morphology. In the field, female spiders actively selected ant sub-castes that were larger than the average prey size...

  1. Ploidy of cell-sorted trophic and cystic forms of Pneumocystis carinii.

    Science.gov (United States)

    Martinez, Anna; Aliouat, El Moukhtar; Standaert-Vitse, Annie; Werkmeister, Elisabeth; Pottier, Muriel; Pinçon, Claire; Dei-Cas, Eduardo; Aliouat-Denis, Cécile-Marie

    2011-01-01

    Once regarded as an AIDS-defining illness, Pneumocystis pneumonia (PcP) is nowadays prevailing in immunocompromised HIV-negative individuals such as patients receiving immunosuppressive therapies or affected by primary immunodeficiency. Moreover, Pneumocystis clinical spectrum is broadening to non-severely-immunocompromised subjects who could be colonized by the fungus while remaining asymptomatic for PcP, thus being able to transmit the infection by airborne route to susceptible hosts. Although the taxonomical position of the Pneumocystis genus has been clarified, several aspects of its life cycle remain elusive such as its mode of proliferation within the alveolus or its ploidy level. As no long-term culture model exists to grow Pneumocystis organisms in vitro, an option was to use a model of immunosuppressed rat infected with Pneumocystis carinii and sort life cycle stage fractions using a high-through-put cytometer. Subsequently, ploidy levels of the P. carinii trophic and cystic form fractions were measured by flow cytometry. In the cystic form, eight contents of DNA were measured thus strengthening the fact that each mature cyst contains eight haploid spores. Following release, each spore evolves into a trophic form. The majority of the trophic form fraction was haploid in our study. Some less abundant trophic forms displayed two contents of DNA indicating that they could undergo (i) mating/fusion leading to a diploid status or (ii) asexual mitotic division or (iii) both. Even less abundant trophic forms with four contents of DNA were suggestive of mitotic divisions occurring following mating in diploid trophic forms. Of interest, was the presence of trophic forms with three contents of DNA, an unusual finding that could be related to asymmetrical mitotic divisions occurring in other fungal species to create genetic diversity at lower energetic expenses than mating. Overall, ploidy data of P. carinii life cycle stages shed new light on the complexity of its

  2. Ploidy of cell-sorted trophic and cystic forms of Pneumocystis carinii.

    Directory of Open Access Journals (Sweden)

    Anna Martinez

    Full Text Available Once regarded as an AIDS-defining illness, Pneumocystis pneumonia (PcP is nowadays prevailing in immunocompromised HIV-negative individuals such as patients receiving immunosuppressive therapies or affected by primary immunodeficiency. Moreover, Pneumocystis clinical spectrum is broadening to non-severely-immunocompromised subjects who could be colonized by the fungus while remaining asymptomatic for PcP, thus being able to transmit the infection by airborne route to susceptible hosts. Although the taxonomical position of the Pneumocystis genus has been clarified, several aspects of its life cycle remain elusive such as its mode of proliferation within the alveolus or its ploidy level. As no long-term culture model exists to grow Pneumocystis organisms in vitro, an option was to use a model of immunosuppressed rat infected with Pneumocystis carinii and sort life cycle stage fractions using a high-through-put cytometer. Subsequently, ploidy levels of the P. carinii trophic and cystic form fractions were measured by flow cytometry. In the cystic form, eight contents of DNA were measured thus strengthening the fact that each mature cyst contains eight haploid spores. Following release, each spore evolves into a trophic form. The majority of the trophic form fraction was haploid in our study. Some less abundant trophic forms displayed two contents of DNA indicating that they could undergo (i mating/fusion leading to a diploid status or (ii asexual mitotic division or (iii both. Even less abundant trophic forms with four contents of DNA were suggestive of mitotic divisions occurring following mating in diploid trophic forms. Of interest, was the presence of trophic forms with three contents of DNA, an unusual finding that could be related to asymmetrical mitotic divisions occurring in other fungal species to create genetic diversity at lower energetic expenses than mating. Overall, ploidy data of P. carinii life cycle stages shed new light on the

  3. Trophic interactions, ecosystem structure and function in the southern Yellow Sea

    Institute of Scientific and Technical Information of China (English)

    LIN Qun; JIN Xianshi; ZHANG Bo

    2013-01-01

    The southern Yellow Sea is an important fishing ground,providing abundant fishery resources.However,overfishing and climate change have caused a decline in the resource and damaged the ecosystem.We developed an ecosystem model to analyze the trophic interactions and ecosystem structure and function to guide sustainable development of the ecosystem.Atrophic mass-balance model of the southern Yellow Sea during 2000-2001 was constructed using Ecopath with Ecosim software.We defined 22 important functional groups and studied their diet composition.The trophic levels of fish,shrimp,crabs,and cephalopods were between 2.78 and 4.39,and the mean trophic level of the fisheries was 3.24.The trophic flows within the food web occurred primarily in the lower trophic levels.The mean trophic transfer efficiency was 8.1%,of which 7.1% was from primary producers and 9.3% was from detritus within the ecosystem.The transfer efficiency between trophic levels Ⅱ to Ⅲ to Ⅳ to Ⅴ to >Ⅴ was 5.0%,5.7%,18.5%,and 19.7%-20.4%,respectively.Of the total flow,phytoplankton contributed 61% and detritus contributed 39%.Fishing is defined as a top predator within the ecosystem,and has a negative impact on most commercial species.Moreover,the ecosystem had a high gross efficiency of the fishery and a high value of primary production required to sustain the fishery.Together,our data suggest there is high fishing pressure in the southern Yellow Sea.Based on analysis of Odum's ecological parameters,this ecosystem was at an immature stage.Our results provide some insights into the structure and development of this ecosystem.

  4. Predictive error analysis for a water resource management model

    Science.gov (United States)

    Gallagher, Mark; Doherty, John

    2007-02-01

    SummaryIn calibrating a model, a set of parameters is assigned to the model which will be employed for the making of all future predictions. If these parameters are estimated through solution of an inverse problem, formulated to be properly posed through either pre-calibration or mathematical regularisation, then solution of this inverse problem will, of necessity, lead to a simplified parameter set that omits the details of reality, while still fitting historical data acceptably well. Furthermore, estimates of parameters so obtained will be contaminated by measurement noise. Both of these phenomena will lead to errors in predictions made by the model, with the potential for error increasing with the hydraulic property detail on which the prediction depends. Integrity of model usage demands that model predictions be accompanied by some estimate of the possible errors associated with them. The present paper applies theory developed in a previous work to the analysis of predictive error associated with a real world, water resource management model. The analysis offers many challenges, including the fact that the model is a complex one that was partly calibrated by hand. Nevertheless, it is typical of models which are commonly employed as the basis for the making of important decisions, and for which such an analysis must be made. The potential errors associated with point-based and averaged water level and creek inflow predictions are examined, together with the dependence of these errors on the amount of averaging involved. Error variances associated with predictions made by the existing model are compared with "optimized error variances" that could have been obtained had calibration been undertaken in such a way as to minimize predictive error variance. The contributions by different parameter types to the overall error variance of selected predictions are also examined.

  5. Models for short term malaria prediction in Sri Lanka

    Directory of Open Access Journals (Sweden)

    Galappaththy Gawrie NL

    2008-05-01

    Full Text Available Abstract Background Malaria in Sri Lanka is unstable and fluctuates in intensity both spatially and temporally. Although the case counts are dwindling at present, given the past history of resurgence of outbreaks despite effective control measures, the control programmes have to stay prepared. The availability of long time series of monitored/diagnosed malaria cases allows for the study of forecasting models, with an aim to developing a forecasting system which could assist in the efficient allocation of resources for malaria control. Methods Exponentially weighted moving average models, autoregressive integrated moving average (ARIMA models with seasonal components, and seasonal multiplicative autoregressive integrated moving average (SARIMA models were compared on monthly time series of district malaria cases for their ability to predict the number of malaria cases one to four months ahead. The addition of covariates such as the number of malaria cases in neighbouring districts or rainfall were assessed for their ability to improve prediction of selected (seasonal ARIMA models. Results The best model for forecasting and the forecasting error varied strongly among the districts. The addition of rainfall as a covariate improved prediction of selected (seasonal ARIMA models modestly in some districts but worsened prediction in other districts. Improvement by adding rainfall was more frequent at larger forecasting horizons. Conclusion Heterogeneity of patterns of malaria in Sri Lanka requires regionally specific prediction models. Prediction error was large at a minimum of 22% (for one of the districts for one month ahead predictions. The modest improvement made in short term prediction by adding rainfall as a covariate to these prediction models may not be sufficient to merit investing in a forecasting system for which rainfall data are routinely processed.

  6. Aggregate driver model to enable predictable behaviour

    Science.gov (United States)

    Chowdhury, A.; Chakravarty, T.; Banerjee, T.; Balamuralidhar, P.

    2015-09-01

    The categorization of driving styles, particularly in terms of aggressiveness and skill is an emerging area of interest under the broader theme of intelligent transportation. There are two possible discriminatory techniques that can be applied for such categorization; a microscale (event based) model and a macro-scale (aggregate) model. It is believed that an aggregate model will reveal many interesting aspects of human-machine interaction; for example, we may be able to understand the propensities of individuals to carry out a given task over longer periods of time. A useful driver model may include the adaptive capability of the human driver, aggregated as the individual propensity to control speed/acceleration. Towards that objective, we carried out experiments by deploying smartphone based application to be used for data collection by a group of drivers. Data is primarily being collected from GPS measurements including position & speed on a second-by-second basis, for a number of trips over a two months period. Analysing the data set, aggregate models for individual drivers were created and their natural aggressiveness were deduced. In this paper, we present the initial results for 12 drivers. It is shown that the higher order moments of the acceleration profile is an important parameter and identifier of journey quality. It is also observed that the Kurtosis of the acceleration profiles stores major information about the driving styles. Such an observation leads to two different ranking systems based on acceleration data. Such driving behaviour models can be integrated with vehicle and road model and used to generate behavioural model for real traffic scenario.

  7. Validating predictions from climate envelope models

    Science.gov (United States)

    Watling, J.; Bucklin, D.; Speroterra, C.; Brandt, L.; Cabal, C.; Romañach, Stephanie S.; Mazzotti, Frank J.

    2013-01-01

    Climate envelope models are a potentially important conservation tool, but their ability to accurately forecast species’ distributional shifts using independent survey data has not been fully evaluated. We created climate envelope models for 12 species of North American breeding birds previously shown to have experienced poleward range shifts. For each species, we evaluated three different approaches to climate envelope modeling that differed in the way they treated climate-induced range expansion and contraction, using random forests and maximum entropy modeling algorithms. All models were calibrated using occurrence data from 1967–1971 (t1) and evaluated using occurrence data from 1998–2002 (t2). Model sensitivity (the ability to correctly classify species presences) was greater using the maximum entropy algorithm than the random forest algorithm. Although sensitivity did not differ significantly among approaches, for many species, sensitivity was maximized using a hybrid approach that assumed range expansion, but not contraction, in t2. Species for which the hybrid approach resulted in the greatest improvement in sensitivity have been reported from more land cover types than species for which there was little difference in sensitivity between hybrid and dynamic approaches, suggesting that habitat generalists may be buffered somewhat against climate-induced range contractions. Specificity (the ability to correctly classify species absences) was maximized using the random forest algorithm and was lowest using the hybrid approach. Overall, our results suggest cautious optimism for the use of climate envelope models to forecast range shifts, but also underscore the importance of considering non-climate drivers of species range limits. The use of alternative climate envelope models that make different assumptions about range expansion and contraction is a new and potentially useful way to help inform our understanding of climate change effects on species.

  8. Functional and neurophysiological evidence of the efficacy of trophic pharmacotherapy using an adrenocorticotrophic hormone4-9 analog in experimental allergic encephalomyelitis, an animal model of multiple sclerosis.

    Science.gov (United States)

    Duckers, H J; van Dokkum, R P; Verhaagen, J; Lopes da Silva, F H; Gispen, W H

    1996-03-01

    Chronic experimental allergic encephalomyelitis (CEAE) is a well-established animal model for the human syndrome, multiple sclerosis. CEAE has striking histological, electrophysiological and clinical analogies with multiple sclerosis and is a valuable animal model for the preclinical pharmacotherapeutical development of new putative therapeutic agents. In this paper, we describe a neurotrophic repair approach in Lewis rats suffering from CEAE. The neurotrophic peptide used is a degradation resistant adrenocorticotrophic hormone4-9 analog. The development of CEAE was examined using a combination of clinical, functional and electrophysiological parameters including somatosensory and motor evoked potentials. The latencies and amplitudes of the various evoked potentials can provide quantitative, objective data regarding the involvement of different nerve tracts in CEAE and the effectiveness of the neurotrophic peptide. Repeated subcutaneous injections of the neurotrophic peptide suppressed the development of CEAE-related clinical symptoms, markedly improved motor performance and reduced the reaction time upon thermal stimulation as compared to saline-treated CEAE animals during a 17 week follow-up study. Prolonged onset latencies of corticomotor evoked potentials and peak latencies of somatosensory evoked potentials due to the demyelination were normalized upon peptide treatment. In addition, peptide treatment substantially prevented total blocking of the corticomotor pathway in CEAE-animals and reduced the attenuation of sensory evoked potentials-related peak amplitudes as compared to saline-treated animals. The functional and electrophysiological improvements observed in CEAE-animals treated with the adrenocorticotrophic hormone4-9 analog, suggest that a neurotrophic repair approach could be of great value to promote the restoration of function in a disabling demyelinating disorder.

  9. Noncausal spatial prediction filtering based on an ARMA model

    Institute of Scientific and Technical Information of China (English)

    Liu Zhipeng; Chen Xiaohong; Li Jingye

    2009-01-01

    Conventional f-x prediction filtering methods are based on an autoregressive model. The error section is first computed as a source noise but is removed as additive noise to obtain the signal, which results in an assumption inconsistency before and after filtering. In this paper, an autoregressive, moving-average model is employed to avoid the model inconsistency. Based on the ARMA model, a noncasual prediction filter is computed and a self-deconvolved projection filter is used for estimating additive noise in order to suppress random noise. The 1-D ARMA model is also extended to the 2-D spatial domain, which is the basis for noncasual spatial prediction filtering for random noise attenuation on 3-D seismic data. Synthetic and field data processing indicate this method can suppress random noise more effectively and preserve the signal simultaneously and does much better than other conventional prediction filtering methods.

  10. Performance Predictable ServiceBSP Model for Grid Computing

    Institute of Scientific and Technical Information of China (English)

    TONG Weiqin; MIAO Weikai

    2007-01-01

    This paper proposes a performance prediction model for grid computing model ServiceBSP to support developing high quality applications in grid environment. In ServiceBSP model,the agents carrying computing tasks are dispatched to the local domain of the selected computation services. By using the IP (integer program) approach, the Service Selection Agent selects the computation services with global optimized QoS (quality of service) consideration. The performance of a ServiceBSP application can be predicted according to the performance prediction model based on the QoS of the selected services. The performance prediction model can help users to analyze their applications and improve them by optimized the factors which affects the performance. The experiment shows that the Service Selection Agent can provide ServiceBSP users with satisfied QoS of applications.

  11. Two Predictions of a Compound Cue Model of Priming

    OpenAIRE

    Walenski, Matthew

    2003-01-01

    This paper examines two predictions of the compound cue model of priming (Ratcliff and McKoon, 1988). While this model has been used to provide an account of a wide range of priming effects, it may not actually predict priming in these or other circumstances. In order to predict priming effects, the compound cue model relies on an assumption that all items have the same number of associates. This assumption may be true in only a restricted number of cases. This paper demonstrates that when th...

  12. Trophically available metal - A variable feast

    Energy Technology Data Exchange (ETDEWEB)

    Rainbow, Philip S., E-mail: p.rainbow@nhm.ac.uk [Department of Zoology, Natural History Museum, Cromwell Rd, London SW7 5BD (United Kingdom); Luoma, Samuel N. [Department of Zoology, Natural History Museum, Cromwell Rd, London SW7 5BD (United Kingdom); John Muir Institute of the Environment, University of California, Davis, CA 95616 (United States); Wang Wenxiong [College of Marine and Environmental Sciences, State Key Laboratory for Marine Environmental Sciences, Xiamen University, Fujian (China)

    2011-10-15

    Assimilation of trace metals by predators from prey is affected by the physicochemical form of the accumulated metal in the prey, leading to the concept of a Trophically Available Metal (TAM) component in the food item definable in terms of particular subcellular fractions of accumulated metal. As originally defined TAM consists of soluble metal forms and metal associated with cell organelles, the combination of separated fractions which best explained particular results involving a decapod crustacean predator feeding on bivalve mollusc tissues. Unfortunately TAM as originally defined has subsequently frequently been used in the literature as an absolute description of that component of accumulated metal that is trophically available in all prey to all consumers. It is now clear that what is trophically available varies between food items, consumers and metals. TAM as originally defined should be seen as a useful starting hypothesis, not as a statement of fact. - Trophically Available Metal (TAM), the component of accumulated metal in food that is taken up by a feeding animal, varies with food type and consumer.

