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Sample records for models predicting trophic

  1. Trophic dynamics of a simple model ecosystem.

    Science.gov (United States)

    Bell, Graham; Fortier-Dubois, Étienne

    2017-09-13

    We have constructed a model of community dynamics that is simple enough to enumerate all possible food webs, yet complex enough to represent a wide range of ecological processes. We use the transition matrix to predict the outcome of succession and then investigate how the transition probabilities are governed by resource supply and immigration. Low-input regimes lead to simple communities whereas trophically complex communities develop when there is an adequate supply of both resources and immigrants. Our interpretation of trophic dynamics in complex communities hinges on a new principle of mutual replenishment, defined as the reciprocal alternation of state in a pair of communities linked by the invasion and extinction of a shared species. Such neutral couples are the outcome of succession under local dispersal and imply that food webs will often be made up of suites of trophically equivalent species. When immigrants arrive from an external pool of fixed composition a similar principle predicts a dynamic core of webs constituting a neutral interchange network, although communities may express an extensive range of other webs whose membership is only in part predictable. The food web is not in general predictable from whole-community properties such as productivity or stability, although it may profoundly influence these properties. © 2017 The Author(s).

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

    DEFF Research Database (Denmark)

    Andersen, Ken Haste; Pedersen, Martin

    2010-01-01

    The largest perturbation on upper trophic levels of many marine ecosystems stems from fishing. The reaction of the ecosystem goes beyond the trophic levels directly targeted by the fishery. This reaction has been described either as a change in slope of the overall size spectrum or as a trophic...... 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...... 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...

  3. 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...

  4. 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.

  5. Coupling of an individual-based model of anchovy with lower trophic level and hydrodynamic models

    Science.gov (United States)

    Wang, Yuheng; Wei, Hao; Kishi, Michio J.

    2013-03-01

    Anchovy ( Engraulis japonicus), 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.

  6. 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.

  7. The exploration of trophic structure modeling using mass balance Ecopath model of Tangerang coastal waters

    Science.gov (United States)

    Dewi, N. N.; Kamal, M.; Wardiatno, Y.; Rozi

    2018-04-01

    Ecopath model approach was used to describe trophic interaction, energy flows and ecosystem condition of Tangerang coastal waters. This model consists of 42 ecological groups, of which 41 are living groups and one is a detritus group. Trophic levels of these groups vary between 1.0 (for primary producers and detritus) to 4.03 (for tetraodontidae). Groups with trophic levels 2≤TL<3 and 3≤TL<4 have a range of ecotropic efficiency from 0 to 0.9719 and 0 to 0.7520 respectively.The Mean transfer efficiency is 9.43% for phytoplankton and 3.39% for detritus. The Mixed trophic impact analysis indicates that phytoplankton havea positive impact on the majority of pelagic fish, while detritus has a positive impact on the majority of demersal fish. Leiognathidae havea negative impact on phytoplankton, zooplankton and several other groups. System omnivory index for this ecosystem is 0.151. System primary production/respiration (P/R) ratio of Tangerang coastal waters is 1.505. This coastal ecosystem is an immatureecosystem because it hasdegraded. Pedigree index for this model is 0.57. This model describes ecosystem condition affected by overfishing and antropogenic activities. Therefore, through Ecopath model we provide some suggestions about the ecosystem-based fisheries management.

  8. 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

  9. Accounting for size-specific predation improves our ability to predict the strength of a trophic cascade.

    Science.gov (United States)

    Stevenson, Christine F; Demes, Kyle W; Salomon, Anne K

    2016-02-01

    Predation can influence the magnitude of herbivory that grazers exert on primary producers by altering both grazer abundance and their per capita consumption rates via changes in behavior, density-dependent effects, and size. Therefore, models based solely on changes in abundance may miss key components of grazing pressure. We estimated shifts in grazing pressure associated with changes in the abundance and per capita consumption rates of sea urchins triggered by size-selective predation by sea otters (Enhydra lutris). Field surveys suggest that sea otters dramatically decreased the abundance and median size of sea urchins. Furthermore, laboratory experiments revealed that kelp consumption by sea urchins varied nonlinearly as a function of urchin size such that consumption rates increased to the 0.56 and 0.68 power of biomass for red and green urchins, respectively. This reveals that shifts in urchin size structure due to size-selective predation by sea otters alter sea urchin per capita grazing rates. Comparison of two quantitative models estimating total consumptive capacity revealed that a model incorporating shifts in urchin abundance while neglecting urchin size structure overestimated grazing pressure compared to a model that incorporated size. Consequently, incorporating shifts in urchin size better predicted field estimates of kelp abundance compared to equivalent models based on urchin abundance alone. We provide strong evidence that incorporating size-specific parameters increases our ability to describe and predict trophic interactions.

  10. 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

  11. Trophic complexity enhances ecosystem functioning in an aquatic detritus-based model system.

    Science.gov (United States)

    Jabiol, Jérémy; McKie, Brendan G; Bruder, Andreas; Bernadet, Caroline; Gessner, Mark O; Chauvet, Eric

    2013-09-01

    1. Understanding the functional significance of species interactions in ecosystems has become a major challenge as biodiversity declines rapidly worldwide. Ecosystem consequences arising from the loss of diversity either within trophic levels (horizontal diversity) or across trophic levels (vertical diversity) are well documented. However, simultaneous losses of species at different trophic levels may also result in interactive effects, with potentially complex outcomes for ecosystem functioning. 2. Because of logistical constraints, the outcomes of such interactions have been difficult to assess in experiments involving large metazoan species. Here, we take advantage of a detritus-based model system to experimentally assess the consequences of biodiversity change within both horizontal and vertical food-web components on leaf-litter decomposition, a fundamental process in a wide range of ecosystems. 3. Our concurrent manipulation of fungal decomposer diversity (0, 1 or 5 species), detritivore diversity (0, 1 or 3 species), and the presence of predatory fish scent showed that trophic complexity is key to eliciting diversity effects on ecosystem functioning. Specifically, although fungi and detritivores tended to promote decomposition individually, rates were highest in the most complete community where all trophic levels were represented at the highest possible species richness. In part, the effects were trait-mediated, reflected in the contrasting foraging responses of the detritivore species to predator scent. 4. Our results thus highlight the importance of interactive effects of simultaneous species loss within multiple trophic levels on ecosystem functioning. If a common phenomenon, this outcome suggests that functional ecosystem impairment resulting from widespread biodiversity loss could be more severe than inferred from previous experiments confined to varying diversity within single trophic levels. © 2013 The Authors. Journal of Animal Ecology © 2013

  12. 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.

  13. Applying the Back-Propagation Neural Network model and fuzzy classification to evaluate the trophic status of a reservoir system.

    Science.gov (United States)

    Chang, C L; Liu, H C

    2015-09-01

    The trophic state index, and in particular, the Carlson Trophic State Index (CTSI), is critical for evaluating reservoir water quality. Despite its common use in evaluating static water quality, the reliability of the CTSI may decrease when water turbidity is high. Therefore, this study examines the reliability of the CTSI and uses the Back-Propagation Neural Network (BPNN) model to create a new trophic state index. Fuzzy theory, rather than binary logic, is implemented to classify the trophic status into its three grades. The results show that compared to the CTSI with traditional classification, the new index with fuzzy classification can improve trophic status evaluation with high water turbidity. A reliable trophic state index can correctly describe reservoir water quality and allow relevant agencies to address proper water quality management strategies for a reservoir system.

  14. 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

  15. Trophic position and metabolic rate predict the long-term decay process of radioactive cesium in fish: a meta-analysis.

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    Hideyuki Doi

    Full Text Available Understanding the long-term behavior of radionuclides in organisms is important for estimating possible associated risks to human beings and ecosystems. As radioactive cesium (¹³⁷Cs can be accumulated in organisms and has a long physical half-life, it is very important to understand its long-term decay in organisms; however, the underlying mechanisms determining the decay process are little known. We performed a meta-analysis to collect published data on the long-term ¹³⁷Cs decay process in fish species to estimate biological (metabolic rate and ecological (trophic position, habitat, and diet type influences on this process. From the linear mixed models, we found that 1 trophic position could predict the day of maximum ¹³⁷Cs activity concentration in fish; and 2 the metabolic rate of the fish species and environmental water temperature could predict ecological half-lives and decay rates for fish species. These findings revealed that ecological and biological traits are important to predict the long-term decay process of ¹³⁷Cs activity concentration in fish.

  16. 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

    A model of trophic flows through the northern Benguela between 1980 and 1989 was constructed using the ECOPATH approach. The model serves to close the temporal gap between models of the system for the 1970s and 1990s. The aim is to provide a workable model, with the intention of encouraging...... 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...

  17. Spatially Explicit Modeling Reveals Cephalopod Distributions Match Contrasting Trophic Pathways in the Western Mediterranean Sea.

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    Patricia Puerta

    Full Text Available Populations of the same species can experience different responses to the environment throughout their distributional range as a result of spatial and temporal heterogeneity in habitat conditions. This highlights the importance of understanding the processes governing species distribution at local scales. However, research on species distribution often averages environmental covariates across large geographic areas, missing variability in population-environment interactions within geographically distinct regions. We used spatially explicit models to identify interactions between species and environmental, including chlorophyll a (Chla and sea surface temperature (SST, and trophic (prey density conditions, along with processes governing the distribution of two cephalopods with contrasting life-histories (octopus and squid across the western Mediterranean Sea. This approach is relevant for cephalopods, since their population dynamics are especially sensitive to variations in habitat conditions and rarely stable in abundance and location. The regional distributions of the two cephalopod species matched two different trophic pathways present in the western Mediterranean Sea, associated with the Gulf of Lion upwelling and the Ebro river discharges respectively. The effects of the studied environmental and trophic conditions were spatially variant in both species, with usually stronger effects along their distributional boundaries. We identify areas where prey availability limited the abundance of cephalopod populations as well as contrasting effects of temperature in the warmest regions. Despite distributional patterns matching productive areas, a general negative effect of Chla on cephalopod densities suggests that competition pressure is common in the study area. Additionally, results highlight the importance of trophic interactions, beyond other common environmental factors, in shaping the distribution of cephalopod populations. Our study presents

  18. Multi-model inference for incorporating trophic and climate uncertainty into stock assessments

    Science.gov (United States)

    Ianelli, James; Holsman, Kirstin K.; Punt, André E.; Aydin, Kerim

    2016-12-01

    Ecosystem-based fisheries management (EBFM) approaches allow a broader and more extensive consideration of objectives than is typically possible with conventional single-species approaches. Ecosystem linkages may include trophic interactions and climate change effects on productivity for the relevant species within the system. Presently, models are evolving to include a comprehensive set of fishery and ecosystem information to address these broader management considerations. The increased scope of EBFM approaches is accompanied with a greater number of plausible models to describe the systems. This can lead to harvest recommendations and biological reference points that differ considerably among models. Model selection for projections (and specific catch recommendations) often occurs through a process that tends to adopt familiar, often simpler, models without considering those that incorporate more complex ecosystem information. Multi-model inference provides a framework that resolves this dilemma by providing a means of including information from alternative, often divergent models to inform biological reference points and possible catch consequences. We apply an example of this approach to data for three species of groundfish in the Bering Sea: walleye pollock, Pacific cod, and arrowtooth flounder using three models: 1) an age-structured "conventional" single-species model, 2) an age-structured single-species model with temperature-specific weight at age, and 3) a temperature-specific multi-species stock assessment model. The latter two approaches also include consideration of alternative future climate scenarios, adding another dimension to evaluate model projection uncertainty. We show how Bayesian model-averaging methods can be used to incorporate such trophic and climate information to broaden single-species stock assessments by using an EBFM approach that may better characterize uncertainty.

  19. 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

    A model of trophic flows through the northern Benguela between 1980 and 1989 was constructed using the ECOPATH approach. The model serves to close the temporal gap between models of the system for the 1970s and 1990s. The aim is to provide a workable model, with the intention of encouraging...... scientists working on different components of the ecosystem to collaborate to improve and update the model for more recent years. Ultimately, this type of model may form a basis for multispecies management approaches in the region. By the 1980s, sardine Sardinops sagax and hake Merluccius spp. stocks...... 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...

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

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    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

  1. 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

    To quantify the effects of a future climate on three morphologically different lakes that varied in trophic status from oligo-mesotrophic to highly eutrophic, we applied the one-dimensional lake ecosystem model DYRESM-CAEDYM to oligo-mesotrophic Lake Okareka, eutrophic Lake Rotoehu, both in the t....... Therefore, future climate effects should be taken into account in the long-term planning and implementation of lake management as strategies may need to be refined and adapted to preserve or improve the present-day lake water quality....

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

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    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.

  3. 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 source of bioavailable orthophosphate (OP) in the catchment. The Soil Water Assessment Tool (SWAT) was used to identify important sources of OP loading in the catchment and to predict changes in the trophic status of four reservoirs associated with three...

  4. Holistic assessment of Chwaka Bay's multi-gear fishery - Using a trophic modeling approach

    Science.gov (United States)

    Rehren, Jennifer; Wolff, Matthias; Jiddawi, Narriman

    2018-04-01

    East African coastal communities highly depend on marine resources for not just income but also protein supply. The multi-species, multi-gear nature of East African fisheries makes this type of fishery particularly difficult to manage, as there is a trade-off between maximizing total catch from all gears and species and minimizing overfishing of target species and the disintegration of the ecosystem. The use and spatio-temporal overlap of multiple gears in Chwaka Bay (Zanzibar) has led to severe conflicts between fishermen. There is a general concern of overfishing in the bay because of the widespread use of small mesh sizes and destructive gears such as dragnets and spear guns. We constructed an Ecopath food web model to describe the current trophic flow structure and fishing pattern of the bay. Based on this model, we explored the impact of different gears on the ecosystem and the fishing community in order to give advice for gear based management in the bay. Results indicate that Chwaka bay is a productive, shallow water system, with biomass concentrations around the first and second trophic level. The system is greatly bottom-up driven and dominated by primary producers and invertebrates. The trophic and network indicators as well as the community energetics characterize Chwaka Bay as relatively mature. Traps and dragnets have the strongest impact on the ecosystem and on the catches obtained by other gears. Both gears potentially destabilize the ecosystem by reducing the biomass of top-down controlling key species (including important herbivores of macroalgae). The dragnet fishery is the least profitable, but provides most jobs for the fishing community. Thus, a complete ban of dragnets in the bay would require the provision of alternative livelihoods. Due to the low resource biomass of fish in the bay and the indication of a loss of structural control of certain fish groups, Chwaka Bay does not seem to provide scope for further expansion of the fishery

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

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    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 m

  6. Assessment, modelization and analysis of 106 Ru experimental transfers through a freshwater trophic system

    International Nuclear Information System (INIS)

    Vray, F.

    1994-01-01

    Experiments are carried out in order to study 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

  7. Predicting species distribution and abundance responses to climate change: why it is essential to include biotic interactions across trophic levels.

    Science.gov (United States)

    Van der Putten, Wim H; Macel, Mirka; Visser, Marcel E

    2010-07-12

    Current predictions on species responses to climate change strongly rely on projecting altered environmental conditions on species distributions. However, it is increasingly acknowledged that climate change also influences species interactions. We review and synthesize literature information on biotic interactions and use it to argue that the abundance of species and the direction of selection during climate change vary depending on how their trophic interactions become disrupted. Plant abundance can be controlled by aboveground and belowground multitrophic level interactions with herbivores, pathogens, symbionts and their enemies. We discuss how these interactions may alter during climate change and the resulting species range shifts. We suggest conceptual analogies between species responses to climate warming and exotic species introduced in new ranges. There are also important differences: the herbivores, pathogens and mutualistic symbionts of range-expanding species and their enemies may co-migrate, and the continuous gene flow under climate warming can make adaptation in the expansion zone of range expanders different from that of cross-continental exotic species. We conclude that under climate change, results of altered species interactions may vary, ranging from species becoming rare to disproportionately abundant. Taking these possibilities into account will provide a new perspective on predicting species distribution under climate change.

  8. Predicting invasiveness of species in trade: Climate match, trophic guild and fecundity influence establishment and impact of non-native freshwater fishes

    Science.gov (United States)

    Howeth, Jennifer G.; Gantz, Crysta A.; Angermeier, Paul; Frimpong, Emmanuel A.; Hoff, Michael H.; Keller, Reuben P.; Mandrak, Nicholas E.; Marchetti, Michael P.; Olden, Julian D.; Romagosa, Christina M.; Lodge, David M.

    2016-01-01

    AimImpacts of non-native species have motivated development of risk assessment tools for identifying introduced species likely to become invasive. Here, we develop trait-based models for the establishment and impact stages of freshwater fish invasion, and use them to screen non-native species common in international trade. We also determine which species in the aquarium, biological supply, live bait, live food and water garden trades are likely to become invasive. Results are compared to historical patterns of non-native fish establishment to assess the relative importance over time of pathways in causing invasions.LocationLaurentian Great Lakes region.MethodsTrait-based classification trees for the establishment and impact stages of invasion were developed from data on freshwater fish species that established or failed to establish in the Great Lakes. Fishes in trade were determined from import data from Canadian and United States regulatory agencies, assigned to specific trades and screened through the developed models.ResultsClimate match between a species’ native range and the Great Lakes region predicted establishment success with 75–81% accuracy. Trophic guild and fecundity predicted potential harmful impacts of established non-native fishes with 75–83% accuracy. Screening outcomes suggest the water garden trade poses the greatest risk of introducing new invasive species, followed by the live food and aquarium trades. Analysis of historical patterns of introduction pathways demonstrates the increasing importance of these trades relative to other pathways. Comparisons among trades reveal that model predictions parallel historical patterns; all fishes previously introduced from the water garden trade have established. The live bait, biological supply, aquarium and live food trades have also contributed established non-native fishes.Main conclusionsOur models predict invasion risk of potential fish invaders to the Great Lakes region and could help managers

  9. 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

  10. Study of a tri-trophic prey-dependent food chain model of interacting populations.

    Science.gov (United States)

    Haque, Mainul; Ali, Nijamuddin; Chakravarty, Santabrata

    2013-11-01

    The current paper accounts for the influence of intra-specific competition among predators in a prey dependent tri-trophic food chain model of interacting populations. We offer a detailed mathematical analysis of the proposed food chain model to illustrate some of the significant results that has arisen from the interplay of deterministic ecological phenomena and processes. Biologically feasible equilibria of the system are observed and the behaviours of the system around each of them are described. In particular, persistence, stability (local and global) and bifurcation (saddle-node, transcritical, Hopf-Andronov) analysis of this model are obtained. Relevant results from previous well known food chain models are compared with the current findings. Global stability analysis is also carried out by constructing appropriate Lyapunov functions. Numerical simulations show that the present system is capable enough to produce chaotic dynamics when the rate of self-interaction is very low. On the other hand such chaotic behaviour disappears for a certain value of the rate of self interaction. In addition, numerical simulations with experimented parameters values confirm the analytical results and shows that intra-specific competitions bears a potential role in controlling the chaotic dynamics of the system; and thus the role of self interactions in food chain model is illustrated first time. Finally, a discussion of the ecological applications of the analytical and numerical findings concludes the paper. Copyright © 2013 Elsevier Inc. All rights reserved.

  11. Cultural Resource Predictive Modeling

    Science.gov (United States)

    2017-10-01

    refining formal, inductive predictive models is the quality of the archaeological and environmental data. To build models efficiently, relevant...geomorphology, and historic information . Lessons Learned: The original model was focused on the identification of prehistoric resources. This...system but uses predictive modeling informally . For example, there is no probability for buried archaeological deposits on the Burton Mesa, but there is

  12. Using a food-web model to assess the trophic structure and energy flows in Daya Bay, China

    Science.gov (United States)

    Chen, Zuozhi; Xu, Shannan; Qiu, Yongsong

    2015-12-01

    Daya Bay, is one of the largest and most important semi-closed bays along the southern coast of China. Due to the favorable geomorphological and climatic conditions, this bay has become an important conservation zone of aquatic germplasm resources in South China Sea. To characterize the trophic structure, ecosystem properties and keystone species, a food-web model for Daya Bay has been developed by the means of a mass-balance approach using the Ecopath with Ecosim software. The mean trophic transfer efficiency for the entire ecosystem as a whole is 10.9% while the trophic level II is 5.1%. The primary- and secondary-producers, including phytoplankton, zooplankton and micro-zoobenthos demonstrated the important overall impacts on the rest of the groups based on mixed trophic impact (MIT) analysis and are classified as the keystone groups. The analysis of ecosystem attributes indicated that ecosystem of Daya Bay can be categorized as an immature one and/or is in the degraded stage. A comparison of this model with other coastal ecosystems, including Kuosheng Bay, Tongoy Bay, Beibu Gulf and Cadiz Gulf, underpinned that the ecosystem of Daye Bay is an obviously stressed system and is more vulnerable to the external disturbance. In general, our study indicates that a holistic approach is needed to minimize the impacts of anthropogenic activities to ensure the sustainability of the ecosystem in the future.

  13. Predictive modeling of complications.

    Science.gov (United States)

    Osorio, Joseph A; Scheer, Justin K; Ames, Christopher P

    2016-09-01

    Predictive analytic algorithms are designed to identify patterns in the data that allow for accurate predictions without the need for a hypothesis. Therefore, predictive modeling can provide detailed and patient-specific information that can be readily applied when discussing the risks of surgery with a patient. There are few studies using predictive modeling techniques in the adult spine surgery literature. These types of studies represent the beginning of the use of predictive analytics in spine surgery outcomes. We will discuss the advancements in the field of spine surgery with respect to predictive analytics, the controversies surrounding the technique, and the future directions.

  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. Species invasion history influences community evolution in a tri-trophic food web model.

    Directory of Open Access Journals (Sweden)

    Akihiko Mougi

    2009-08-01

    Full Text Available Recent experimental studies have demonstrated the importance of invasion history for evolutionary formation of community. However, only few theoretical studies on community evolution have focused on such views.We used a tri-trophic food web model to analyze the coevolutionary effects of ecological invasions by a mutant and by a predator and/or resource species of a native consumer species community and found that ecological invasions can lead to various evolutionary histories. The invasion of a predator makes multiple evolutionary community histories possible, and the evolutionary history followed can determine both the invasion success of the predator into the native community and the fate of the community. A slight difference in the timing of an ecological invasion can lead to a greatly different fate. In addition, even greatly different community histories can converge as a result of environmental changes such as a predator trait shift or a productivity change. Furthermore, the changes to the evolutionary history may be irreversible.Our modeling results suggest that the timing of ecological invasion of a species into a focal community can largely change the evolutionary consequences of the community. Our approach based on adaptive dynamics will be a useful tool to understand the effect of invasion history on evolutionary formation of community.

  16. Species invasion history influences community evolution in a tri-trophic food web model.

    Science.gov (United States)

    Mougi, Akihiko; Nishimura, Kinya

    2009-08-24

    Recent experimental studies have demonstrated the importance of invasion history for evolutionary formation of community. However, only few theoretical studies on community evolution have focused on such views. We used a tri-trophic food web model to analyze the coevolutionary effects of ecological invasions by a mutant and by a predator and/or resource species of a native consumer species community and found that ecological invasions can lead to various evolutionary histories. The invasion of a predator makes multiple evolutionary community histories possible, and the evolutionary history followed can determine both the invasion success of the predator into the native community and the fate of the community. A slight difference in the timing of an ecological invasion can lead to a greatly different fate. In addition, even greatly different community histories can converge as a result of environmental changes such as a predator trait shift or a productivity change. Furthermore, the changes to the evolutionary history may be irreversible. Our modeling results suggest that the timing of ecological invasion of a species into a focal community can largely change the evolutionary consequences of the community. Our approach based on adaptive dynamics will be a useful tool to understand the effect of invasion history on evolutionary formation of community.

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

    Science.gov (United States)

    2015-09-30

    ecosystem models may be implemented (e.g., Southern Ocean ). A main advantage in using model output to diagnose the occurrence and persistence of...may impact suitable habitat distributions in the CCLME or other regions of the world oceans . REFERENCES Peterson, W. T., & Schwing, F. B. (2003...in the California Current Ecosystem Jerome Fiechter UC Santa Cruz Institute of Marine Sciences 1156 High Street Santa Cruz, CA 95064 phone

  18. Archaeological predictive model set.

    Science.gov (United States)

    2015-03-01

    This report is the documentation for Task 7 of the Statewide Archaeological Predictive Model Set. The goal of this project is to : develop a set of statewide predictive models to assist the planning of transportation projects. PennDOT is developing t...

  19. Modeling Microbial Dynamics in Aquifers Considering the Interaction Between the Higher Trophic Levels

    Science.gov (United States)

    Bajracharya, B. M.; Cirpka, O. A.; Lu, C.

    2014-12-01

    Models of microbial dynamics coupled to solute transport in aquifers typically require the introduction of a bacterial carrying capacity term to prevent excessive microbial growth close to substrate-injection boundaries. The factors controlling this carrying capacity, however, are not fully understood. Most explanations for the occurrence of a carrying capacity discussed are based on the assumption of a bottom-up control of groundwater ecosystems. An alternative explanation is based on top-down control. Our model considers substrate, bacteria and higher trophic levels, such as grazers or bacteriophages. The dissolved substrate is transported with water flow whereas the biomasses of bacteria and grazers are considered essentially immobile. The one-dimensional reactive transport model also accounts for substrate dispersion and a random walk of grazers influenced by the bacteria concentration. The grazers grow on the bacteria, leading to a negative feedback on the bacteria concentration which may limit the turnover of the substrate. A single retentostat model with Monod kinetics of bacterial growth and a second-order grazing shows that the system oscillates but approaches a stable steady state with non-zero concentrations of substrate, bacteria, and grazers. The steady-state concentration of the bacteria biomass is independent of the substrate concentration in the inflow. When coupling several retentostats in a series to mimic a groundwater column, the steady-state bacteria concentrations remain at a constant level over a significant travel distance. The results show that grazing is a possible explanation of the carrying capacity, provided that there is enough substrate to sustain bacteria and grazers.

  20. 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.

  1. Zephyr - the prediction models

    DEFF Research Database (Denmark)

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

    2001-01-01

    utilities as partners and users. The new models are evaluated for five wind farms in Denmark as well as one wind farm in Spain. It is shown that the predictions based on conditional parametric models are superior to the predictions obatined by state-of-the-art parametric models.......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 Danish...

  2. Inverse and Predictive Modeling

    Energy Technology Data Exchange (ETDEWEB)

    Syracuse, Ellen Marie [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

    2017-09-27

    The LANL Seismo-Acoustic team has a strong capability in developing data-driven models that accurately predict a variety of observations. These models range from the simple – one-dimensional models that are constrained by a single dataset and can be used for quick and efficient predictions – to the complex – multidimensional models that are constrained by several types of data and result in more accurate predictions. Team members typically build models of geophysical characteristics of Earth and source distributions at scales of 1 to 1000s of km, the techniques used are applicable for other types of physical characteristics at an even greater range of scales. The following cases provide a snapshot of some of the modeling work done by the Seismo- Acoustic team at LANL.

  3. Exploring the Use of Multimedia Fate and Bioaccumulation Models to Calculate Trophic Magnification Factors (TMFs)

    Science.gov (United States)

    The trophic magnification factor (TMF) is considered to be a key metric for assessing the bioaccumulation potential of organic chemicals in food webs. Fugacity is an equilibrium criterion and thus reflects the relative thermodynamic status of a chemical in the environment and in ...

  4. Strong Impact of Chronic Cerebral Hypoperfusion on Neurovascular Unit, Cerebrovascular Remodeling, and Neurovascular Trophic Coupling in Alzheimer's Disease Model Mouse.

    Science.gov (United States)

    Shang, Jingwei; Yamashita, Toru; Zhai, Yun; Nakano, Yumiko; Morihara, Ryuta; Fukui, Yusuke; Hishikawa, Nozomi; Ohta, Yasuyuki; Abe, Koji

    2016-03-05

    Although chronic cerebral hypoperfusion (CCH) may affect Alzheimer's disease (AD) pathogenesis, the mechanism remains elusive. In the present study, we investigated the role of CCH on an AD mouse model in neurovascular unit, cerebrovascular remodeling, and neurovascular trophic coupling. Moreover, examined protective effect of galantamine. Alzheimer's disease transgenic mice (APP23) were subjected to bilateral common carotid arteries stenosis with ameroid constrictors for slowly progressive cerebral hypoperfusion. CCH exacerbated neuronal loss and decrease of α7 subunit of nicotinic acetylcholine receptors (α7-nAChRs) expression in hippocampus and thalamus at 12 months. Meanwhile, CCH greatly induced advanced glycation end products expression, and blood-brain barrier leakage through observing IgG and MMP9 expressions. Furthermore, a significant number of dramatic enlarged cerebral vessels with remodeling, BDNF/TrkB decreased in neurovascular trophic coupling. The present study demonstrated that CCH strongly enhanced primary AD pathology including neurodegeneration, neurovascular unit disruption, cerebrovascular remodeling and neurovascular trophic coupling damage in AD mice, and that galantamine treatment greatly ameliorated such neuropathologic abnormalities.

  5. 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.

  6. Trophic mass-balance model of Alaska's Prince William Sound ecosystem, for the post-spill period 1994-1996

    International Nuclear Information System (INIS)

    Okey, T.A.; Pauly, D.

    1998-01-01

    The Ecopath modelling approach for the Prince William Sound (PWS) ecosystem was described. The area is the site of the 1989 Exxon Valdez oil spill (EVOS), the largest spill in U.S. history. 36,000 tonnes of crude oil spread throughout the central and southwestern PWS into the Gulf of Alaska and along the Kenai and Alaska Peninsula. The initial effects of the oil spill were catastrophic. The Ecopath modelling approach discussed in this report is aimed at providing a cohesive picture of the PWS ecosystem by constructing a mass-balanced model of food-web interactions and trophic flows using information collected since the EVOS. The model includes all biotic components of the ecosystem and provides a quantitative description of food-web interactions and relationships, as well as energy flows among components. The model can provide an understanding of how ecosystems respond to disturbances, such as oil spills. 216 refs., 74 tabs., 13 figs., 8 appendices

  7. Echinocandin treatment of pneumocystis pneumonia in rodent models depletes cysts leaving trophic burdens that cannot transmit the infection.

    Directory of Open Access Journals (Sweden)

    Melanie T Cushion

    2010-01-01

    Full Text Available Fungi in the genus Pneumocystis cause pneumonia (PCP in hosts with debilitated immune systems and are emerging as co-morbidity factors associated with chronic diseases such as COPD. Limited therapeutic choices and poor understanding of the life cycle are a result of the inability of these fungi to grow outside the mammalian lung. Within the alveolar lumen, Pneumocystis spp., appear to have a bi-phasic life cycle consisting of an asexual phase characterized by binary fission of trophic forms and a sexual cycle resulting in formation of cysts, but the life cycle stage that transmits the infection is not known. The cysts, but not the trophic forms, express beta -1,3-D-glucan synthetase and contain abundant beta -1,3-D-glucan. Here we show that therapeutic and prophylactic treatment of PCP with echinocandins, compounds which inhibit the synthesis of beta -1,3-D-glucan, depleted cysts in rodent models of PCP, while sparing the trophic forms which remained in significant numbers. Survival was enhanced in the echincandin treated mice, likely due to the decreased beta -1,3-D-glucan content in the lungs of treated mice and rats which coincided with reductions of cyst numbers, and dramatic remodeling of organism morphology. Strong evidence for the cyst as the agent of transmission was provided by the failure of anidulafungin-treated mice to transmit the infection. We show for the first time that withdrawal of anidulafungin treatment with continued immunosuppression permitted the repopulation of cyst forms. Treatment of PCP with an echinocandin alone will not likely result in eradication of infection and cessation of echinocandin treatment while the patient remains immunosuppressed could result in relapse. Importantly, the echinocandins provide novel and powerful chemical tools to probe the still poorly understood bi-phasic life cycle of this genus of fungal pathogens.

  8. Modeling of PCB trophic transfer in the Gulf of Lions; 3D coupled model application.

    Science.gov (United States)

    Alekseenko, E; Thouvenin, B; Tronczyński, J; Carlotti, F; Garreau, P; Tixier, C; Baklouti, M

    2018-03-01

    3D coupled modeling approach is used for the PCB dispersion assessment in the Gulf of Lion and its transfer to zooplankton via biogeochemical processes. PCB budgets and fluxes between the different species of PCB: dissolved, particulate, biosorbed on plankton, assimilated by zooplankton, which are governed by different processes: adsorption/desorption, bacteria and plankton mortality, zooplankton excretion, grazing, mineralization, volatilization have been estimated. Model outputs were compared with the available in situ data. It was found that the Rhone River outflows play an important role in the organism contamination in the coastal zone, whereas the atmospheric depositions are rather more important in the offshore zones. The transfer of the available contaminant to bacteria and phytoplankton species is mainly related to the biomass present in the water column. Absorption fluxes (grazing) to zooplankton are rather higher than the passive sorption fluxes, which are themselves also linked to the sorption coefficient. Copyright © 2018 Elsevier Ltd. All rights reserved.

  9. 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.

  10. 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.

  11. Competition influence in the segregation of the trophic niche of otariids: a case study using isotopic Bayesian mixing models in Galapagos pinnipeds.

    Science.gov (United States)

    Páez-Rosas, Diego; Rodríguez-Pérez, Mónica; Riofrío-Lazo, Marjorie

    2014-12-15

    The feeding success of predators is associated with the competition level for resources, and, thus, sympatric species are exposed to a potential trophic overlap. Isotopic Bayesian mixing models should provide a better understanding of the contribution of preys to the diet of predators and the feeding behavior of a species over time. The carbon and nitrogen isotopic signatures from pup hair samples of 93 Galapagos sea lions and 48 Galapagos fur seals collected between 2003 and 2009 in different regions (east and west) of the archipelago were analyzed. A PDZ Europa ANCA-GSL elemental analyzer interfaced with a PDZ Europa 20-20 continuous flow gas source mass spectrometer was employed. Bayesian models, SIAR and SIBER, were used to estimate the contribution of prey to the diet of predators, the niche breadth, and the trophic overlap level between the populations. Statistical differences in the isotopic values of both predators were observed over the time. The mixing model determined that Galapagos fur seals had a primarily teutophagous diet, whereas the Galapagos sea lions fed exclusively on fish in both regions of the archipelago. The SIBER analysis showed differences in the trophic niche between the two sea lion populations, with the western rookery of the Galapagos sea lion being the population with the largest trophic niche area. A trophic niche partitioning between Galapagos fur seals and Galapagos sea lions in the west of the archipelago is suggested by our results. At intraspecific level, the western population of the Galapagos sea lion (ZwW) showed higher trophic breadth than the eastern population, a strategy adopted by the ZwW to decrease the interspecific competition levels in the western region. Copyright © 2014 John Wiley & Sons, Ltd.

  12. 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

  13. 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...

  14. Study of silver-110M transfer mechanisms in freshwater. Conceiving and utilization of an experimental model of ecosystem and of a mathematical model to simulate the radionuclide through a trophic chain

    International Nuclear Information System (INIS)

    Garnier-Laplace, J.

    1990-10-01

    Uptake and retention of 110m Ag are quantified from laboratory studies carried out on an experimental freshwater ecosystem composed by two abiotic units, water and sediment, and by four trophic levels: primary producer (Scenedesmus obliquus), first order consumers (Daphnia magna, Gammarus pulex, Chrionomus sp.), second order consumer (Cyprinus carpio) and third order one (Salmo trutta). The chosen analytical process consists in expressing each transfer by a mathematical equation which formulation is based on a theoric analysis. Experiments allow to calibrate parameters of these equations for each unit of the food chain. All experimental data concerning 110m Ag uptake emphasize the radioprotection implications of this radioelement, because of the high values of the estimated radioecological parameters. On the basis of the results obtained, a determinist mathematical model has been conceived to simulate the radionuclide distribution in the food chain as a function of a chronic or acute contamination mode. Its application gives the development with time of the mean 110m Ag concentration values for each trophic level. The first approaches based on the analysis of the results of field studies, carried out on ecosystems affected by chronic pollution (Rhone river) or acute one (as a consequence of the Chernobyl accident), give to the model an important explicative and global predictive quality. The age of the fish, their dietary habits which vary according to the annual cycle of the prey species and with theirposition in the food chain, appear such as essential parameters. The trophic pathway is clearly predominant whatever the contamination mode and, explains, for acute exposure, why accumulation of 110m Ag can be prolonged for a long time after the surrounding environment contamination [fr

  15. Models of Plankton Community Changes during a Warm Water Anomaly in Arctic Waters Show Altered Trophic Pathways with Minimal Changes in Carbon Export

    Directory of Open Access Journals (Sweden)

    Maria Vernet

    2017-05-01

    Full Text Available Carbon flow through pelagic food webs is an expression of the composition, biomass and activity of phytoplankton as primary producers. In the near future, severe environmental changes in the Arctic Ocean are expected to lead to modifications of phytoplankton communities. Here, we used a combination of linear inverse modeling and ecological network analysis to study changes in food webs before, during, and after an anomalous warm water event in the eastern Fram Strait of the West Spitsbergen Current (WSC that resulted in a shift from diatoms to flagellates during the summer (June–July. The model predicts substantial differences in the pathways of carbon flow in diatom- vs. Phaeocystis/nanoflagellate-dominated phytoplankton communities, but relatively small differences in carbon export. The model suggests a change in the zooplankton community and activity through increasing microzooplankton abundance and the switching of meso- and macrozooplankton feeding from strict herbivory to omnivory, detritivory and coprophagy. When small cells and flagellates dominated, the phytoplankton carbon pathway through the food web was longer and the microbial loop more active. Furthermore, one step was added in the flow from phytoplankton to mesozooplankton, and phytoplankton carbon to higher trophic levels is available via detritus or microzooplankton. Model results highlight how specific changes in phytoplankton community composition, as expected in a climate change scenario, do not necessarily lead to a reduction in carbon export.

  16. Network structure beyond food webs: mapping non-trophic and trophic interactions on Chilean rocky shores.

    Science.gov (United States)

    Sonia Kéfi; Berlow, Eric L; Wieters, Evie A; Joppa, Lucas N; Wood, Spencer A; Brose, Ulrich; Navarrete, Sergio A

    2015-01-01

    How multiple types of non-trophic interactions map onto trophic networks in real communities remains largely unknown. We present the first effort, to our knowledge, describing a comprehensive ecological network that includes all known trophic and diverse non-trophic links among >100 coexisting species for the marine rocky intertidal community of the central Chilean coast. Our results suggest that non-trophic interactions exhibit highly nonrandom structures both alone and with respect to food web structure. The occurrence of different types of interactions, relative to all possible links, was well predicted by trophic structure and simple traits of the source and target species. In this community, competition for space and positive interactions related to habitat/refuge provisioning by sessile and/or basal species were by far the most abundant non-trophic interactions. If these patterns are orroborated in other ecosystems, they may suggest potentially important dynamic constraints on the combined architecture of trophic and non-trophic interactions. The nonrandom patterning of non-trophic interactions suggests a path forward for developing a more comprehensive ecological network theory to predict the functioning and resilience of ecological communities.

  17. 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

  18. Trophic redundancy reduces vulnerability to extinction cascades.

    Science.gov (United States)

    Sanders, Dirk; Thébault, Elisa; Kehoe, Rachel; Frank van Veen, F J

    2018-03-06

    Current species extinction rates are at unprecedentedly high levels. While human activities can be the direct cause of some extinctions, it is becoming increasingly clear that species extinctions themselves can be the cause of further extinctions, since species affect each other through the network of ecological interactions among them. There is concern that the simplification of ecosystems, due to the loss of species and ecological interactions, increases their vulnerability to such secondary extinctions. It is predicted that more complex food webs will be less vulnerable to secondary extinctions due to greater trophic redundancy that can buffer against the effects of species loss. Here, we demonstrate in a field experiment with replicated plant-insect communities, that the probability of secondary extinctions is indeed smaller in food webs that include trophic redundancy. Harvesting one species of parasitoid wasp led to secondary extinctions of other, indirectly linked, species at the same trophic level. This effect was markedly stronger in simple communities than for the same species within a more complex food web. We show that this is due to functional redundancy in the more complex food webs and confirm this mechanism with a food web simulation model by highlighting the importance of the presence and strength of trophic links providing redundancy to those links that were lost. Our results demonstrate that biodiversity loss, leading to a reduction in redundant interactions, can increase the vulnerability of ecosystems to secondary extinctions, which, when they occur, can then lead to further simplification and run-away extinction cascades. Copyright © 2018 the Author(s). Published by PNAS.

  19. Confidence scores for prediction models

    DEFF Research Database (Denmark)

    Gerds, Thomas Alexander; van de Wiel, MA

    2011-01-01

    In medical statistics, many alternative strategies are available for building a prediction model based on training data. Prediction models are routinely compared by means of their prediction performance in independent validation data. If only one data set is available for training and validation......, then rival strategies can still be compared based on repeated bootstraps of the same data. Often, however, the overall performance of rival strategies is similar and it is thus difficult to decide for one model. Here, we investigate the variability of the prediction models that results when the same...... 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...

  20. Optimal harvesting of a stochastic delay tri-trophic food-chain model with Lévy jumps

    Science.gov (United States)

    Qiu, Hong; Deng, Wenmin

    2018-02-01

    In this paper, the optimal harvesting of a stochastic delay tri-trophic food-chain model with Lévy jumps is considered. We introduce two kinds of environmental perturbations in this model. One is called white noise which is continuous and is described by a stochastic integral with respect to the standard Brownian motion. And the other one is jumping noise which is modeled by a Lévy process. Under some mild assumptions, the critical values between extinction and persistent in the mean of each species are established. The sufficient and necessary criteria for the existence of optimal harvesting policy are established and the optimal harvesting effort and the maximum of sustainable yield are also obtained. We utilize the ergodic method to discuss the optimal harvesting problem. The results show that white noises and Lévy noises significantly affect the optimal harvesting policy while time delays is harmless for the optimal harvesting strategy in some cases. At last, some numerical examples are introduced to show the validity of our results.

  1. Contrasting effects of heat pulses on different trophic levels, an experiment with a herbivore-parasitoid model system.

    Science.gov (United States)

    Schreven, Stijn J J; Frago, Enric; Stens, Annemiek; de Jong, Peter W; van Loon, Joop J A

    2017-01-01

    Under predicted global climate change, species will be gradually exposed to warmer temperatures, and to a more variable climate including more intense and more frequent heatwaves. Increased climatic variability is expected to have different effects on species and ecosystems than gradual warming. A key challenge to predict the impact of climate change is to understand how temperature changes will affect species interactions. Herbivorous insects and their natural enemies belong to some of the largest groups of terrestrial animals, and thus they have a great impact on the functioning of ecosystems and on the services these ecosystems provide. Here we studied the life history traits of the plant-feeding insect Plutella xylostella and its specialist endoparasitoid Diadegma semiclausum, when exposed to a daily heat pulse of 5 or 10°C temperature increase during their entire immature phase. Growth and developmental responses differed with the amplitude of the heat pulse and they were different between host and parasitoid, indicating different thermal sensitivity of the two trophic levels. With a +5°C heat pulse, the adult parasitoids were larger which may result in a higher fitness, whereas a +10°C heat pulse retarded parasitoid development. These results show that the parasitoid is more sensitive than its host to brief intervals of temperature change, and this results in either positive or negative effects on life history traits, depending on the amplitude of the heat pulse. These findings suggest that more extreme fluctuations may disrupt host-parasitoid synchrony, whereas moderate fluctuations may improve parasitoid fitness.

  2. Predicting species distribution and abundance responses to climate change: why it is essential to include biotic interactions across trophic levels

    NARCIS (Netherlands)

    Van der Putten, W.H.; Macel, M.; Visser, M.E.

    2010-01-01

    Current predictions on species responses to climate change strongly rely on projecting altered environmental conditions on species distributions. However, it is increasingly acknowledged that climate change also influences species interactions. We review and synthesize literature information on

  3. 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

  4. Habitat fragmentation differentially affects trophic levels and alters behavior in a multi-trophic marine system.

    Science.gov (United States)

    Rielly-Carroll, Elizabeth; Freestone, Amy L

    2017-03-01

    Seagrass, an important subtidal marine ecosystem, is being lost at a rate of 110 km 2  year -1 , leading to fragmented seagrass seascapes. Habitat fragmentation is predicted to affect trophic levels differently, with higher trophic levels being more sensitive, stressing the importance of a multi-trophic perspective. Utilizing the trophic relationship between the blue crab (Callinectes sapidus) and hard clam (Mercenaria mercenaria), where adult blue crabs prey on juvenile blue crabs, and juvenile blue crabs prey on small hard clams, we examined whether predation rates, abundance, and behavior of predators and prey differed between continuous and fragmented seagrass in a multi-trophic context at two sites in Barnegat Bay, NJ. We tested the hypothesis that fragmented habitats would differentially affect trophic levels within a tri-trophic system, and our results supported this hypothesis. Densities of adult blue crabs were higher in fragmented than continuous habitats. Densities of juvenile blue crabs, the primary predator of hard clams, were lower in fragmented habitats than continuous, potentially due to increased predation by adult blue crabs. Clams experienced lower predation and burrowed to a shallower depth in fragmented habitats than in continuous habitat, likely due in part to the low densities of juvenile blue crabs, their primary predator. Our results suggest that while trophic levels are differentially affected, the impact of habitat fragmentation may be stronger on intermediate rather than top trophic levels in some marine systems.

  5. 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...

  6. Bootstrap prediction and Bayesian prediction under misspecified models

    OpenAIRE

    Fushiki, Tadayoshi

    2005-01-01

    We consider a statistical prediction problem under misspecified models. In a sense, Bayesian prediction is an optimal prediction method when an assumed model is true. Bootstrap prediction is obtained by applying Breiman's `bagging' method to a plug-in prediction. Bootstrap prediction can be considered to be an approximation to the Bayesian prediction under the assumption that the model is true. However, in applications, there are frequently deviations from the assumed model. In this paper, bo...

  7. 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...... the performance of HIRLAM in particular with respect to wind predictions. To estimate the performance of the model two spatial resolutions (0,5 Deg. and 0.2 Deg.) and different sets of HIRLAM variables were used to predict wind speed and energy production. The predictions of energy production for the wind farms...... 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...

  8. 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...

  9. 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...

  10. MODEL PREDICTIVE CONTROL FUNDAMENTALS

    African Journals Online (AJOL)

    2012-07-02

    Jul 2, 2012 ... Linear MPC. 1. Uses linear model: ˙x = Ax + Bu. 2. Quadratic cost function: F = xT Qx + uT Ru. 3. Linear constraints: Hx + Gu < 0. 4. Quadratic program. Nonlinear MPC. 1. Nonlinear model: ˙x = f(x, u). 2. Cost function can be nonquadratic: F = (x, u). 3. Nonlinear constraints: h(x, u) < 0. 4. Nonlinear program.

  11. From eutrophic to mesotrophic: modelling watershed management scenarios to change the trophic status of a reservoir.

    Science.gov (United States)

    Mateus, Marcos; Almeida, Carina; Brito, David; Neves, Ramiro

    2014-03-12

    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.

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

    International Nuclear Information System (INIS)

    Beckon, William N.

    2016-01-01

    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).

  13. 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).

  14. 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

  15. A model of trophic flows in the northern Benguela upwelling system ...

    African Journals Online (AJOL)

    Ultimately, this type of model may form a basis for multispecies management approaches in the region. By the 1980s, sardine Sardinops sagax and hake Merluccius spp. stocks in the northern Benguela had both undergone a decline, yet were still heavily fished. Horse mackerel Trachurus trachurus capensis had increased ...

  16. Modelling bankruptcy prediction models in Slovak companies

    Directory of Open Access Journals (Sweden)

    Kovacova Maria

    2017-01-01

    Full Text Available An intensive research from academics and practitioners has been provided regarding models for bankruptcy prediction and credit risk management. In spite of numerous researches focusing on forecasting bankruptcy using traditional statistics techniques (e.g. discriminant analysis and logistic regression and early artificial intelligence models (e.g. artificial neural networks, there is a trend for transition to machine learning models (support vector machines, bagging, boosting, and random forest to predict bankruptcy one year prior to the event. Comparing the performance of this with unconventional approach with results obtained by discriminant analysis, logistic regression, and neural networks application, it has been found that bagging, boosting, and random forest models outperform the others techniques, and that all prediction accuracy in the testing sample improves when the additional variables are included. On the other side the prediction accuracy of old and well known bankruptcy prediction models is quiet high. Therefore, we aim to analyse these in some way old models on the dataset of Slovak companies to validate their prediction ability in specific conditions. Furthermore, these models will be modelled according to new trends by calculating the influence of elimination of selected variables on the overall prediction ability of these models.

  17. Trophic modelling of the Peruvian upwelling ecosystem: Towards reconciliation of multiple datasets

    Science.gov (United States)

    Guénette, Sylvie; Christensen, Villy; Pauly, Daniel

    2008-10-01

    In the 1980s, personnel from the Instituto del Mar del Peru collaborated with foreign experts to reconstruct time series of (1) catch and biomass of the Peruvian anchovy Engraulis ringens back to 1953, along with parallel time series of (2) abundance of anchovy predators and competitors, and (3) abiotic parameters indicative of the dynamics of the Peruvian upwelling system. This contribution documents an attempt to build an ecosystem model of the Peruvian upwelling ecosystem and recreate the observed biomass trends through the period 1953-1984, using the Ecopath with Ecosim (EwE) software. The time series of biomass, particularly of Peruvian anchovy and its various predators, are not reproduced by the EwE model based solely on the original parameters. Instead, to model the anchovy abundance fluctuations caused by El Niño and other oceanographic events, it is necessary to include mechanisms that were not part of the original description of the ecosystem, which focused on mass-balance. For example, a switch between large and small phytoplankton appears to be required to induce the observed abundance shifts between sardine and anchovy. Similarly, a ‘mitigating’ relationship must be assumed between bonito ( Sarda chilensis) and seabirds for the 1965 collapse of seabirds to be reproduced by the model. Mechanisms of this sort, here proposed in a very tentative fashion, will have to be firmly established and quantified before a model can successfully explain both the older data series (1953-1984) as done here, and eventually the new series on the Peruvian upwelling system that became available only recently. With a total of now over 50 years of data, this would represent one of the best documented marine ecosystems in the world, matching its status as one of the most productive.

  18. 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.

  19. Predictive models of moth development

    Science.gov (United States)

    Degree-day models link ambient temperature to insect life-stages, making such models valuable tools in integrated pest management. These models increase management efficacy by predicting pest phenology. In Wisconsin, the top insect pest of cranberry production is the cranberry fruitworm, Acrobasis v...

  20. Predictive Models and Computational Embryology

    Science.gov (United States)

    EPA’s ‘virtual embryo’ project is building an integrative systems biology framework for predictive models of developmental toxicity. One schema involves a knowledge-driven adverse outcome pathway (AOP) framework utilizing information from public databases, standardized ontologies...

  1. Food-web models predict species abundances in response to habitat change.

    Directory of Open Access Journals (Sweden)

    Nicholas J Gotelli

    2006-10-01

    Full Text Available Plant and animal population sizes inevitably change following habitat loss, but the mechanisms underlying these changes are poorly understood. We experimentally altered habitat volume and eliminated top trophic levels of the food web of invertebrates that inhabit rain-filled leaves of the carnivorous pitcher plant Sarracenia purpurea. Path models that incorporated food-web structure better predicted population sizes of food-web constituents than did simple keystone species models, models that included only autecological responses to habitat volume, or models including both food-web structure and habitat volume. These results provide the first experimental confirmation that trophic structure can determine species abundances in the face of habitat loss.