  13. Aerodynamic Noise Prediction Using stochastic Turbulence Modeling

    Directory of Open Access Journals (Sweden)

    Arash Ahmadzadegan

    2008-01-01

    Full Text Available Amongst many approaches to determine the sound propagated from turbulent flows, hybrid methods, in which the turbulent noise source field is computed or modeled separately from the far field calculation, are frequently used. For basic estimation of sound propagation, less computationally intensive methods can be developed using stochastic models of the turbulent fluctuations (turbulent noise source field. A simple and easy to use stochastic model for generating turbulent velocity fluctuations called continuous filter white noise (CFWN model was used. This method based on the use of classical Langevian-equation to model the details of fluctuating field superimposed on averaged computed quantities. The resulting sound field due to the generated unsteady flow field was evaluated using Lighthill's acoustic analogy. Volume integral method used for evaluating the acoustic analogy. This formulation presents an advantage, as it confers the possibility to determine separately the contribution of the different integral terms and also integration regions to the radiated acoustic pressure. Our results validated by comparing the directivity and the overall sound pressure level (OSPL magnitudes with the available experimental results. Numerical results showed reasonable agreement with the experiments, both in maximum directivity and magnitude of the OSPL. This method presents a very suitable tool for the noise calculation of different engineering problems in early stages of the design process where rough estimates using cheaper methods are needed for different geometries.

  14. A Predictive Model of High Shear Thrombus Growth.

    Science.gov (United States)

    Mehrabadi, Marmar; Casa, Lauren D C; Aidun, Cyrus K; Ku, David N

    2016-08-01

    The ability to predict the timescale of thrombotic occlusion in stenotic vessels may improve patient risk assessment for thrombotic events. In blood contacting devices, thrombosis predictions can lead to improved designs to minimize thrombotic risks. We have developed and validated a model of high shear thrombosis based on empirical correlations between thrombus growth and shear rate. A mathematical model was developed to predict the growth of thrombus based on the hemodynamic shear rate. The model predicts thrombus deposition based on initial geometric and fluid mechanic conditions, which are updated throughout the simulation to reflect the changing lumen dimensions. The model was validated by comparing predictions against actual thrombus growth in six separate in vitro experiments: stenotic glass capillary tubes (diameter = 345 µm) at three shear rates, the PFA-100(®) system, two microfluidic channel dimensions (heights = 300 and 82 µm), and a stenotic aortic graft (diameter = 5.5 mm). Comparison of the predicted occlusion times to experimental results shows excellent agreement. The model is also applied to a clinical angiography image to illustrate the time course of thrombosis in a stenotic carotid artery after plaque cap rupture. Our model can accurately predict thrombotic occlusion time over a wide range of hemodynamic conditions.

  15. The application of modeling and prediction with MRA wavelet network

    Institute of Scientific and Technical Information of China (English)

    LU Shu-ping; YANG Xue-jing; ZHAO Xi-ren

    2004-01-01

    As there are lots of non-linear systems in the real engineering, it is very important to do more researches on the modeling and prediction of non-linear systems. Based on the multi-resolution analysis (MRA) of wavelet theory, this paper combined the wavelet theory with neural network and established a MRA wavelet network with the scaling function and wavelet function as its neurons. From the analysis in the frequency domain, the results indicated that MRA wavelet network was better than other wavelet networks in the ability of approaching to the signals. An essential research was carried out on modeling and prediction with MRA wavelet network in the non-linear system. Using the lengthwise sway data received from the experiment of ship model, a model of offline prediction was established and was applied to the short-time prediction of ship motion. The simulation results indicated that the forecasting model improved the prediction precision effectively, lengthened the forecasting time and had a better prediction results than that of AR linear model.The research indicates that it is feasible to use the MRA wavelet network in the short -time prediction of ship motion.

  16. Taxonomy, ecology and fishery of Lake Victoria haplochromine trophic groups

    NARCIS (Netherlands)

    Witte, F.; Oijen, van M.J.P

    1990-01-01

    Based on ecological and morphological features, the 300 or more haplochromine cichlid species of Lake Victoria are classified into fifteen (sub)trophic groups. A key to the trophic groups, mainly based on external morphological characters, is presented. Of each trophic group a description is given c

  17. Taxonomy, ecology and fishery of Lake Victoria haplochromine trophic groups

    NARCIS (Netherlands)

    Witte, F.; Oijen, van M.J.P

    1990-01-01

    Based on ecological and morphological features, the 300 or more haplochromine cichlid species of Lake Victoria are classified into fifteen (sub)trophic groups. A key to the trophic groups, mainly based on external morphological characters, is presented. Of each trophic group a description is given c

  18. A COMPARISON BETWEEN THREE PREDICTIVE MODELS OF COMPUTATIONAL INTELLIGENCE

    Directory of Open Access Journals (Sweden)

    DUMITRU CIOBANU

    2013-12-01

    Full Text Available Time series prediction is an open problem and many researchers are trying to find new predictive methods and improvements for the existing ones. Lately methods based on neural networks are used extensively for time series prediction. Also, support vector machines have solved some of the problems faced by neural networks and they began to be widely used for time series prediction. The main drawback of those two methods is that they are global models and in the case of a chaotic time series it is unlikely to find such model. In this paper it is presented a comparison between three predictive from computational intelligence field one based on neural networks one based on support vector machine and another based on chaos theory. We show that the model based on chaos theory is an alternative to the other two methods.

  19. Model estimates of the impact of bioirrigation activity of MARENZELLERIA spp. on the trophic state of the Gulf of Finland ecosystem

    Science.gov (United States)

    Isaev, Alexey; Eremina, Tatjana; Savchuk, Oleg; Ryabchenko, Vladimir

    2016-04-01

    Since the end of the 2000s, a sparcely populated soft bottom area in the deeps of the Eastern Gulf of Finland has been affected by invasive species Marenzelleria spp. The burrow flushing of sediments by Marenzelleria results in deeper penetration of oxygen into the sediments with corresponding redox alterations of nutrient cycling. In oxic conditions, a lager fraction of mineralized phosphates is transformed into particulate form due to formation of iron-humate complexes, thus increasing sediment phosphorus retention and burial. On the contrary, increased oxygen availability reduces nitrogen removal by denitrification, thus leading to larger release of nitrate into the water column. Indeed, as was revealed by long-term observations, the large-scale invasion of Marenzelleria spp. into the Eastern Gulf of Finland was accompanied by significant increase of the N:P ratio (Maximov et al., 2014). For a quantitative assessment of the bioirrigation activity of Marenzelleria spp., sediment parameterizations in SPBEM (St. Petersburg Baltic eutrophication model) have been modified as follows. Intensified burrow flushing is considered as the enlarged water-sediment contact area between water and sediments and described with the coefficient of bioirrigation determined by the abundance of polychaete and radius of their burrows. These parameterizations have been applied for simulation of the Gulf of Finland nutrient dynamics during 2010-2040. Preliminary results of simulations showed that vital activity of invasive Marenzelleria spp. might lead to slowdown or decrease of eutrophication in the Gulf of Finland due to reduction of cyanobacterial blooms

  20. MJO prediction skill, predictability, and teleconnection impacts in the Beijing Climate Center Atmospheric General Circulation Model

    Science.gov (United States)

    Wu, Jie; Ren, Hong-Li; Zuo, Jinqing; Zhao, Chongbo; Chen, Lijuan; Li, Qiaoping

    2016-09-01

    This study evaluates performance of Madden-Julian oscillation (MJO) prediction in the Beijing Climate Center Atmospheric General Circulation Model (BCC_AGCM2.2). By using the real-time multivariate MJO (RMM) indices, it is shown that the MJO prediction skill of BCC_AGCM2.2 extends to about 16-17 days before the bivariate anomaly correlation coefficient drops to 0.5 and the root-mean-square error increases to the level of the climatological prediction. The prediction skill showed a seasonal dependence, with the highest skill occurring in boreal autumn, and a phase dependence with higher skill for predictions initiated from phases 2-4. The results of the MJO predictability analysis showed that the upper bounds of the prediction skill can be extended to 26 days by using a single-member estimate, and to 42 days by using the ensemble-mean estimate, which also exhibited an initial amplitude and phase dependence. The observed relationship between the MJO and the North Atlantic Oscillation was accurately reproduced by BCC_AGCM2.2 for most initial phases of the MJO, accompanied with the Rossby wave trains in the Northern Hemisphere extratropics driven by MJO convection forcing. Overall, BCC_AGCM2.2 displayed a significant ability to predict the MJO and its teleconnections without interacting with the ocean, which provided a useful tool for fully extracting the predictability source of subseasonal prediction.

  1. Predicting Market Impact Costs Using Nonparametric Machine Learning Models.

    Directory of Open Access Journals (Sweden)

    Saerom Park

    Full Text Available Market impact cost is the most significant portion of implicit transaction costs that can reduce the overall transaction cost, although it cannot be measured directly. In this paper, we employed the state-of-the-art nonparametric machine learning models: neural networks, Bayesian neural network, Gaussian process, and support vector regression, to predict market impact cost accurately and to provide the predictive model that is versatile in the number of variables. We collected a large amount of real single transaction data of US stock market from Bloomberg Terminal and generated three independent input variables. As a result, most nonparametric machine learning models outperformed a-state-of-the-art benchmark parametric model such as I-star model in four error measures. Although these models encounter certain difficulties in separating the permanent and temporary cost directly, nonparametric machine learning models can be good alternatives in reducing transaction costs by considerably improving in prediction performance.

  2. Predicting Market Impact Costs Using Nonparametric Machine Learning Models.

    Science.gov (United States)

    Park, Saerom; Lee, Jaewook; Son, Youngdoo

    2016-01-01

    Market impact cost is the most significant portion of implicit transaction costs that can reduce the overall transaction cost, although it cannot be measured directly. In this paper, we employed the state-of-the-art nonparametric machine learning models: neural networks, Bayesian neural network, Gaussian process, and support vector regression, to predict market impact cost accurately and to provide the predictive model that is versatile in the number of variables. We collected a large amount of real single transaction data of US stock market from Bloomberg Terminal and generated three independent input variables. As a result, most nonparametric machine learning models outperformed a-state-of-the-art benchmark parametric model such as I-star model in four error measures. Although these models encounter certain difficulties in separating the permanent and temporary cost directly, nonparametric machine learning models can be good alternatives in reducing transaction costs by considerably improving in prediction performance.

  3. New Approaches for Channel Prediction Based on Sinusoidal Modeling

    Directory of Open Access Journals (Sweden)

    Ekman Torbjörn

    2007-01-01

    Full Text Available Long-range channel prediction is considered to be one of the most important enabling technologies to future wireless communication systems. The prediction of Rayleigh fading channels is studied in the frame of sinusoidal modeling in this paper. A stochastic sinusoidal model to represent a Rayleigh fading channel is proposed. Three different predictors based on the statistical sinusoidal model are proposed. These methods outperform the standard linear predictor (LP in Monte Carlo simulations, but underperform with real measurement data, probably due to nonstationary model parameters. To mitigate these modeling errors, a joint moving average and sinusoidal (JMAS prediction model and the associated joint least-squares (LS predictor are proposed. It combines the sinusoidal model with an LP to handle unmodeled dynamics in the signal. The joint LS predictor outperforms all the other sinusoidal LMMSE predictors in suburban environments, but still performs slightly worse than the standard LP in urban environments.

  4. Prediction model for spring dust weather frequency in North China

    Institute of Scientific and Technical Information of China (English)

    LANG XianMei

    2008-01-01

    It is of great social and scientific importance and also very difficult to make reliable prediction for dust weather frequency (DWF) in North China. In this paper, the correlation between spring DWF in Beijing and Tianjin observation stations, taken as examples in North China, and seasonally averaged surface air temperature, precipitation, Arctic Oscillation, Antarctic Oscillation, South Oscillation, near surface meridional wind and Eurasian westerly index is respectively calculated so as to construct a prediction model for spring DWF in North China by using these climatic factors. Two prediction models, I.e. Model-Ⅰ and model-Ⅱ, are then set up respectively based on observed climate data and the 32-year (1970--2001) extra-seasonal hindcast experiment data as reproduced by the nine-level Atmospheric General Circulation Model developed at the Institute of Atmospheric Physics (IAP9L-AGCM). It is indicated that the correlation coefficient between the observed and predicted DWF reaches 0.933 in the model-Ⅰ, suggesting a high prediction skill one season ahead. The corresponding value is high up to 0.948 for the subsequent model-Ⅱ, which involves synchronous spring climate data reproduced by the IAP9L-AGCM relative to the model-Ⅰ. The model-Ⅱ can not only make more precise prediction but also can bring forward the lead time of real-time prediction from the model-Ⅰ's one season to half year. At last, the real-time predictability of the two models is evaluated. It follows that both the models display high prediction skill for both the interannual variation and linear trend of spring DWF in North China, and each is also featured by different advantages. As for the model-Ⅱ, the prediction skill is much higher than that of original approach by use of the IAP9L-AGCM alone. Therefore, the prediction idea put forward here should be popularized in other regions in China where dust weather occurs frequently.

  5. Prediction model for spring dust weather frequency in North China

    Institute of Scientific and Technical Information of China (English)

    2008-01-01

    It is of great social and scientific importance and also very difficult to make reliable prediction for dust weather frequency (DWF) in North China. In this paper, the correlation between spring DWF in Beijing and Tianjin observation stations, taken as examples in North China, and seasonally averaged surface air temperature, precipitation, Arctic Oscillation, Antarctic Oscillation, South Oscillation, near surface meridional wind and Eurasian westerly index is respectively calculated so as to construct a prediction model for spring DWF in North China by using these climatic factors. Two prediction models, i.e. model-I and model-II, are then set up respectively based on observed climate data and the 32-year (1970 -2001) extra-seasonal hindcast experiment data as reproduced by the nine-level Atmospheric General Circulation Model developed at the Institute of Atmospheric Physics (IAP9L-AGCM). It is indicated that the correlation coefficient between the observed and predicted DWF reaches 0.933 in the model-I, suggesting a high prediction skill one season ahead. The corresponding value is high up to 0.948 for the subsequent model-II, which involves synchronous spring climate data reproduced by the IAP9L-AGCM relative to the model-I. The model-II can not only make more precise prediction but also can bring forward the lead time of real-time prediction from the model-I’s one season to half year. At last, the real-time predictability of the two models is evaluated. It follows that both the models display high prediction skill for both the interannual variation and linear trend of spring DWF in North China, and each is also featured by different advantages. As for the model-II, the prediction skill is much higher than that of original approach by use of the IAP9L-AGCM alone. Therefore, the prediction idea put forward here should be popularized in other regions in China where dust weather occurs frequently.

  6. Trophic interactions in northern Chile upwelling ecosystem, year 1997

    Directory of Open Access Journals (Sweden)

    Mónica E Barros

    2014-11-01

    Full Text Available A food web model is constructed to describe predator-prey interactions, community structure and trophic flows in northern Chile upwelling ecosystem (18°20'S, 24°S, for the year 1997. The model is built using the Ecopath with Ecosim software version 6.4, and encompasses 21 functional groups, ranging from primary producers (phytoplankton to top predators (birds and marine mammals, the principal fishing resources and the fishery. Input parameters required to build the model were gathered from specialized literature, grey literature and our own estimates. The results indicated that the total biomass (B T was estimated at 624.7 ton km-2. The combined biomass of small pelagic fish represented 26% of B T, while the combined biomass of demersal fish represented only 0.1% of B T. These results highlight the importance of pelagic fish in this system. Predation mortality resulted to be the main source of mortality. Nevertheless, fishing mortality was important in anchovy, mackerel, common dolphinfish and jack mackerel. The mean trophic level of the fishery was estimated as 3.7, with landings sustained mainly by anchovy. Primary production required to sustain the landings (PPR was estimated at 7.5% of calculated total net primary production, which is lower than PPR estimates in other upwelling ecosystems. The average trophic transfer efficiency was 18%, which is in the range (10-20% informed for marine ecosystems. Results indicate that in 1997 the northern Chile marine ecosystem was characterized for being a system far from maturity, dominated in terms of biomass and flows by the pelagic realm.

  7. Model Predictive Control of Sewer Networks

    DEFF Research Database (Denmark)

    Pedersen, Einar B.; Herbertsson, Hannes R.; Niemann, Henrik;

    2016-01-01

    The developments in solutions for management of urban drainage are of vital importance, as the amount of sewer water from urban areas continues to increase due to the increase of the world’s population and the change in the climate conditions. How a sewer network is structured, monitored...... and controlled have thus become essential factors for efficient performance of waste water treatment plants. This paper examines methods for simplified modelling and controlling a sewer network. A practical approach to the problem is used by analysing simplified design model, which is based on the Barcelona...

  8. A burnout prediction model based around char morphology

    Energy Technology Data Exchange (ETDEWEB)

    Tao Wu; Edward Lester; Michael Cloke [University of Nottingham, Nottingham (United Kingdom). School of Chemical, Environmental and Mining Engineering

    2006-05-15

    Several combustion models have been developed that can make predictions about coal burnout and burnout potential. Most of these kinetic models require standard parameters such as volatile content and particle size to make a burnout prediction. This article presents a new model called the char burnout (ChB) model, which also uses detailed information about char morphology in its prediction. The input data to the model is based on information derived from two different image analysis techniques. One technique generates characterization data from real char samples, and the other predicts char types based on characterization data from image analysis of coal particles. The pyrolyzed chars in this study were created in a drop tube furnace operating at 1300{sup o}C, 200 ms, and 1% oxygen. Modeling results were compared with a different carbon burnout kinetic model as well as the actual burnout data from refiring the same chars in a drop tube furnace operating at 1300{sup o}C, 5% oxygen, and residence times of 200, 400, and 600 ms. A good agreement between ChB model and experimental data indicates that the inclusion of char morphology in combustion models could well improve model predictions. 38 refs., 5 figs., 6 tabs.

  9. An evaluation of mathematical models for predicting skin permeability.

    Science.gov (United States)

    Lian, Guoping; Chen, Longjian; Han, Lujia

    2008-01-01

    A number of mathematical models have been proposed for predicting skin permeability, mostly empirical and very few are deterministic. Early empirical models use simple lipophilicity parameters. The recent trend is to use more complicated molecular structure descriptors. There has been much debate on which models best predict skin permeability. This article evaluates various mathematical models using a comprehensive experimental dataset of skin permeability for 124 chemical compounds compiled from various sources. Of the seven models compared, the deterministic model of Mitragotri gives the best prediction. The simple quantitative structure permeability relationships (QSPR) model of Potts and Guy gives the second best prediction. The two models have many features in common. Both assume the lipid matrix as the pathway of transdermal permeation. Both use octanol-water partition coefficient and molecular size. Even the mathematical formulae are similar. All other empirical QSPR models that use more complicated molecular structure descriptors fail to provide satisfactory prediction. The molecular structure descriptors in the more complicated QSPR models are empirically related to skin permeation. The mechanism on how these descriptors affect transdermal permeation is not clear. Mathematically it is an ill-defined approach to use many colinearly related parameters rather than fewer independent parameters in multi-linear regression.

  10. Bayesian Age-Period-Cohort Modeling and Prediction - BAMP

    Directory of Open Access Journals (Sweden)

    Volker J. Schmid

    2007-10-01

    Full Text Available The software package BAMP provides a method of analyzing incidence or mortality data on the Lexis diagram, using a Bayesian version of an age-period-cohort model. A hierarchical model is assumed with a binomial model in the first-stage. As smoothing priors for the age, period and cohort parameters random walks of first and second order, with and without an additional unstructured component are available. Unstructured heterogeneity can also be included in the model. In order to evaluate the model fit, posterior deviance, DIC and predictive deviances are computed. By projecting the random walk prior into the future, future death rates can be predicted.

  11. Validation of a tuber blight (Phytophthora infestans) prediction model

    Science.gov (United States)

    Potato tuber blight caused by Phytophthora infestans accounts for significant losses in storage. There is limited published quantitative data on predicting tuber blight. We validated a tuber blight prediction model developed in New York with cultivars Allegany, NY 101, and Katahdin using independent...

  12. Prediction of bypass transition with differential Reynolds stress models

    NARCIS (Netherlands)

    Westin, K.J.A.; Henkes, R.A.W.M.

    1998-01-01

    Boundary layer transition induced by high levels of free stream turbulence (FSl), so called bypass transition, can not be predicted with conventional stability calculations (e.g. the en-method). The use of turbulence models for transition prediction has shown some success for this type of flows, and

  13. Prediction Models of Free-Field Vibrations from Railway Traffic

    DEFF Research Database (Denmark)

    Malmborg, Jens; Persson, Kent; Persson, Peter

    2017-01-01

    and railways close to where people work and live. Annoyance from traffic-induced vibrations and noise is expected to be a growing issue. To predict the level of vibration and noise in buildings caused by railway and road traffic, calculation models are needed. In the present paper, a simplified prediction...

  14. A new ensemble model for short term wind power prediction

    DEFF Research Database (Denmark)

    Madsen, Henrik; Albu, Razvan-Daniel; Felea, Ioan

    2012-01-01

    As the objective of this study, a non-linear ensemble system is used to develop a new model for predicting wind speed in short-term time scale. Short-term wind power prediction becomes an extremely important field of research for the energy sector. Regardless of the recent advancements in the re-search...

  15. Space Weather: Measurements, Models and Predictions

    Science.gov (United States)

    2014-03-21

    and record high levels of cosmic ray flux. There were broad-ranging terrestrial responses to this inactivity of the Sun. BC was involved in the...techniques for converting from one coordinate system (e.g., the invariant coordinate system used for the model) to another (e.g., the latitude- radius

  16. Monotone models for prediction in data mining

    NARCIS (Netherlands)

    Velikova, M.V.

    2006-01-01

    This dissertation studies the incorporation of monotonicity constraints as a type of domain knowledge into a data mining process. Monotonicity constraints are enforced at two stages¿data preparation and data modeling. The main contributions of the research are a novel procedure to test the degree of

  17. Predicting Magazine Audiences with a Loglinear Model.

    Science.gov (United States)

    1987-07-01

    important use of e.d. estimates is in media selection ( Aaker 1975; Lee 1962, 1963; Little and Lodish 1969). All advertising campaigns have a budget. It...N.Z. Listener 6061 39.0 4 0 22 References Aaker , D.A. (1975), "ADMOD:An Advertising Decision Model," Journal of Marketing Research, February, 37-45

  18. Trophic Niche in a Raptor Species: The Relationship between Diet Diversity, Habitat Diversity and Territory Quality.

    Science.gov (United States)

    Navarro-López, Juan; Fargallo, Juan Antonio

    2015-01-01

    Recent research reports that many populations of species showing a wide trophic niche (generalists) are made up of both generalist individuals and individuals with a narrow trophic niche (specialists), suggesting trophic specializations at an individual level. If true, foraging strategies should be associated with individual quality and fitness. Optimal foraging theory predicts that individuals will select the most favourable habitats for feeding. In addition, the "landscape heterogeneity hypothesis" predicts a higher number of species in more diverse landscapes. Thus, it can be predicted that individuals with a wider realized trophic niche should have foraging territories with greater habitat diversity, suggesting that foraging strategies, territory quality and habitat diversity are inter-correlated. This was tested for a population of common kestrels Falco tinnunculus. Diet diversity, territory occupancy (as a measure of territory quality) and habitat diversity of territories were measured over an 8-year period. Our results show that: 1) territory quality was quadratically correlated with habitat diversity, with the best territories being the least and most diverse; 2) diet diversity was not correlated with territory quality; and 3) diet diversity was negatively correlated with landscape heterogeneity. Our study suggests that niche generalist foraging strategies are based on an active search for different prey species within or between habitats rather than on the selection of territories with high habitat diversity.

  19. Trophic Niche in a Raptor Species: The Relationship between Diet Diversity, Habitat Diversity and Territory Quality.

    Directory of Open Access Journals (Sweden)

    Juan Navarro-López

    Full Text Available Recent research reports that many populations of species showing a wide trophic niche (generalists are made up of both generalist individuals and individuals with a narrow trophic niche (specialists, suggesting trophic specializations at an individual level. If true, foraging strategies should be associated with individual quality and fitness. Optimal foraging theory predicts that individuals will select the most favourable habitats for feeding. In addition, the "landscape heterogeneity hypothesis" predicts a higher number of species in more diverse landscapes. Thus, it can be predicted that individuals with a wider realized trophic niche should have foraging territories with greater habitat diversity, suggesting that foraging strategies, territory quality and habitat diversity are inter-correlated. This was tested for a population of common kestrels Falco tinnunculus. Diet diversity, territory occupancy (as a measure of territory quality and habitat diversity of territories were measured over an 8-year period. Our results show that: 1 territory quality was quadratically correlated with habitat diversity, with the best territories being the least and most diverse; 2 diet diversity was not correlated with territory quality; and 3 diet diversity was negatively correlated with landscape heterogeneity. Our study suggests that niche generalist foraging strategies are based on an active search for different prey species within or between habitats rather than on the selection of territories with high habitat diversity.

  20. Scanpath Based N-Gram Models for Predicting Reading Behavior

    DEFF Research Database (Denmark)

    Mishra, Abhijit; Bhattacharyya, Pushpak; Carl, Michael

    2013-01-01

    Predicting reading behavior is a difficult task. Reading behavior depends on various linguistic factors (e.g. sentence length, structural complexity etc.) and other factors (e.g individual's reading style, age etc.). Ideally, a reading model should be similar to a language model where the model i...

  1. Better predictions when models are wrong or underspecified

    NARCIS (Netherlands)

    Ommen, Matthijs van

    2015-01-01

    Many statistical methods rely on models of reality in order to learn from data and to make predictions about future data. By necessity, these models usually do not match reality exactly, but are either wrong (none of the hypotheses in the model provides an accurate description of reality) or undersp

  2. A biogeochemical model of Lake Pusiano (North Italy and its use in the predictability of phytoplankton blooms: first preliminary results

    Directory of Open Access Journals (Sweden)

    Alessandro OGGIONI

    2006-02-01

    Full Text Available This study reports the first preliminary results of the DYRESM-CAEDYM model application to a mid size sub-alpine lake (Lake Pusiano North Italy. The in-lake modelling is a part of a more general project called Pusiano Integrated Lake/Catchment project (PILE whose final goal is to understand the hydrological and trophic relationship between lake and catchment, supporting the restoration plan of the lake through field data analysis and numerical models. DYRESM is a 1D-3D hydrodynamics model for predicting the vertical profile of temperature, salinity and density. CAEDYM is multi-component ecological model, used here as a phytoplankton-zooplankton processes based model, which includes algorithms to simulate the nutrient cycles within the water column as well as the air-water gas exchanges and the water-sediments fluxes. The first results of the hydrodynamics simulations underline the capability of the model to accurately simulate the surface temperature seasonal trend and the thermal gradient whereas, during summer stratification, the model underestimates the bottom temperature of around 2 °C. The ecological model describes the epilimnetic reactive phosphorus (PO4 depletion (due to the phytoplankton uptake and the increase in PO4 concentrations in the deepest layers of the lake (due to the mineralization processes and the sediments release. In terms of phytoplankton dynamics the model accounts for the Planktothrix rubescens dominance during the whole season, whereas it seems to underestimate the peak in primary production related to both the simulated algal groups (P. rubescens and the rest of the other species aggregated in a single class. The future aims of the project are to complete the model parameterization and to connect the in-lake and the catchment modelling in order to gain an integrated view of the lake-catchment ecosystem as well as to develop a three dimensional model of the lake.

  3. Ontogenetic, spatial and temporal variation in trophic level and diet of Chukchi Sea fishes

    Science.gov (United States)

    Marsh, Jennifer M.; Mueter, Franz J.; Iken, Katrin; Danielson, Seth

    2017-01-01

    Climate warming and increasing development are expected to alter the ecosystem of the Chukchi Sea, including its fish communities. As a component of the Arctic Ecosystem Integrated Survey, we assessed the ontogenetic, spatial and temporal variability of the trophic level and diet of key fish species in the Chukchi Sea using N and C stable isotopes. During August and September of 2012 and 2013, 16 common fish species and two primary, invertebrate consumers were collected from surface, midwater and bottom trawls within the eastern Chukchi Sea. Linear mixed-effects models were used to detect possible variation in the relationship between body length and either δ13C or δ15N values among water masses and years for 13 fish species with an emphasis on Arctic cod (Boreogadus saida). We also examined the fish community isotopic niche space, trophic redundancy, and trophic separation within each water mass as measures of resiliency of the fish food web. Ontogenetic shifts in trophic level and diet were observed for most species and these changes tended to vary by water mass. As they increased in length, most fish species relied more on benthic prey with the exception of three forage fish species (walleye pollock, Gadus chalcogrammus, capelin, Mallotus villosus, and Pacific sandlance, Ammodytes hexapterus). Species that exhibited interannual differences in diet and trophic level were feeding at lower trophic levels and consumed a more pelagic diet in 2012 when zooplankton densities were higher. Fish communities occupied different isotopic niche spaces depending on water mass association. In more northerly Arctic waters, the fish community occupied the smallest isotopic niche space and relied heavily on a limited range of intermediate δ13C prey, whereas in warmer, nutrient-rich Bering Chukchi Summer Water, pelagic prey was important. In the warmest, Pacific-derived coastal water, fish consumed both benthic and pelagic prey. Examining how spatial gradients in trophic

  4. Hybrid Corporate Performance Prediction Model Considering Technical Capability

    Directory of Open Access Journals (Sweden)

    Joonhyuck Lee

    2016-07-01

    Full Text Available Many studies have tried to predict corporate performance and stock prices to enhance investment profitability using qualitative approaches such as the Delphi method. However, developments in data processing technology and machine-learning algorithms have resulted in efforts to develop quantitative prediction models in various managerial subject areas. We propose a quantitative corporate performance prediction model that applies the support vector regression (SVR algorithm to solve the problem of the overfitting of training data and can be applied to regression problems. The proposed model optimizes the SVR training parameters based on the training data, using the genetic algorithm to achieve sustainable predictability in changeable markets and managerial environments. Technology-intensive companies represent an increasing share of the total economy. The performance and stock prices of these companies are affected by their financial standing and their technological capabilities. Therefore, we apply both financial indicators and technical indicators to establish the proposed prediction model. Here, we use time series data, including financial, patent, and corporate performance information of 44 electronic and IT companies. Then, we predict the performance of these companies as an empirical verification of the prediction performance of the proposed model.

  5. A Multistep Chaotic Model for Municipal Solid Waste Generation Prediction.

    Science.gov (United States)

    Song, Jingwei; He, Jiaying

    2014-08-01

    In this study, a univariate local chaotic model is proposed to make one-step and multistep forecasts for daily municipal solid waste (MSW) generation in Seattle, Washington. For MSW generation prediction with long history data, this forecasting model was created based on a nonlinear dynamic method called phase-space reconstruction. Compared with other nonlinear predictive models, such as artificial neural network (ANN) and partial least square-support vector machine (PLS-SVM), and a commonly used linear seasonal autoregressive integrated moving average (sARIMA) model, this method has demonstrated better prediction accuracy from 1-step ahead prediction to 14-step ahead prediction assessed by both mean absolute percentage error (MAPE) and root mean square error (RMSE). Max error, MAPE, and RMSE show that chaotic models were more reliable than the other three models. As chaotic models do not involve random walk, their performance does not vary while ANN and PLS-SVM make different forecasts in each trial. Moreover, this chaotic model was less time consuming than ANN and PLS-SVM models.

  6. Using Pareto points for model identification in predictive toxicology.

    Science.gov (United States)

    Palczewska, Anna; Neagu, Daniel; Ridley, Mick

    2013-03-22

    : Predictive toxicology is concerned with the development of models that are able to predict the toxicity of chemicals. A reliable prediction of toxic effects of chemicals in living systems is highly desirable in cosmetics, drug design or food protection to speed up the process of chemical compound discovery while reducing the need for lab tests. There is an extensive literature associated with the best practice of model generation and data integration but management and automated identification of relevant models from available collections of models is still an open problem. Currently, the decision on which model should be used for a new chemical compound is left to users. This paper intends to initiate the discussion on automated model identification. We present an algorithm, based on Pareto optimality, which mines model collections and identifies a model that offers a reliable prediction for a new chemical compound. The performance of this new approach is verified for two endpoints: IGC50 and LogP. The results show a great potential for automated model identification methods in predictive toxicology.

  7. On the Predictiveness of Single-Field Inflationary Models

    CERN Document Server

    Burgess, C.P.; Trott, Michael

    2014-01-01

    We re-examine the predictiveness of single-field inflationary models and discuss how an unknown UV completion can complicate determining inflationary model parameters from observations, even from precision measurements. Besides the usual naturalness issues associated with having a shallow inflationary potential, we describe another issue for inflation, namely, unknown UV physics modifies the running of Standard Model (SM) parameters and thereby introduces uncertainty into the potential inflationary predictions. We illustrate this point using the minimal Higgs Inflationary scenario, which is arguably the most predictive single-field model on the market, because its predictions for $A_s$, $r$ and $n_s$ are made using only one new free parameter beyond those measured in particle physics experiments, and run up to the inflationary regime. We find that this issue can already have observable effects. At the same time, this UV-parameter dependence in the Renormalization Group allows Higgs Inflation to occur (in prin...

  8. A Composite Model Predictive Control Strategy for Furnaces

    Institute of Scientific and Technical Information of China (English)

    Hao Zang; Hongguang Li; Jingwen Huang; Jia Wang

    2014-01-01

    Tube furnaces are essential and primary energy intensive facilities in petrochemical plants. Operational optimi-zation of furnaces could not only help to improve product quality but also benefit to reduce energy consumption and exhaust emission. Inspired by this idea, this paper presents a composite model predictive control (CMPC) strategy, which, taking advantage of distributed model predictive control architectures, combines tracking nonlinear model predictive control and economic nonlinear model predictive control metrics to keep process running smoothly and optimize operational conditions. The control ers connected with two kinds of communi-cation networks are easy to organize and maintain, and stable to process interferences. A fast solution algorithm combining interior point solvers and Newton's method is accommodated to the CMPC realization, with reason-able CPU computing time and suitable online applications. Simulation for industrial case demonstrates that the proposed approach can ensure stable operations of furnaces, improve heat efficiency, and reduce the emission effectively.