  2. An abundant small sized fish as keystone species? The effect of Pomatoschistus microps on food webs and its trophic role in two intertidal benthic communities: A modeling approach

    Science.gov (United States)

    Pockberger, Moritz; Kellnreitner, Florian; Ahnelt, Harald; Asmus, Ragnhild; Asmus, Harald

    2014-02-01

    Ecological network analysis (ENA) was used to study the effects of Pomatoschistus microps on energy transport through the food web, its impact on other compartments and its possible role as a keystone species in the trophic webs of an Arenicola tidal flat ecosystem and a sparse Zostera noltii bed ecosystem. Three ENA models were constructed: (a) model 1 contains data of the original food web from prior research in the investigated area by Baird et al. (2007), (b) an updated model 2 which included biomass and diet data of P. microps from recent sampling, and (c) model 3 simulating a food web without P. microps. A comparison of energy transport between the different models revealed that more energy is transported from lower trophic levels up the food chain, in the presence of P. microps (models 1 and 2) than in its absence (model 3). Calculations of the keystone index (KSi) revealed the high overall impact (measured as εi) of this fish species on food webs. In model 1, P. microps was assigned a low KSi in the Arenicola flat and in the sparse Z. noltii bed. Calculations in model 2 ranked P. microps first for keystoneness and εi in both communities, the Arenicola flat and the sparse Z. noltii bed. Taken together, our results give insight into the role of P. microps when considering a whole food web and reveal direct and indirect trophic interactions of this small-sized fish species. These results might illustrate the impact and importance of abundant, widespread species in food webs and facilitate further investigations.

  3. 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.

  4. 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.

  5. Predictions models with neural nets

    Directory of Open Access Journals (Sweden)

    Vladimír Konečný

    2008-01-01

    Full Text Available The contribution is oriented to basic problem trends solution of economic pointers, using neural networks. Problems include choice of the suitable model and consequently configuration of neural nets, choice computational function of neurons and the way prediction learning. The contribution contains two basic models that use structure of multilayer neural nets and way of determination their configuration. It is postulate a simple rule for teaching period of neural net, to get most credible prediction.Experiments are executed with really data evolution of exchange rate Kč/Euro. The main reason of choice this time series is their availability for sufficient long period. In carry out of experiments the both given basic kind of prediction models with most frequent use functions of neurons are verified. Achieve prediction results are presented as in numerical and so in graphical forms.

  6. 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.

  7. 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...

  8. 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.

  9. What do saliency models predict?

    Science.gov (United States)

    Koehler, Kathryn; Guo, Fei; Zhang, Sheng; Eckstein, Miguel P.

    2014-01-01

    Saliency models have been frequently used to predict eye movements made during image viewing without a specified task (free viewing). Use of a single image set to systematically compare free viewing to other tasks has never been performed. We investigated the effect of task differences on the ability of three models of saliency to predict the performance of humans viewing a novel database of 800 natural images. We introduced a novel task where 100 observers made explicit perceptual judgments about the most salient image region. Other groups of observers performed a free viewing task, saliency search task, or cued object search task. Behavior on the popular free viewing task was not best predicted by standard saliency models. Instead, the models most accurately predicted the explicit saliency selections and eye movements made while performing saliency judgments. Observers' fixations varied similarly across images for the saliency and free viewing tasks, suggesting that these two tasks are related. The variability of observers' eye movements was modulated by the task (lowest for the object search task and greatest for the free viewing and saliency search tasks) as well as the clutter content of the images. Eye movement variability in saliency search and free viewing might be also limited by inherent variation of what observers consider salient. Our results contribute to understanding the tasks and behavioral measures for which saliency models are best suited as predictors of human behavior, the relationship across various perceptual tasks, and the factors contributing to observer variability in fixational eye movements. PMID:24618107

  10. 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...

  11. 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

  12. 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.

  13. Modelling exposure of oceanic higher trophic-level consumers to polychlorinated biphenyls: pollution 'hotspots' in relation to mass mortality events of marine mammals.

    Science.gov (United States)

    Handoh, Itsuki C; Kawai, Toru

    2014-08-30

    Marine mammals in the past mass mortality events may have been susceptible to infection because their immune systems were suppressed through the bioaccumulation of environmental pollutants such as polychlorinated biphenyls (PCBs). We compiled mortality event data sets of 33 marine mammal species, and employed a Finely-Advanced Transboundary Environmental model (FATE) to model the exposure of the global fish community to PCB congeners, in order to define critical exposure levels (CELs) of PCBs above which mass mortality events are likely to occur. Our modelling approach enabled us to describe the mass mortality events in the context of exposure of higher-trophic consumers to PCBs and to identify marine pollution 'hotspots' such as the Mediterranean Sea and north-western European coasts. We demonstrated that the CELs can be applied to quantify a chemical pollution Planetary Boundary, under which a safe operating space for marine mammals and humanity can exist. Copyright © 2014 Elsevier Ltd. All rights reserved.

  14. 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

  15. 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

  16. A Trophic Flow Model of the Caeté Mangrove Estuary (North Brazil) with Considerations for the Sustainable Use of its Resources

    Science.gov (United States)

    Wolff, M.; Koch, V.; Isaac, V.

    2000-06-01

    The Caeté Estuary lies within the world's second largest mangrove region, 200 km south-east of the Amazon delta. It has an extension of about 220 km2and is subjected to a considerable human impact through intensive harvest of mangrove crabs (Ucides cordatus) and logging of mangroves. In order to integrate available information on biomass, catches, food spectrum and dynamics of the main species populations of the system, a trophic steady state model of 19 compartments was constructed using the ECOPATH II software (Christensen & Pauly, 1992). Ninety-nine percent of total system biomass is made up by mangroves (Rhizophora mangle, Avicennia germinans andLaguncularia racemosa), which are assumed to cover about 45% of the total area and contribute about 60% to the system's primary production. The remaining biomass (132 g m-2) is distributed between the pelagic and benthic domains in proportions of 10% and 90% respectively. Through litter fall, mangroves inject the main primary food source into the system, which is either consumed directly by herbivores (principally land crabs, Ucides cordatus) or, when already metabolized by bacteria, by detritivors (principally fiddler crabs, Uca spp.). These two groups are prominent in terms of biomass (80 g and 14·5 g m-2), and food intake (1120 g m-2 yr-1and 1378 g m-2 yr-1respectively). According to the model estimates, energy flow through the fish and shrimp compartments is of relatively low importance for the energy cycling within the system, a finding which is contrary to the situation in other mangrove estuaries reported in the literature. The dominance of mangrove epibenthos is attributed to the fact that a large part of the system's production remains within the mangrove forest as material export to the estuary is restricted to spring tides, when the forest is completely indundated. This is also the reason for the low abundance of suspension feeders, which are restricted to a small belt along the Caeté River and the small

  17. 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.

  18. Using stable isotopes to determine seabird trophic relationships

    Science.gov (United States)

    Hobson, Keith A.; Piatt, John F.; Pitocchelli, Jay

    1994-01-01

    1. The stable isotopes of nitrogen (δ15N) and carbon (δ13C) were analysed in 22 species of marine birds from coastal waters of the northeast Pacific Ocean. Analyses confirm that stable nitrogen isotopes can predict seabird trophic positions.2. Based on δ15N analyses, seabird trophic-level inferences generally agree with those of conventional dietary studies, but suggest that lower trophic-level organisms are more important to several seabirds than was recognized previously.3. Stable-carbon isotope analysis may be a good indicator of inshore vs. offshore feeding preference.4. In general, stable-isotope analysis to determine trophic level offers many advantages over conventional dietary approaches since trophic inferences are based on time-integrated estimates of assimilated and not just ingested foods, and isotopic abundance represents a continuous variable that is amenable to statistical analysis.

  19. 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.

  20. Use of a Human Artificial Chromosome for Delivering Trophic Factors in a Rodent Model of Amyotrophic Lateral Sclerosis

    Directory of Open Access Journals (Sweden)

    Yasuhiro Watanabe

    2015-01-01

    Full Text Available A human artificial chromosome (HAC is maintained as an episome within a cell and avoids random integration into the host genome. It can transfer multiple and/or large transgenes along with their regulatory elements thereby resembling native chromosomes. Using this HAC system, we established mesenchymal stem cells (MSCs that simultaneously expressed hepatocyte growth factor, glial cell line-derived neurotrophic factor, and insulin-like growth factor 1, termed HAC-MSCs. This cell line provides an opportunity for stable transplantation and thorough analyses. We then introduced the cells for the treatment of a neurodegenerative disorder, amyotrophic lateral sclerosis. The HAC-MSCs were transplanted via the fourth cerebral ventricle (CV or intravenous (i.v. infusion at various ages of recipient mice. Littermate- and sex-matched mice underwent a sham procedure. Compared to the controls, there was an encouraging trend of increased life span via CV transplantation and delayed onset in i.v. infusion 60 days after transplantation. Further, we confirmed a statistically significant increase in life span via CV transplantation at 100 days. This effect was not seen in mice transplanted with MSCs lacking the HAC. We successfully enhanced the trophic potential of the MSCs using the HAC. This strategy could be a promising direction for the treatment of neurodegenerative disorders.

  1. 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. © 2012 The Authors. Journal of Evolutionary Biology © 2012 European Society For Evolutionary Biology.

  2. Trophic levels of fish species of commercial importance in the Colombian Caribbean.

    Science.gov (United States)

    García, Camilo B; Contreras, Cristian Camilo

    2011-09-01

    Ecological studies on commercial important fish species are of great value to support resource management issues. This study calculated trophic levels of those Colombian Caribbean fish species whose diet has been locally described. Usable diet data of 119 species resulted in 164 trophic level estimates. An ordinary regression model relating trophic level and fish size was formulated. The regression slope was positive and significantly different from zero (p fish size. Both the list of trophic levels and the regression model should be of help in the formulation of trophic indicators and models of neotropical ecosystems.

  3. Trophic strategies of unicellular plankton

    DEFF Research Database (Denmark)

    Chakraborty, Subhendu; Nielsen, Lasse Tor; Andersen, Ken Haste

    2017-01-01

    Unicellular plankton employ trophic strategies ranging from pure photoautotrophs over mixotrophy to obligate heterotrophs (phagotrophs), with cell sizes from 10-8 to 1 μg C. A full understanding of how trophic strategy and cell size depend on resource environment and predation is lacking. To this......Unicellular plankton employ trophic strategies ranging from pure photoautotrophs over mixotrophy to obligate heterotrophs (phagotrophs), with cell sizes from 10-8 to 1 μg C. A full understanding of how trophic strategy and cell size depend on resource environment and predation is lacking...... unicellulars are colimited by organic carbon and nutrients, and only large photoautotrophs and smaller mixotrophs are nutrient limited; (2) trophic strategy is bottom-up selected by the environment, while optimal size is top-down selected by predation. The focus on cell size and trophic strategies facilitates...

  4. Iowa calibration of MEPDG performance prediction models.

    Science.gov (United States)

    2013-06-01

    This study aims to improve the accuracy of AASHTO Mechanistic-Empirical Pavement Design Guide (MEPDG) pavement : performance predictions for Iowa pavement systems through local calibration of MEPDG prediction models. A total of 130 : representative p...

  5. Model complexity control for hydrologic prediction

    NARCIS (Netherlands)

    Schoups, G.; Van de Giesen, N.C.; Savenije, H.H.G.

    2008-01-01

    A common concern in hydrologic modeling is overparameterization of complex models given limited and noisy data. This leads to problems of parameter nonuniqueness and equifinality, which may negatively affect prediction uncertainties. A systematic way of controlling model complexity is therefore

  6. 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

  7. Staying Power of Churn Prediction Models

    NARCIS (Netherlands)

    Risselada, Hans; Verhoef, Peter C.; Bijmolt, Tammo H. A.

    In this paper, we study the staying power of various churn prediction models. Staying power is defined as the predictive performance of a model in a number of periods after the estimation period. We examine two methods, logit models and classification trees, both with and without applying a bagging

  8. 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.

  9. Comparison of Prediction-Error-Modelling Criteria

    DEFF Research Database (Denmark)

    Jørgensen, John Bagterp; Jørgensen, Sten Bay

    2007-01-01

    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 is a r...

  10. Predictive models for monitoring and analysis of the total zooplankton

    Directory of Open Access Journals (Sweden)

    Obradović Milica

    2014-01-01

    Full Text Available In recent years, modeling and prediction of total zooplankton abundance have been performed by various tools and techniques, among which data mining tools have been less frequent. The purpose of this paper is to automatically determine the dependency degree and the influence of physical, chemical and biological parameters on the total zooplankton abundance, through design of the specific data mining models. For this purpose, the analysis of key influencers was used. The analysis is based on the data obtained from the SeLaR information system - specifically, the data from the two reservoirs (Gruža and Grošnica with different morphometric characteristics and trophic state. The data is transformed into optimal structure for data analysis, upon which, data mining model based on the Naïve Bayes algorithm is constructed. The results of the analysis imply that in both reservoirs, parameters of groups and species of zooplankton have the greatest influence on the total zooplankton abundance. If these inputs (group and zooplankton species are left out, differences in the impact of physical, chemical and other biological parameters in dependences of reservoirs can be noted. In the Grošnica reservoir, analysis showed that the temporal dimension (months, nitrates, water temperature, chemical oxygen demand, chlorophyll and chlorides, had the key influence with strong relative impact. In the Gruža reservoir, key influence parameters for total zooplankton are: spatial dimension (location, water temperature and physiological groups of bacteria. The results show that the presented data mining model is usable on any kind of aquatic ecosystem and can also serve for the detection of inputs which could be the basis for the future analysis and modeling.

  11. Calibration of PMIS pavement performance prediction models.

    Science.gov (United States)

    2012-02-01

    Improve the accuracy of TxDOTs existing pavement performance prediction models through calibrating these models using actual field data obtained from the Pavement Management Information System (PMIS). : Ensure logical performance superiority patte...

  12. Predictive Model Assessment for Count Data

    National Research Council Canada - National Science Library

    Czado, Claudia; Gneiting, Tilmann; Held, Leonhard

    2007-01-01

    .... In case studies, we critique count regression models for patent data, and assess the predictive performance of Bayesian age-period-cohort models for larynx cancer counts in Germany. Key words: Calibration...

  13. Modeling and Prediction Using Stochastic Differential Equations

    DEFF Research Database (Denmark)

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

    2016-01-01

    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......) for modeling and forecasting. It is argued that this gives models and predictions which better reflect reality. The SDE approach also offers a more adequate framework for modeling and a number of efficient tools for model building. A software package (CTSM-R) for SDE-based modeling is briefly described....... 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...

  14. Predictive models for arteriovenous fistula maturation.

    Science.gov (United States)

    Al Shakarchi, Julien; McGrogan, Damian; Van der Veer, Sabine; Sperrin, Matthew; Inston, Nicholas

    2016-05-07

    Haemodialysis (HD) is a lifeline therapy for patients with end-stage renal disease (ESRD). A critical factor in the survival of renal dialysis patients is the surgical creation of vascular access, and international guidelines recommend arteriovenous fistulas (AVF) as the gold standard of vascular access for haemodialysis. Despite this, AVFs have been associated with high failure rates. Although risk factors for AVF failure have been identified, their utility for predicting AVF failure through predictive models remains unclear. The objectives of this review are to systematically and critically assess the methodology and reporting of studies developing prognostic predictive models for AVF outcomes and assess them for suitability in clinical practice. Electronic databases were searched for studies reporting prognostic predictive models for AVF outcomes. Dual review was conducted to identify studies that reported on the development or validation of a model constructed to predict AVF outcome following creation. Data were extracted on study characteristics, risk predictors, statistical methodology, model type, as well as validation process. We included four different studies reporting five different predictive models. Parameters identified that were common to all scoring system were age and cardiovascular disease. This review has found a small number of predictive models in vascular access. The disparity between each study limits the development of a unified predictive model.

  15. Model Predictive Control Fundamentals | Orukpe | Nigerian Journal ...

    African Journals Online (AJOL)

    Model Predictive Control (MPC) has developed considerably over the last two decades, both within the research control community and in industries. MPC strategy involves the optimization of a performance index with respect to some future control sequence, using predictions of the output signal based on a process model, ...

  16. Unreachable Setpoints in Model Predictive Control

    DEFF Research Database (Denmark)

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

    2008-01-01

    In this work, a new model predictive controller is developed that handles unreachable setpoints better than traditional model predictive control methods. The new controller induces an interesting fast/slow asymmetry in the tracking response of the system. Nominal asymptotic stability of the optim...

  17. Application of Landsat 8 OLI Image and Empirical Model for Water Trophic Status Identification of Riam Kanan Reservoir, Banjar, South Kalimantan

    Science.gov (United States)

    Saputra, A. N.; Danoedoro, P.; Kamal, M.

    2017-12-01

    Remote sensing has a potential for observing, mapping and monitoring the quality of lake water. Riam Kanan is a reservoir which has a water resource from Riam Kanan River with the area width of its watershed about 1043 km2. The accumulation of nutrient in this reservoir simultaneously deteriorates the condition of waters, which can cause an increasingly growth of harm micro algae or Harmful Algal Blooms (HABs). This research applied Carlson’s trophic status index (CTSI) at Riam Kanan Reservoir using Landsat-8 OLI satellite image. The Landsat 8 OLI image was recorded on 14 August 2016 and was used in this research based on its surface reflectance values. The result of correlation test shows that band 3 of the image as coefficient of chlorophyll-a parameter, channel 2 as coefficient of phosphate, and band ratio of SDT as coefficient of SDT. Based on the result of modelling using CTSI, the majority scale of CTSI score at Riam Kanan Reservoir is between 60 to70 in medium eutrophic class. The class of medium eutrophic at Riam Kanan Reservoir potentially emerges the threat both of the improvement of water fertility and the reduction of water quality. Improvement of the fertility is apprehensive since it can trigger an explosion of micro algae which will endanger the ecological condition at the area of Riam Kanan Reservoir.

  18. Clinical Prediction Models for Cardiovascular Disease: Tufts Predictive Analytics and Comparative Effectiveness Clinical Prediction Model Database.

    Science.gov (United States)

    Wessler, Benjamin S; Lai Yh, Lana; Kramer, Whitney; Cangelosi, Michael; Raman, Gowri; Lutz, Jennifer S; Kent, David M

    2015-07-01

    Clinical prediction models (CPMs) estimate the probability of clinical outcomes and hold the potential to improve decision making and individualize care. For patients with cardiovascular disease, there are numerous CPMs available although the extent of this literature is not well described. We conducted a systematic review for articles containing CPMs for cardiovascular disease published between January 1990 and May 2012. Cardiovascular disease includes coronary heart disease, heart failure, arrhythmias, stroke, venous thromboembolism, and peripheral vascular disease. We created a novel database and characterized CPMs based on the stage of development, population under study, performance, covariates, and predicted outcomes. There are 796 models included in this database. The number of CPMs published each year is increasing steadily over time. Seven hundred seventeen (90%) are de novo CPMs, 21 (3%) are CPM recalibrations, and 58 (7%) are CPM adaptations. This database contains CPMs for 31 index conditions, including 215 CPMs for patients with coronary artery disease, 168 CPMs for population samples, and 79 models for patients with heart failure. There are 77 distinct index/outcome pairings. Of the de novo models in this database, 450 (63%) report a c-statistic and 259 (36%) report some information on calibration. There is an abundance of CPMs available for a wide assortment of cardiovascular disease conditions, with substantial redundancy in the literature. The comparative performance of these models, the consistency of effects and risk estimates across models and the actual and potential clinical impact of this body of literature is poorly understood. © 2015 American Heart Association, Inc.

  19. Hybrid approaches to physiologic modeling and prediction

    Science.gov (United States)

    Olengü, Nicholas O.; Reifman, Jaques

    2005-05-01

    This paper explores how the accuracy of a first-principles physiological model can be enhanced by integrating data-driven, "black-box" models with the original model to form a "hybrid" model system. Both linear (autoregressive) and nonlinear (neural network) data-driven techniques are separately combined with a first-principles model to predict human body core temperature. Rectal core temperature data from nine volunteers, subject to four 30/10-minute cycles of moderate exercise/rest regimen in both CONTROL and HUMID environmental conditions, are used to develop and test the approach. The results show significant improvements in prediction accuracy, with average improvements of up to 30% for prediction horizons of 20 minutes. The models developed from one subject's data are also used in the prediction of another subject's core temperature. Initial results for this approach for a 20-minute horizon show no significant improvement over the first-principles model by itself.

  20. Evaluating the Predictive Value of Growth Prediction Models

    Science.gov (United States)

    Murphy, Daniel L.; Gaertner, Matthew N.

    2014-01-01

    This study evaluates four growth prediction models--projection, student growth percentile, trajectory, and transition table--commonly used to forecast (and give schools credit for) middle school students' future proficiency. Analyses focused on vertically scaled summative mathematics assessments, and two performance standards conditions (high…

  1. 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...

  2. A Global Model for Bankruptcy Prediction.

    Science.gov (United States)

    Alaminos, David; Del Castillo, Agustín; Fernández, Manuel Ángel

    2016-01-01

    The recent world financial crisis has increased the number of bankruptcies in numerous countries and has resulted in a new area of research which responds to the need to predict this phenomenon, not only at the level of individual countries, but also at a global level, offering explanations of the common characteristics shared by the affected companies. Nevertheless, few studies focus on the prediction of bankruptcies globally. In order to compensate for this lack of empirical literature, this study has used a methodological framework of logistic regression to construct predictive bankruptcy models for Asia, Europe and America, and other global models for the whole world. The objective is to construct a global model with a high capacity for predicting bankruptcy in any region of the world. The results obtained have allowed us to confirm the superiority of the global model in comparison to regional models over periods of up to three years prior to bankruptcy.

  3. Fingerprint verification prediction model in hand dermatitis.

    Science.gov (United States)

    Lee, Chew K; Chang, Choong C; Johor, Asmah; Othman, Puwira; Baba, Roshidah

    2015-07-01

    Hand dermatitis associated fingerprint changes is a significant problem and affects fingerprint verification processes. This study was done to develop a clinically useful prediction model for fingerprint verification in patients with hand dermatitis. A case-control study involving 100 patients with hand dermatitis. All patients verified their thumbprints against their identity card. Registered fingerprints were randomized into a model derivation and model validation group. Predictive model was derived using multiple logistic regression. Validation was done using the goodness-of-fit test. The fingerprint verification prediction model consists of a major criterion (fingerprint dystrophy area of ≥ 25%) and two minor criteria (long horizontal lines and long vertical lines). The presence of the major criterion predicts it will almost always fail verification, while presence of both minor criteria and presence of one minor criterion predict high and low risk of fingerprint verification failure, respectively. When none of the criteria are met, the fingerprint almost always passes the verification. The area under the receiver operating characteristic curve was 0.937, and the goodness-of-fit test showed agreement between the observed and expected number (P = 0.26). The derived fingerprint verification failure prediction model is validated and highly discriminatory in predicting risk of fingerprint verification in patients with hand dermatitis. © 2014 The International Society of Dermatology.

  4. Massive Predictive Modeling using Oracle R Enterprise

    CERN Multimedia

    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...

  5. 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.

  6. Predictive Model of Systemic Toxicity (SOT)

    Science.gov (United States)

    In an effort to ensure chemical safety in light of regulatory advances away from reliance on animal testing, USEPA and L’Oréal have collaborated to develop a quantitative systemic toxicity prediction model. Prediction of human systemic toxicity has proved difficult and remains a ...

  7. 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.

  8. 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.

  9. 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.

  10. 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.

  11. 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.

  12. 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.

  13. 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.

  14. 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.

  15. 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.

  16. 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.

  17. 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.

  18. 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…

  19. Predicting and Modeling RNA Architecture

    Science.gov (United States)

    Westhof, Eric; Masquida, Benoît; Jossinet, Fabrice

    2011-01-01

    SUMMARY A general approach for modeling the architecture of large and structured RNA molecules is described. The method exploits the modularity and the hierarchical folding of RNA architecture that is viewed as the assembly of preformed double-stranded helices defined by Watson-Crick base pairs and RNA modules maintained by non-Watson-Crick base pairs. Despite the extensive molecular neutrality observed in RNA structures, specificity in RNA folding is achieved through global constraints like lengths of helices, coaxiality of helical stacks, and structures adopted at the junctions of helices. The Assemble integrated suite of computer tools allows for sequence and structure analysis as well as interactive modeling by homology or ab initio assembly with possibilities for fitting within electronic density maps. The local key role of non-Watson-Crick pairs guides RNA architecture formation and offers metrics for assessing the accuracy of three-dimensional models in a more useful way than usual root mean square deviation (RMSD) values. PMID:20504963

  20. Multiple Steps Prediction with Nonlinear ARX Models

    OpenAIRE

    Zhang, Qinghua; Ljung, Lennart

    2007-01-01

    NLARX (NonLinear AutoRegressive with eXogenous inputs) models are frequently used in black-box nonlinear system identication. Though it is easy to make one step ahead prediction with such models, multiple steps prediction is far from trivial. The main difficulty is that in general there is no easy way to compute the mathematical expectation of an output conditioned by past measurements. An optimal solution would require intensive numerical computations related to nonlinear filltering. The pur...

  1. 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.

  2. Model complexity control for hydrologic prediction

    Science.gov (United States)

    Schoups, G.; van de Giesen, N. C.; Savenije, H. H. G.