  9. Submission Form for Peer-Reviewed Cancer Risk Prediction Models

    Science.gov (United States)

    If you have information about a peer-reviewd cancer risk prediction model that you would like to be considered for inclusion on this list, submit as much information as possible through the form on this page.

  10. ACCIDENT PREDICTION MODELS FOR UNSIGNALISED URBAN JUNCTIONS IN GHANA

    Directory of Open Access Journals (Sweden)

    Mohammed SALIFU, MSc., PhD, MIHT, MGhIE

    2004-01-01

    The accident prediction models developed have a potentially wide area of application and their systematic use is likely to improve considerably the quality and delivery of the engineering aspects of accident mitigation and prevention in Ghana.

  11. Using a Prediction Model to Manage Cyber Security Threats

    National Research Council Canada - National Science Library

    Jaganathan, Venkatesh; Cherurveettil, Priyesh; Muthu Sivashanmugam, Premapriya

    2015-01-01

    .... The cost impact due to worms, viruses, or other malicious software is significant. This paper proposes a mathematical model to predict the impact of an attack based on significant factors that influence cyber security...

  12. Development of a multi-year climate prediction model | Alexander ...

    African Journals Online (AJOL)

    Development of a multi-year climate prediction model. ... The available water resources in Southern Africa are rapidly approaching the limits of economic exploitation. ... that could be attributed to climate change arising from human activities.

  13. Compensatory versus noncompensatory models for predicting consumer preferences

    Directory of Open Access Journals (Sweden)

    Anja Dieckmann

    2009-04-01

    Full Text Available Standard preference models in consumer research assume that people weigh and add all attributes of the available options to derive a decision, while there is growing evidence for the use of simplifying heuristics. Recently, a greedoid algorithm has been developed (Yee, Dahan, Hauser and Orlin, 2007; Kohli and Jedidi, 2007 to model lexicographic heuristics from preference data. We compare predictive accuracies of the greedoid approach and standard conjoint analysis in an online study with a rating and a ranking task. The lexicographic model derived from the greedoid algorithm was better at predicting ranking compared to rating data, but overall, it achieved lower predictive accuracy for hold-out data than the compensatory model estimated by conjoint analysis. However, a considerable minority of participants was better predicted by lexicographic strategies. We conclude that the new algorithm will not replace standard tools for analyzing preferences, but can boost the study of situational and individual differences in preferential choice processes.

  14. Determining the trophic guilds of fishes and macroinvertebrates in a seagrass food web

    Science.gov (United States)

    Luczkovich, J.J.; Ward, G.P.; Johnson, J.C.; Christian, R.R.; Baird, D.; Neckles, H.; Rizzo, W.M.

    2002-01-01

    We established trophic guilds of macroinvertebrate and fish taxa using correspondence analysis and a hierarchical clustering strategy for a seagrass food web in winter in the northeastern Gulf of Mexico. To create the diet matrix, we characterized the trophic linkages of macroinvertebrate and fish taxa present in Halodule wrightii seagrass habitat areas within the St. Marks National Wildlife Refuge (Florida) using binary data, combining dietary links obtained from relevant literature for macroinvertebrates with stomach analysis of common fishes collected during January and February of 1994. Heirarchical average-linkage cluster analysis of the 73 taxa of fishes and macroinvertebrates in the diet matrix yielded 14 clusters with diet similarity ??? 0.60. We then used correspondence analysis with three factors to jointly plot the coordinates of the consumers (identified by cluster membership) and of the 33 food sources. Correspondence analysis served as a visualization tool for assigning each taxon to one of eight trophic guilds: herbivores, detritivores, suspension feeders, omnivores, molluscivores, meiobenthos consumers, macrobenthos consumers, and piscivores. These trophic groups, cross-classified with major taxonomic groups, were further used to develop consumer compartments in a network analysis model of carbon flow in this seagrass ecosystem. The method presented here should greatly improve the development of future network models of food webs by providing an objective procedure for aggregating trophic groups.

  15. Haskell financial data modeling and predictive analytics

    CERN Document Server

    Ryzhov, Pavel

    2013-01-01

    This book is a hands-on guide that teaches readers how to use Haskell's tools and libraries to analyze data from real-world sources in an easy-to-understand manner.This book is great for developers who are new to financial data modeling using Haskell. A basic knowledge of functional programming is not required but will be useful. An interest in high frequency finance is essential.

  16. Mesoscale Wind Predictions for Wave Model Evaluation

    Science.gov (United States)

    2016-06-07

    N0001400WX20041(B) http://www.nrlmry.navy.mil LONG TERM GOALS The long-term goal is to demonstrate the significance and importance of high...ocean waves by an appropriate wave model. OBJECTIVES The main objectives of this project are to: 1. Build the infrastructure to generate the...temperature for all COAMPS grids at the resolution of each of these grids. These analyses are important for the proper 2 specification of the lower

  17. Tissue turnover rates and isotopic trophic discrimination factors in the endothermic teleost, pacific bluefin tuna (Thunnus orientalis.

    Directory of Open Access Journals (Sweden)

    Daniel J Madigan

    Full Text Available Stable isotope analysis (SIA of highly migratory marine pelagic animals can improve understanding of their migratory patterns and trophic ecology. However, accurate interpretation of isotopic analyses relies on knowledge of isotope turnover rates and tissue-diet isotope discrimination factors. Laboratory-derived turnover rates and discrimination factors have been difficult to obtain due to the challenges of maintaining these species in captivity. We conducted a study to determine tissue- (white muscle and liver and isotope- (nitrogen and carbon specific turnover rates and trophic discrimination factors (TDFs using archived tissues from captive Pacific bluefin tuna (PBFT, Thunnus orientalis, 1-2914 days after a diet shift in captivity. Half-life values for (15N turnover in white muscle and liver were 167 and 86 days, and for (13C were 255 and 162 days, respectively. TDFs for white muscle and liver were 1.9 and 1.1‰ for δ(15N and 1.8 and 1.2‰ for δ(13C, respectively. Our results demonstrate that turnover of (15N and (13C in bluefin tuna tissues is well described by a single compartment first-order kinetics model. We report variability in turnover rates between tissue types and their isotope dynamics, and hypothesize that metabolic processes play a large role in turnover of nitrogen and carbon in PBFT white muscle and liver tissues. (15N in white muscle tissue showed the most predictable change with diet over time, suggesting that white muscle δ(15N data may provide the most reliable inferences for diet and migration studies using stable isotopes in wild fish. These results allow more accurate interpretation of field data and dramatically improve our ability to use stable isotope data from wild tunas to better understand their migration patterns and trophic ecology.

  18. Modeling Seizure Self-Prediction: An E-Diary Study

    Science.gov (United States)

    Haut, Sheryl R.; Hall, Charles B.; Borkowski, Thomas; Tennen, Howard; Lipton, Richard B.

    2013-01-01

    Purpose A subset of patients with epilepsy successfully self-predicted seizures in a paper diary study. We conducted an e-diary study to ensure that prediction precedes seizures, and to characterize the prodromal features and time windows that underlie self-prediction. Methods Subjects 18 or older with LRE and ≥3 seizures/month maintained an e-diary, reporting AM/PM data daily, including mood, premonitory symptoms, and all seizures. Self-prediction was rated by, “How likely are you to experience a seizure [time frame]”? Five choices ranged from almost certain (>95% chance) to very unlikely. Relative odds of seizure (OR) within time frames was examined using Poisson models with log normal random effects to adjust for multiple observations. Key Findings Nineteen subjects reported 244 eligible seizures. OR for prediction choices within 6hrs was as high as 9.31 (1.92,45.23) for “almost certain”. Prediction was most robust within 6hrs of diary entry, and remained significant up to 12hrs. For 9 best predictors, average sensitivity was 50%. Older age contributed to successful self-prediction, and self-prediction appeared to be driven by mood and premonitory symptoms. In multivariate modeling of seizure occurrence, self-prediction (2.84; 1.68,4.81), favorable change in mood (0.82; 0.67,0.99) and number of premonitory symptoms (1,11; 1.00,1.24) were significant. Significance Some persons with epilepsy can self-predict seizures. In these individuals, the odds of a seizure following a positive prediction are high. Predictions were robust, not attributable to recall bias, and were related to self awareness of mood and premonitory features. The 6-hour prediction window is suitable for the development of pre-emptive therapy. PMID:24111898

  19. Personalized Predictive Modeling and Risk Factor Identification using Patient Similarity.

    Science.gov (United States)

    Ng, Kenney; Sun, Jimeng; Hu, Jianying; Wang, Fei

    2015-01-01

    Personalized predictive models are customized for an individual patient and trained using information from similar patients. Compared to global models trained on all patients, they have the potential to produce more accurate risk scores and capture more relevant risk factors for individual patients. This paper presents an approach for building personalized predictive models and generating personalized risk factor profiles. A locally supervised metric learning (LSML) similarity measure is trained for diabetes onset and used to find clinically similar patients. Personalized risk profiles are created by analyzing the parameters of the trained personalized logistic regression models. A 15,000 patient data set, derived from electronic health records, is used to evaluate the approach. The predictive results show that the personalized models can outperform the global model. Cluster analysis of the risk profiles show groups of patients with similar risk factors, differences in the top risk factors for different groups of patients and differences between the individual and global risk factors.

  20. Model predictive control of P-time event graphs

    Science.gov (United States)

    Hamri, H.; Kara, R.; Amari, S.

    2016-12-01

    This paper deals with model predictive control of discrete event systems modelled by P-time event graphs. First, the model is obtained by using the dater evolution model written in the standard algebra. Then, for the control law, we used the finite-horizon model predictive control. For the closed-loop control, we used the infinite-horizon model predictive control (IH-MPC). The latter is an approach that calculates static feedback gains which allows the stability of the closed-loop system while respecting the constraints on the control vector. The problem of IH-MPC is formulated as a linear convex programming subject to a linear matrix inequality problem. Finally, the proposed methodology is applied to a transportation system.

  1. Prediction of cloud droplet number in a general circulation model

    Energy Technology Data Exchange (ETDEWEB)

    Ghan, S.J.; Leung, L.R. [Pacific Northwest National Lab., Richland, WA (United States)

    1996-04-01

    We have applied the Colorado State University Regional Atmospheric Modeling System (RAMS) bulk cloud microphysics parameterization to the treatment of stratiform clouds in the National Center for Atmospheric Research Community Climate Model (CCM2). The RAMS predicts mass concentrations of cloud water, cloud ice, rain and snow, and number concnetration of ice. We have introduced the droplet number conservation equation to predict droplet number and it`s dependence on aerosols.

  2. Using connectome-based predictive modeling to predict individual behavior from brain connectivity.

    Science.gov (United States)

    Shen, Xilin; Finn, Emily S; Scheinost, Dustin; Rosenberg, Monica D; Chun, Marvin M; Papademetris, Xenophon; Constable, R Todd

    2017-03-01

    Neuroimaging is a fast-developing research area in which anatomical and functional images of human brains are collected using techniques such as functional magnetic resonance imaging (fMRI), diffusion tensor imaging (DTI), and electroencephalography (EEG). Technical advances and large-scale data sets have allowed for the development of models capable of predicting individual differences in traits and behavior using brain connectivity measures derived from neuroimaging data. Here, we present connectome-based predictive modeling (CPM), a data-driven protocol for developing predictive models of brain-behavior relationships from connectivity data using cross-validation. This protocol includes the following steps: (i) feature selection, (ii) feature summarization, (iii) model building, and (iv) assessment of prediction significance. We also include suggestions for visualizing the most predictive features (i.e., brain connections). The final result should be a generalizable model that takes brain connectivity data as input and generates predictions of behavioral measures in novel subjects, accounting for a considerable amount of the variance in these measures. It has been demonstrated that the CPM protocol performs as well as or better than many of the existing approaches in brain-behavior prediction. As CPM focuses on linear modeling and a purely data-driven approach, neuroscientists with limited or no experience in machine learning or optimization will find it easy to implement these protocols. Depending on the volume of data to be processed, the protocol can take 10-100 min for model building, 1-48 h for permutation testing, and 10-20 min for visualization of results.

  3. Mixed models for predictive modeling in actuarial science

    NARCIS (Netherlands)

    Antonio, K.; Zhang, Y.

    2012-01-01

    We start with a general discussion of mixed (also called multilevel) models and continue with illustrating specific (actuarial) applications of this type of models. Technical details on (linear, generalized, non-linear) mixed models follow: model assumptions, specifications, estimation techniques

  4. Webinar of paper 2013, Which method predicts recidivism best? A comparison of statistical, machine learning and data mining predictive models

    NARCIS (Netherlands)

    Tollenaar, N.; Van der Heijden, P.G.M.|info:eu-repo/dai/nl/073087998

    2013-01-01

    Using criminal population criminal conviction history information, prediction models are developed that predict three types of criminal recidivism: general recidivism, violent recidivism and sexual recidivism. The research question is whether prediction techniques from modern statistics, data mining

  5. Webinar of paper 2013, Which method predicts recidivism best? A comparison of statistical, machine learning and data mining predictive models

    NARCIS (Netherlands)

    Tollenaar, N.; Van der Heijden, P.G.M.

    2013-01-01

    Using criminal population criminal conviction history information, prediction models are developed that predict three types of criminal recidivism: general recidivism, violent recidivism and sexual recidivism. The research question is whether prediction techniques from modern statistics, data mining

  6. Catalytic cracking models developed for predictive control purposes

    Directory of Open Access Journals (Sweden)

    Dag Ljungqvist

    1993-04-01

    Full Text Available The paper deals with state-space modeling issues in the context of model-predictive control, with application to catalytic cracking. Emphasis is placed on model establishment, verification and online adjustment. Both the Fluid Catalytic Cracking (FCC and the Residual Catalytic Cracking (RCC units are discussed. Catalytic cracking units involve complex interactive processes which are difficult to operate and control in an economically optimal way. The strong nonlinearities of the FCC process mean that the control calculation should be based on a nonlinear model with the relevant constraints included. However, the model can be simple compared to the complexity of the catalytic cracking plant. Model validity is ensured by a robust online model adjustment strategy. Model-predictive control schemes based on linear convolution models have been successfully applied to the supervisory dynamic control of catalytic cracking units, and the control can be further improved by the SSPC scheme.

  7. Technical note: A linear model for predicting δ13 Cprotein.

    Science.gov (United States)

    Pestle, William J; Hubbe, Mark; Smith, Erin K; Stevenson, Joseph M

    2015-08-01

    Development of a model for the prediction of δ(13) Cprotein from δ(13) Ccollagen and Δ(13) Cap-co . Model-generated values could, in turn, serve as "consumer" inputs for multisource mixture modeling of paleodiet. Linear regression analysis of previously published controlled diet data facilitated the development of a mathematical model for predicting δ(13) Cprotein (and an experimentally generated error term) from isotopic data routinely generated during the analysis of osseous remains (δ(13) Cco and Δ(13) Cap-co ). Regression analysis resulted in a two-term linear model (δ(13) Cprotein (%) = (0.78 × δ(13) Cco ) - (0.58× Δ(13) Cap-co ) - 4.7), possessing a high R-value of 0.93 (r(2)  = 0.86, P < 0.01), and experimentally generated error terms of ±1.9% for any predicted individual value of δ(13) Cprotein . This model was tested using isotopic data from Formative Period individuals from northern Chile's Atacama Desert. The model presented here appears to hold significant potential for the prediction of the carbon isotope signature of dietary protein using only such data as is routinely generated in the course of stable isotope analysis of human osseous remains. These predicted values are ideal for use in multisource mixture modeling of dietary protein source contribution. © 2015 Wiley Periodicals, Inc.

  8. Traffic Prediction Scheme based on Chaotic Models in Wireless Networks

    Directory of Open Access Journals (Sweden)

    Xiangrong Feng

    2013-09-01

    Full Text Available Based on the local support vector algorithm of chaotic time series analysis, the Hannan-Quinn information criterion and SAX symbolization are introduced. Then a novel prediction algorithm is proposed, which is successfully applied to the prediction of wireless network traffic. For the correct prediction problems of short-term flow with smaller data set size, the weakness of the algorithms during model construction is analyzed by study and comparison to LDK prediction algorithm. It is verified the Hannan-Quinn information principle can be used to calculate the number of neighbor points to replace pervious empirical method, which uses the number of neighbor points to acquire more accurate prediction model. Finally, actual flow data is applied to confirm the accuracy rate of the proposed algorithm LSDHQ. It is testified by our experiments that it also has higher performance in adaptability than that of LSDHQ algorithm.

  9. Toward a predictive model for elastomer seals

    Science.gov (United States)

    Molinari, Nicola; Khawaja, Musab; Sutton, Adrian; Mostofi, Arash

    Nitrile butadiene rubber (NBR) and hydrogenated-NBR (HNBR) are widely used elastomers, especially as seals in oil and gas applications. During exposure to well-hole conditions, ingress of gases causes degradation of performance, including mechanical failure. We use computer simulations to investigate this problem at two different length and time-scales. First, we study the solubility of gases in the elastomer using a chemically-inspired description of HNBR based on the OPLS all-atom force-field. Starting with a model of NBR, C=C double bonds are saturated with either hydrogen or intramolecular cross-links, mimicking the hydrogenation of NBR to form HNBR. We validate against trends for the mass density and glass transition temperature for HNBR as a function of cross-link density, and for NBR as a function of the fraction of acrylonitrile in the copolymer. Second, we study mechanical behaviour using a coarse-grained model that overcomes some of the length and time-scale limitations of an all-atom approach. Nanoparticle fillers added to the elastomer matrix to enhance mechanical response are also included. Our initial focus is on understanding the mechanical properties at the elevated temperatures and pressures experienced in well-hole conditions.