    2008-12-01

    A common concern in hydrologic modeling is overparameterization of complex models given limited and noisy data. This leads to problems of parameter nonuniqueness and equifinality, which may negatively affect prediction uncertainties. A systematic way of controlling model complexity is therefore needed. We compare three model complexity control methods for hydrologic prediction, namely, cross validation (CV), Akaike's information criterion (AIC), and structural risk minimization (SRM). Results show that simulation of water flow using non-physically-based models (polynomials in this case) leads to increasingly better calibration fits as the model complexity (polynomial order) increases. However, prediction uncertainty worsens for complex non-physically-based models because of overfitting of noisy data. Incorporation of physically based constraints into the model (e.g., storage-discharge relationship) effectively bounds prediction uncertainty, even as the number of parameters increases. The conclusion is that overparameterization and equifinality do not lead to a continued increase in prediction uncertainty, as long as models are constrained by such physical principles. Complexity control of hydrologic models reduces parameter equifinality and identifies the simplest model that adequately explains the data, thereby providing a means of hydrologic generalization and classification. SRM is a promising technique for this purpose, as it (1) provides analytic upper bounds on prediction uncertainty, hence avoiding the computational burden of CV, and (2) extends the applicability of classic methods such as AIC to finite data. The main hurdle in applying SRM is the need for an a priori estimation of the complexity of the hydrologic model, as measured by its Vapnik-Chernovenkis (VC) dimension. Further research is needed in this area.

  3. 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.

  4. Quantifying predictive accuracy in survival models.

    Science.gov (United States)

    Lirette, Seth T; Aban, Inmaculada

    2017-12-01

    For time-to-event outcomes in medical research, survival models are the most appropriate to use. Unlike logistic regression models, quantifying the predictive accuracy of these models is not a trivial task. We present the classes of concordance (C) statistics and R 2 statistics often used to assess the predictive ability of these models. The discussion focuses on Harrell's C, Kent and O'Quigley's R 2 , and Royston and Sauerbrei's R 2 . We present similarities and differences between the statistics, discuss the software options from the most widely used statistical analysis packages, and give a practical example using the Worcester Heart Attack Study dataset.

  5. Predictive power of nuclear-mass models

    Directory of Open Access Journals (Sweden)

    Yu. A. Litvinov

    2013-12-01

    Full Text Available Ten different theoretical models are tested for their predictive power in the description of nuclear masses. Two sets of experimental masses are used for the test: the older set of 2003 and the newer one of 2011. The predictive power is studied in two regions of nuclei: the global region (Z, N ≥ 8 and the heavy-nuclei region (Z ≥ 82, N ≥ 126. No clear correlation is found between the predictive power of a model and the accuracy of its description of the masses.

  6. 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...... find that confidence sets are very wide, change significantly with the predictor variables, and frequently include expected utilities for which the investor prefers not to invest. The latter motivates a robust investment strategy maximizing the minimal element of the confidence set. The robust investor...... allocates a much lower share of wealth to stocks compared to a standard investor....

  7. Improvement, Verification, and Refinement of Spatially-Explicit Exposure Models in Risk Assessment - FishRand Spatially-Explicit Bioaccumulation Model Demonstration

    Science.gov (United States)

    2015-08-01

    assessments. Site data provides an indication of current conditions. However, predictive models are required to evaluate the impact of potential...sediment sites. Bioaccumulation modeling approaches range from deriving empirical trophic relationships based on site-specific data, to dynamic...aquatic food webs incorporating site-specific data on trophic levels, species foraging strategies, and feeding preferences. Frequently, the

  8. 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.

  9. Food chain model to predict westslope cutthroat trout ovary selenium concentrations from water concentrations in the Elk Valley, BC

    International Nuclear Information System (INIS)

    Orr, P.; Wiramanaden, C.; Franklin, W.; Fraser, C.

    2010-01-01

    The 5 coal mines operated by Teck Coal Ltd. in British Columbia's Elk River watershed release selenium during weathering of mine waste rock. Since 1966, several field studies have been conducted in which selenium concentrations in biota were measured. They revealed that tissue concentrations are higher in aquatic biota sampled in lentic compared to lotic habitats of the watershed with similar water selenium concentrations. Two food chain models were developed based on the available data. The models described dietary selenium accumulation in the ovaries of lotic versus lentic westslope cutthroat trout (WCT), a valued aquatic resource in the Elk River system. The following 3 trophic transfer relationships were characterized for each model: (1) water to base of the food web, (2) base of the food web to benthic invertebrates, and (3) benthic invertebrates to WCT ovaries. The lotic and lentic models combined the resulting equations for each trophic transfer relationships to predict WCT ovary concentrations from water concentrations. The models were in very good agreement with the available data, despite fish movement and the fact that composite benthic invertebrate sample data were only an approximation of the feeding preferences of individual fish. Based on the observed rates of increase in water selenium concentrations throughout the watershed, the models predicted very small/slow increases in WCT ovary concentrations with time.

  10. Accuracy assessment of landslide prediction models

    International Nuclear Information System (INIS)

    Othman, A N; Mohd, W M N W; Noraini, S

    2014-01-01

    The increasing population and expansion of settlements over hilly areas has greatly increased the impact of natural disasters such as landslide. Therefore, it is important to developed models which could accurately predict landslide hazard zones. Over the years, various techniques and models have been developed to predict landslide hazard zones. The aim of this paper is to access the accuracy of landslide prediction models developed by the authors. The methodology involved the selection of study area, data acquisition, data processing and model development and also data analysis. The development of these models are based on nine different landslide inducing parameters i.e. slope, land use, lithology, soil properties, geomorphology, flow accumulation, aspect, proximity to river and proximity to road. Rank sum, rating, pairwise comparison and AHP techniques are used to determine the weights for each of the parameters used. Four (4) different models which consider different parameter combinations are developed by the authors. Results obtained are compared to landslide history and accuracies for Model 1, Model 2, Model 3 and Model 4 are 66.7, 66.7%, 60% and 22.9% respectively. From the results, rank sum, rating and pairwise comparison can be useful techniques to predict landslide hazard zones

  11. Are trophic and diversity indices based on macrophyte communities pertinent tools to monitor water quality?

    Science.gov (United States)

    Thiebaut, Gabrielle; Guérold, François; Muller, Serge

    2002-08-01

    Diversity and trophic indices based on macrophyte communities were calculated to test their pertinence to monitoring water quality in the Northern Vosges (NE of France). Highly significant correlations were found between the four tested chemical variables (bicarbonate, calcium, phosphorus and ammonium nitrogen) and trophic indices and between them and abundance and richness. Trophic indices and McIntosh's index appeared to be more effective in predicting water quality than diversity indices. Diversity indices do not necessarily provide any direct information on the quality or degree of degradation of the environment from which the sample was taken, whereas trophic indices do.

  12. Predictive validation of an influenza spread model.

    Directory of Open Access Journals (Sweden)

    Ayaz Hyder

    Full Text Available BACKGROUND: Modeling plays a critical role in mitigating impacts of seasonal influenza epidemics. Complex simulation models are currently at the forefront of evaluating optimal mitigation strategies at multiple scales and levels of organization. Given their evaluative role, these models remain limited in their ability to predict and forecast future epidemics leading some researchers and public-health practitioners to question their usefulness. The objective of this study is to evaluate the predictive ability of an existing complex simulation model of influenza spread. METHODS AND FINDINGS: We used extensive data on past epidemics to demonstrate the process of predictive validation. This involved generalizing an individual-based model for influenza spread and fitting it to laboratory-confirmed influenza infection data from a single observed epidemic (1998-1999. Next, we used the fitted model and modified two of its parameters based on data on real-world perturbations (vaccination coverage by age group and strain type. Simulating epidemics under these changes allowed us to estimate the deviation/error between the expected epidemic curve under perturbation and observed epidemics taking place from 1999 to 2006. Our model was able to forecast absolute intensity and epidemic peak week several weeks earlier with reasonable reliability and depended on the method of forecasting-static or dynamic. CONCLUSIONS: Good predictive ability of influenza epidemics is critical for implementing mitigation strategies in an effective and timely manner. Through the process of predictive validation applied to a current complex simulation model of influenza spread, we provided users of the model (e.g. public-health officials and policy-makers with quantitative metrics and practical recommendations on mitigating impacts of seasonal influenza epidemics. This methodology may be applied to other models of communicable infectious diseases to test and potentially improve

  13. Predictive Validation of an Influenza Spread Model

    Science.gov (United States)

    Hyder, Ayaz; Buckeridge, David L.; Leung, Brian

    2013-01-01

    Background Modeling plays a critical role in mitigating impacts of seasonal influenza epidemics. Complex simulation models are currently at the forefront of evaluating optimal mitigation strategies at multiple scales and levels of organization. Given their evaluative role, these models remain limited in their ability to predict and forecast future epidemics leading some researchers and public-health practitioners to question their usefulness. The objective of this study is to evaluate the predictive ability of an existing complex simulation model of influenza spread. Methods and Findings We used extensive data on past epidemics to demonstrate the process of predictive validation. This involved generalizing an individual-based model for influenza spread and fitting it to laboratory-confirmed influenza infection data from a single observed epidemic (1998–1999). Next, we used the fitted model and modified two of its parameters based on data on real-world perturbations (vaccination coverage by age group and strain type). Simulating epidemics under these changes allowed us to estimate the deviation/error between the expected epidemic curve under perturbation and observed epidemics taking place from 1999 to 2006. Our model was able to forecast absolute intensity and epidemic peak week several weeks earlier with reasonable reliability and depended on the method of forecasting-static or dynamic. Conclusions Good predictive ability of influenza epidemics is critical for implementing mitigation strategies in an effective and timely manner. Through the process of predictive validation applied to a current complex simulation model of influenza spread, we provided users of the model (e.g. public-health officials and policy-makers) with quantitative metrics and practical recommendations on mitigating impacts of seasonal influenza epidemics. This methodology may be applied to other models of communicable infectious diseases to test and potentially improve their predictive

  14. Simulated tri-trophic networks reveal complex relationships between species diversity and interaction diversity.

    Science.gov (United States)

    Pardikes, Nicholas A; Lumpkin, Will; Hurtado, Paul J; Dyer, Lee A

    2018-01-01

    Most of earth's biodiversity is comprised of interactions among species, yet it is unclear what causes variation in interaction diversity across space and time. We define interaction diversity as the richness and relative abundance of interactions linking species together at scales from localized, measurable webs to entire ecosystems. Large-scale patterns suggest that two basic components of interaction diversity differ substantially and predictably between different ecosystems: overall taxonomic diversity and host specificity of consumers. Understanding how these factors influence interaction diversity, and quantifying the causes and effects of variation in interaction diversity are important goals for community ecology. While previous studies have examined the effects of sampling bias and consumer specialization on determining patterns of ecological networks, these studies were restricted to two trophic levels and did not incorporate realistic variation in species diversity and consumer diet breadth. Here, we developed a food web model to generate tri-trophic ecological networks, and evaluated specific hypotheses about how the diversity of trophic interactions and species diversity are related under different scenarios of species richness, taxonomic abundance, and consumer diet breadth. We investigated the accumulation of species and interactions and found that interactions accumulate more quickly; thus, the accumulation of novel interactions may require less sampling effort than sampling species in order to get reliable estimates of either type of diversity. Mean consumer diet breadth influenced the correlation between species and interaction diversity significantly more than variation in both species richness and taxonomic abundance. However, this effect of diet breadth on interaction diversity is conditional on the number of observed interactions included in the models. The results presented here will help develop realistic predictions of the relationships

  15. Trophic signatures of seabirds suggest shifts in oceanic ecosystems.

    Science.gov (United States)

    Gagne, Tyler O; Hyrenbach, K David; Hagemann, Molly E; Van Houtan, Kyle S

    2018-02-01

    Pelagic ecosystems are dynamic ocean regions whose immense natural capital is affected by climate change, pollution, and commercial fisheries. Trophic level-based indicators derived from fishery catch data may reveal the food web status of these systems, but the utility of these metrics has been debated because of targeting bias in fisheries catch. We analyze a unique, fishery-independent data set of North Pacific seabird tissues to inform ecosystem trends over 13 decades (1890s to 2010s). Trophic position declined broadly in five of eight species sampled, indicating a long-term shift from higher-trophic level to lower-trophic level prey. No species increased their trophic position. Given species prey preferences, Bayesian diet reconstructions suggest a shift from fishes to squids, a result consistent with both catch reports and ecosystem models. Machine learning models further reveal that trophic position trends have a complex set of drivers including climate, commercial fisheries, and ecomorphology. Our results show that multiple species of fish-consuming seabirds may track the complex changes occurring in marine ecosystems.

  16. Trophic levels of fish species of commercial importance in the Colombian Caribbean

    Directory of Open Access Journals (Sweden)

    Camilo B García

    2011-09-01

    Full Text Available Ecological studies on commercial important fish species are of great value to support resource management issues. This study calculated trophic levels of those Colombian Caribbean fish species whose diet has been locally described. Usable diet data of 119 species resulted in 164 trophic level estimates. An ordinary regression model relating trophic level and fish size was formulated. The regression slope was positive and significantly different from zero (p<0.05 suggesting a scaling of trophic level with fish size. Both the list of trophic levels and the regression model should be of help in the formulation of trophic indicators and models of neotropical ecosystems. Rev. Biol. Trop. 59 (3: 1195-1203. Epub 2011 September 01.

  17. Posterior predictive checking of multiple imputation models.

    Science.gov (United States)

    Nguyen, Cattram D; Lee, Katherine J; Carlin, John B

    2015-07-01

    Multiple imputation is gaining popularity as a strategy for handling missing data, but there is a scarcity of tools for checking imputation models, a critical step in model fitting. Posterior predictive checking (PPC) has been recommended as an imputation diagnostic. PPC involves simulating "replicated" data from the posterior predictive distribution of the model under scrutiny. Model fit is assessed by examining whether the analysis from the observed data appears typical of results obtained from the replicates produced by the model. A proposed diagnostic measure is the posterior predictive "p-value", an extreme value of which (i.e., a value close to 0 or 1) suggests a misfit between the model and the data. The aim of this study was to evaluate the performance of the posterior predictive p-value as an imputation diagnostic. Using simulation methods, we deliberately misspecified imputation models to determine whether posterior predictive p-values were effective in identifying these problems. When estimating the regression parameter of interest, we found that more extreme p-values were associated with poorer imputation model performance, although the results highlighted that traditional thresholds for classical p-values do not apply in this context. A shortcoming of the PPC method was its reduced ability to detect misspecified models with increasing amounts of missing data. Despite the limitations of posterior predictive p-values, they appear to have a valuable place in the imputer's toolkit. In addition to automated checking using p-values, we recommend imputers perform graphical checks and examine other summaries of the test quantity distribution. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  18. 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...... in the Markov model for this task. Classifications that are purely based on statistical models might not always be biologically meaningful. We present combinatorial methods to incorporate biological background knowledge to enhance the prediction performance....

  19. 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...... on underlying basic assumptions, such as diffuse fields, high modal overlap, resonant field being dominant, etc., and the consequences of these in terms of limitations in the theory and in the practical use of the models....

  20. Comparative Study of Bancruptcy Prediction Models

    Directory of Open Access Journals (Sweden)

    Isye Arieshanti

    2013-09-01

    Full Text Available Early indication of bancruptcy is important for a company. If companies aware of  potency of their bancruptcy, they can take a preventive action to anticipate the bancruptcy. In order to detect the potency of a bancruptcy, a company can utilize a a model of bancruptcy prediction. The prediction model can be built using a machine learning methods. However, the choice of machine learning methods should be performed carefully. Because the suitability of a model depends on the problem specifically. Therefore, in this paper we perform a comparative study of several machine leaning methods for bancruptcy prediction. According to the comparative study, the performance of several models that based on machine learning methods (k-NN, fuzzy k-NN, SVM, Bagging Nearest Neighbour SVM, Multilayer Perceptron(MLP, Hybrid of MLP + Multiple Linear Regression, it can be showed that fuzzy k-NN method achieve the best performance with accuracy 77.5%

  1. Prediction Models for Dynamic Demand Response

    Energy Technology Data Exchange (ETDEWEB)

    Aman, Saima; Frincu, Marc; Chelmis, Charalampos; Noor, Muhammad; Simmhan, Yogesh; Prasanna, Viktor K.

    2015-11-02

    As Smart Grids move closer to dynamic curtailment programs, Demand Response (DR) events will become necessary not only on fixed time intervals and weekdays predetermined by static policies, but also during changing decision periods and weekends to react to real-time demand signals. Unique challenges arise in this context vis-a-vis demand prediction and curtailment estimation and the transformation of such tasks into an automated, efficient dynamic demand response (D2R) process. While existing work has concentrated on increasing the accuracy of prediction models for DR, there is a lack of studies for prediction models for D2R, which we address in this paper. Our first contribution is the formal definition of D2R, and the description of its challenges and requirements. Our second contribution is a feasibility analysis of very-short-term prediction of electricity consumption for D2R over a diverse, large-scale dataset that includes both small residential customers and large buildings. Our third, and major contribution is a set of insights into the predictability of electricity consumption in the context of D2R. Specifically, we focus on prediction models that can operate at a very small data granularity (here 15-min intervals), for both weekdays and weekends - all conditions that characterize scenarios for D2R. We find that short-term time series and simple averaging models used by Independent Service Operators and utilities achieve superior prediction accuracy. We also observe that workdays are more predictable than weekends and holiday. Also, smaller customers have large variation in consumption and are less predictable than larger buildings. Key implications of our findings are that better models are required for small customers and for non-workdays, both of which are critical for D2R. Also, prediction models require just few days’ worth of data indicating that small amounts of

  2. Are animal models predictive for humans?

    Directory of Open Access Journals (Sweden)

    Greek Ray

    2009-01-01

    Full Text Available Abstract It is one of the central aims of the philosophy of science to elucidate the meanings of scientific terms and also to think critically about their application. The focus of this essay is the scientific term predict and whether there is credible evidence that animal models, especially in toxicology and pathophysiology, can be used to predict human outcomes. Whether animals can be used to predict human response to drugs and other chemicals is apparently a contentious issue. However, when one empirically analyzes animal models using scientific tools they fall far short of being able to predict human responses. This is not surprising considering what we have learned from fields such evolutionary and developmental biology, gene regulation and expression, epigenetics, complexity theory, and comparative genomics.

  3. 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.

  4. Trophic transfer of pyrene metabolites between aquatic invertebrates

    International Nuclear Information System (INIS)

    Carrasco Navarro, V.; Leppänen, M.T.; Kukkonen, J.V.K.; Godoy Olmos, S.

    2013-01-01

    The trophic transfer of pyrene metabolites was studied using Gammarus setosus as a predator and the invertebrates Lumbriculus variegatus and Chironomus riparius as prey. The results obtained by liquid scintillation counting confirmed that the pyrene metabolites produced by the aquatic invertebrates L. variegatus and C. riparius were transferred to G. setosus through the diet. More detailed analyses by liquid chromatography discovered that two of the metabolites produced by C. riparius appeared in the chromatograms of G. setosus tissue extracts, proving their trophic transfer. These metabolites were not present in chromatograms of G. setosus exclusively exposed to pyrene. The present study supports the trophic transfer of PAH metabolites between benthic macroinvertebrates and common species of an arctic amphipod. As some PAH metabolites are more toxic than the parent compounds, the present study raises concerns about the consequences of their trophic transfer and the fate and effects of PAHs in natural environments. - Highlights: ► The trophic transfer of pyrene metabolites between invertebrates was evaluated. ► Biotransformation of pyrene by L. variegatus and C. riparius is different. ► Metabolites produced by L. variegatus and C. riparius are transferred to G. setosus. ► Specifically, two metabolites produced by C. riparius were transferred. - Some of the pyrene metabolites produced by the model invertebrates L. variegatus and C. riparius are transferred to G. setosus through the diet, proving their trophic transfer.

  5. Model predictive controller design of hydrocracker reactors

    OpenAIRE

    GÖKÇE, Dila

    2014-01-01

    This study summarizes the design of a Model Predictive Controller (MPC) in Tüpraş, İzmit Refinery Hydrocracker Unit Reactors. Hydrocracking process, in which heavy vacuum gasoil is converted into lighter and valuable products at high temperature and pressure is described briefly. Controller design description, identification and modeling studies are examined and the model variables are presented. WABT (Weighted Average Bed Temperature) equalization and conversion increase are simulate...

  6. Multi-Model Ensemble Wake Vortex Prediction

    Science.gov (United States)

    Koerner, Stephan; Holzaepfel, Frank; Ahmad, Nash'at N.

    2015-01-01

    Several multi-model ensemble methods are investigated for predicting wake vortex transport and decay. This study is a joint effort between National Aeronautics and Space Administration and Deutsches Zentrum fuer Luft- und Raumfahrt to develop a multi-model ensemble capability using their wake models. An overview of different multi-model ensemble methods and their feasibility for wake applications is presented. The methods include Reliability Ensemble Averaging, Bayesian Model Averaging, and Monte Carlo Simulations. The methodologies are evaluated using data from wake vortex field experiments.

  7. Thermodynamic modeling of activity coefficient and prediction of solubility: Part 1. Predictive models.

    Science.gov (United States)

    Mirmehrabi, Mahmoud; Rohani, Sohrab; Perry, Luisa

    2006-04-01

    A new activity coefficient model was developed from excess Gibbs free energy in the form G(ex) = cA(a) x(1)(b)...x(n)(b). The constants of the proposed model were considered to be function of solute and solvent dielectric constants, Hildebrand solubility parameters and specific volumes of solute and solvent molecules. The proposed model obeys the Gibbs-Duhem condition for activity coefficient models. To generalize the model and make it as a purely predictive model without any adjustable parameters, its constants were found using the experimental activity coefficient and physical properties of 20 vapor-liquid systems. The predictive capability of the proposed model was tested by calculating the activity coefficients of 41 binary vapor-liquid equilibrium systems and showed good agreement with the experimental data in comparison with two other predictive models, the UNIFAC and Hildebrand models. The only data used for the prediction of activity coefficients, were dielectric constants, Hildebrand solubility parameters, and specific volumes of the solute and solvent molecules. Furthermore, the proposed model was used to predict the activity coefficient of an organic compound, stearic acid, whose physical properties were available in methanol and 2-butanone. The predicted activity coefficient along with the thermal properties of the stearic acid were used to calculate the solubility of stearic acid in these two solvents and resulted in a better agreement with the experimental data compared to the UNIFAC and Hildebrand predictive models.

  8. 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.

  9. A revised prediction model for natural conception.

    Science.gov (United States)

    Bensdorp, Alexandra J; van der Steeg, Jan Willem; Steures, Pieternel; Habbema, J Dik F; Hompes, Peter G A; Bossuyt, Patrick M M; van der Veen, Fulco; Mol, Ben W J; Eijkemans, Marinus J C

    2017-06-01

    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 was to assess whether additional predictors can refine the Hunault model and extend its applicability. Consecutive subfertile couples with unexplained and mild male subfertility presenting in fertility clinics were asked to participate in a prospective cohort study. We constructed a multivariable prediction model with the predictors from the Hunault model and new potential predictors. The primary outcome, natural conception leading to an ongoing pregnancy, was observed in 1053 women of the 5184 included couples (20%). All predictors of the Hunault model were selected into the revised model plus an additional seven (woman's body mass index, cycle length, basal FSH levels, tubal status,history of previous pregnancies in the current relationship (ongoing pregnancies after natural conception, fertility treatment or miscarriages), semen volume, and semen morphology. Predictions from the revised model seem to concur better with observed pregnancy rates compared with the Hunault model; c-statistic of 0.71 (95% CI 0.69 to 0.73) compared with 0.59 (95% CI 0.57 to 0.61). Copyright © 2017. Published by Elsevier Ltd.

  10. Modelling the predictive performance of credit scoring

    Directory of Open Access Journals (Sweden)

    Shi-Wei Shen

    2013-07-01

    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.

  11. 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.

  12. 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 cont...... benchmark model. Due to the inherent constraints the applied approach is based on Model Predictive Control....... 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...

  13. Bayesian Predictive Models for Rayleigh Wind Speed

    DEFF Research Database (Denmark)

    Shahirinia, Amir; Hajizadeh, Amin; Yu, David C

    2017-01-01

    predictive model of the wind speed aggregates the non-homogeneous distributions into a single continuous distribution. Therefore, the result is able to capture the variation among the probability distributions of the wind speeds at the turbines’ locations in a wind farm. More specifically, instead of using...... a wind speed distribution whose parameters are known or estimated, the parameters are considered as random whose variations are according to probability distributions. The Bayesian predictive model for a Rayleigh which only has a single model scale parameter has been proposed. Also closed-form posterior......One of the major challenges with the increase in wind power generation is the uncertain nature of wind speed. So far the uncertainty about wind speed has been presented through probability distributions. Also the existing models that consider the uncertainty of the wind speed primarily view...

  14. Comparison of two ordinal prediction models

    DEFF Research Database (Denmark)

    Kattan, Michael W; Gerds, Thomas A

    2015-01-01

    system (i.e. old or new), such as the level of evidence for one or more factors included in the system or the general opinions of expert clinicians. However, given the major objective of estimating prognosis on an ordinal scale, we argue that the rival staging system candidates should be compared...... on their ability to predict outcome. We sought to outline an algorithm that would compare two rival ordinal systems on their predictive ability. RESULTS: We devised an algorithm based largely on the concordance index, which is appropriate for comparing two models in their ability to rank observations. We...... demonstrate our algorithm with a prostate cancer staging system example. CONCLUSION: We have provided an algorithm for selecting the preferred staging system based on prognostic accuracy. It appears to be useful for the purpose of selecting between two ordinal prediction models....

  15. Predictive analytics can support the ACO model.

    Science.gov (United States)

    Bradley, Paul

    2012-04-01

    Predictive analytics can be used to rapidly spot hard-to-identify opportunities to better manage care--a key tool in accountable care. When considering analytics models, healthcare providers should: Make value-based care a priority and act on information from analytics models. Create a road map that includes achievable steps, rather than major endeavors. Set long-term expectations and recognize that the effectiveness of an analytics program takes time, unlike revenue cycle initiatives that may show a quick return.

  16. Predictive modeling in homogeneous catalysis: a tutorial

    NARCIS (Netherlands)

    Maldonado, A.G.; Rothenberg, G.

    2010-01-01

    Predictive modeling has become a practical research tool in homogeneous catalysis. It can help to pinpoint ‘good regions’ in the catalyst space, narrowing the search for the optimal catalyst for a given reaction. Just like any other new idea, in silico catalyst optimization is accepted by some

  17. Model predictive control of smart microgrids

    DEFF Research Database (Denmark)

    Hu, Jiefeng; Zhu, Jianguo; Guerrero, Josep M.

    2014-01-01

    required to realise high-performance of distributed generations and will realise innovative control techniques utilising model predictive control (MPC) to assist in coordinating the plethora of generation and load combinations, thus enable the effective exploitation of the clean renewable energy sources...

  18. Feedback model predictive control by randomized algorithms

    NARCIS (Netherlands)

    Batina, Ivo; Stoorvogel, Antonie Arij; Weiland, Siep

    2001-01-01

    In this paper we present a further development of an algorithm for stochastic disturbance rejection in model predictive control with input constraints based on randomized algorithms. The algorithm presented in our work can solve the problem of stochastic disturbance rejection approximately but with

  19. A Robustly Stabilizing Model Predictive Control Algorithm

    Science.gov (United States)

    Ackmece, A. Behcet; Carson, John M., III

    2007-01-01

    A model predictive control (MPC) algorithm that differs from prior MPC algorithms has been developed for controlling an uncertain nonlinear system. This algorithm guarantees the resolvability of an associated finite-horizon optimal-control problem in a receding-horizon implementation.

  20. 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...

  1. 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 ...

  2. Comparing trophic flows in the southern Benguela to those in other ...

    African Journals Online (AJOL)

    A balanced trophic flow model of the southern Benguela ecosystem is presented, averaging the period 1980–1989 and emphasizing upper trophic levels. The model is based largely on studies conducted within the framework of the Benguela Ecology Programme and updates the results of an expert workshop held in Cape ...

  3. 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

  4. 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.

  5. Link Prediction via Sparse Gaussian Graphical Model

    Directory of Open Access Journals (Sweden)

    Liangliang Zhang

    2016-01-01

    Full Text Available Link prediction is an important task in complex network analysis. Traditional link prediction methods are limited by network topology and lack of node property information, which makes predicting links challenging. In this study, we address link prediction using a sparse Gaussian graphical model and demonstrate its theoretical and practical effectiveness. In theory, link prediction is executed by estimating the inverse covariance matrix of samples to overcome information limits. The proposed method was evaluated with four small and four large real-world datasets. The experimental results show that the area under the curve (AUC value obtained by the proposed method improved by an average of 3% and 12.5% compared to 13 mainstream similarity methods, respectively. This method outperforms the baseline method, and the prediction accuracy is superior to mainstream methods when using only 80% of the training set. The method also provides significantly higher AUC values when using only 60% in Dolphin and Taro datasets. Furthermore, the error rate of the proposed method demonstrates superior performance with all datasets compared to mainstream methods.

  6. 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.)

  7. 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.

  8. Genetic models of homosexuality: generating testable predictions

    Science.gov (United States)

    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 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. PMID:17015344

  9. A statistical model for predicting muscle performance

    Science.gov (United States)

    Byerly, Diane Leslie De Caix

    The objective of these studies was to develop a capability for predicting muscle performance and fatigue to be utilized for both space- and ground-based applications. To develop this predictive model, healthy test subjects performed a defined, repetitive dynamic exercise to failure using a Lordex spinal machine. Throughout the exercise, surface electromyography (SEMG) data were collected from the erector spinae using a Mega Electronics ME3000 muscle tester and surface electrodes placed on both sides of the back muscle. These data were analyzed using a 5th order Autoregressive (AR) model and statistical regression analysis. It was determined that an AR derived parameter, the mean average magnitude of AR poles, significantly correlated with the maximum number of repetitions (designated Rmax) that a test subject was able to perform. Using the mean average magnitude of AR poles, a test subject's performance to failure could be predicted as early as the sixth repetition of the exercise. This predictive model has the potential to provide a basis for improving post-space flight recovery, monitoring muscle atrophy in astronauts and assessing the effectiveness of countermeasures, monitoring astronaut performance and fatigue during Extravehicular Activity (EVA) operations, providing pre-flight assessment of the ability of an EVA crewmember to perform a given task, improving the design of training protocols and simulations for strenuous International Space Station assembly EVA, and enabling EVA work task sequences to be planned enhancing astronaut performance and safety. Potential ground-based, medical applications of the predictive model include monitoring muscle deterioration and performance resulting from illness, establishing safety guidelines in the industry for repetitive tasks, monitoring the stages of rehabilitation for muscle-related injuries sustained in sports and accidents, and enhancing athletic performance through improved training protocols while reducing

  10. 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

  11. 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.

  12. 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.

  13. Trophic interaction modifications: an empirical and theoretical framework.

    Science.gov (United States)

    Terry, J Christopher D; Morris, Rebecca J; Bonsall, Michael B

    2017-10-01

    Consumer-resource interactions are often influenced by other species in the community. At present these 'trophic interaction modifications' are rarely included in ecological models despite demonstrations that they can drive system dynamics. Here, we advocate and extend an approach that has the potential to unite and represent this key group of non-trophic interactions by emphasising the change to trophic interactions induced by modifying species. We highlight the opportunities this approach brings in comparison to frameworks that coerce trophic interaction modifications into pairwise relationships. To establish common frames of reference and explore the value of the approach, we set out a range of metrics for the 'strength' of an interaction modification which incorporate increasing levels of contextual information about the system. Through demonstrations in three-species model systems, we establish that these metrics capture complimentary aspects of interaction modifications. We show how the approach can be used in a range of empirical contexts; we identify as specific gaps in current understanding experiments with multiple levels of modifier species and the distributions of modifications in networks. The trophic interaction modification approach we propose can motivate and unite empirical and theoretical studies of system dynamics, providing a route to confront ecological complexity. © 2017 The Authors. Ecology Letters published by CNRS and John Wiley & Sons Ltd.

  14. 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.

  15. Trophic state monitoring of lakes and reservoirs using remote sensing

    Science.gov (United States)

    Aten, Michelle L.

    Lakes and reservoirs are important resources that provide water for critical needs, such as drinking water, agriculture, recreation, fisheries, wildlife, and other uses. However, there is increasing concern that anthropogenic eutrophication threatens the usability of these natural resources. Therefore, this research investigates these complex hydrologic ecosystems and recommends a methodology for monitoring the trophic state of lakes and reservoirs using remote sensing data. The Mississippi Department of Environmental Quality provided in situ data for seven Mississippi lakes including, Arkabutla, Bay Springs, Enid, Grenada, Okatibbee, Ross Barnett, and Sardis lakes. This research explored the relationships between the Secchi depth (SD), chlorophyll-a (CHL), and total phosphorus (TP) in situ data and Moderate Resolution Imaging Spectroradiometer (MODIS) spectral reflectance data. This was accomplished by deriving Carlson Trophic State Index values for each in situ measurements and using these TSI(SD), TSI(CHL), and TSI(TP) values to evaluate potential predictive methods. Simple linear regression was performed to quantify the strength of the relationships between the in situ data and MODIS surface reflectance values. However, R-square values were too low and inconsistent to justify additional analyses. Therefore, machine learning models from the Waikato Environment for Knowledge Analysis (WEKA) software workbench were explored and tested. Optimal predictive models and settings were investigated for two meta-learner classifiers, three Bayesian classifiers and three decision tree classifiers. The Classification Via Regression yielded the best results when using large datasets, the all-but-one iteration setting, MODIS A1 individual bands as predictors, and TSI(SD) as targets. For this model and these settings, the percentages of correctly classified instances ranged from 77.74% to 81.98% and kappa values ranged from 0.41 to 0.48. The percentage of correctly classified

  16. 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.

  17. 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.

  18. Predictive Models for Carcinogenicity and Mutagenicity ...

    Science.gov (United States)

    Mutagenicity and carcinogenicity are endpoints of major environmental and regulatory concern. These endpoints are also important targets for development of alternative methods for screening and prediction due to the large number of chemicals of potential concern and the tremendous cost (in time, money, animals) of rodent carcinogenicity bioassays. Both mutagenicity and carcinogenicity involve complex, cellular processes that are only partially understood. Advances in technologies and generation of new data will permit a much deeper understanding. In silico methods for predicting mutagenicity and rodent carcinogenicity based on chemical structural features, along with current mutagenicity and carcinogenicity data sets, have performed well for local prediction (i.e., within specific chemical classes), but are less successful for global prediction (i.e., for a broad range of chemicals). The predictivity of in silico methods can be improved by improving the quality of the data base and endpoints used for modelling. In particular, in vitro assays for clastogenicity need to be improved to reduce false positives (relative to rodent carcinogenicity) and to detect compounds that do not interact directly with DNA or have epigenetic activities. New assays emerging to complement or replace some of the standard assays include VitotoxTM, GreenScreenGC, and RadarScreen. The needs of industry and regulators to assess thousands of compounds necessitate the development of high-t

  19. 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

  20. 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...

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

    DEFF Research Database (Denmark)

    Stolfi, A.

    2015-01-01

    values of L (0.254 mm, 0.330 mm) was produced by comparing predicted values with external face-to-face measurements. After removing outliers, the results show that the developed two-parameter model can serve as tool for modeling the FDM dimensional behavior in a wide range of deposition angles....

  2. 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...... values of L (0.254 mm, 0.330 mm) was produced by comparing predicted values with external face-to-face measurements. After removing outliers, the results show that the developed two-parameter model can serve as tool for modeling the FDM dimensional behavior in a wide range of deposition angles....

  3. 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

  4. 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

  5. The effects of spatial scale on trophic interactions

    NARCIS (Netherlands)

    Van de Koppel, J.; Bardgett, R.D.; Bengtsson, J.; Rodriguez-Barrueco, C.; Rietkerk, M.; 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

  6. 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.

  7. Predictive modelling of evidence informed teaching

    OpenAIRE

    Zhang, Dell; Brown, C.

    2017-01-01

    In this paper, we analyse the questionnaire survey data collected from 79 English primary schools about the situation of evidence informed teaching, where the evidences could come from research journals or conferences. Specifically, we build a predictive model to see what external factors could help to close the gap between teachers’ belief and behaviour in evidence informed teaching, which is the first of its kind to our knowledge. The major challenge, from the data mining perspective, is th...

  8. A Predictive Model for Cognitive Radio

    Science.gov (United States)

    2006-09-14

    response in a given situation. Vadde et al. interest and produce a model for prediction of the response. have applied response surface methodology and...34 2000. [3] K. K. Vadde and V. R. Syrotiuk, "Factor interaction on service configurations to those that best meet our communication delivery in mobile ad...resulting set of configurations randomly or apply additional 2004. screening criteria. [4] K. K. Vadde , M.-V. R. Syrotiuk, and D. C. Montgomery

  9. Tectonic predictions with mantle convection models

    Science.gov (United States)

    Coltice, Nicolas; Shephard, Grace E.

    2018-04-01

    Over the past 15 yr, numerical models of convection in Earth's mantle have made a leap forward: they can now produce self-consistent plate-like behaviour at the surface together with deep mantle circulation. These digital tools provide a new window into the intimate connections between plate tectonics and mantle dynamics, and can therefore be used for tectonic predictions, in principle. This contribution explores this assumption. First, initial conditions at 30, 20, 10 and 0 Ma are generated by driving a convective flow with imposed plate velocities at the surface. We then compute instantaneous mantle flows in response to the guessed temperature fields without imposing any boundary conditions. Plate boundaries self-consistently emerge at correct locations with respect to reconstructions, except for small plates close to subduction zones. As already observed for other types of instantaneous flow calculations, the structure of the top boundary layer and upper-mantle slab is the dominant character that leads to accurate predictions of surface velocities. Perturbations of the rheological parameters have little impact on the resulting surface velocities. We then compute fully dynamic model evolution from 30 and 10 to 0 Ma, without imposing plate boundaries or plate velocities. Contrary to instantaneous calculations, errors in kinematic predictions are substantial, although the plate layout and kinematics in several areas remain consistent with the expectations for the Earth. For these calculations, varying the rheological parameters makes a difference for plate boundary evolution. Also, identified errors in initial conditions contribute to first-order kinematic errors. This experiment shows that the tectonic predictions of dynamic models over 10 My are highly sensitive to uncertainties of rheological parameters and initial temperature field in comparison to instantaneous flow calculations. Indeed, the initial conditions and the rheological parameters can be good enough

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

    DEFF Research Database (Denmark)

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

    2011-01-01

    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...

  11. Predictive Modeling of the CDRA 4BMS

    Science.gov (United States)

    Coker, Robert F.; Knox, James C.

    2016-01-01

    As part of NASA's Advanced Exploration Systems (AES) program and the Life Support Systems Project (LSSP), fully predictive models of the Four Bed Molecular Sieve (4BMS) of the Carbon Dioxide Removal Assembly (CDRA) on the International Space Station (ISS) 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.

  12. Predictive Modeling by the Cerebellum Improves Proprioception

    Science.gov (United States)

    Bhanpuri, Nasir H.; Okamura, Allison M.

    2013-01-01

    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. PMID:24005283

  13. Prediction of Chemical Function: Model Development and ...

    Science.gov (United States)

    The United States Environmental Protection Agency’s Exposure Forecaster (ExpoCast) project is developing both statistical and mechanism-based computational models for predicting exposures to thousands of chemicals, including those in consumer products. The high-throughput (HT) screening-level exposures developed under ExpoCast can be combined with HT screening (HTS) bioactivity data for the risk-based prioritization of chemicals for further evaluation. The functional role (e.g. solvent, plasticizer, fragrance) that a chemical performs can drive both the types of products in which it is found and the concentration in which it is present and therefore impacting exposure potential. However, critical chemical use information (including functional role) is lacking for the majority of commercial chemicals for which exposure estimates are needed. A suite of machine-learning based models for classifying chemicals in terms of their likely functional roles in products based on structure were developed. This effort required collection, curation, and harmonization of publically-available data sources of chemical functional use information from government and industry bodies. Physicochemical and structure descriptor data were generated for chemicals with function data. Machine-learning classifier models for function were then built in a cross-validated manner from the descriptor/function data using the method of random forests. The models were applied to: 1) predict chemi

  14. 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...

  15. 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.

  16. 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.

  17. Evaluating predictive models of software quality

    International Nuclear Information System (INIS)

    Ciaschini, V; Canaparo, M; Ronchieri, E; Salomoni, D

    2014-01-01

    Applications from High Energy Physics scientific community are constantly growing and implemented by a large number of developers. This implies a strong churn on the code and an associated risk of faults, which is unavoidable as long as the software undergoes active evolution. However, the necessities of production systems run counter to this. Stability and predictability are of paramount importance; in addition, a short turn-around time for the defect discovery-correction-deployment cycle is required. A way to reconcile these opposite foci is to use a software quality model to obtain an approximation of the risk before releasing a program to only deliver software with a risk lower than an agreed threshold. In this article we evaluated two quality predictive models to identify the operational risk and the quality of some software products. We applied these models to the development history of several EMI packages with intent to discover the risk factor of each product and compare it with its real history. We attempted to determine if the models reasonably maps reality for the applications under evaluation, and finally we concluded suggesting directions for further studies.

  18. 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

  19. PREDICTION MODELS OF GRAIN YIELD AND CHARACTERIZATION

    Directory of Open Access Journals (Sweden)

    Narciso Ysac Avila Serrano

    2009-06-01

    Full Text Available With the objective to characterize the grain yield of five cowpea cultivars and to find linear regression models to predict it, a study was developed in La Paz, Baja California Sur, Mexico. A complete randomized blocks design was used. Simple and multivariate analyses of variance were carried out using the canonical variables to characterize the cultivars. The variables cluster per plant, pods per plant, pods per cluster, seeds weight per plant, seeds hectoliter weight, 100-seed weight, seeds length, seeds wide, seeds thickness, pods length, pods wide, pods weight, seeds per pods, and seeds weight per pods, showed significant differences (P≤ 0.05 among cultivars. Paceño and IT90K-277-2 cultivars showed the higher seeds weight per plant. The linear regression models showed correlation coefficients ≥0.92. In these models, the seeds weight per plant, pods per cluster, pods per plant, cluster per plant and pods length showed significant correlations (P≤ 0.05. In conclusion, the results showed that grain yield differ among cultivars and for its estimation, the prediction models showed determination coefficients highly dependable.

  20. Predictive Models for Normal Fetal Cardiac Structures.

    Science.gov (United States)

    Krishnan, Anita; Pike, Jodi I; McCarter, Robert; Fulgium, Amanda L; Wilson, Emmanuel; Donofrio, Mary T; Sable, Craig A

    2016-12-01

    Clinicians rely on age- and size-specific measures of cardiac structures to diagnose cardiac disease. No universally accepted normative data exist for fetal cardiac structures, and most fetal cardiac centers do not use the same standards. The aim of this study was to derive predictive models for Z scores for 13 commonly evaluated fetal cardiac structures using a large heterogeneous population of fetuses without structural cardiac defects. The study used archived normal fetal echocardiograms in representative fetuses aged 12 to 39 weeks. Thirteen cardiac dimensions were remeasured by a blinded echocardiographer from digitally stored clips. Studies with inadequate imaging views were excluded. Regression models were developed to relate each dimension to estimated gestational age (EGA) by dates, biparietal diameter, femur length, and estimated fetal weight by the Hadlock formula. Dimension outcomes were transformed (e.g., using the logarithm or square root) as necessary to meet the normality assumption. Higher order terms, quadratic or cubic, were added as needed to improve model fit. Information criteria and adjusted R 2 values were used to guide final model selection. Each Z-score equation is based on measurements derived from 296 to 414 unique fetuses. EGA yielded the best predictive model for the majority of dimensions; adjusted R 2 values ranged from 0.72 to 0.893. However, each of the other highly correlated (r > 0.94) biometric parameters was an acceptable surrogate for EGA. In most cases, the best fitting model included squared and cubic terms to introduce curvilinearity. For each dimension, models based on EGA provided the best fit for determining normal measurements of fetal cardiac structures. Nevertheless, other biometric parameters, including femur length, biparietal diameter, and estimated fetal weight provided results that were nearly as good. Comprehensive Z-score results are available on the basis of highly predictive models derived from gestational

  1. An analytical model for climatic predictions

    International Nuclear Information System (INIS)

    Njau, E.C.

    1990-12-01

    A climatic model based upon analytical expressions is presented. This model is capable of making long-range predictions of heat energy variations on regional or global scales. These variations can then be transformed into corresponding variations of some other key climatic parameters since weather and climatic changes are basically driven by differential heating and cooling around the earth. On the basis of the mathematical expressions upon which the model is based, it is shown that the global heat energy structure (and hence the associated climatic system) are characterized by zonally as well as latitudinally propagating fluctuations at frequencies downward of 0.5 day -1 . We have calculated the propagation speeds for those particular frequencies that are well documented in the literature. The calculated speeds are in excellent agreement with the measured speeds. (author). 13 refs

  2. An Anisotropic Hardening Model for Springback Prediction

    International Nuclear Information System (INIS)

    Zeng, Danielle; Xia, Z. Cedric

    2005-01-01

    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

  3. Web tools for predictive toxicology model building.

    Science.gov (United States)

    Jeliazkova, Nina

    2012-07-01

    The development and use of web tools in chemistry has accumulated more than 15 years of history already. Powered by the advances in the Internet technologies, the current generation of web systems are starting to expand into areas, traditional for desktop applications. The web platforms integrate data storage, cheminformatics and data analysis tools. The ease of use and the collaborative potential of the web is compelling, despite the challenges. The topic of this review is a set of recently published web tools that facilitate predictive toxicology model building. The focus is on software platforms, offering web access to chemical structure-based methods, although some of the frameworks could also provide bioinformatics or hybrid data analysis functionalities. A number of historical and current developments are cited. In order to provide comparable assessment, the following characteristics are considered: support for workflows, descriptor calculations, visualization, modeling algorithms, data management and data sharing capabilities, availability of GUI or programmatic access and implementation details. The success of the Web is largely due to its highly decentralized, yet sufficiently interoperable model for information access. The expected future convergence between cheminformatics and bioinformatics databases provides new challenges toward management and analysis of large data sets. The web tools in predictive toxicology will likely continue to evolve toward the right mix of flexibility, performance, scalability, interoperability, sets of unique features offered, friendly user interfaces, programmatic access for advanced users, platform independence, results reproducibility, curation and crowdsourcing utilities, collaborative sharing and secure access.

  4. [Endometrial cancer: Predictive models and clinical impact].

    Science.gov (United States)

    Bendifallah, Sofiane; Ballester, Marcos; Daraï, Emile

    2017-12-01

    In France, in 2015, endometrial cancer (CE) is the first gynecological cancer in terms of incidence and the fourth cause of cancer of the woman. About 8151 new cases and nearly 2179 deaths have been reported. Treatments (surgery, external radiotherapy, brachytherapy and chemotherapy) are currently delivered on the basis of an estimation of the recurrence risk, an estimation of lymph node metastasis or an estimate of survival probability. This risk is determined on the basis of prognostic factors (clinical, histological, imaging, biological) taken alone or grouped together in the form of classification systems, which are currently insufficient to account for the evolutionary and prognostic heterogeneity of endometrial cancer. For endometrial cancer, the concept of mathematical modeling and its application to prediction have developed in recent years. These biomathematical tools have opened a new era of care oriented towards the promotion of targeted therapies and personalized treatments. Many predictive models have been published to estimate the risk of recurrence and lymph node metastasis, but a tiny fraction of them is sufficiently relevant and of clinical utility. The optimization tracks are multiple and varied, suggesting the possibility in the near future of a place for these mathematical models. The development of high-throughput genomics is likely to offer a more detailed molecular characterization of the disease and its heterogeneity. Copyright © 2017 Société Française du Cancer. Published by Elsevier Masson SAS. All rights reserved.

  5. 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.

  6. Predictions of models for environmental radiological assessment

    International Nuclear Information System (INIS)

    Peres, Sueli da Silva; Lauria, Dejanira da Costa; Mahler, Claudio Fernando

    2011-01-01

    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 137 Cs and 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)

  7. 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.

  8. 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.

  9. Trophic state index of a lake system using IRS (P6-LISS III) satellite imagery.

    Science.gov (United States)

    Sheela, A M; Letha, J; Joseph, Sabu; Ramachandran, K K; Sanalkumar, S P

    2011-06-01

    Water pollution has now become a major threat to the existence of living beings and water quality monitoring is an effective step towards the restoration of water quality. Lakes are versatile ecosystems and their eutrophication is a serious problem. Carlson Trophic State Index (CTSI) provides an insight into the trophic condition of a lake. CTSI has been modified for the study area and is used in this study. Satellite imagery analysis now plays a prominent role in the quick assessment of water quality in a vast area. This study is an attempt to assess the trophic state index based on secchi disk depth and chlorophyll a of a lake system (Akkulam-Veli lake, Kerala, India) using Indian Remote Sensing (IRS) P6 LISS III imagery. Field data were collected on the date of the overpass of the satellite. Multiple regression equation is found to yield superior results than the simple regression equations using spectral ratios and radiance from the individual bands, for the prediction of trophic state index from satellite imagery. The trophic state index based on secchi disk depth, derived from the satellite imagery, provides an accurate prediction of the trophic status of the lake. IRS P6-LISS III imagery can be effectively used for the assessment of the trophic condition of a lake system.

  10. Changes in the trophic structure of the southern Benguela before ...

    African Journals Online (AJOL)

    Ecopath with Ecosim was used to construct and compare mass-balanced foodweb models of the southern Benguela ecosystem, representing the following eras of ... trophic control mechanism becoming the dominant driver of ecosystem dynamics, increasing the impact of environmental events including climate change.

  11. Combining GPS measurements and IRI model predictions

    International Nuclear Information System (INIS)

    Hernandez-Pajares, M.; Juan, J.M.; Sanz, J.; Bilitza, D.

    2002-01-01

    The free electrons distributed in the ionosphere (between one hundred and thousands of km in height) produce a frequency-dependent effect on Global Positioning System (GPS) signals: a delay in the pseudo-orange and an advance in the carrier phase. These effects are proportional to the columnar electron density between the satellite and receiver, i.e. the integrated electron density along the ray path. Global ionospheric TEC (total electron content) maps can be obtained with GPS data from a network of ground IGS (international GPS service) reference stations with an accuracy of few TEC units. The comparison with the TOPEX TEC, mainly measured over the oceans far from the IGS stations, shows a mean bias and standard deviation of about 2 and 5 TECUs respectively. The discrepancies between the STEC predictions and the observed values show an RMS typically below 5 TECUs (which also includes the alignment code noise). he existence of a growing database 2-hourly global TEC maps and with resolution of 5x2.5 degrees in longitude and latitude can be used to improve the IRI prediction capability of the TEC. When the IRI predictions and the GPS estimations are compared for a three month period around the Solar Maximum, they are in good agreement for middle latitudes. An over-determination of IRI TEC has been found at the extreme latitudes, the IRI predictions being, typically two times higher than the GPS estimations. Finally, local fits of the IRI model can be done by tuning the SSN from STEC GPS observations

  12. Mathematical models for indoor radon prediction

    International Nuclear Information System (INIS)

    Malanca, A.; Pessina, V.; Dallara, G.

    1995-01-01

    It is known that the indoor radon (Rn) concentration can be predicted by means of mathematical models. The simplest model relies on two variables only: the Rn source strength and the air exchange rate. In the Lawrence Berkeley Laboratory (LBL) model several environmental parameters are combined into a complex equation; besides, a correlation between the ventilation rate and the Rn entry rate from the soil is admitted. The measurements were carried out using activated carbon canisters. Seventy-five measurements of Rn concentrations were made inside two rooms placed on the second floor of a building block. One of the rooms had a single-glazed window whereas the other room had a double pane window. During three different experimental protocols, the mean Rn concentration was always higher into the room with a double-glazed window. That behavior can be accounted for by the simplest model. A further set of 450 Rn measurements was collected inside a ground-floor room with a grounding well in it. This trend maybe accounted for by the LBL model

  13. A Predictive Maintenance Model for Railway Tracks

    DEFF Research Database (Denmark)

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

    2015-01-01

    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......). Five technical and economic aspects are taken into account to schedule tamping: (1) track degradation of the standard deviation of the longitudinal level over time; (2) track geometrical alignment; (3) track quality thresholds based on the train speed limits; (4) the dependency of the track quality...... 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...