  10. Using a Prediction Model to Manage Cyber Security Threats

    Directory of Open Access Journals (Sweden)

    Venkatesh Jaganathan

    2015-01-01

    Full Text Available Cyber-attacks are an important issue faced by all organizations. Securing information systems is critical. Organizations should be able to understand the ecosystem and predict attacks. Predicting attacks quantitatively should be part of risk management. The cost impact due to worms, viruses, or other malicious software is significant. This paper proposes a mathematical model to predict the impact of an attack based on significant factors that influence cyber security. This model also considers the environmental information required. It is generalized and can be customized to the needs of the individual organization.

  11. Using a Prediction Model to Manage Cyber Security Threats.

    Science.gov (United States)

    Jaganathan, Venkatesh; Cherurveettil, Priyesh; Muthu Sivashanmugam, Premapriya

    2015-01-01

    Cyber-attacks are an important issue faced by all organizations. Securing information systems is critical. Organizations should be able to understand the ecosystem and predict attacks. Predicting attacks quantitatively should be part of risk management. The cost impact due to worms, viruses, or other malicious software is significant. This paper proposes a mathematical model to predict the impact of an attack based on significant factors that influence cyber security. This model also considers the environmental information required. It is generalized and can be customized to the needs of the individual organization.

  12. Active diagnosis of hybrid systems - A model predictive approach

    DEFF Research Database (Denmark)

    Tabatabaeipour, Seyed Mojtaba; Ravn, Anders P.; Izadi-Zamanabadi, Roozbeh;

    2009-01-01

    A method for active diagnosis of hybrid systems is proposed. The main idea is to predict the future output of both normal and faulty model of the system; then at each time step an optimization problem is solved with the objective of maximizing the difference between the predicted normal and faulty...... outputs constrained by tolerable performance requirements. As in standard model predictive control, the first element of the optimal input is applied to the system and the whole procedure is repeated until the fault is detected by a passive diagnoser. It is demonstrated how the generated excitation signal...

  13. Aero-acoustic noise of wind turbines. Noise prediction models

    Energy Technology Data Exchange (ETDEWEB)

    Maribo Pedersen, B. [ed.

    1997-12-31

    Semi-empirical and CAA (Computational AeroAcoustics) noise prediction techniques are the subject of this expert meeting. The meeting presents and discusses models and methods. The meeting may provide answers to the following questions: What Noise sources are the most important? How are the sources best modeled? What needs to be done to do better predictions? Does it boil down to correct prediction of the unsteady aerodynamics around the rotor? Or is the difficult part to convert the aerodynamics into acoustics? (LN)

  14. Model Predictive Control of a Wave Energy Converter

    DEFF Research Database (Denmark)

    Andersen, Palle; Pedersen, Tom Søndergård; Nielsen, Kirsten Mølgaard;

    2015-01-01

    In this paper reactive control and Model Predictive Control (MPC) for a Wave Energy Converter (WEC) are compared. The analysis is based on a WEC from Wave Star A/S designed as a point absorber. The model predictive controller uses wave models based on the dominating sea states combined with a model...... connecting undisturbed wave sequences to sequences of torque. Losses in the conversion from mechanical to electrical power are taken into account in two ways. Conventional reactive controllers are tuned for each sea state with the assumption that the converter has the same efficiency back and forth. MPC...

  15. Modelling and prediction of non-stationary optical turbulence behaviour

    Science.gov (United States)

    Doelman, Niek; Osborn, James

    2016-07-01

    There is a strong need to model the temporal fluctuations in turbulence parameters, for instance for scheduling, simulation and prediction purposes. This paper aims at modelling the dynamic behaviour of the turbulence coherence length r0, utilising measurement data from the Stereo-SCIDAR instrument installed at the Isaac Newton Telescope at La Palma. Based on an estimate of the power spectral density function, a low order stochastic model to capture the temporal variability of r0 is proposed. The impact of this type of stochastic model on the prediction of the coherence length behaviour is shown.

  16. Research on Drag Torque Prediction Model for the Wet Clutches

    Institute of Scientific and Technical Information of China (English)

    2008-01-01

    Considering the surface tension effect and centrifugal effect, a mathematical model based on Reynolds equation for predicting the drag torque of disengage wet clutches is presented. The model indicates that the equivalent radius is a function of clutch speed and flow rate. The drag torque achieves its peak at a critical speed. Above this speed, drag torque drops due to the shrinking of the oil film. The model also points out that viscosity and flow rate effects on drag torque. Experimental results indicate that the model is reasonable and it performs well for predicting the drag torque peak.

  17. Model output statistics applied to wind power prediction

    Energy Technology Data Exchange (ETDEWEB)

    Joensen, A.; Giebel, G.; Landberg, L. [Risoe National Lab., Roskilde (Denmark); Madsen, H.; Nielsen, H.A. [The Technical Univ. of Denmark, Dept. of Mathematical Modelling, Lyngby (Denmark)

    1999-03-01

    Being able to predict the output of a wind farm online for a day or two in advance has significant advantages for utilities, such as better possibility to schedule fossil fuelled power plants and a better position on electricity spot markets. In this paper prediction methods based on Numerical Weather Prediction (NWP) models are considered. The spatial resolution used in NWP models implies that these predictions are not valid locally at a specific wind farm. Furthermore, due to the non-stationary nature and complexity of the processes in the atmosphere, and occasional changes of NWP models, the deviation between the predicted and the measured wind will be time dependent. If observational data is available, and if the deviation between the predictions and the observations exhibits systematic behavior, this should be corrected for; if statistical methods are used, this approaches is usually referred to as MOS (Model Output Statistics). The influence of atmospheric turbulence intensity, topography, prediction horizon length and auto-correlation of wind speed and power is considered, and to take the time-variations into account, adaptive estimation methods are applied. Three estimation techniques are considered and compared, Extended Kalman Filtering, recursive least squares and a new modified recursive least squares algorithm. (au) EU-JOULE-3. 11 refs.

  18. Development and application of chronic disease risk prediction models.

    Science.gov (United States)

    Oh, Sun Min; Stefani, Katherine M; Kim, Hyeon Chang

    2014-07-01

    Currently, non-communicable chronic diseases are a major cause of morbidity and mortality worldwide, and a large proportion of chronic diseases are preventable through risk factor management. However, the prevention efficacy at the individual level is not yet satisfactory. Chronic disease prediction models have been developed to assist physicians and individuals in clinical decision-making. A chronic disease prediction model assesses multiple risk factors together and estimates an absolute disease risk for the individual. Accurate prediction of an individual's future risk for a certain disease enables the comparison of benefits and risks of treatment, the costs of alternative prevention strategies, and selection of the most efficient strategy for the individual. A large number of chronic disease prediction models, especially targeting cardiovascular diseases and cancers, have been suggested, and some of them have been adopted in the clinical practice guidelines and recommendations of many countries. Although few chronic disease prediction tools have been suggested in the Korean population, their clinical utility is not as high as expected. This article reviews methodologies that are commonly used for developing and evaluating a chronic disease prediction model and discusses the current status of chronic disease prediction in Korea.

  19. Evaluation of burst pressure prediction models for line pipes

    Energy Technology Data Exchange (ETDEWEB)

    Zhu, Xian-Kui, E-mail: zhux@battelle.org [Battelle Memorial Institute, 505 King Avenue, Columbus, OH 43201 (United States); Leis, Brian N. [Battelle Memorial Institute, 505 King Avenue, Columbus, OH 43201 (United States)

    2012-01-15

    Accurate prediction of burst pressure plays a central role in engineering design and integrity assessment of oil and gas pipelines. Theoretical and empirical solutions for such prediction are evaluated in this paper relative to a burst pressure database comprising more than 100 tests covering a variety of pipeline steel grades and pipe sizes. Solutions considered include three based on plasticity theory for the end-capped, thin-walled, defect-free line pipe subjected to internal pressure in terms of the Tresca, von Mises, and ZL (or Zhu-Leis) criteria, one based on a cylindrical instability stress (CIS) concept, and a large group of analytical and empirical models previously evaluated by Law and Bowie (International Journal of Pressure Vessels and Piping, 84, 2007: 487-492). It is found that these models can be categorized into either a Tresca-family or a von Mises-family of solutions, except for those due to Margetson and Zhu-Leis models. The viability of predictions is measured via statistical analyses in terms of a mean error and its standard deviation. Consistent with an independent parallel evaluation using another large database, the Zhu-Leis solution is found best for predicting burst pressure, including consideration of strain hardening effects, while the Tresca strength solutions including Barlow, Maximum shear stress, Turner, and the ASME boiler code provide reasonably good predictions for the class of line-pipe steels with intermediate strain hardening response. - Highlights: Black-Right-Pointing-Pointer This paper evaluates different burst pressure prediction models for line pipes. Black-Right-Pointing-Pointer The existing models are categorized into two major groups of Tresca and von Mises solutions. Black-Right-Pointing-Pointer Prediction quality of each model is assessed statistically using a large full-scale burst test database. Black-Right-Pointing-Pointer The Zhu-Leis solution is identified as the best predictive model.

  20. Outcome Prediction in Mathematical Models of Immune Response to Infection.

    Directory of Open Access Journals (Sweden)

    Manuel Mai

    Full Text Available Clinicians need to predict patient outcomes with high accuracy as early as possible after disease inception. In this manuscript, we show that patient-to-patient variability sets a fundamental limit on outcome prediction accuracy for a general class of mathematical models for the immune response to infection. However, accuracy can be increased at the expense of delayed prognosis. We investigate several systems of ordinary differential equations (ODEs that model the host immune response to a pathogen load. Advantages of systems of ODEs for investigating the immune response to infection include the ability to collect data on large numbers of 'virtual patients', each with a given set of model parameters, and obtain many time points during the course of the infection. We implement patient-to-patient variability v in the ODE models by randomly selecting the model parameters from distributions with coefficients of variation v that are centered on physiological values. We use logistic regression with one-versus-all classification to predict the discrete steady-state outcomes of the system. We find that the prediction algorithm achieves near 100% accuracy for v = 0, and the accuracy decreases with increasing v for all ODE models studied. The fact that multiple steady-state outcomes can be obtained for a given initial condition, i.e. the basins of attraction overlap in the space of initial conditions, limits the prediction accuracy for v > 0. Increasing the elapsed time of the variables used to train and test the classifier, increases the prediction accuracy, while adding explicit external noise to the ODE models decreases the prediction accuracy. Our results quantify the competition between early prognosis and high prediction accuracy that is frequently encountered by clinicians.

  1. Development of Interpretable Predictive Models for BPH and Prostate Cancer

    Science.gov (United States)

    Bermejo, Pablo; Vivo, Alicia; Tárraga, Pedro J; Rodríguez-Montes, JA

    2015-01-01

    BACKGROUND Traditional methods for deciding whether to recommend a patient for a prostate biopsy are based on cut-off levels of stand-alone markers such as prostate-specific antigen (PSA) or any of its derivatives. However, in the last decade we have seen the increasing use of predictive models that combine, in a non-linear manner, several predictives that are better able to predict prostate cancer (PC), but these fail to help the clinician to distinguish between PC and benign prostate hyperplasia (BPH) patients. We construct two new models that are capable of predicting both PC and BPH. METHODS An observational study was performed on 150 patients with PSA ≥3 ng/mL and age >50 years. We built a decision tree and a logistic regression model, validated with the leave-one-out methodology, in order to predict PC or BPH, or reject both. RESULTS Statistical dependence with PC and BPH was found for prostate volume (P-value < 0.001), PSA (P-value < 0.001), international prostate symptom score (IPSS; P-value < 0.001), digital rectal examination (DRE; P-value < 0.001), age (P-value < 0.002), antecedents (P-value < 0.006), and meat consumption (P-value < 0.08). The two predictive models that were constructed selected a subset of these, namely, volume, PSA, DRE, and IPSS, obtaining an area under the ROC curve (AUC) between 72% and 80% for both PC and BPH prediction. CONCLUSION PSA and volume together help to build predictive models that accurately distinguish among PC, BPH, and patients without any of these pathologies. Our decision tree and logistic regression models outperform the AUC obtained in the compared studies. Using these models as decision support, the number of unnecessary biopsies might be significantly reduced. PMID:25780348

  2. Model Predictive Control of Wind Turbines

    DEFF Research Database (Denmark)

    Henriksen, Lars Christian

    the need for maintenance of the wind turbine. Either way, better total-cost-of-ownership for wind turbine operators can be achieved by improved control of the wind turbines. Wind turbine control can be improved in two ways, by improving the model on which the controller bases its design or by improving......Wind turbines play a major role in the transformation from a fossil fuel based energy production to a more sustainable production of energy. Total-cost-of-ownership is an important parameter when investors decide in which energy technology they should place their capital. Modern wind turbines...... are controlled by pitching the blades and by controlling the electro-magnetic torque of the generator, thus slowing the rotation of the blades. Improved control of wind turbines, leading to reduced fatigue loads, can be exploited by using less materials in the construction of the wind turbine or by reducing...

  3. Numerical modeling capabilities to predict repository performance

    Energy Technology Data Exchange (ETDEWEB)

    1979-09-01

    This report presents a summary of current numerical modeling capabilities that are applicable to the design and performance evaluation of underground repositories for the storage of nuclear waste. The report includes codes that are available in-house, within Golder Associates and Lawrence Livermore Laboratories; as well as those that are generally available within the industry and universities. The first listing of programs are in-house codes in the subject areas of hydrology, solute transport, thermal and mechanical stress analysis, and structural geology. The second listing of programs are divided by subject into the following categories: site selection, structural geology, mine structural design, mine ventilation, hydrology, and mine design/construction/operation. These programs are not specifically designed for use in the design and evaluation of an underground repository for nuclear waste; but several or most of them may be so used.

  4. A new statistical tool to predict phenology under climate change scenarios

    NARCIS (Netherlands)

    Gienapp, P.; Hemerik, L.; Visser, M.E.

    2005-01-01

    Climate change will likely affect the phenology of trophic levels differently and thereby disrupt the phenological synchrony between predators and prey. To predict this disruption of the synchrony under different climate change scenarios, good descriptive models for the phenology of the different sp

  5. Comparison of Linear Prediction Models for Audio Signals

    Directory of Open Access Journals (Sweden)

    2009-03-01

    Full Text Available While linear prediction (LP has become immensely popular in speech modeling, it does not seem to provide a good approach for modeling audio signals. This is somewhat surprising, since a tonal signal consisting of a number of sinusoids can be perfectly predicted based on an (all-pole LP model with a model order that is twice the number of sinusoids. We provide an explanation why this result cannot simply be extrapolated to LP of audio signals. If noise is taken into account in the tonal signal model, a low-order all-pole model appears to be only appropriate when the tonal components are uniformly distributed in the Nyquist interval. Based on this observation, different alternatives to the conventional LP model can be suggested. Either the model should be changed to a pole-zero, a high-order all-pole, or a pitch prediction model, or the conventional LP model should be preceded by an appropriate frequency transform, such as a frequency warping or downsampling. By comparing these alternative LP models to the conventional LP model in terms of frequency estimation accuracy, residual spectral flatness, and perceptual frequency resolution, we obtain several new and promising approaches to LP-based audio modeling.

  6. Comparison of Linear Prediction Models for Audio Signals

    Directory of Open Access Journals (Sweden)

    van Waterschoot Toon

    2008-01-01

    Full Text Available While linear prediction (LP has become immensely popular in speech modeling, it does not seem to provide a good approach for modeling audio signals. This is somewhat surprising, since a tonal signal consisting of a number of sinusoids can be perfectly predicted based on an (all-pole LP model with a model order that is twice the number of sinusoids. We provide an explanation why this result cannot simply be extrapolated to LP of audio signals. If noise is taken into account in the tonal signal model, a low-order all-pole model appears to be only appropriate when the tonal components are uniformly distributed in the Nyquist interval. Based on this observation, different alternatives to the conventional LP model can be suggested. Either the model should be changed to a pole-zero, a high-order all-pole, or a pitch prediction model, or the conventional LP model should be preceded by an appropriate frequency transform, such as a frequency warping or downsampling. By comparing these alternative LP models to the conventional LP model in terms of frequency estimation accuracy, residual spectral flatness, and perceptual frequency resolution, we obtain several new and promising approaches to LP-based audio modeling.

  7. Hidden Markov models for prediction of protein features

    DEFF Research Database (Denmark)

    Bystroff, Christopher; Krogh, Anders

    2008-01-01

    Hidden Markov Models (HMMs) are an extremely versatile statistical representation that can be used to model any set of one-dimensional discrete symbol data. HMMs can model protein sequences in many ways, depending on what features of the protein are represented by the Markov states. For protein...... structure prediction, states have been chosen to represent either homologous sequence positions, local or secondary structure types, or transmembrane locality. The resulting models can be used to predict common ancestry, secondary or local structure, or membrane topology by applying one of the two standard...... algorithms for comparing a sequence to a model. In this chapter, we review those algorithms and discuss how HMMs have been constructed and refined for the purpose of protein structure prediction....

  8. Modelling of physical properties - databases, uncertainties and predictive power

    DEFF Research Database (Denmark)

    Gani, Rafiqul

    Physical and thermodynamic property in the form of raw data or estimated values for pure compounds and mixtures are important pre-requisites for performing tasks such as, process design, simulation and optimization; computer aided molecular/mixture (product) design; and, product-process analysis...... connectivity approach. The development of these models requires measured property data and based on them, the regression of model parameters is performed. Although this class of models is empirical by nature, they do allow extrapolation from the regressed model parameters to predict properties of chemicals...... not included in the measured data-set. Therefore, they are also considered as predictive models. The paper will highlight different issues/challenges related to the role of the databases and the mathematical and thermodynamic consistency of the measured/estimated data, the predictive nature of the developed...

  9. Modeling, Prediction, and Control of Heating Temperature for Tube Billet

    Directory of Open Access Journals (Sweden)

    Yachun Mao

    2015-01-01

    Full Text Available Annular furnaces have multivariate, nonlinear, large time lag, and cross coupling characteristics. The prediction and control of the exit temperature of a tube billet are important but difficult. We establish a prediction model for the final temperature of a tube billet through OS-ELM-DRPLS method. We address the complex production characteristics, integrate the advantages of PLS and ELM algorithms in establishing linear and nonlinear models, and consider model update and data lag. Based on the proposed model, we design a prediction control algorithm for tube billet temperature. The algorithm is validated using the practical production data of Baosteel Co., Ltd. Results show that the model achieves the precision required in industrial applications. The temperature of the tube billet can be controlled within the required temperature range through compensation control method.

  10. Predicting artificailly drained areas by means of selective model ensemble

    DEFF Research Database (Denmark)

    Møller, Anders Bjørn; Beucher, Amélie; Iversen, Bo Vangsø

    . The approaches employed include decision trees, discriminant analysis, regression models, neural networks and support vector machines amongst others. Several models are trained with each method, using variously the original soil covariates and principal components of the covariates. With a large ensemble...... out since the mid-19th century, and it has been estimated that half of the cultivated area is artificially drained (Olesen, 2009). A number of machine learning approaches can be used to predict artificially drained areas in geographic space. However, instead of choosing the most accurate model....... The study aims firstly to train a large number of models to predict the extent of artificially drained areas using various machine learning approaches. Secondly, the study will develop a method for selecting the models, which give a good prediction of artificially drained areas, when used in conjunction...

  11. Ecosystem structure and trophic analysis of Angolan fishery landings

    Directory of Open Access Journals (Sweden)

    Ronaldo Angelini

    2011-06-01

    Full Text Available Information on the mean trophic level of fishery landings in Angola and the output from a preliminary Ecopath with Ecosim (EwE model were used to examine the dynamics of the Angolan marine ecosystem. Results were compared with the nearby Namibian and South African ecosystems, which share some of the exploited fish populations. The results show that: (i The mean trophic level of Angola’s fish landings has not decreased over the years; (ii There are significant correlations between the landings of Angola, Namibia and South Africa; (iii The ecosystem attributes calculated by the EwE models for the three ecosystems were similar, and the main differences were related to the magnitude of flows and biomass; (iv The similarity among ecosystem trends for Namibia, South Africa and Angola re-emphasizes the need to continue collaborative regional studies on the fish stocks and their ecosystems. To improve the Angolan model it is necessary to gain a better understanding of plankton dynamics because plankton are essential for Sardinella spp. An expanded analysis of the gut contents of the fish species occupying Angola’s coastline is also necessary.

  12. Experimental study on prediction model for maximum rebound ratio

    Institute of Scientific and Technical Information of China (English)

    LEI Wei-dong; TENG Jun; A.HEFNY; ZHAO Jian; GUAN Jiong

    2007-01-01

    The proposed prediction model for estimating the maximum rebound ratio was applied to a field explosion test, Mandai test in Singapore.The estimated possible maximum Deak particle velocities(PPVs)were compared with the field records.Three of the four available field-recorded PPVs lie exactly below the estimated possible maximum values as expected.while the fourth available field-recorded PPV lies close to and a bit higher than the estimated maximum possible PPV The comparison results show that the predicted PPVs from the proposed prediction model for the maximum rebound ratio match the field.recorded PPVs better than those from two empirical formulae.The very good agreement between the estimated and field-recorded values validates the proposed prediction model for estimating PPV in a rock mass with a set of ipints due to application of a two dimensional compressional wave at the boundary of a tunnel or a borehole.

  13. Groundwater Level Prediction using M5 Model Trees

    Science.gov (United States)

    Nalarajan, Nitha Ayinippully; Mohandas, C.

    2015-01-01

    Groundwater is an important resource, readily available and having high economic value and social benefit. Recently, it had been considered a dependable source of uncontaminated water. During the past two decades, increased rate of extraction and other greedy human actions have resulted in the groundwater crisis, both qualitatively and quantitatively. Under prevailing circumstances, the availability of predicted groundwater levels increase the importance of this valuable resource, as an aid in the planning of groundwater resources. For this purpose, data-driven prediction models are widely used in the present day world. M5 model tree (MT) is a popular soft computing method emerging as a promising method for numeric prediction, producing understandable models. The present study discusses the groundwater level predictions using MT employing only the historical groundwater levels from a groundwater monitoring well. The results showed that MT can be successively used for forecasting groundwater levels.

  14. A Fusion Model for CPU Load Prediction in Cloud Computing

    Directory of Open Access Journals (Sweden)

    Dayu Xu

    2013-11-01

    Full Text Available Load prediction plays a key role in cost-optimal resource allocation and datacenter energy saving. In this paper, we use real-world traces from Cloud platform and propose a fusion model to forecast the future CPU loads. First, long CPU load time series data are divided into short sequences with same length from the historical data on the basis of cloud control cycle. Then we use kernel fuzzy c-means clustering algorithm to put the subsequences into different clusters. For each cluster, with current load sequence, a genetic algorithm optimized wavelet Elman neural network prediction model is exploited to predict the CPU load in next time interval. Finally, we obtain the optimal cloud computing CPU load prediction results from the cluster and its corresponding predictor with minimum forecasting error. Experimental results show that our algorithm performs better than other models reported in previous works.

  15. Modelling proteins' hidden conformations to predict antibiotic resistance

    Science.gov (United States)

    Hart, Kathryn M.; Ho, Chris M. W.; Dutta, Supratik; Gross, Michael L.; Bowman, Gregory R.

    2016-10-01

    TEM β-lactamase confers bacteria with resistance to many antibiotics and rapidly evolves activity against new drugs. However, functional changes are not easily explained by differences in crystal structures. We employ Markov state models to identify hidden conformations and explore their role in determining TEM's specificity. We integrate these models with existing drug-design tools to create a new technique, called Boltzmann docking, which better predicts TEM specificity by accounting for conformational heterogeneity. Using our MSMs, we identify hidden states whose populations correlate with activity against cefotaxime. To experimentally detect our predicted hidden states, we use rapid mass spectrometric footprinting and confirm our models' prediction that increased cefotaxime activity correlates with reduced Ω-loop flexibility. Finally, we design novel variants to stabilize the hidden cefotaximase states, and find their populations predict activity against cefotaxime in vitro and in vivo. Therefore, we expect this framework to have numerous applications in drug and protein design.

  16. A Hybrid Neural Network Prediction Model of Air Ticket Sales

    Directory of Open Access Journals (Sweden)

    Han-Chen Huang

    2013-11-01

    Full Text Available Air ticket sales revenue is an important source of revenue for travel agencies, and if future air ticket sales revenue can be accurately forecast, travel agencies will be able to advance procurement to achieve a sufficient amount of cost-effective tickets. Therefore, this study applied the Artificial Neural Network (ANN and Genetic Algorithms (GA to establish a prediction model of travel agency air ticket sales revenue. By verifying the empirical data, this study proved that the established prediction model has accurate prediction power, and MAPE (mean absolute percentage error is only 9.11%. The established model can provide business operators with reliable and efficient prediction data as a reference for operational decisions.

  17. E-commerce business model mining and prediction

    Institute of Scientific and Technical Information of China (English)

    Zhou-zhou HE; Zhong-fei ZHANG; Chun-ming CHEN; Zheng-gang WANG

    2015-01-01

    We study the problem of business model mining and prediction in the e-commerce context. Unlike most existing approaches where this is typically formulated as a regression problem or a time-series prediction problem, we take a different formulation to this problem by noting that these existing approaches fail to consider the potential relationships both among the consumers (consumer infl uence) and among the shops (competitions or collaborations). Taking this observation into consideration, we propose a new method for e-commerce business model mining and prediction, called EBMM, which combines regression with community analysis. The challenge is that the links in the network are typically not directly observed, which is addressed by applying information diffusion theory through the consumer-shop network. Extensive evaluations using Alibaba Group e-commerce data demonstrate the promise and superiority of EBMM to the state-of-the-art methods in terms of business model mining and prediction.

  18. Model for Predicting Passage of Invasive Fish Species Through Culverts

    Science.gov (United States)

    Neary, V.

    2010-12-01

    Conservation efforts to promote or inhibit fish passage include the application of simple fish passage models to determine whether an open channel flow allows passage of a given fish species. Derivations of simple fish passage models for uniform and nonuniform flow conditions are presented. For uniform flow conditions, a model equation is developed that predicts the mean-current velocity threshold in a fishway, or velocity barrier, which causes exhaustion at a given maximum distance of ascent. The derivation of a simple expression for this exhaustion-threshold (ET) passage model is presented using kinematic principles coupled with fatigue curves for threatened and endangered fish species. Mean current velocities at or above the threshold predict failure to pass. Mean current velocities below the threshold predict successful passage. The model is therefore intuitive and easily applied to predict passage or exclusion. The ET model’s simplicity comes with limitations, however, including its application only to uniform flow, which is rarely found in the field. This limitation is addressed by deriving a model that accounts for nonuniform conditions, including backwater profiles and drawdown curves. Comparison of these models with experimental data from volitional swimming studies of fish indicates reasonable performance, but limitations are still present due to the difficulty in predicting fish behavior and passage strategies that can vary among individuals and different fish species.

  19. Development of predictive models for geosmin-related taste and odor in Kansas, USA, drinking water reservoirs.

    Science.gov (United States)

    Dzialowski, Andrew R; Smith, Val H; Huggins, Donald G; Denoyelles, Frank; Lim, Niang-Choo; Baker, Debbie S; Beury, Jason H

    2009-06-01

    The presence of taste and odor compounds can greatly reduce the quality of drinking water supplies. Because the monetary costs associated with the removal of these compounds can be high, it is impractical for most facilities to continuously treat their raw water. Instead, new tools are needed to help predict when taste and odor events may be most likely to occur. Water quality data were collected between June and October in 2006-2007 from five Kansas (USA) reservoirs in order to develop predictive models for geosmin, a major taste and odor compound; two of these reservoirs were also sampled during specific taste and odor events in December 2006 and January 2007. Lake trophic state alone was not a good predictor of geosmin concentrations as the highest average geosmin concentration was observed in the reservoir with the lowest nutrient and chlorophyll a concentrations. In addition, taste and odor events were not confined to summer months; elevated geosmin concentrations were observed in several reservoirs during the winter. Growth limitation by inorganic phosphorus appeared to be the primary determinant of geosmin production by algal cells in these reservoirs.

  20. Some Remarks on CFD Drag Prediction of an Aircraft Model

    Science.gov (United States)

    Peng, S. H.; Eliasson, P.

    Observed in CFD drag predictions for the DLR-F6 aircraft model with various configurations, some issues are addressed. The emphasis is placed on the effect of turbulence modeling and grid resolution. With several different turbulence models, the predicted flow feature around the aircraft is highlighted. It is shown that the prediction of the separation bubble in the wing-body junction is closely related to the inherent modeling mechanism of turbulence production. For the configuration with an additional fairing, which has effectively removed the separation bubble, it is illustrated that the drag prediction may be altered even for attached turbulent boundary layer when different turbulence models are used. Grid sensitivity studies are performed with two groups of subsequently refined grids. It is observed that, in contrast to the lift, the drag prediction is rather sensitive to the grid refinement, as well as to the artificial diffusion added for solving the turbulence transport equation. It is demonstrated that an effective grid refinement should drive the predicted drag components monotonically and linearly converged to a finite value.

  1. Signature prediction for model-based automatic target recognition

    Science.gov (United States)

    Keydel, Eric R.; Lee, Shung W.

    1996-06-01

    The moving and stationary target recognition (MSTAR) model- based automatic target recognition (ATR) system utilizes a paradigm which matches features extracted form an unknown SAR target signature against predictions of those features generated from models of the sensing process and candidate target geometries. The candidate target geometry yielding the best match between predicted and extracted features defines the identify of the unknown target. MSTAR will extend the current model-based ATR state-of-the-art in a number of significant directions. These include: use of Bayesian techniques for evidence accrual, reasoning over target subparts, coarse-to-fine hypothesis search strategies, and explicit reasoning over target articulation, configuration, occlusion, and lay-over. These advances also imply significant technical challenges, particularly for the MSTAR feature prediction module (MPM). In addition to accurate electromagnetics, the MPM must provide traceback between input target geometry and output features, on-line target geometry manipulation, target subpart feature prediction, explicit models for local scene effects, and generation of sensitivity and uncertainty measures for the predicted features. This paper describes the MPM design which is being developed to satisfy these requirements. The overall module structure is presented, along with the specific deign elements focused on MSTAR requirements. Particular attention is paid to design elements that enable on-line prediction of features within the time constraints mandated by model-driven ATR. Finally, the current status, development schedule, and further extensions in the module design are described.

  2. Multi-model ensemble hydrologic prediction and uncertainties analysis

    Directory of Open Access Journals (Sweden)

    S. Jiang

    2014-09-01

    Full Text Available Modelling uncertainties (i.e. input errors, parameter uncertainties and model structural errors inevitably exist in hydrological prediction. A lot of recent attention has focused on these, of which input error modelling, parameter optimization and multi-model ensemble strategies are the three most popular methods to demonstrate the impacts of modelling uncertainties. In this paper the Xinanjiang model, the Hybrid rainfall–runoff model and the HYMOD model were applied to the Mishui Basin, south China, for daily streamflow ensemble simulation and uncertainty analysis. The three models were first calibrated by two parameter optimization algorithms, namely, the Shuffled Complex Evolution method (SCE-UA and the Shuffled Complex Evolution Metropolis method (SCEM-UA; next, the input uncertainty was accounted for by introducing a normally-distributed error multiplier; then, the simulation sets calculated from the three models were combined by Bayesian model averaging (BMA. The results show that both these parameter optimization algorithms generate good streamflow simulations; specifically the SCEM-UA can imply parameter uncertainty and give the posterior distribution of the parameters. Considering the precipitation input uncertainty, the streamflow simulation precision does not improve very much. While the BMA combination not only improves the streamflow prediction precision, it also gives quantitative uncertainty bounds for the simulation sets. The SCEM-UA calculated prediction interval is better than the SCE-UA calculated one. These results suggest that considering the model parameters' uncertainties and doing multi-model ensemble simulations are very practical for streamflow prediction and flood forecasting, from which more precision prediction and more reliable uncertainty bounds can be generated.

  3. Model predictive torque control with an extended prediction horizon for electrical drive systems

    Science.gov (United States)

    Wang, Fengxiang; Zhang, Zhenbin; Kennel, Ralph; Rodríguez, José

    2015-07-01

    This paper presents a model predictive torque control method for electrical drive systems. A two-step prediction horizon is achieved by considering the reduction of the torque ripples. The electromagnetic torque and the stator flux error between predicted values and the references, and an over-current protection are considered in the cost function design. The best voltage vector is selected by minimising the value of the cost function, which aims to achieve a low torque ripple in two intervals. The study is carried out experimentally. The results show that the proposed method achieves good performance in both steady and transient states.

  4. Embryo quality predictive models based on cumulus cells gene expression

    Directory of Open Access Journals (Sweden)

    Devjak R

    2016-06-01

    Full Text Available Since the introduction of in vitro fertilization (IVF in clinical practice of infertility treatment, the indicators for high quality embryos were investigated. Cumulus cells (CC have a specific gene expression profile according to the developmental potential of the oocyte they are surrounding, and therefore, specific gene expression could be used as a biomarker. The aim of our study was to combine more than one biomarker to observe improvement in prediction value of embryo development. In this study, 58 CC samples from 17 IVF patients were analyzed. This study was approved by the Republic of Slovenia National Medical Ethics Committee. Gene expression analysis [quantitative real time polymerase chain reaction (qPCR] for five genes, analyzed according to embryo quality level, was performed. Two prediction models were tested for embryo quality prediction: a binary logistic and a decision tree model. As the main outcome, gene expression levels for five genes were taken and the area under the curve (AUC for two prediction models were calculated. Among tested genes, AMHR2 and LIF showed significant expression difference between high quality and low quality embryos. These two genes were used for the construction of two prediction models: the binary logistic model yielded an AUC of 0.72 ± 0.08 and the decision tree model yielded an AUC of 0.73 ± 0.03. Two different prediction models yielded similar predictive power to differentiate high and low quality embryos. In terms of eventual clinical decision making, the decision tree model resulted in easy-to-interpret rules that are highly applicable in clinical practice.