  14. 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.

  15. An Operational Model for the Prediction of Jet Blast

    Science.gov (United States)

    2012-01-09

    This paper presents an operational model for the prediction of jet blast. The model was : developed based upon three modules including a jet exhaust model, jet centerline decay : model and aircraft motion model. The final analysis was compared with d...

  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. 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

    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...... 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...

  18. 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.

  19. 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. Copyright © 2012 Elsevier Ltd. All rights reserved.

  20. Trophic magnification of PCBs and its relationship to the octanol-water partition coefficient

    Science.gov (United States)

    Walters, D.M.; Mills, M.A.; Cade, B.S.; Burkard, L.P.

    2011-01-01

    We investigated polychlorinated biphenyl (PCB) bioaccumulation relative to octanol-water partition coefficient (KOW) and organism trophic position (TP) at the Lake Hartwell Superfund site (South Carolina). We measured PCBs (127 congeners) and stable isotopes (??15N) in sediment, organic matter, phytoplankton, zooplankton, macroinvertebrates, and fish. TP, as calculated from ??15N, 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 KOW, as did the predictive power (r2) of individual TP-PCB regression models used to calculate TMFs. We developed log KOW-TMF models for eight food webs with vastly different environments (freshwater, marine, arctic, temperate) and species composition (cold- vs warmblooded consumers). The effect of KOW 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 KOW effect on TMFs (no difference in model slopes among food webs). Our findings underscore the importance of hydrophobicity (as characterized by KOW) in regulating bioaccumulation of recalcitrant compounds in aquatic systems, and demonstrate that relationships between chemical KOW and bioaccumulation from field studies are more generalized than previously recognized. ?? This article not subject to U.S. Copyright. Published 2011 by the American Chemical Society.

  1. Predictive modeling: potential application in prevention services.

    Science.gov (United States)

    Wilson, Moira L; Tumen, Sarah; Ota, Rissa; Simmers, Anthony G

    2015-05-01

    In 2012, the New Zealand Government announced a proposal to introduce predictive risk models (PRMs) to help professionals identify and assess children at risk of abuse or neglect as part of a preventive early intervention strategy, subject to further feasibility study and trialing. The purpose of this study is to examine technical feasibility and predictive validity of the proposal, focusing on a PRM that would draw on population-wide linked administrative data to identify newborn children who are at high priority for intensive preventive services. Data analysis was conducted in 2013 based on data collected in 2000-2012. A PRM was developed using data for children born in 2010 and externally validated for children born in 2007, examining outcomes to age 5 years. Performance of the PRM in predicting administratively recorded substantiations of maltreatment was good compared to the performance of other tools reviewed in the literature, both overall, and for indigenous Māori children. Some, but not all, of the children who go on to have recorded substantiations of maltreatment could be identified early using PRMs. PRMs should be considered as a potential complement to, rather than a replacement for, professional judgment. Trials are needed to establish whether risks can be mitigated and PRMs can make a positive contribution to frontline practice, engagement in preventive services, and outcomes for children. Deciding whether to proceed to trial requires balancing a range of considerations, including ethical and privacy risks and the risk of compounding surveillance bias. Crown Copyright © 2015. Published by Elsevier Inc. All rights reserved.

  2. 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.

  3. Modeling the effects of dispersal on predicted contemporary and future fisher (Martes pennanti) distribution in the U.S

    Science.gov (United States)

    Lucretia Olson; M. Schwartz

    2013-01-01

    Many species at high trophic levels are predicted to be impacted by shifts in habitat associated with climate change. While temperate coniferous forests are predicted to be one of the least affected ecosystems, the impact of shifting habitat on terrestrial carnivores that live within these ecosystems may depend on the dispersal rates of the species and the patchiness...

  4. Heuristic Modeling for TRMM Lifetime Predictions

    Science.gov (United States)

    Jordan, P. S.; Sharer, P. J.; DeFazio, R. L.

    1996-01-01

    Analysis time for computing the expected mission lifetimes of proposed frequently maneuvering, tightly altitude constrained, Earth orbiting spacecraft have been significantly reduced by means of a heuristic modeling method implemented in a commercial-off-the-shelf spreadsheet product (QuattroPro) running on a personal computer (PC). The method uses a look-up table to estimate the maneuver frequency per month as a function of the spacecraft ballistic coefficient and the solar flux index, then computes the associated fuel use by a simple engine model. Maneuver frequency data points are produced by means of a single 1-month run of traditional mission analysis software for each of the 12 to 25 data points required for the table. As the data point computations are required only a mission design start-up and on the occasion of significant mission redesigns, the dependence on time consuming traditional modeling methods is dramatically reduced. Results to date have agreed with traditional methods to within 1 to 1.5 percent. The spreadsheet approach is applicable to a wide variety of Earth orbiting spacecraft with tight altitude constraints. It will be particularly useful to such missions as the Tropical Rainfall Measurement Mission scheduled for launch in 1997, whose mission lifetime calculations are heavily dependent on frequently revised solar flux predictions.

  5. A Computational Model for Predicting Gas Breakdown

    Science.gov (United States)

    Gill, Zachary

    2017-10-01

    Pulsed-inductive discharges are a common method of producing a plasma. They provide a mechanism for quickly and efficiently generating a large volume of plasma for rapid use and are seen in applications including propulsion, fusion power, and high-power lasers. However, some common designs see a delayed response time due to the plasma forming when the magnitude of the magnetic field in the thruster is at a minimum. New designs are difficult to evaluate due to the amount of time needed to construct a new geometry and the high monetary cost of changing the power generation circuit. To more quickly evaluate new designs and better understand the shortcomings of existing designs, a computational model is developed. This model uses a modified single-electron model as the basis for a Mathematica code to determine how the energy distribution in a system changes with regards to time and location. By analyzing this energy distribution, the approximate time and location of initial plasma breakdown can be predicted. The results from this code are then compared to existing data to show its validity and shortcomings. Missouri S&T APLab.

  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. 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.

  8. Which method predicts recidivism best?: A comparison of statistical, machine learning, and data mining predictive models

    OpenAIRE

    Tollenaar, N.; van der Heijden, P.G.M.

    2012-01-01

    Using criminal population conviction histories of recent offenders, prediction mod els 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 and machine learning provide an improvement in predictive performance over classical statistical methods, namely logistic regression and linear discrim inant analysis. These models are compared ...

  9. 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.

  10. Computational Approaches to Predict Indices of ...

    Science.gov (United States)

    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 classifications are good predictors of ecosystem health and the potential for ecosystem services (e.g., recreation, aesthetics, and fisheries). Additionally, some ecosystem disservices, such as cyanobacteria blooms, are also associated with increased nutrient inputs. Thus, trophic state can be used as a proxy for cyanobacteria bloom risk. To explore this idea, we construct two random forest models of trophic state (as determined by chlorophyll a concentration). First we define an “All Variable” model that estimates trophic state with both in situ and universally available data, and then we reduce this to a “GIS Only” model that uses only the universally available data. The “All Variables” model had a root mean square error (RMSE) of 0.09 and R2 of 0.8; whereas, the “GIS Only” model was 0.22 and 0.48 for RMSE and R2, respectively. Examining the “GIS Only” model (i.e., the model that has broadest applicability) we see that in spite of lower overall accuracy, it still has better than even odds (i.e., prediction probability is > 50%) of being correct in more than 1091 of the 1138 lakes included in this model. The “GIS Only” model has tremendous potential for exploring spatial trends at the national level since the datasets required to parameterize the

  11. 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...

  12. 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.

  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

  14. 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

  15. Model Predictive Control for an Industrial SAG Mill

    DEFF Research Database (Denmark)

    Ohan, Valeriu; Steinke, Florian; Metzger, Michael

    2012-01-01

    We discuss Model Predictive Control (MPC) based on ARX models and a simple lower order disturbance model. The advantage of this MPC formulation is that it has few tuning parameters and is based on an ARX prediction model that can readily be identied using standard technologies from system identic...

  16. Uncertainties in spatially aggregated predictions from a logistic regression model

    NARCIS (Netherlands)

    Horssen, P.W. van; Pebesma, E.J.; Schot, P.P.

    2002-01-01

    This paper presents a method to assess the uncertainty of an ecological spatial prediction model which is based on logistic regression models, using data from the interpolation of explanatory predictor variables. The spatial predictions are presented as approximate 95% prediction intervals. The

  17. Dealing with missing predictor values when applying clinical prediction models.

    NARCIS (Netherlands)

    Janssen, K.J.; Vergouwe, Y.; Donders, A.R.T.; Harrell Jr, F.E.; Chen, Q.; Grobbee, D.E.; Moons, K.G.

    2009-01-01

    BACKGROUND: Prediction models combine patient characteristics and test results to predict the presence of a disease or the occurrence of an event in the future. In the event that test results (predictor) are unavailable, a strategy is needed to help users applying a prediction model to deal with

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

    Directory of Open Access Journals (Sweden)

    Stano Pekár

    2011-01-01

    Full Text Available 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.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.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-differences in the route to successful reproduction where females are

  19. 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.

  20. 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.

  1. Predictive capabilities of various constitutive models for arterial tissue.

    Science.gov (United States)

    Schroeder, Florian; Polzer, Stanislav; Slažanský, Martin; Man, Vojtěch; Skácel, Pavel

    2018-02-01

    Aim of this study is to validate some constitutive models by assessing their capabilities in describing and predicting uniaxial and biaxial behavior of porcine aortic tissue. 14 samples from porcine aortas were used to perform 2 uniaxial and 5 biaxial tensile tests. Transversal strains were furthermore stored for uniaxial data. The experimental data were fitted by four constitutive models: Holzapfel-Gasser-Ogden model (HGO), model based on generalized structure tensor (GST), Four-Fiber-Family model (FFF) and Microfiber model. Fitting was performed to uniaxial and biaxial data sets separately and descriptive capabilities of the models were compared. Their predictive capabilities were assessed in two ways. Firstly each model was fitted to biaxial data and its accuracy (in term of R 2 and NRMSE) in prediction of both uniaxial responses was evaluated. Then this procedure was performed conversely: each model was fitted to both uniaxial tests and its accuracy in prediction of 5 biaxial responses was observed. Descriptive capabilities of all models were excellent. In predicting uniaxial response from biaxial data, microfiber model was the most accurate while the other models showed also reasonable accuracy. Microfiber and FFF models were capable to reasonably predict biaxial responses from uniaxial data while HGO and GST models failed completely in this task. HGO and GST models are not capable to predict biaxial arterial wall behavior while FFF model is the most robust of the investigated constitutive models. Knowledge of transversal strains in uniaxial tests improves robustness of constitutive models. Copyright © 2017 Elsevier Ltd. All rights reserved.

  2. 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

    International Nuclear Information System (INIS)

    Wallace, Joshua S.; Blersch, David M.

    2015-01-01

    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. - Highlights: • A dynamic model assesses continued bioaccumulation of PBDEs in predators of invasive prey. • The model incorporates novel benthic-pelagic energy links due to invasive prey. • Increases in total PBDEs in smallmouth bass due to invasive energy pathways are simulated. • The model is validated to archival data obtained prior to invasion of zebra mussels and round goby. - A dynamic model is developed to simulate continued bioaccumulation of polybrominated diphenyl ethers (PBDEs) in smallmouth bass due to emerging benthic-pelagic energy pathways.

  3. Comparing National Water Model Inundation Predictions with Hydrodynamic Modeling

    Science.gov (United States)

    Egbert, R. J.; Shastry, A.; Aristizabal, F.; Luo, C.

    2017-12-01

    The National Water Model (NWM) simulates the hydrologic cycle and produces streamflow forecasts, runoff, and other variables for 2.7 million reaches along the National Hydrography Dataset for the continental United States. NWM applies Muskingum-Cunge channel routing which is based on the continuity equation. However, the momentum equation also needs to be considered to obtain better estimates of streamflow and stage in rivers especially for applications such as flood inundation mapping. Simulation Program for River NeTworks (SPRNT) is a fully dynamic model for large scale river networks that solves the full nonlinear Saint-Venant equations for 1D flow and stage height in river channel networks with non-uniform bathymetry. For the current work, the steady-state version of the SPRNT model was leveraged. An evaluation on SPRNT's and NWM's abilities to predict inundation was conducted for the record flood of Hurricane Matthew in October 2016 along the Neuse River in North Carolina. This event was known to have been influenced by backwater effects from the Hurricane's storm surge. Retrospective NWM discharge predictions were converted to stage using synthetic rating curves. The stages from both models were utilized to produce flood inundation maps using the Height Above Nearest Drainage (HAND) method which uses the local relative heights to provide a spatial representation of inundation depths. In order to validate the inundation produced by the models, Sentinel-1A synthetic aperture radar data in the VV and VH polarizations along with auxiliary data was used to produce a reference inundation map. A preliminary, binary comparison of the inundation maps to the reference, limited to the five HUC-12 areas of Goldsboro, NC, yielded that the flood inundation accuracies for NWM and SPRNT were 74.68% and 78.37%, respectively. The differences for all the relevant test statistics including accuracy, true positive rate, true negative rate, and positive predictive value were found

  4. Predictive models for moving contact line flows

    Science.gov (United States)

    Rame, Enrique; Garoff, Stephen

    2003-01-01

    Modeling flows with moving contact lines poses the formidable challenge that the usual assumptions of Newtonian fluid and no-slip condition give rise to a well-known singularity. This singularity prevents one from satisfying the contact angle condition to compute the shape of the fluid-fluid interface, a crucial calculation without which design parameters such as the pressure drop needed to move an immiscible 2-fluid system through a solid matrix cannot be evaluated. Some progress has been made for low Capillary number spreading flows. Combining experimental measurements of fluid-fluid interfaces very near the moving contact line with an analytical expression for the interface shape, we can determine a parameter that forms a boundary condition for the macroscopic interface shape when Ca much les than l. This parameter, which plays the role of an "apparent" or macroscopic dynamic contact angle, is shown by the theory to depend on the system geometry through the macroscopic length scale. This theoretically established dependence on geometry allows this parameter to be "transferable" from the geometry of the measurement to any other geometry involving the same material system. Unfortunately this prediction of the theory cannot be tested on Earth.

  5. Developmental prediction model for early alcohol initiation in Dutch adolescents

    NARCIS (Netherlands)

    Geels, L.M.; Vink, J.M.; Beijsterveldt, C.E.M. van; Bartels, M.; Boomsma, D.I.

    2013-01-01

    Objective: Multiple factors predict early alcohol initiation in teenagers. Among these are genetic risk factors, childhood behavioral problems, life events, lifestyle, and family environment. We constructed a developmental prediction model for alcohol initiation below the Dutch legal drinking age

  6. Seasonal predictability of Kiremt rainfall in coupled general circulation models

    Science.gov (United States)

    Gleixner, Stephanie; Keenlyside, Noel S.; Demissie, Teferi D.; Counillon, François; Wang, Yiguo; Viste, Ellen

    2017-11-01

    The Ethiopian economy and population is strongly dependent on rainfall. Operational seasonal predictions for the main rainy season (Kiremt, June-September) are based on statistical approaches with Pacific sea surface temperatures (SST) as the main predictor. Here we analyse dynamical predictions from 11 coupled general circulation models for the Kiremt seasons from 1985-2005 with the forecasts starting from the beginning of May. We find skillful predictions from three of the 11 models, but no model beats a simple linear prediction model based on the predicted Niño3.4 indices. The skill of the individual models for dynamically predicting Kiremt rainfall depends on the strength of the teleconnection between Kiremt rainfall and concurrent Pacific SST in the models. Models that do not simulate this teleconnection fail to capture the observed relationship between Kiremt rainfall and the large-scale Walker circulation.

  7. MODELLING OF DYNAMIC SPEED LIMITS USING THE MODEL PREDICTIVE CONTROL

    Directory of Open Access Journals (Sweden)

    Andrey Borisovich Nikolaev

    2017-09-01

    Full Text Available The article considers the issues of traffic management using intelligent system “Car-Road” (IVHS, which consist of interacting intelligent vehicles (IV and intelligent roadside controllers. Vehicles are organized in convoy with small distances between them. All vehicles are assumed to be fully automated (throttle control, braking, steering. Proposed approaches for determining speed limits for traffic cars on the motorway using a model predictive control (MPC. The article proposes an approach to dynamic speed limit to minimize the downtime of vehicles in traffic.

  8. 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

  9. Trophic levels and trophic tangles: the prevalence of omnivory in real food webs.

    Science.gov (United States)

    Thompson, Ross M; Hemberg, Martin; Starzomski, Brian M; Shurin, Jonathan B

    2007-03-01

    The concept of trophic levels is one of the oldest in ecology and informs our understanding of energy flow and top-down control within food webs, but it has been criticized for ignoring omnivory. We tested whether trophic levels were apparent in 58 real food webs in four habitat types by examining patterns of trophic position. A large proportion of taxa (64.4%) occupied integer trophic positions, suggesting that discrete trophic levels do exist. Importantly however, the majority of those trophic positions were aggregated around integer values of 0 and 1, representing plants and herbivores. For the majority of the real food webs considered here, secondary consumers were no more likely to occupy an integer trophic position than in randomized food webs. This means that, above the herbivore trophic level, food webs are better characterized as a tangled web of omnivores. Omnivory was most common in marine systems, rarest in streams, and intermediate in lakes and terrestrial food webs. Trophic-level-based concepts such as trophic cascades may apply to systems with short food chains, but they become less valid as food chains lengthen.

  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. 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…

  12. Models for predicting compressive strength and water absorption of ...

    African Journals Online (AJOL)

    This work presents a mathematical model for predicting the compressive strength and water absorption of laterite-quarry dust cement block using augmented Scheffe's simplex lattice design. The statistical models developed can predict the mix proportion that will yield the desired property. The models were tested for lack of ...

  13. Regression models for predicting anthropometric measurements of ...

    African Journals Online (AJOL)

    measure anthropometric dimensions to predict difficult-to-measure dimensions required for ergonomic design of school furniture. A total of 143 students aged between 16 and 18 years from eight public secondary schools in Ogbomoso, Nigeria ...

  14. FINITE ELEMENT MODEL FOR PREDICTING RESIDUAL ...

    African Journals Online (AJOL)

    direction (σx) had a maximum value of 375MPa (tensile) and minimum value of ... These results shows that the residual stresses obtained by prediction from the finite element method are in fair agreement with the experimental results.

  15. 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......). 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...... visualization to improve our understanding of the different attained performances, effectively compiling all the conducted experiments in a meaningful way. We complete our study with an entropy-based analysis that highlights the uncertainty handling properties provided by the GP, crucial for prediction tasks...

  16. 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.

  17. From Predictive Models to Instructional Policies

    Science.gov (United States)

    Rollinson, Joseph; Brunskill, Emma

    2015-01-01

    At their core, Intelligent Tutoring Systems consist of a student model and a policy. The student model captures the state of the student and the policy uses the student model to individualize instruction. Policies require different properties from the student model. For example, a mastery threshold policy requires the student model to have a way…

  18. Diversity and stability of an estuarine trophic network

    OpenAIRE

    Lobry, Jeremy; David, V; Pasquaud, S; Lepage, M; Sautour, B; Rochard, E

    2008-01-01

    Estuarine areas provide highly valuable ecosystem benefits for human populations, despite being under intense demographic, economic and ecological pressures. Hence, an understanding of the structure and function of estuarine ecosystems is essential for understanding the persistence and stability of these ecosystems and their response to perturbations. This study synthesises available data and knowledge about the Gironde estuary (SW France) in a mass-balanced trophic model to illustrate potent...

  19. 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.

  20. Comparison of the performance of two indices of trophic status for depicting the status of Pulicat lagoon ecosystem.

    Science.gov (United States)

    Santhanam, Harini; Raj, S Amal; Thanasekaran, K

    2011-10-01

    In the recent years, several indices of trophic status of coastal ecosytems are being used worldwide. However, not all of them can be applied successfully to depict the true trophic status of shallow coastal ecosystems due to their inherent complexities in nutrient circulation and interactions between biotic organisms. The most widely used index is Carlson's Trophic State Index (TSI), while more recently, several environmental indices have been constructed using soft computing techniques such as fuzzy logic. In the present investigation, a comparison of the performances of a conventional trophic state index and a newly developed fuzzy-based trophic state index has been presented in detail. The aim of the work was to highlight the advantages of the newer technique over the existing techniques in light of the inherent variability of both data as well as the sampling procedures undertaken for studying a coastal lagoon ecosystem called Pulicat lagoon in the southeast coast of India. The results of the study have shown that while there are discrepancies in depicting the trophic status of the lagoon using Carlson's index, the newer logic-based index predicted the trophic status accurately which was verified by field assessments. Thus the successful application of a more reliable index to estimate trophic status has been presented.

  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. Tempo of trophic evolution and its impact on mammalian diversification.

    Science.gov (United States)

    Price, Samantha A; Hopkins, Samantha S B; Smith, Kathleen K; Roth, V Louise

    2012-05-01

    Mammals are characterized by the complex adaptations of their dentition, which are an indication that diet has played a critical role in their evolutionary history. Although much attention has focused on diet and the adaptations of specific taxa, the role of diet in large-scale diversification patterns remains unresolved. Contradictory hypotheses have been proposed, making prediction of the expected relationship difficult. We show that net diversification rate (the cumulative effect of speciation and extinction), differs significantly among living mammals, depending upon trophic strategy. Herbivores diversify fastest, carnivores are intermediate, and omnivores are slowest. The tempo of transitions between the trophic strategies is also highly biased: the fastest rates occur into omnivory from herbivory and carnivory and the lowest transition rates are between herbivory and carnivory. Extant herbivore and carnivore diversity arose primarily through diversification within lineages, whereas omnivore diversity evolved by transitions into the strategy. The ability to specialize and subdivide the trophic niche allowed herbivores and carnivores to evolve greater diversity than omnivores.

  3. Effect of stock size, climate, predation, and trophic status on recruitment of alewives in Lake Ontario, 1978-2000

    Science.gov (United States)

    O'Gorman, Robert; Lantry, Brian F.; Schneider, Clifford P.

    2004-01-01

    The population of alewives Alosa pseudoharengus in Lake Ontario is of great concern to fishery managers because alewives are the principal prey of introduced salmonines and because alewives negatively influence many endemic fishes. We used spring bottom trawl catches of alewives to investigate the roles of stock size, climate, predation, and lake trophic status on recruitment of alewives to age 2 in Lake Ontario during 1978–2000. Climate was indexed from the temperature of water entering a south-shore municipal treatment plant, lake trophic status was indexed by the mean concentration of total phosphorus (TP) in surface water in spring, and predation was indexed by the product of the number of salmonines stocked and relative, first-year survival of Chinook salmonOncorhynchus tshawytscha. A Ricker-type parent–progeny model suggested that peak production of age-1 alewives could occur over a broad range of spawning stock sizes, and the fit of the model was improved most by the addition of terms for spring water temperature and winter duration. With the addition of the two climate terms, the Ricker model indicated that when water was relatively warm in spring and the winter was relatively short, peak potential production of young was nine times higher than when water temperature and winters were average, and 73 times higher than when water was cold in spring and winters were long. Relative survival from age 1 to recruitment at age 2 was best described by a multiple linear regression with terms for adult abundance, TP, and predation. Mean recruitment of age-2 fish in the 1978–1998 year-classes predicted by using the two models in sequence was only about 20% greater than the observed mean recruitment. Model estimates fit the measured data exceptionally well for all but the largest four year-classes, which suggests that the models will facilitate improvement in estimates of trophic transfer due to alewives.

  4. 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...

  5. Ecological opportunities and intraspecific competition alter trophic niche specialization in an opportunistic stream predator.

    Science.gov (United States)

    Evangelista, Charlotte; Boiche, Anatole; Lecerf, Antoine; Cucherousset, Julien

    2014-09-01

    Many generalist populations are composed of specialized individuals that use a narrow part of the population's niche. Ecological theories predict that individual specialization and population trophic niche are determined by biotic interactions and resource diversity emerging from environmental variations (i.e. ecological opportunities). However, due to the paucity of empirical and experimental demonstrations, the genuine importance of each of these drivers in determining trophic niche attributes is not fully appreciated. The present study aimed at determining the population level and individual responses of brown trout (Salmo trutta) to variations in ecological opportunities (terrestrial prey inputs) and autochthonous prey communities among 10 stream reaches along a riparian condition gradient using individual longitudinal monitoring and stable isotope analyses. Our results suggested that trophic niche diversity varied along the environmental gradient, while individual trophic specialization was indirectly driven by ecological opportunities through strengthened intraspecific competition. Individual diet was repeatable over the study period, and the growth rate of juvenile brown trout increased with their specialization for aquatic predatory invertebrates. Our findings highlight the dual influences of intraspecific competition and ecological opportunities on individual trophic specialization and population trophic niche. © 2014 The Authors. Journal of Animal Ecology © 2014 British Ecological Society.

  6. A model to predict the beginning of the pollen season

    DEFF Research Database (Denmark)

    Toldam-Andersen, Torben Bo

    1991-01-01

    In order to predict the beginning of the pollen season, a model comprising the Utah phenoclirnatography Chill Unit (CU) and ASYMCUR-Growing Degree Hour (GDH) submodels were used to predict the first bloom in Alms, Ulttirrs and Berirln. The model relates environmental temperatures to rest completion...... and bud development. As phenologic parameter 14 years of pollen counts were used. The observed datcs for the beginning of the pollen seasons were defined from the pollen counts and compared with the model prediction. The CU and GDH submodels were used as: 1. A fixed day model, using only the GDH model...... for fruit trees are generally applicable, and give a reasonable description of the growth processes of other trees. This type of model can therefore be of value in predicting the start of the pollen season. The predicted dates were generally within 3-5 days of the observed. Finally the possibility of frost...

  7. Risk prediction model: Statistical and artificial neural network approach

    Science.gov (United States)

    Paiman, Nuur Azreen; Hariri, Azian; Masood, Ibrahim

    2017-04-01

    Prediction models are increasingly gaining popularity and had been used in numerous areas of studies to complement and fulfilled clinical reasoning and decision making nowadays. The adoption of such models assist physician's decision making, individual's behavior, and consequently improve individual outcomes and the cost-effectiveness of care. The objective of this paper is to reviewed articles related to risk prediction model in order to understand the suitable approach, development and the validation process of risk prediction model. A qualitative review of the aims, methods and significant main outcomes of the nineteen published articles that developed risk prediction models from numerous fields were done. This paper also reviewed on how researchers develop and validate the risk prediction models based on statistical and artificial neural network approach. From the review done, some methodological recommendation in developing and validating the prediction model were highlighted. According to studies that had been done, artificial neural network approached in developing the prediction model were more accurate compared to statistical approach. However currently, only limited published literature discussed on which approach is more accurate for risk prediction model development.

  8. Evaluation of the US Army fallout prediction model

    International Nuclear Information System (INIS)

    Pernick, A.; Levanon, I.

    1987-01-01

    The US Army fallout prediction method was evaluated against an advanced fallout prediction model--SIMFIC (Simplified Fallout Interpretive Code). The danger zone areas of the US Army method were found to be significantly greater (up to a factor of 8) than the areas of corresponding radiation hazard as predicted by SIMFIC. Nonetheless, because the US Army's method predicts danger zone lengths that are commonly shorter than the corresponding hot line distances of SIMFIC, the US Army's method is not reliably conservative

  9. Comparative Evaluation of Some Crop Yield Prediction Models ...

    African Journals Online (AJOL)

    A computer program was adopted from the work of Hill et al. (1982) to calibrate and test three of the existing yield prediction models using tropical cowpea yieldÐweather data. The models tested were Hanks Model (first and second versions). Stewart Model (first and second versions) and HallÐButcher Model. Three sets of ...

  10. Comparative Evaluation of Some Crop Yield Prediction Models ...

    African Journals Online (AJOL)

    (1982) to calibrate and test three of the existing yield prediction models using tropical cowpea yieldÐweather data. The models tested were Hanks Model (first and second versions). Stewart Model (first and second versions) and HallÐButcher Model. Three sets of cowpea yield-water use and weather data were collected.

  11. 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

    Classical speech intelligibility models, such as the speech transmission index (STI) and the speech intelligibility index (SII) are based on calculations on the physical acoustic signals. The present study predicts speech intelligibility by combining a psychoacoustically validated model of auditory...

  12. 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

  13. Ocean wave prediction using numerical and neural network models

    Digital Repository Service at National Institute of Oceanography (India)

    Mandal, S.; Prabaharan, N.

    This paper presents an overview of the development of the numerical wave prediction models and recently used neural networks for ocean wave hindcasting and forecasting. The numerical wave models express the physical concepts of the phenomena...

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

    Directory of Open Access Journals (Sweden)

    W S Xu

    Full Text Available 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.

  15. 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.

  16. 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

  17. 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

  18. Statistical model based gender prediction for targeted NGS clinical panels

    Directory of Open Access Journals (Sweden)

    Palani Kannan Kandavel

    2017-12-01

    The reference test dataset are being used to test the model. The sensitivity on predicting the gender has been increased from the current “genotype composition in ChrX” based approach. In addition, the prediction score given by the model can be used to evaluate the quality of clinical dataset. The higher prediction score towards its respective gender indicates the higher quality of sequenced data.

  19. comparative analysis of two mathematical models for prediction

    African Journals Online (AJOL)

    Abstract. A mathematical modeling for prediction of compressive strength of sandcrete blocks was performed using statistical analysis for the sandcrete block data ob- tained from experimental work done in this study. The models used are Scheffes and Osadebes optimization theories to predict the compressive strength of ...

  20. Comparison of predictive models for the early diagnosis of diabetes

    NARCIS (Netherlands)

    M. Jahani (Meysam); M. Mahdavi (Mahdi)

    2016-01-01

    textabstractObjectives: This study develops neural network models to improve the prediction of diabetes using clinical and lifestyle characteristics. Prediction models were developed using a combination of approaches and concepts. Methods: We used memetic algorithms to update weights and to improve

  1. 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...

  2. Hidden Markov Model for quantitative prediction of snowfall

    Indian Academy of Sciences (India)

    A Hidden Markov Model (HMM) has been developed for prediction of quantitative snowfall in Pir-Panjal and Great Himalayan mountain ranges of Indian Himalaya. The model predicts snowfall for two days in advance using daily recorded nine meteorological variables of past 20 winters from 1992–2012. There are six ...

  3. Bayesian variable order Markov models: Towards Bayesian predictive state representations

    NARCIS (Netherlands)

    Dimitrakakis, C.

    2009-01-01

    We present a Bayesian variable order Markov model that shares many similarities with predictive state representations. The resulting models are compact and much easier to specify and learn than classical predictive state representations. Moreover, we show that they significantly outperform a more

  4. Demonstrating the improvement of predictive maturity of a computational model

    Energy Technology Data Exchange (ETDEWEB)

    Hemez, Francois M [Los Alamos National Laboratory; Unal, Cetin [Los Alamos National Laboratory; Atamturktur, Huriye S [CLEMSON UNIV.

    2010-01-01

    We demonstrate an improvement of predictive capability brought to a non-linear material model using a combination of test data, sensitivity analysis, uncertainty quantification, and calibration. A model that captures increasingly complicated phenomena, such as plasticity, temperature and strain rate effects, is analyzed. Predictive maturity is defined, here, as the accuracy of the model to predict multiple Hopkinson bar experiments. A statistical discrepancy quantifies the systematic disagreement (bias) between measurements and predictions. Our hypothesis is that improving the predictive capability of a model should translate into better agreement between measurements and predictions. This agreement, in turn, should lead to a smaller discrepancy. We have recently proposed to use discrepancy and coverage, that is, the extent to which the physical experiments used for calibration populate the regime of applicability of the model, as basis to define a Predictive Maturity Index (PMI). It was shown that predictive maturity could be improved when additional physical tests are made available to increase coverage of the regime of applicability. This contribution illustrates how the PMI changes as 'better' physics are implemented in the model. The application is the non-linear Preston-Tonks-Wallace (PTW) strength model applied to Beryllium metal. We demonstrate that our framework tracks the evolution of maturity of the PTW model. Robustness of the PMI with respect to the selection of coefficients needed in its definition is also studied.

  5. 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

  6. 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

  7. Wind turbine control and model predictive control for uncertain systems

    DEFF Research Database (Denmark)

    Thomsen, Sven Creutz

    as disturbance models for controller design. The theoretical study deals with Model Predictive Control (MPC). MPC is an optimal control method which is characterized by the use of a receding prediction horizon. MPC has risen in popularity due to its inherent ability to systematically account for time...

  8. Hidden Markov Model for quantitative prediction of snowfall and ...

    Indian Academy of Sciences (India)

    A Hidden Markov Model (HMM) has been developed for prediction of quantitative snowfall in Pir-Panjal and Great Himalayan mountain ranges of Indian Himalaya. The model predicts snowfall for two days in advance using daily recorded nine meteorological variables of past 20 winters from 1992–2012. There are six ...

  9. Model predictive control of a 3-DOF helicopter system using ...

    African Journals Online (AJOL)

    ... by simulation, and its performance is compared with that achieved by linear model predictive control (LMPC). Keywords: nonlinear systems, helicopter dynamics, MIMO systems, model predictive control, successive linearization. International Journal of Engineering, Science and Technology, Vol. 2, No. 10, 2010, pp. 9-19 ...

  10. Models for predicting fuel consumption in sagebrush-dominated ecosystems

    Science.gov (United States)

    Clinton S. Wright

    2013-01-01

    Fuel consumption predictions are necessary to accurately estimate or model fire effects, including pollutant emissions during wildland fires. Fuel and environmental measurements on a series of operational prescribed fires were used to develop empirical models for predicting fuel consumption in big sagebrush (Artemisia tridentate Nutt.) ecosystems....

  11. Comparative Analysis of Two Mathematical Models for Prediction of ...

    African Journals Online (AJOL)

    A mathematical modeling for prediction of compressive strength of sandcrete blocks was performed using statistical analysis for the sandcrete block data obtained from experimental work done in this study. The models used are Scheffe's and Osadebe's optimization theories to predict the compressive strength of sandcrete ...

  12. A mathematical model for predicting earthquake occurrence ...

    African Journals Online (AJOL)

    We consider the continental crust under damage. We use the observed results of microseism in many seismic stations of the world which was established to study the time series of the activities of the continental crust with a view to predicting possible time of occurrence of earthquake. We consider microseism time series ...

  13. Model for predicting the injury severity score.

    Science.gov (United States)

    Hagiwara, Shuichi; Oshima, Kiyohiro; Murata, Masato; Kaneko, Minoru; Aoki, Makoto; Kanbe, Masahiko; Nakamura, Takuro; Ohyama, Yoshio; Tamura, Jun'ichi

    2015-07-01

    To determine the formula that predicts the injury severity score from parameters that are obtained in the emergency department at arrival. We reviewed the medical records of trauma patients who were transferred to the emergency department of Gunma University Hospital between January 2010 and December 2010. The injury severity score, age, mean blood pressure, heart rate, Glasgow coma scale, hemoglobin, hematocrit, red blood cell count, platelet count, fibrinogen, international normalized ratio of prothrombin time, activated partial thromboplastin time, and fibrin degradation products, were examined in those patients on arrival. To determine the formula that predicts the injury severity score, multiple linear regression analysis was carried out. The injury severity score was set as the dependent variable, and the other parameters were set as candidate objective variables. IBM spss Statistics 20 was used for the statistical analysis. Statistical significance was set at P  Watson ratio was 2.200. A formula for predicting the injury severity score in trauma patients was developed with ordinary parameters such as fibrin degradation products and mean blood pressure. This formula is useful because we can predict the injury severity score easily in the emergency department.

  14. Trophic magnification of organic chemicals: A global synthesis

    Science.gov (United States)

    Walter, W. 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 compounds, regardless of KOW, and lowest for chemicals with rapid transformation rates (kM > 0.2 day–1). This probabilistic model provides a new global tool for screening existing and new OCs for their biomagnification potential.

  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. Fixed recurrence and slip models better predict earthquake behavior than the time- and slip-predictable models 1: repeating earthquakes

    Science.gov (United States)

    Rubinstein, Justin L.; Ellsworth, William L.; Chen, Kate Huihsuan; Uchida, Naoki

    2012-01-01

    The behavior of individual events in repeating earthquake sequences in California, Taiwan and Japan is better predicted by a model with fixed inter-event time or fixed slip than it is by the time- and slip-predictable models for earthquake occurrence. Given that repeating earthquakes are highly regular in both inter-event time and seismic moment, the time- and slip-predictable models seem ideally suited to explain their behavior. Taken together with evidence from the companion manuscript that shows similar results for laboratory experiments we conclude that the short-term predictions of the time- and slip-predictable models should be rejected in favor of earthquake models that assume either fixed slip or fixed recurrence interval. This implies that the elastic rebound model underlying the time- and slip-predictable models offers no additional value in describing earthquake behavior in an event-to-event sense, but its value in a long-term sense cannot be determined. These models likely fail because they rely on assumptions that oversimplify the earthquake cycle. We note that the time and slip of these events is predicted quite well by fixed slip and fixed recurrence models, so in some sense they are time- and slip-predictable. While fixed recurrence and slip models better predict repeating earthquake behavior than the time- and slip-predictable models, we observe a correlation between slip and the preceding recurrence time for many repeating earthquake sequences in Parkfield, California. This correlation is not found in other regions, and the sequences with the correlative slip-predictable behavior are not distinguishable from nearby earthquake sequences that do not exhibit this behavior.

  17. 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.

  18. 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.

  19. 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.

  20. 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

  1. Towards a generalized energy prediction model for machine tools.

    Science.gov (United States)

    Bhinge, Raunak; Park, Jinkyoo; Law, Kincho H; Dornfeld, David A; Helu, Moneer; Rachuri, Sudarsan

    2017-04-01

    Energy prediction of machine tools can deliver many advantages to a manufacturing enterprise, ranging from energy-efficient process planning to machine tool monitoring. Physics-based, energy prediction models have been proposed in the past to understand the energy usage pattern of a machine tool. However, uncertainties in both the machine and the operating environment make it difficult to predict the energy consumption of the target machine reliably. Taking advantage of the opportunity to collect extensive, contextual, energy-consumption data, we discuss a data-driven approach to develop an energy prediction model of a machine tool in this paper. First, we present a methodology that can efficiently and effectively collect and process data extracted from a machine tool and its sensors. We then present a data-driven model that can be used to predict the energy consumption of the machine tool for machining a generic part. Specifically, we use Gaussian Process (GP) Regression, a non-parametric machine-learning technique, to develop the prediction model. The energy prediction model is then generalized over multiple process parameters and operations. Finally, we apply this generalized model with a method to assess uncertainty intervals to predict the energy consumed to machine any part using a Mori Seiki NVD1500 machine tool. Furthermore, the same model can be used during process planning to optimize the energy-efficiency of a machining process.

  2. Poisson Mixture Regression Models for Heart Disease Prediction

    Science.gov (United States)

    Erol, Hamza

    2016-01-01

    Early heart disease control can be achieved by high disease prediction and diagnosis efficiency. This paper focuses on the use of model based clustering techniques to predict and diagnose heart disease via Poisson mixture regression models. Analysis and application of Poisson mixture regression models is here addressed under two different classes: standard and concomitant variable mixture regression models. Results show that a two-component concomitant variable Poisson mixture regression model predicts heart disease better than both the standard Poisson mixture regression model and the ordinary general linear Poisson regression model due to its low Bayesian Information Criteria value. Furthermore, a Zero Inflated Poisson Mixture Regression model turned out to be the best model for heart prediction over all models as it both clusters individuals into high or low risk category and predicts rate to heart disease componentwise given clusters available. It is deduced that heart disease prediction can be effectively done by identifying the major risks componentwise using Poisson mixture regression model. PMID:27999611

  3. Comparison of Predictive Models for the Early Diagnosis of Diabetes.

    Science.gov (United States)

    Jahani, Meysam; Mahdavi, Mahdi

    2016-04-01

    This study develops neural network models to improve the prediction of diabetes using clinical and lifestyle characteristics. Prediction models were developed using a combination of approaches and concepts. We used memetic algorithms to update weights and to improve prediction accuracy of models. In the first step, the optimum amount for neural network parameters such as momentum rate, transfer function, and error function were obtained through trial and error and based on the results of previous studies. In the second step, optimum parameters were applied to memetic algorithms in order to improve the accuracy of prediction. This preliminary analysis showed that the accuracy of neural networks is 88%. In the third step, the accuracy of neural network models was improved using a memetic algorithm and resulted model was compared with a logistic regression model using a confusion matrix and receiver operating characteristic curve (ROC). The memetic algorithm improved the accuracy from 88.0% to 93.2%. We also found that memetic algorithm had a higher accuracy than the model from the genetic algorithm and a regression model. Among models, the regression model has the least accuracy. For the memetic algorithm model the amount of sensitivity, specificity, positive predictive value, negative predictive value, and ROC are 96.2, 95.3, 93.8, 92.4, and 0.958 respectively. The results of this study provide a basis to design a Decision Support System for risk management and planning of care for individuals at risk of diabetes.

  4. Applications of modeling in polymer-property prediction

    Science.gov (United States)

    Case, F. H.

    1996-08-01

    A number of molecular modeling techniques have been applied for the prediction of polymer properties and behavior. Five examples illustrate the range of methodologies used. A simple atomistic simulation of small polymer fragments is used to estimate drug compatibility with a polymer matrix. The analysis of molecular dynamics results from a more complex model of a swollen hydrogel system is used to study gas diffusion in contact lenses. Statistical mechanics are used to predict conformation dependent properties — an example is the prediction of liquid-crystal formation. The effect of the molecular weight distribution on phase separation in polyalkanes is predicted using thermodynamic models. In some cases, the properties of interest cannot be directly predicted using simulation methods or polymer theory. Correlation methods may be used to bridge the gap between molecular structure and macroscopic properties. The final example shows how connectivity-indices-based quantitative structure-property relationships were used to predict properties for candidate polyimids in an electronics application.

  5. Artificial Neural Network Model for Predicting Compressive

    OpenAIRE

    Salim T. Yousif; Salwa M. Abdullah

    2013-01-01

      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...

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

    Science.gov (United States)

    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. A trophic 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.

  7. Prediction of hourly solar radiation with multi-model framework

    International Nuclear Information System (INIS)

    Wu, Ji; Chan, Chee Keong

    2013-01-01

    Highlights: • A novel approach to predict solar radiation through the use of clustering paradigms. • Development of prediction models based on the intrinsic pattern observed in each cluster. • Prediction based on proper clustering and selection of model on current time provides better results than other methods. • Experiments were conducted on actual solar radiation data obtained from a weather station in Singapore. - Abstract: In this paper, a novel multi-model prediction framework for prediction of solar radiation is proposed. The framework started with the assumption that there are several patterns embedded in the solar radiation series. To extract the underlying pattern, the solar radiation series is first segmented into smaller subsequences, and the subsequences are further grouped into different clusters. For each cluster, an appropriate prediction model is trained. Hence a procedure for pattern identification is developed to identify the proper pattern that fits the current period. Based on this pattern, the corresponding prediction model is applied to obtain the prediction value. The prediction result of the proposed framework is then compared to other techniques. It is shown that the proposed framework provides superior performance as compared to others

  8. Size-based predictions of food web patterns

    DEFF Research Database (Denmark)

    Zhang, Lai; Hartvig, Martin; Knudsen, Kim

    2014-01-01

    We employ size-based theoretical arguments to derive simple analytic predictions of ecological patterns and properties of natural communities: size-spectrum exponent, maximum trophic level, and susceptibility to invasive species. The predictions are brought about by assuming that an infinite number...... simulations with varying species richness. To this end, we develop a new size- and trait-based food web model that can be simplified into an analytically solvable size-based model. We confirm existing solutions for the size distribution and derive novel predictions for maximum trophic level and invasion...... of species are continuously distributed on a size-trait axis. It is, however, an open question whether such predictions are valid for a food web with a finite number of species embedded in a network structure. We address this question by comparing the size-based predictions to results from dynamic food web...

  9. Posterior Predictive Model Checking for Multidimensionality in Item Response Theory

    Science.gov (United States)

    Levy, Roy; Mislevy, Robert J.; Sinharay, Sandip

    2009-01-01

    If data exhibit multidimensionality, key conditional independence assumptions of unidimensional models do not hold. The current work pursues posterior predictive model checking, a flexible family of model-checking procedures, as a tool for criticizing models due to unaccounted for dimensions in the context of item response theory. Factors…

  10. Model predictive control of a crude oil distillation column

    Directory of Open Access Journals (Sweden)

    Morten Hovd

    1999-04-01

    Full Text Available The project of designing and implementing model based predictive control on the vacuum distillation column at the Nynäshamn Refinery of Nynäs AB is described in this paper. The paper describes in detail the modeling for the model based control, covers the controller implementation, and documents the benefits gained from the model based controller.

  11. Enhancing Flood Prediction Reliability Using Bayesian Model Averaging

    Science.gov (United States)

    Liu, Z.; Merwade, V.

    2017-12-01

    Uncertainty analysis is an indispensable part of modeling the hydrology and hydrodynamics of non-idealized environmental systems. Compared to reliance on prediction from one model simulation, using on ensemble of predictions that consider uncertainty from different sources is more reliable. In this study, Bayesian model averaging (BMA) is applied to Black River watershed in Arkansas and Missouri by combining multi-model simulations to get reliable deterministic water stage and probabilistic inundation extent predictions. The simulation ensemble is generated from 81 LISFLOOD-FP subgrid model configurations that include uncertainty from channel shape, channel width, channel roughness and discharge. Model simulation outputs are trained with observed water stage data during one flood event, and BMA prediction ability is validated for another flood event. Results from this study indicate that BMA does not always outperform all members in the ensemble, but it provides relatively robust deterministic flood stage predictions across the basin. Station based BMA (BMA_S) water stage prediction has better performance than global based BMA (BMA_G) prediction which is superior to the ensemble mean prediction. Additionally, high-frequency flood inundation extent (probability greater than 60%) in BMA_G probabilistic map is more accurate than the probabilistic flood inundation extent based on equal weights.

  12. Genomic prediction of complex human traits: relatedness, trait architecture and predictive meta-models

    Science.gov (United States)

    Spiliopoulou, Athina; Nagy, Reka; Bermingham, Mairead L.; Huffman, Jennifer E.; Hayward, Caroline; Vitart, Veronique; Rudan, Igor; Campbell, Harry; Wright, Alan F.; Wilson, James F.; Pong-Wong, Ricardo; Agakov, Felix; Navarro, Pau; Haley, Chris S.

    2015-01-01

    We explore the prediction of individuals' phenotypes for complex traits using genomic data. We compare several widely used prediction models, including Ridge Regression, LASSO and Elastic Nets estimated from cohort data, and polygenic risk scores constructed using published summary statistics from genome-wide association meta-analyses (GWAMA). We evaluate the interplay between relatedness, trait architecture and optimal marker density, by predicting height, body mass index (BMI) and high-density lipoprotein level (HDL) in two data cohorts, originating from Croatia and Scotland. We empirically demonstrate that dense models are better when all genetic effects are small (height and BMI) and target individuals are related to the training samples, while sparse models predict better in unrelated individuals and when some effects have moderate size (HDL). For HDL sparse models achieved good across-cohort prediction, performing similarly to the GWAMA risk score and to models trained within the same cohort, which indicates that, for predicting traits with moderately sized effects, large sample sizes and familial structure become less important, though still potentially useful. Finally, we propose a novel ensemble of whole-genome predictors with GWAMA risk scores and demonstrate that the resulting meta-model achieves higher prediction accuracy than either model on its own. We conclude that although current genomic predictors are not accurate enough for diagnostic purposes, performance can be improved without requiring access to large-scale individual-level data. Our methodologically simple meta-model is a means of performing predictive meta-analysis for optimizing genomic predictions and can be easily extended to incorporate multiple population-level summary statistics or other domain knowledge. PMID:25918167

  13. 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

  14. 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

  15. Predictive models for acute kidney injury following cardiac surgery.

    Science.gov (United States)

    Demirjian, Sevag; Schold, Jesse D; Navia, Jose; Mastracci, Tara M; Paganini, Emil P; Yared, Jean-Pierre; Bashour, Charles A

    2012-03-01

    Accurate prediction of cardiac surgery-associated acute kidney injury (AKI) would improve clinical decision making and facilitate timely diagnosis and treatment. The aim of the study was to develop predictive models for cardiac surgery-associated AKI using presurgical and combined pre- and intrasurgical variables. Prospective observational cohort. 25,898 patients who underwent cardiac surgery at Cleveland Clinic in 2000-2008. Presurgical and combined pre- and intrasurgical variables were used to develop predictive models. Dialysis therapy and a composite of doubling of serum creatinine level or dialysis therapy within 2 weeks (or discharge if sooner) after cardiac surgery. Incidences of dialysis therapy and the composite of doubling of serum creatinine level or dialysis therapy were 1.7% and 4.3%, respectively. Kidney function parameters were strong independent predictors in all 4 models. Surgical complexity reflected by type and history of previous cardiac surgery were robust predictors in models based on presurgical variables. However, the inclusion of intrasurgical variables accounted for all explained variance by procedure-related information. Models predictive of dialysis therapy showed good calibration and superb discrimination; a combined (pre- and intrasurgical) model performed better than the presurgical model alone (C statistics, 0.910 and 0.875, respectively). Models predictive of the composite end point also had excellent discrimination with both presurgical and combined (pre- and intrasurgical) variables (C statistics, 0.797 and 0.825, respectively). However, the presurgical model predictive of the composite end point showed suboptimal calibration (P predictive models in other cohorts is required before wide-scale application. We developed and internally validated 4 new models that accurately predict cardiac surgery-associated AKI. These models are based on readily available clinical information and can be used for patient counseling, clinical

  16. Modeling number of claims and prediction of total claim amount

    Science.gov (United States)

    Acar, Aslıhan Şentürk; Karabey, Uǧur

    2017-07-01

    In this study we focus on annual number of claims of a private health insurance data set which belongs to a local insurance company in Turkey. In addition to Poisson model and negative binomial model, zero-inflated Poisson model and zero-inflated negative binomial model are used to model the number of claims in order to take into account excess zeros. To investigate the impact of different distributional assumptions for the number of claims on the prediction of total claim amount, predictive performances of candidate models are compared by using root mean square error (RMSE) and mean absolute error (MAE) criteria.

  17. 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

  18. Ecosystem regime shifts disrupt trophic structure.

    Science.gov (United States)

    Hempson, Tessa N; Graham, Nicholas A J; MacNeil, M Aaron; Hoey, Andrew S; Wilson, Shaun K

    2018-01-01

    Regime shifts between alternative stable ecosystem states are becoming commonplace due to the combined effects of local stressors and global climate change. Alternative states are characterized as substantially different in form and function from pre-disturbance states, disrupting the delivery of ecosystem services and functions. On coral reefs, regime shifts are typically characterized by a change in the benthic composition from coral to macroalgal dominance. Such fundamental shifts in the benthos are anticipated to impact associated fish communities that are reliant on the reef for food and shelter, yet there is limited understanding of how regime shifts propagate through the fish community over time, relative to initial or recovery conditions. This study addresses this knowledge gap using long-term data of coral reef regime shifts and recovery on Seychelles reefs following the 1998 mass bleaching event. It shows how trophic structure of the reef fish community becomes increasingly dissimilar between alternative reef ecosystem states (regime-shifted vs. recovering) with time since disturbance. Regime-shifted reefs developed a concave trophic structure, with increased biomass in base trophic levels as herbivorous species benefitted from increased algal resources. Mid trophic level species, including specialists such as corallivores, declined with loss of coral habitat, while biomass was retained in upper trophic levels by large-bodied, generalist invertivores. Recovering reefs also experienced an initial decline in mid trophic level biomass, but moved toward a bottom-heavy pyramid shape, with a wide range of feeding groups (e.g., planktivores, corallivores, omnivores) represented at mid trophic levels. Given the importance of coral reef fishes in maintaining the ecological function of coral reef ecosystems and their associated fisheries, understanding the effects of regime shifts on these communities is essential to inform decisions that enhance ecological

  19. Model-based uncertainty in species range prediction

    DEFF Research Database (Denmark)

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

    2006-01-01

    algorithm when extrapolating beyond the range of data used to build the model. The effects of these factors should be carefully considered when using this modelling approach to predict species ranges. Main conclusions We highlight an important source of uncertainty in assessments of the impacts of climate......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......, identify key reasons why model output may differ and discuss the implications that model uncertainty has for policy-guiding applications. Location The Western Cape of South Africa. Methods We applied nine of the most widely used modelling techniques to model potential distributions under current...