  5. Validation of Biomarker-based risk prediction models

    OpenAIRE

    Taylor, Jeremy M.G.; Ankerst, Donna P.; Andridge, Rebecca R.

    2008-01-01

    The increasing availability and use of predictive models to facilitate informed decision making highlights the need for careful assessment of the validity of these models. In particular, models involving biomarkers require careful validation for two reasons: issues with overfitting when complex models involve a large number of biomarkers, and inter-laboratory variation in assays used to measure biomarkers. In this paper we distinguish between internal and external statistical validation. Inte...

  6. Prediction error, ketamine and psychosis: An updated model.

    Science.gov (United States)

    Corlett, Philip R; Honey, Garry D; Fletcher, Paul C

    2016-11-01

    In 2007, we proposed an explanation of delusion formation as aberrant prediction error-driven associative learning. Further, we argued that the NMDA receptor antagonist ketamine provided a good model for this process. Subsequently, we validated the model in patients with psychosis, relating aberrant prediction error signals to delusion severity. During the ensuing period, we have developed these ideas, drawing on the simple principle that brains build a model of the world and refine it by minimising prediction errors, as well as using it to guide perceptual inferences. While previously we focused on the prediction error signal per se, an updated view takes into account its precision, as well as the precision of prior expectations. With this expanded perspective, we see several possible routes to psychotic symptoms - which may explain the heterogeneity of psychotic illness, as well as the fact that other drugs, with different pharmacological actions, can produce psychotomimetic effects. In this article, we review the basic principles of this model and highlight specific ways in which prediction errors can be perturbed, in particular considering the reliability and uncertainty of predictions. The expanded model explains hallucinations as perturbations of the uncertainty mediated balance between expectation and prediction error. Here, expectations dominate and create perceptions by suppressing or ignoring actual inputs. Negative symptoms may arise due to poor reliability of predictions in service of action. By mapping from biology to belief and perception, the account proffers new explanations of psychosis. However, challenges remain. We attempt to address some of these concerns and suggest future directions, incorporating other symptoms into the model, building towards better understanding of psychosis. © The Author(s) 2016.

  7. Trophic ecology of sea urchins in coral-rocky reef systems, Ecuador.

    Science.gov (United States)

    Cabanillas-Terán, Nancy; Loor-Andrade, Peggy; Rodríguez-Barreras, Ruber; Cortés, Jorge

    2016-01-01

    Sea urchins are important grazers and influence reef development in the Eastern Tropical Pacific (ETP). Diadema mexicanum and Eucidaris thouarsii are the most important sea urchins on the Ecuadorian coastal reefs. This study provided a trophic scenario for these two species of echinoids in the coral-rocky reef bottoms of the Ecuadorian coast, using stable isotopes. We evaluated the relative proportion of algal resources assimilated, and trophic niche of the two sea urchins in the most southern coral-rocky reefs of the ETP in two sites with different disturbance level. Bayesian models were used to estimate the contribution of algal sources, niche breadth, and trophic overlap between the two species. The sea urchins behaved as opportunistic feeders, although they showed differential resource assimilation. Eucidaris thouarsii is the dominant species in disturbed environments; likewise, their niche amplitude was broader than that of D. mexicanum when conditions were not optimal. However, there was no niche overlap between the species. The Stable Isotope Analysis in R (SIAR) indicated that both sea urchins shared limiting resources in the disturbed area, mainly Dictyota spp. (contributions of up to 85% for D. mexicanum and up to 75% for E. thouarsii). The Stable Isotope Bayesian Ellipses in R (SIBER) analysis results indicated less interspecific competition in the undisturbed site. Our results suggested a trophic niche partitioning between sympatric sea urchin species in coastal areas of the ETP, but the limitation of resources could lead to trophic overlap and stronger habitat degradation.

  8. Trophic ecology of sea urchins in coral-rocky reef systems, Ecuador

    Directory of Open Access Journals (Sweden)

    Nancy Cabanillas-Terán

    2016-01-01

    Full Text Available Sea urchins are important grazers and influence reef development in the Eastern Tropical Pacific (ETP. Diadema mexicanum and Eucidaris thouarsii are the most important sea urchins on the Ecuadorian coastal reefs. This study provided a trophic scenario for these two species of echinoids in the coral-rocky reef bottoms of the Ecuadorian coast, using stable isotopes. We evaluated the relative proportion of algal resources assimilated, and trophic niche of the two sea urchins in the most southern coral-rocky reefs of the ETP in two sites with different disturbance level. Bayesian models were used to estimate the contribution of algal sources, niche breadth, and trophic overlap between the two species. The sea urchins behaved as opportunistic feeders, although they showed differential resource assimilation. Eucidaris thouarsii is the dominant species in disturbed environments; likewise, their niche amplitude was broader than that of D. mexicanum when conditions were not optimal. However, there was no niche overlap between the species. The Stable Isotope Analysis in R (SIAR indicated that both sea urchins shared limiting resources in the disturbed area, mainly Dictyota spp. (contributions of up to 85% for D. mexicanum and up to 75% for E. thouarsii. The Stable Isotope Bayesian Ellipses in R (SIBER analysis results indicated less interspecific competition in the undisturbed site. Our results suggested a trophic niche partitioning between sympatric sea urchin species in coastal areas of the ETP, but the limitation of resources could lead to trophic overlap and stronger habitat degradation.

  9. Variability of Lekanesphaera monodi metabolic rates with habitat trophic status

    Science.gov (United States)

    Vignes, Fabio; Fedele, Marialaura; Pinna, Maurizio; Mancinelli, Giorgio; Basset, Alberto

    2012-05-01

    Regulation of metabolism is a common strategy used by individuals to respond to a changing environment. The mechanisms underlying the variability of metabolic rates in macroinvertebrates are of primary importance in studying benthic-pelagic energy transfer in transitional water ecosystems. Lekanesphaera monodi is an isopod endemic to transitional water ecosystems that can modify its metabolic rate in response to environmental changes. Therefore it is a useful model in studying the influence of environmental factors on metabolism. This study focused on the interpopulation variability of standard metabolic rates (SMR) in L. monodi populations sampled in three transitional water ecosystems differing in their trophic status. The standard metabolic rates of L. monodi individuals across the same range of body size spectra were inferred from oxygen consumption measurements in a flow-through respirometer in the three populations and a body condition index was assessed for each population. Habitat trophic status was evaluated by monthly measurement of the basic physical-chemical parameters of the water column in the ecosystems for one year. Standard metabolic rates showed high variability, ranging from 0.27 to 10.14 J d-1. Body size accounted for more than 38% of total variability. In terms of trophic status, individuals from the eutrophic ecosystem had significantly higher standard metabolic rates than individuals from the other ecosystems (SMR = 2.3 J d-1 in Spunderati Sud vs. 1.36 J d-1 in Alimini and 0.69 J d-1 in Acquatina). The body conditions index was also higher in the population from the eutrophic ecosystem. Results show that standard metabolic rates and growth rates are directly related to habitat productivity in accordance with the expectations of the food habits hypothesis. A possible extension of this hypothesis to benthic invertebrates is proposed.

  10. Predicting Solar Cycle 25 using Surface Flux Transport Model

    Science.gov (United States)

    Imada, Shinsuke; Iijima, Haruhisa; Hotta, Hideyuki; Shiota, Daiko; Kusano, Kanya

    2017-08-01

    It is thought that the longer-term variations of the solar activity may affect the Earth’s climate. Therefore, predicting the next solar cycle is crucial for the forecast of the “solar-terrestrial environment”. To build prediction schemes for the next solar cycle is a key for the long-term space weather study. Recently, the relationship between polar magnetic field at the solar minimum and next solar activity is intensively discussed. Because we can determine the polar magnetic field at the solar minimum roughly 3 years before the next solar maximum, we may discuss the next solar cycle 3years before. Further, the longer term (~5 years) prediction might be achieved by estimating the polar magnetic field with the Surface Flux Transport (SFT) model. Now, we are developing a prediction scheme by SFT model as a part of the PSTEP (Project for Solar-Terrestrial Environment Prediction) and adapting to the Cycle 25 prediction. The predicted polar field strength of Cycle 24/25 minimum is several tens of percent smaller than Cycle 23/24 minimum. The result suggests that the amplitude of Cycle 25 is weaker than the current cycle. We also try to obtain the meridional flow, differential rotation, and turbulent diffusivity from recent modern observations (Hinode and Solar Dynamics Observatory). These parameters will be used in the SFT models to predict the polar magnetic fields strength at the solar minimum. In this presentation, we will explain the outline of our strategy to predict the next solar cycle and discuss the initial results for Cycle 25 prediction.

  11. Predicting soil acidification trends at Plynlimon using the SAFE model

    Directory of Open Access Journals (Sweden)

    B. Reynolds

    1997-01-01

    Full Text Available The SAFE model has been applied to an acid grassland site, located on base-poor stagnopodzol soils derived from Lower Palaeozoic greywackes. The model predicts that acidification of the soil has occurred in response to increased acid deposition following the industrial revolution. Limited recovery is predicted following the decline in sulphur deposition during the mid to late 1970s. Reducing excess sulphur and NOx deposition in 1998 to 40% and 70% of 1980 levels results in further recovery but soil chemical conditions (base saturation, soil water pH and ANC do not return to values predicted in pre-industrial times. The SAFE model predicts that critical loads (expressed in terms of the (Ca+Mg+K:Alcrit ratio for six vegetation species found in acid grassland communities are not exceeded despite the increase in deposited acidity following the industrial revolution. The relative growth response of selected vegetation species characteristic of acid grassland swards has been predicted using a damage function linking growth to soil solution base cation to aluminium ratio. The results show that very small growth reductions can be expected for 'acid tolerant' plants growing in acid upland soils. For more sensitive species such as Holcus lanatus, SAFE predicts that growth would have been reduced by about 20% between 1951 and 1983, when acid inputs were greatest. Recovery to c. 90% of normal growth (under laboratory conditions is predicted as acidic inputs decline.

  12. Physics-Informed Machine Learning for Predictive Turbulence Modeling: A Priori Assessment of Prediction Confidence

    CERN Document Server

    Wu, Jin-Long; Xiao, Heng; Ling, Julia

    2016-01-01

    Although Reynolds-Averaged Navier-Stokes (RANS) equations are still the dominant tool for engineering design and analysis applications involving turbulent flows, standard RANS models are known to be unreliable in many flows of engineering relevance, including flows with separation, strong pressure gradients or mean flow curvature. With increasing amounts of 3-dimensional experimental data and high fidelity simulation data from Large Eddy Simulation (LES) and Direct Numerical Simulation (DNS), data-driven turbulence modeling has become a promising approach to increase the predictive capability of RANS simulations. Recently, a data-driven turbulence modeling approach via machine learning has been proposed to predict the Reynolds stress anisotropy of a given flow based on high fidelity data from closely related flows. In this work, the closeness of different flows is investigated to assess the prediction confidence a priori. Specifically, the Mahalanobis distance and the kernel density estimation (KDE) technique...

  13. Extended Range Hydrological Predictions: Uncertainty Associated with Model Parametrization

    Science.gov (United States)

    Joseph, J.; Ghosh, S.; Sahai, A. K.

    2016-12-01

    The better understanding of various atmospheric processes has led to improved predictions of meteorological conditions at various temporal scale, ranging from short term which cover a period up to 2 days to long term covering a period of more than 10 days. Accurate prediction of hydrological variables can be done using these predicted meteorological conditions, which would be helpful in proper management of water resources. Extended range hydrological simulation includes the prediction of hydrological variables for a period more than 10 days. The main sources of uncertainty in hydrological predictions include the uncertainty in the initial conditions, meteorological forcing and model parametrization. In the present study, the Extended Range Prediction developed for India for monsoon by Indian Institute of Tropical Meteorology (IITM), Pune is used as meteorological forcing for the Variable Infiltration Capacity (VIC) model. Sensitive hydrological parameters, as derived from literature, along with a few vegetation parameters are assumed to be uncertain and 1000 random values are generated given their prescribed ranges. Uncertainty bands are generated by performing Monte-Carlo Simulations (MCS) for the generated sets of parameters and observed meteorological forcings. The basins with minimum human intervention, within the Indian Peninsular region, are identified and validation of results are carried out using the observed gauge discharge. Further, the uncertainty bands are generated for the extended range hydrological predictions by performing MCS for the same set of parameters and extended range meteorological predictions. The results demonstrate the uncertainty associated with the model parametrisation for the extended range hydrological simulations. Keywords: Extended Range Prediction, Variable Infiltration Capacity model, Monte Carlo Simulation.

  14. Predictive modeling of coral disease distribution within a reef system.

    Directory of Open Access Journals (Sweden)

    Gareth J Williams

    Full Text Available Diseases often display complex and distinct associations with their environment due to differences in etiology, modes of transmission between hosts, and the shifting balance between pathogen virulence and host resistance. Statistical modeling has been underutilized in coral disease research to explore the spatial patterns that result from this triad of interactions. We tested the hypotheses that: 1 coral diseases show distinct associations with multiple environmental factors, 2 incorporating interactions (synergistic collinearities among environmental variables is important when predicting coral disease spatial patterns, and 3 modeling overall coral disease prevalence (the prevalence of multiple diseases as a single proportion value will increase predictive error relative to modeling the same diseases independently. Four coral diseases: Porites growth anomalies (PorGA, Porites tissue loss (PorTL, Porites trematodiasis (PorTrem, and Montipora white syndrome (MWS, and their interactions with 17 predictor variables were modeled using boosted regression trees (BRT within a reef system in Hawaii. Each disease showed distinct associations with the predictors. Environmental predictors showing the strongest overall associations with the coral diseases were both biotic and abiotic. PorGA was optimally predicted by a negative association with turbidity, PorTL and MWS by declines in butterflyfish and juvenile parrotfish abundance respectively, and PorTrem by a modal relationship with Porites host cover. Incorporating interactions among predictor variables contributed to the predictive power of our models, particularly for PorTrem. Combining diseases (using overall disease prevalence as the model response, led to an average six-fold increase in cross-validation predictive deviance over modeling the diseases individually. We therefore recommend coral diseases to be modeled separately, unless known to have etiologies that respond in a similar manner to

  15. A CHAID Based Performance Prediction Model in Educational Data Mining

    Directory of Open Access Journals (Sweden)

    R. Bhaskaran

    2010-01-01

    Full Text Available The performance in higher secondary school education in India is a turning point in the academic lives of all students. As this academic performance is influenced by many factors, it is essential to develop predictive data mining model for students' performance so as to identify the slow learners and study the influence of the dominant factors on their academic performance. In the present investigation, a survey cum experimental methodology was adopted to generate a database and it was constructed from a primary and a secondary source. While the primary data was collected from the regular students, the secondary data was gathered from the school and office of the Chief Educational Officer (CEO. A total of 1000 datasets of the year 2006 from five different schools in three different districts of Tamilnadu were collected. The raw data was preprocessed in terms of filling up missing values, transforming values in one form into another and relevant attribute/ variable selection. As a result, we had 772 student records, which were used for CHAID prediction model construction. A set of prediction rules were extracted from CHIAD prediction model and the efficiency of the generated CHIAD prediction model was found. The accuracy of the present model was compared with other model and it has been found to be satisfactory.

  16. Precise methods for conducted EMI modeling,analysis,and prediction

    Institute of Scientific and Technical Information of China (English)

    2008-01-01

    Focusing on the state-of-the-art conducted EMI prediction, this paper presents a noise source lumped circuit modeling and identification method, an EMI modeling method based on multiple slope approximation of switching transitions, and dou-ble Fourier integral method modeling PWM conversion units to achieve an accurate modeling of EMI noise source. Meanwhile, a new sensitivity analysis method, a general coupling model for steel ground loops, and a partial element equivalent circuit method are proposed to identify and characterize conducted EMI coupling paths. The EMI noise and propagation modeling provide an accurate prediction of conducted EMI in the entire frequency range (0―10 MHz) with good practicability and generality. Finally a new measurement approach is presented to identify the surface current of large dimensional metal shell. The proposed analytical modeling methodology is verified by experimental results.

  17. Precise methods for conducted EMI modeling,analysis, and prediction

    Institute of Scientific and Technical Information of China (English)

    MA WeiMing; ZHAO ZhiHua; MENG Jin; PAN QiJun; ZHANG Lei

    2008-01-01

    Focusing on the state-of-the-art conducted EMI prediction, this paper presents a noise source lumped circuit modeling and identification method, an EMI modeling method based on multiple slope approximation of switching transitions, and dou-ble Fourier integral method modeling PWM conversion units to achieve an accurate modeling of EMI noise source. Meanwhile, a new sensitivity analysis method, a general coupling model for steel ground loops, and a partial element equivalent circuit method are proposed to identify and characterize conducted EMI coupling paths. The EMI noise and propagation modeling provide an accurate prediction of conducted EMI in the entire frequency range (0-10 MHz) with good practicability and generality. Finally a new measurement approach is presented to identify the surface current of large dimensional metal shell. The proposed analytical modeling methodology is verified by experimental results.

  18. Predictive Control, Competitive Model Business Planning, and Innovation ERP

    DEFF Research Database (Denmark)

    Nourani, Cyrus F.; Lauth, Codrina

    2015-01-01

    is not viewed as the sum of its component elements, but the product of their interactions. The paper starts with introducing a systems approach to business modeling. A competitive business modeling technique, based on the author's planning techniques is applied. Systemic decisions are based on common......New optimality principles are put forth based on competitive model business planning. A Generalized MinMax local optimum dynamic programming algorithm is presented and applied to business model computing where predictive techniques can determine local optima. Based on a systems model an enterprise...... Loops, are applied to complex management decisions. Predictive modeling specifics are briefed. A preliminary optimal game modeling technique is presented in brief with applications to innovation and R&D management. Conducting gap and risk analysis can assist with this process. Example application areas...

  19. Response in the trophic state of stratified lakes to changes in hydrology and water level: potential effects of climate change

    Science.gov (United States)

    Robertson, Dale M.; Rose, William J.

    2011-01-01

    To determine how climate-induced changes in hydrology and water level may affect the trophic state (productivity) of stratified lakes, two relatively pristine dimictic temperate lakes in Wisconsin, USA, were examined. Both are closed-basin lakes that experience changes in water level and degradation in water quality during periods of high water. One, a seepage lake with no inlets or outlets, has a small drainage basin and hydrology dominated by precipitation and groundwater exchange causing small changes in water and phosphorus (P) loading, which resulted in small changes in water level, P concentrations, and productivity. The other, a terminal lake with inlets but no outlets, has a large drainage basin and hydrology dominated by runoff causing large changes in water and P loading, which resulted in large changes in water level, P concentrations, and productivity. Eutrophication models accurately predicted the effects of changes in hydrology, P loading, and water level on their trophic state. If climate changes, larger changes in hydrology and water levels than previously observed could occur. If this causes increased water and P loading, stratified (dimictic and monomictic) lakes are expected to experience higher water levels and become more eutrophic, especially those with large developed drainage basins.

  20. Predicting nucleosome positioning using a duration Hidden Markov Model

    Directory of Open Access Journals (Sweden)

    Widom Jonathan

    2010-06-01

    Full Text Available Abstract Background The nucleosome is the fundamental packing unit of DNAs in eukaryotic cells. Its detailed positioning on the genome is closely related to chromosome functions. Increasing evidence has shown that genomic DNA sequence itself is highly predictive of nucleosome positioning genome-wide. Therefore a fast software tool for predicting nucleosome positioning can help understanding how a genome's nucleosome organization may facilitate genome function. Results We present a duration Hidden Markov model for nucleosome positioning prediction by explicitly modeling the linker DNA length. The nucleosome and linker models trained from yeast data are re-scaled when making predictions for other species to adjust for differences in base composition. A software tool named NuPoP is developed in three formats for free download. Conclusions Simulation studies show that modeling the linker length distribution and utilizing a base composition re-scaling method both improve the prediction of nucleosome positioning regarding sensitivity and false discovery rate. NuPoP provides a user-friendly software tool for predicting the nucleosome occupancy and the most probable nucleosome positioning map for genomic sequences of any size. When compared with two existing methods, NuPoP shows improved performance in sensitivity.

  1. Experience-based model predictive control using reinforcement learning

    NARCIS (Netherlands)

    Negenborn, R.R.; De Schutter, B.; Wiering, M.A.; Hellendoorn, J.

    2004-01-01

    Model predictive control (MPC) is becoming an increasingly popular method to select actions for controlling dynamic systems. TraditionallyMPC uses a model of the system to be controlled and a performance function to characterize the desired behavior of the system. The MPC agent finds actions over a

  2. Evaluation of preformance of Predictive Models for Deoxynivalenol in Wheat

    NARCIS (Netherlands)

    Fels, van der H.J.

    2014-01-01

    The aim of this study was to evaluate the performance of two predictive models for deoxynivalenol contamination of wheat at harvest in the Netherlands, including the use of weather forecast data and external model validation. Data were collected in a different year and from different wheat fields th

  3. Katz model prediction of Caenorhabditis elegans mutagenesis on STS-42

    Science.gov (United States)

    Cucinotta, Francis A.; Wilson, John W.; Katz, Robert; Badhwar, Gautam D.

    1992-01-01

    Response parameters that describe the production of recessive lethal mutations in C. elegans from ionizing radiation are obtained with the Katz track structure model. The authors used models of the space radiation environment and radiation transport to predict and discuss mutation rates for C. elegans on the IML-1 experiment aboard STS-42.

  4. A model to predict the sound reflection from forests

    NARCIS (Netherlands)

    Wunderli, J.M.; Salomons, E.M.

    2009-01-01

    A model is presented to predict the reflection of sound at forest edges. A single tree is modelled as a vertical cylinder. For the reflection at a cylinder an analytical solution is given based on the theory of scattering of spherical waves. The entire forest is represented by a line of cylinders

  5. Atmospheric modelling for seasonal prediction at the CSIR

    CSIR Research Space (South Africa)

    Landman, WA

    2014-10-01

    Full Text Available by observed monthly sea-surface temperature (SST) and sea-ice fields. The AGCM is the conformal-cubic atmospheric model (CCAM) administered by the Council for Scientific and Industrial Research. Since the model is forced with observed rather than predicted...

  6. A model to predict the sound reflection from forests

    NARCIS (Netherlands)

    Wunderli, J.M.; Salomons, E.M.

    2009-01-01

    A model is presented to predict the reflection of sound at forest edges. A single tree is modelled as a vertical cylinder. For the reflection at a cylinder an analytical solution is given based on the theory of scattering of spherical waves. The entire forest is represented by a line of cylinders pl

  7. Validation of a multi-objective, predictive urban traffic model

    NARCIS (Netherlands)

    Wilmink, I.R.; Haak, P. van den; Woldeab, Z.; Vreeswijk, J.

    2013-01-01

    This paper describes the results of the verification and validation of the ecoStrategic Model, which was developed, implemented and tested in the eCoMove project. The model uses real-time and historical traffic information to determine the current, predicted and desired state of traffic in a network

  8. Prediction of speech intelligibility based on an auditory preprocessing model

    DEFF Research Database (Denmark)

    Christiansen, Claus Forup Corlin; Pedersen, Michael Syskind; Dau, Torsten

    2010-01-01

    in noise experiment was used for training and an ideal binary mask experiment was used for evaluation. All three models were able to capture the trends in the speech in noise training data well, but the proposed model provides a better prediction of the binary mask test data, particularly when the binary...... masks degenerate to a noise vocoder....

  9. Evaluation of preformance of Predictive Models for Deoxynivalenol in Wheat

    NARCIS (Netherlands)

    Fels, van der H.J.

    2014-01-01

    The aim of this study was to evaluate the performance of two predictive models for deoxynivalenol contamination of wheat at harvest in the Netherlands, including the use of weather forecast data and external model validation. Data were collected in a different year and from different wheat fields

  10. A Climate System Model, Numerical Simulation and Climate Predictability

    Institute of Scientific and Technical Information of China (English)

    ZENG Qingcun; WANG Huijun; LIN Zhaohui; ZHOU Guangqing; YU Yongqiang

    2007-01-01

    @@ The implementation of the project has lasted for more than 20 years. As a result, the following key innovative achievements have been obtained, ranging from the basic theory of climate dynamics, numerical model development and its related computational theory to the dynamical climate prediction using the climate system models:

  11. Trigeminal trophic syndrome: A rare entity

    Directory of Open Access Journals (Sweden)

    Sunil N Mishra

    2011-01-01

    Full Text Available Trigeminal trophic syndrome is a rare condition resulting from self-manipulation of the skin after a peripheral or central injury to the trigeminal system. The syndrome consists of a classic triad of anaesthesia, paraesthesia, and a secondary persistent or recurrent facial ulceration. We describe a 60 year-old woman who developed this syndrome as a sequel to the gasserian ganglion block for trigeminal neuralgia. She had also developed melasma within 1 year. A remarkable benefit was achieved by proper patient education and topical antibiotics which led to the healing of all ulcerations within 4 weeks. In the case reported here, the diagnosis of the trigeminal trophic syndrome was made primarily as a result of the physician′s experience with the syndrome previously.

  12. Prediction horizon effects on stochastic modelling hints for neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Drossu, R.; Obradovic, Z. [Washington State Univ., Pullman, WA (United States)

    1995-12-31

    The objective of this paper is to investigate the relationship between stochastic models and neural network (NN) approaches to time series modelling. Experiments on a complex real life prediction problem (entertainment video traffic) indicate that prior knowledge can be obtained through stochastic analysis both with respect to an appropriate NN architecture as well as to an appropriate sampling rate, in the case of a prediction horizon larger than one. An improvement of the obtained NN predictor is also proposed through a bias removal post-processing, resulting in much better performance than the best stochastic model.

  13. Three-model ensemble wind prediction in southern Italy

    Science.gov (United States)

    Torcasio, Rosa Claudia; Federico, Stefano; Calidonna, Claudia Roberta; Avolio, Elenio; Drofa, Oxana; Landi, Tony Christian; Malguzzi, Piero; Buzzi, Andrea; Bonasoni, Paolo

    2016-03-01

    Quality of wind prediction is of great importance since a good wind forecast allows the prediction of available wind power, improving the penetration of renewable energies into the energy market. Here, a 1-year (1 December 2012 to 30 November 2013) three-model ensemble (TME) experiment for wind prediction is considered. The models employed, run operationally at National Research Council - Institute of Atmospheric Sciences and Climate (CNR-ISAC), are RAMS (Regional Atmospheric Modelling System), BOLAM (BOlogna Limited Area Model), and MOLOCH (MOdello LOCale in H coordinates). The area considered for the study is southern Italy and the measurements used for the forecast verification are those of the GTS (Global Telecommunication System). Comparison with observations is made every 3 h up to 48 h of forecast lead time. Results show that the three-model ensemble outperforms the forecast of each individual model. The RMSE improvement compared to the best model is between 22 and 30 %, depending on the season. It is also shown that the three-model ensemble outperforms the IFS (Integrated Forecasting System) of the ECMWF (European Centre for Medium-Range Weather Forecast) for the surface wind forecasts. Notably, the three-model ensemble forecast performs better than each unbiased model, showing the added value of the ensemble technique. Finally, the sensitivity of the three-model ensemble RMSE to the length of the training period is analysed.

  14. Predicting the Yield Stress of SCC using Materials Modelling

    DEFF Research Database (Denmark)

    Thrane, Lars Nyholm; Hasholt, Marianne Tange; Pade, Claus

    2005-01-01

    A conceptual model for predicting the Bingham rheological parameter yield stress of SCC has been established. The model used here is inspired by previous work of Oh et al. (1), predicting that the yield stress of concrete relative to the yield stress of paste is a function of the relative thickness...... of excess paste around the aggregate. The thickness of excess paste is itself a function of particle shape, particle size distribution, and particle packing. Seven types of SCC were tested at four different excess paste contents in order to verify the conceptual model. Paste composition and aggregate shape...... and distribution were varied between SCC types. The results indicate that yield stress of SCC may be predicted using the model....

  15. Stability of theoretical model for catastrophic weather prediction

    Institute of Scientific and Technical Information of China (English)

    SHI Wei-hui; WANG Yue-peng

    2007-01-01

    Stability related to theoretical model for catastrophic weather prediction,which includes non-hydrostatic perfect elastic model and anelastic model, is discussed and analyzed in detail. It is proved that non-hydrostatic perfect elastic equations set is stable in the class of infinitely differentiable function. However, for the anelastic equations set, its continuity equation is changed in form because of the particular hypothesis for fluid, so "the matching consisting of both viscosity coefficient and incompressible assumption" appears, thereby the most important equations set of this class in practical prediction shows the same instability in topological property as Navier-Stokes equation,which should be avoided first in practical numerical prediction. In light of this, the referenced suggestions to amend the applied model are finally presented.

  16. Comparison of tropospheric scintillation prediction models of the Indonesian climate

    Science.gov (United States)

    Chen, Cheng Yee; Singh, Mandeep Jit

    2014-12-01

    Tropospheric scintillation is a phenomenon that will cause signal degradation in satellite communication with low fade margin. Few studies of scintillation have been conducted in tropical regions. To analyze tropospheric scintillation, we obtain data from a satellite link installed at Bandung, Indonesia, at an elevation angle of 64.7° and a frequency of 12.247 GHz from 1999 to 2000. The data are processed and compared with the predictions of several well-known scintillation prediction models. From the analysis, we found that the ITU-R model gives the lowest error rate when predicting the scintillation intensity for fade at 4.68%. However, the model should be further tested using data from higher-frequency bands, such as the K and Ka bands, to verify the accuracy of the model.

  17. [Predicting suicide or predicting the unpredictable in an uncertain world: Reinforcement Learning Model-Based analysis].

    Science.gov (United States)

    Desseilles, Martin

    2012-01-01

    In general, it appears that the suicidal act is highly unpredictable with the current scientific means available. In this article, the author submits the hypothesis that predicting suicide is complex because it results in predicting a choice, in itself unpredictable. The article proposes a Reinforcement learning model-based analysis. In this model, we integrate on the one hand, four ascending modulatory neurotransmitter systems (acetylcholine, noradrenalin, serotonin, and dopamine) with their regions of respective projections and afferences, and on the other hand, various observations of brain imaging identified until now in the suicidal process.

  18. Trophic redundancy among fishes in an East African nearshore seagrass community inferred from stable-isotope analysis.

    Science.gov (United States)

    Matich, P; Kiszka, J J; Gastrich, K R; Heithaus, M R

    2017-08-01

    Stable-isotope analysis supplemented with stomach contents data from published sources was used to quantify the trophic niches, trophic niche overlaps and potential trophic redundancy for the most commonly caught fish species from an East African nearshore seagrass community. This assessment is an important first step in quantifying food-web structure in a region subject to intense fishing activities. Nearshore food webs were driven by at least two isotopically distinct trophic pathways, algal and seagrass, with a greater proportion of the sampled species feeding within the seagrass food web (57%) compared with the algal food web (33%). There was considerable isotopic niche overlap among species (92% of species overlapped with at least one other species). Narrow isotopic niche widths of most (83%) species sampled, low isotopic similarity (only 23% of species exhibited no differences in δ(13) C and δ(15) N) and low predicted trophic redundancy among fishes most commonly caught by fishermen (15%), however, suggest that adjustments to resource management concerning harvesting and gear selectivity may be needed for the persistence of artisanal fishing in northern Tanzania. More detailed trophic studies paired with information on spatio-temporal variation in fish abundance, especially for heavily targeted species, will assist in the development and implementation of management strategies to maintain coastal food-web integrity. © 2017 The Fisheries Society of the British Isles.

  19. Statistical characteristics of irreversible predictability time in regional ocean models

    Directory of Open Access Journals (Sweden)

    P. C. Chu

    2005-01-01

    Full Text Available Probabilistic aspects of regional ocean model predictability is analyzed using the probability density function (PDF of the irreversible predictability time (IPT (called τ-PDF computed from an unconstrained ensemble of stochastic perturbations in initial conditions, winds, and open boundary conditions. Two-attractors (a chaotic attractor and a small-amplitude stable limit cycle are found in the wind-driven circulation. Relationship between attractor's residence time and IPT determines the τ-PDF for the short (up to several weeks and intermediate (up to two months predictions. The τ-PDF is usually non-Gaussian but not multi-modal for red-noise perturbations in initial conditions and perturbations in the wind and open boundary conditions. Bifurcation of τ-PDF occurs as the tolerance level varies. Generally, extremely successful predictions (corresponding to the τ-PDF's tail toward large IPT domain are not outliers and share the same statistics as a whole ensemble of predictions.

  20. Determining the prediction limits of models and classifiers with applications for disruption prediction in JET

    Science.gov (United States)

    Murari, A.; Peluso, E.; Vega, J.; Gelfusa, M.; Lungaroni, M.; Gaudio, P.; Martínez, F. J.; Contributors, JET

    2017-01-01

    Understanding the many aspects of tokamak physics requires the development of quite sophisticated models. Moreover, in the operation of the devices, prediction of the future evolution of discharges can be of crucial importance, particularly in the case of the prediction of disruptions, which can cause serious damage to various parts of the machine. The determination of the limits of predictability is therefore an important issue for modelling, classifying and forecasting. In all these cases, once a certain level of performance has been reached, the question typically arises as to whether all the information available in the data has been exploited, or whether there are still margins for improvement of the tools being developed. In this paper, a theoretical information approach is proposed to address this issue. The excellent properties of the developed indicator, called the prediction factor (PF), have been proved with the help of a series of numerical tests. Its application to some typical behaviour relating to macroscopic instabilities in tokamaks has shown very positive results. The prediction factor has also been used to assess the performance of disruption predictors running in real time in the JET system, including the one systematically deployed in the feedback loop for mitigation purposes. The main conclusion is that the most advanced predictors basically exploit all the information contained in the locked mode signal on which they are based. Therefore, qualitative improvements in disruption prediction performance in JET would need the processing of additional signals, probably profiles.