  20. Prediction Model for Gastric Cancer Incidence in Korean Population.

    Science.gov (United States)

    Eom, Bang Wool; Joo, Jungnam; Kim, Sohee; Shin, Aesun; Yang, Hye-Ryung; Park, Junghyun; Choi, Il Ju; Kim, Young-Woo; Kim, Jeongseon; Nam, Byung-Ho

    2015-01-01

    Predicting high risk groups for gastric cancer and motivating these groups to receive regular checkups is required for the early detection of gastric cancer. The aim of this study is was to develop a prediction model for gastric cancer incidence based on a large population-based cohort in Korea. Based on the National Health Insurance Corporation data, we analyzed 10 major risk factors for gastric cancer. The Cox proportional hazards model was used to develop gender specific prediction models for gastric cancer development, and the performance of the developed model in terms of discrimination and calibration was also validated using an independent cohort. Discrimination ability was evaluated using Harrell's C-statistics, and the calibration was evaluated using a calibration plot and slope. During a median of 11.4 years of follow-up, 19,465 (1.4%) and 5,579 (0.7%) newly developed gastric cancer cases were observed among 1,372,424 men and 804,077 women, respectively. The prediction models included age, BMI, family history, meal regularity, salt preference, alcohol consumption, smoking and physical activity for men, and age, BMI, family history, salt preference, alcohol consumption, and smoking for women. This prediction model showed good accuracy and predictability in both the developing and validation cohorts (C-statistics: 0.764 for men, 0.706 for women). In this study, a prediction model for gastric cancer incidence was developed that displayed a good performance.

  1. AN EFFICIENT PATIENT INFLOW PREDICTION MODEL FOR HOSPITAL RESOURCE MANAGEMENT

    Directory of Open Access Journals (Sweden)

    Kottalanka Srikanth

    2017-07-01

    Full Text Available There has been increasing demand in improving service provisioning in hospital resources management. Hospital industries work with strict budget constraint at the same time assures quality care. To achieve quality care with budget constraint an efficient prediction model is required. Recently there has been various time series based prediction model has been proposed to manage hospital resources such ambulance monitoring, emergency care and so on. These models are not efficient as they do not consider the nature of scenario such climate condition etc. To address this artificial intelligence is adopted. The issues with existing prediction are that the training suffers from local optima error. This induces overhead and affects the accuracy in prediction. To overcome the local minima error, this work presents a patient inflow prediction model by adopting resilient backpropagation neural network. Experiment are conducted to evaluate the performance of proposed model inter of RMSE and MAPE. The outcome shows the proposed model reduces RMSE and MAPE over existing back propagation based artificial neural network. The overall outcomes show the proposed prediction model improves the accuracy of prediction which aid in improving the quality of health care management.

  2. Risk Prediction Model for Severe Postoperative Complication in Bariatric Surgery.

    Science.gov (United States)

    Stenberg, Erik; Cao, Yang; Szabo, Eva; Näslund, Erik; Näslund, Ingmar; Ottosson, Johan

    2018-01-12

    Factors associated with risk for adverse outcome are important considerations in the preoperative assessment of patients for bariatric surgery. As yet, prediction models based on preoperative risk factors have not been able to predict adverse outcome sufficiently. This study aimed to identify preoperative risk factors and to construct a risk prediction model based on these. Patients who underwent a bariatric surgical procedure in Sweden between 2010 and 2014 were identified from the Scandinavian Obesity Surgery Registry (SOReg). Associations between preoperative potential risk factors and severe postoperative complications were analysed using a logistic regression model. A multivariate model for risk prediction was created and validated in the SOReg for patients who underwent bariatric surgery in Sweden, 2015. Revision surgery (standardized OR 1.19, 95% confidence interval (CI) 1.14-0.24, p prediction model. Despite high specificity, the sensitivity of the model was low. Revision surgery, high age, low BMI, large waist circumference, and dyspepsia/GERD were associated with an increased risk for severe postoperative complication. The prediction model based on these factors, however, had a sensitivity that was too low to predict risk in the individual patient case.

  3. Prediction Model for Gastric Cancer Incidence in Korean Population.

    Directory of Open Access Journals (Sweden)

    Bang Wool Eom

    Full Text Available Predicting high risk groups for gastric cancer and motivating these groups to receive regular checkups is required for the early detection of gastric cancer. The aim of this study is was to develop a prediction model for gastric cancer incidence based on a large population-based cohort in Korea.Based on the National Health Insurance Corporation data, we analyzed 10 major risk factors for gastric cancer. The Cox proportional hazards model was used to develop gender specific prediction models for gastric cancer development, and the performance of the developed model in terms of discrimination and calibration was also validated using an independent cohort. Discrimination ability was evaluated using Harrell's C-statistics, and the calibration was evaluated using a calibration plot and slope.During a median of 11.4 years of follow-up, 19,465 (1.4% and 5,579 (0.7% newly developed gastric cancer cases were observed among 1,372,424 men and 804,077 women, respectively. The prediction models included age, BMI, family history, meal regularity, salt preference, alcohol consumption, smoking and physical activity for men, and age, BMI, family history, salt preference, alcohol consumption, and smoking for women. This prediction model showed good accuracy and predictability in both the developing and validation cohorts (C-statistics: 0.764 for men, 0.706 for women.In this study, a prediction model for gastric cancer incidence was developed that displayed a good performance.

  4. Stage-specific predictive models for breast cancer survivability.

    Science.gov (United States)

    Kate, Rohit J; Nadig, Ramya

    2017-01-01

    Survivability rates vary widely among various stages of breast cancer. Although machine learning models built in past to predict breast cancer survivability were given stage as one of the features, they were not trained or evaluated separately for each stage. To investigate whether there are differences in performance of machine learning models trained and evaluated across different stages for predicting breast cancer survivability. Using three different machine learning methods we built models to predict breast cancer survivability separately for each stage and compared them with the traditional joint models built for all the stages. We also evaluated the models separately for each stage and together for all the stages. Our results show that the most suitable model to predict survivability for a specific stage is the model trained for that particular stage. In our experiments, using additional examples of other stages during training did not help, in fact, it made it worse in some cases. The most important features for predicting survivability were also found to be different for different stages. By evaluating the models separately on different stages we found that the performance widely varied across them. We also demonstrate that evaluating predictive models for survivability on all the stages together, as was done in the past, is misleading because it overestimates performance. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

  5. Factors regulating trophic status in a large subtropical reservoir, China.

    Science.gov (United States)

    Xu, Yaoyang; Cai, Qinghua; Han, Xinqin; Shao, Meiling; Liu, Ruiqiu

    2010-10-01

    We evaluated a 4-year data set (July 2003 to June 2007) to assess the trophic state and its limiting factors of Three-Gorges Reservoir (TGR), China, a large subtropical reservoir. Based on Carlson-type trophic state index (TSI)(CHL), the trophic state of the system was oligotrophic (TSI(S) TSI(TP) and TSI(TN) were higher than the critical value of eutrophic state (TSI(S) > 50). Using Carlson's (1991) two-dimensional approach, deviations of the TSI(S) indicated that factors other than phosphorus and nitrogen limited algal growth and that nonalgal particles affected light attenuation. These findings were further supported by the significant correlation among the values of TSI(CHL) - TSI(SD) and nonvolatile suspended solids and water residence time. The logarithmic model showed that an equivalent TSI(CHL) and TSI(SD) could be found at tau = 54 days in the TGR (Fig. 7). Accordingly, nonalgal particulates dominated light attenuation and limited algal biomass of the reservoir when tau < 54 days.

  6. Evaluation of wave runup predictions from numerical and parametric models

    Science.gov (United States)

    Stockdon, Hilary F.; Thompson, David M.; Plant, Nathaniel G.; Long, Joseph W.

    2014-01-01

    Wave runup during storms is a primary driver of coastal evolution, including shoreline and dune erosion and barrier island overwash. Runup and its components, setup and swash, can be predicted from a parameterized model that was developed by comparing runup observations to offshore wave height, wave period, and local beach slope. Because observations during extreme storms are often unavailable, a numerical model is used to simulate the storm-driven runup to compare to the parameterized model and then develop an approach to improve the accuracy of the parameterization. Numerically simulated and parameterized runup were compared to observations to evaluate model accuracies. The analysis demonstrated that setup was accurately predicted by both the parameterized model and numerical simulations. Infragravity swash heights were most accurately predicted by the parameterized model. The numerical model suffered from bias and gain errors that depended on whether a one-dimensional or two-dimensional spatial domain was used. Nonetheless, all of the predictions were significantly correlated to the observations, implying that the systematic errors can be corrected. The numerical simulations did not resolve the incident-band swash motions, as expected, and the parameterized model performed best at predicting incident-band swash heights. An assimilated prediction using a weighted average of the parameterized model and the numerical simulations resulted in a reduction in prediction error variance. Finally, the numerical simulations were extended to include storm conditions that have not been previously observed. These results indicated that the parameterized predictions of setup may need modification for extreme conditions; numerical simulations can be used to extend the validity of the parameterized predictions of infragravity swash; and numerical simulations systematically underpredict incident swash, which is relatively unimportant under extreme conditions.

  7. Femtocells Sharing Management using mobility prediction model

    OpenAIRE

    Barth, Dominique; Choutri, Amira; Kloul, Leila; Marcé, Olivier

    2013-01-01

    Bandwidth sharing paradigm constitutes an incentive solution for the serious capacity management problem faced by operators as femtocells owners are able to offer a QoS guaranteed network access to mobile users in their femtocell coverage. In this paper, we consider a technico-economic bandwidth sharing model based on a reinforcement learning algorithm. Because such a model does not allow the convergence of the learning algorithm, due to the small size of the femtocells, the mobile users velo...

  8. 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.

  9. Validating predictions from climate envelope models.

    Directory of Open Access Journals (Sweden)

    James I Watling

    Full Text Available 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

  10. North Atlantic climate model bias influence on multiyear predictability

    Science.gov (United States)

    Wu, Y.; Park, T.; Park, W.; Latif, M.

    2018-01-01

    The influences of North Atlantic biases on multiyear predictability of unforced surface air temperature (SAT) variability are examined in the Kiel Climate Model (KCM). By employing a freshwater flux correction over the North Atlantic to the model, which strongly alleviates both North Atlantic sea surface salinity (SSS) and sea surface temperature (SST) biases, the freshwater flux-corrected integration depicts significantly enhanced multiyear SAT predictability in the North Atlantic sector in comparison to the uncorrected one. The enhanced SAT predictability in the corrected integration is due to a stronger and more variable Atlantic Meridional Overturning Circulation (AMOC) and its enhanced influence on North Atlantic SST. Results obtained from preindustrial control integrations of models participating in the Coupled Model Intercomparison Project Phase 5 (CMIP5) support the findings obtained from the KCM: models with large North Atlantic biases tend to have a weak AMOC influence on SAT and exhibit a smaller SAT predictability over the North Atlantic sector.

  11. 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.

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

    Science.gov (United States)

    Osman, Marisol; Vera, C. S.

    2017-10-01

    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

  13. Prediction skill of rainstorm events over India in the TIGGE weather prediction models

    Science.gov (United States)

    Karuna Sagar, S.; Rajeevan, M.; Vijaya Bhaskara Rao, S.; Mitra, A. K.

    2017-12-01

    Extreme rainfall events pose a serious threat of leading to severe floods in many countries worldwide. Therefore, advance prediction of its occurrence and spatial distribution is very essential. In this paper, an analysis has been made to assess the skill of numerical weather prediction models in predicting rainstorms over India. Using gridded daily rainfall data set and objective criteria, 15 rainstorms were identified during the monsoon season (June to September). The analysis was made using three TIGGE (THe Observing System Research and Predictability Experiment (THORPEX) Interactive Grand Global Ensemble) models. The models considered are the European Centre for Medium-Range Weather Forecasts (ECMWF), National Centre for Environmental Prediction (NCEP) and the UK Met Office (UKMO). Verification of the TIGGE models for 43 observed rainstorm days from 15 rainstorm events has been made for the period 2007-2015. The comparison reveals that rainstorm events are predictable up to 5 days in advance, however with a bias in spatial distribution and intensity. The statistical parameters like mean error (ME) or Bias, root mean square error (RMSE) and correlation coefficient (CC) have been computed over the rainstorm region using the multi-model ensemble (MME) mean. The study reveals that the spread is large in ECMWF and UKMO followed by the NCEP model. Though the ensemble spread is quite small in NCEP, the ensemble member averages are not well predicted. The rank histograms suggest that the forecasts are under prediction. The modified Contiguous Rain Area (CRA) technique was used to verify the spatial as well as the quantitative skill of the TIGGE models. Overall, the contribution from the displacement and pattern errors to the total RMSE is found to be more in magnitude. The volume error increases from 24 hr forecast to 48 hr forecast in all the three models.

  14. Micro-mechanical studies on graphite strength prediction models

    Science.gov (United States)

    Kanse, Deepak; Khan, I. A.; Bhasin, V.; Vaze, K. K.

    2013-06-01

    The influence of type of loading and size-effects on the failure strength of graphite were studied using Weibull model. It was observed that this model over-predicts size effect in tension. However, incorporation of grain size effect in Weibull model, allows a more realistic simulation of size effects. Numerical prediction of strength of four-point bend specimen was made using the Weibull parameters obtained from tensile test data. Effective volume calculations were carried out and subsequently predicted strength was compared with experimental data. It was found that Weibull model can predict mean flexural strength with reasonable accuracy even when grain size effect was not incorporated. In addition, the effects of microstructural parameters on failure strength were analyzed using Rose and Tucker model. Uni-axial tensile, three-point bend and four-point bend strengths were predicted using this model and compared with the experimental data. It was found that this model predicts flexural strength within 10%. For uni-axial tensile strength, difference was 22% which can be attributed to less number of tests on tensile specimens. In order to develop failure surface of graphite under multi-axial state of stress, an open ended hollow tube of graphite was subjected to internal pressure and axial load and Batdorf model was employed to calculate failure probability of the tube. Bi-axial failure surface was generated in the first and fourth quadrant for 50% failure probability by varying both internal pressure and axial load.

  15. 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.

  16. 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.

  17. Modeling for prediction of restrained shrinkage effect in concrete repair

    International Nuclear Information System (INIS)

    Yuan Yingshu; Li Guo; Cai Yue

    2003-01-01

    A general model of autogenous shrinkage caused by chemical reaction (chemical shrinkage) is developed by means of Arrhenius' law and a degree of chemical reaction. Models of tensile creep and relaxation modulus are built based on a viscoelastic, three-element model. Tests of free shrinkage and tensile creep were carried out to determine some coefficients in the models. Two-dimensional FEM analysis based on the models and other constitutions can predict the development of tensile strength and cracking. Three groups of patch-repaired beams were designed for analysis and testing. The prediction from the analysis shows agreement with the test results. The cracking mechanism after repair is discussed

  18. 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.

  19. 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 so...... decisions need to be made in terms of statistical distributions of walking parameters and in terms of the parameters describing the statistical distributions. The paper explores how sensitive computations of bridge response are to some of the decisions to be made in this respect. This is useful...

  20. Uncertainties in model-based outcome predictions for treatment planning

    International Nuclear Information System (INIS)

    Deasy, Joseph O.; Chao, K.S. Clifford; Markman, Jerry

    2001-01-01

    Purpose: Model-based treatment-plan-specific outcome predictions (such as normal tissue complication probability [NTCP] or the relative reduction in salivary function) are typically presented without reference to underlying uncertainties. We provide a method to assess the reliability of treatment-plan-specific dose-volume outcome model predictions. Methods and Materials: A practical method is proposed for evaluating model prediction based on the original input data together with bootstrap-based estimates of parameter uncertainties. The general framework is applicable to continuous variable predictions (e.g., prediction of long-term salivary function) and dichotomous variable predictions (e.g., tumor control probability [TCP] or NTCP). Using bootstrap resampling, a histogram of the likelihood of alternative parameter values is generated. For a given patient and treatment plan we generate a histogram of alternative model results by computing the model predicted outcome for each parameter set in the bootstrap list. Residual uncertainty ('noise') is accounted for by adding a random component to the computed outcome values. The residual noise distribution is estimated from the original fit between model predictions and patient data. Results: The method is demonstrated using a continuous-endpoint model to predict long-term salivary function for head-and-neck cancer patients. Histograms represent the probabilities for the level of posttreatment salivary function based on the input clinical data, the salivary function model, and the three-dimensional dose distribution. For some patients there is significant uncertainty in the prediction of xerostomia, whereas for other patients the predictions are expected to be more reliable. In contrast, TCP and NTCP endpoints are dichotomous, and parameter uncertainties should be folded directly into the estimated probabilities, thereby improving the accuracy of the estimates. Using bootstrap parameter estimates, competing treatment

  1. 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...

  2. Geospatial application of the Water Erosion Prediction Project (WEPP) Model

    Science.gov (United States)

    D. C. Flanagan; J. R. Frankenberger; T. A. Cochrane; C. S. Renschler; W. J. Elliot

    2011-01-01

    The Water Erosion Prediction Project (WEPP) model is a process-based technology for prediction of soil erosion by water at hillslope profile, field, and small watershed scales. In particular, WEPP utilizes observed or generated daily climate inputs to drive the surface hydrology processes (infiltration, runoff, ET) component, which subsequently impacts the rest of the...

  3. Reduced order modelling and predictive control of multivariable ...

    Indian Academy of Sciences (India)

    Anuj Abraham

    2018-03-16

    Mar 16, 2018 ... The performance of constraint generalized predictive control scheme is found to be superior to that of the conventional PID controller in terms of overshoot, settling time and performance indices, mainly ISE, IAE and MSE. Keywords. Predictive control; distillation column; reduced order model; dominant pole; ...

  4. Exposure and food web transfer of pharmaceuticals in ospreys (Pandion haliaetus): Predictive model and empirical data

    Science.gov (United States)

    Lazarus, Rebecca S.; Rattner, Barnett A.; Du, Bowen; McGowan, Peter C.; Blazer, Vicki; Ottinger, Mary Ann

    2015-01-01

    The osprey (Pandion haliaetus) is a well-known sentinel of environmental contamination, yet no studies have traced pharmaceuticals through the water–fish–osprey food web. A screening-level exposure assessment was used to evaluate the bioaccumulation potential of 113 pharmaceuticals and metabolites, and an artificial sweetener in this food web. Hypothetical concentrations in water reflecting “wastewater effluent dominated” or “dilution dominated” scenarios were combined with pH-specific bioconcentration factors (BCFs) to predict uptake in fish. Residues in fish and osprey food intake rate were used to calculate the daily intake (DI) of compounds by an adult female osprey. Fourteen pharmaceuticals and a drug metabolite with a BCF greater than 100 and a DI greater than 20 µg/kg were identified as being most likely to exceed the adult human therapeutic dose (HTD). These 15 compounds were also evaluated in a 40 day cumulative dose exposure scenario using first-order kinetics to account for uptake and elimination. Assuming comparable absorption to humans, the half-lives (t1/2) for an adult osprey to reach the HTD within 40 days were calculated. For 3 of these pharmaceuticals, the estimated t1/2 in ospreys was less than that for humans, and thus an osprey might theoretically reach or exceed the HTD in 3 to 7 days. To complement the exposure model, 24 compounds were quantified in water, fish plasma, and osprey nestling plasma from 7 potentially impaired locations in Chesapeake Bay. Of the 18 analytes detected in water, 8 were found in fish plasma, but only 1 in osprey plasma (the antihypertensive diltiazem). Compared to diltiazem detection rate and concentrations in water (10/12 detects, time, and there is no evidence to suggest adverse effects. This screening-level exposure model can help identify those compounds that warrant further investigation in high-trophic level species.

  5. 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

  6. Consensus models to predict endocrine disruption for all ...

    Science.gov (United States)

    Humans are potentially exposed to tens of thousands of man-made chemicals in the environment. It is well known that some environmental chemicals mimic natural hormones and thus have the potential to be endocrine disruptors. Most of these environmental chemicals have never been tested for their ability to disrupt the endocrine system, in particular, their ability to interact with the estrogen receptor. EPA needs tools to prioritize thousands of chemicals, for instance in the Endocrine Disruptor Screening Program (EDSP). Collaborative Estrogen Receptor Activity Prediction Project (CERAPP) was intended to be a demonstration of the use of predictive computational models on HTS data including ToxCast and Tox21 assays to prioritize a large chemical universe of 32464 unique structures for one specific molecular target – the estrogen receptor. CERAPP combined multiple computational models for prediction of estrogen receptor activity, and used the predicted results to build a unique consensus model. Models were developed in collaboration between 17 groups in the U.S. and Europe and applied to predict the common set of chemicals. Structure-based techniques such as docking and several QSAR modeling approaches were employed, mostly using a common training set of 1677 compounds provided by U.S. EPA, to build a total of 42 classification models and 8 regression models for binding, agonist and antagonist activity. All predictions were evaluated on ToxCast data and on an exte

  7. Dietary information improves cardiovascular disease risk prediction models.

    Science.gov (United States)

    Baik, I; Cho, N H; Kim, S H; Shin, C

    2013-01-01

    Data are limited on cardiovascular disease (CVD) risk prediction models that include dietary predictors. Using known risk factors and dietary information, we constructed and evaluated CVD risk prediction models. Data for modeling were from population-based prospective cohort studies comprised of 9026 men and women aged 40-69 years. At baseline, all were free of known CVD and cancer, and were followed up for CVD incidence during an 8-year period. We used Cox proportional hazard regression analysis to construct a traditional risk factor model, an office-based model, and two diet-containing models and evaluated these models by calculating Akaike information criterion (AIC), C-statistics, integrated discrimination improvement (IDI), net reclassification improvement (NRI) and calibration statistic. We constructed diet-containing models with significant dietary predictors such as poultry, legumes, carbonated soft drinks or green tea consumption. Adding dietary predictors to the traditional model yielded a decrease in AIC (delta AIC=15), a 53% increase in relative IDI (P-value for IDI NRI (category-free NRI=0.14, P NRI (category-free NRI=0.08, P<0.01) compared with the office-based model. The calibration plots for risk prediction demonstrated that the inclusion of dietary predictors contributes to better agreement in persons at high risk for CVD. C-statistics for the four models were acceptable and comparable. We suggest that dietary information may be useful in constructing CVD risk prediction models.

  8. 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...

  9. Unsupervised ship trajectory modeling and prediction using compression and clustering

    NARCIS (Netherlands)

    de Vries, G.; van Someren, M.; van Erp, M.; Stehouwer, H.; van Zaanen, M.

    2009-01-01

    In this paper we show how to build a model of ship trajectories in a certain maritime region and use this model to predict future ship movements. The presented method is unsupervised and based on existing compression (line-simplification) and clustering techniques. We evaluate the model with a

  10. Prediction of annual rainfall pattern using Hidden Markov Model ...

    African Journals Online (AJOL)

    A hidden Markov model to predict annual rainfall pattern has been presented in this paper. The model is developed to provide necessary information for the farmers, agronomists, water resource management scientists and policy makers to enable them plan for the uncertainty of annual rainfall. The model classified annual ...

  11. The Selection of Turbulence Models for Prediction of Room Airflow

    DEFF Research Database (Denmark)

    Nielsen, Peter V.

    This paper discusses the use of different turbulence models and their advantages in given situations. As an example, it is shown that a simple zero-equation model can be used for the prediction of special situations as flow with a low level of turbulence. A zero-equation model with compensation...

  12. Model Predictive Control of Wind Turbines using Uncertain LIDAR Measurements

    DEFF Research Database (Denmark)

    Mirzaei, Mahmood; Soltani, Mohsen; Poulsen, Niels Kjølstad

    2013-01-01

    The problem of Model predictive control (MPC) of wind turbines using uncertain LIDAR (LIght Detection And Ranging) measurements is considered. A nonlinear dynamical model of the wind turbine is obtained. We linearize the obtained nonlinear model for different operating points, which are determined...

  13. Using Pareto points for model identification in predictive toxicology

    Science.gov (United States)

    2013-01-01

    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. PMID:23517649

  14. Integrating geophysics and hydrology for reducing the uncertainty of groundwater model predictions and improved prediction performance

    DEFF Research Database (Denmark)

    Christensen, Nikolaj Kruse; Christensen, Steen; Ferre, Ty

    constructed from geological and hydrological data. However, geophysical data are increasingly used to inform hydrogeologic models because they are collected at lower cost and much higher density than geological and hydrological data. Despite increased use of geophysics, it is still unclear whether...... the integration of geophysical data in the construction of a groundwater model increases the prediction performance. We suggest that modelers should perform a hydrogeophysical “test-bench” analysis of the likely value of geophysics data for improving groundwater model prediction performance before actually...... collecting geophysical data. At a minimum, an analysis should be conducted assuming settings that are favorable for the chosen geophysical method. If the analysis suggests that data collected by the geophysical method is unlikely to improve model prediction performance under these favorable settings...

  15. 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.

  16. Preoperative prediction model of outcome after cholecystectomy for symptomatic gallstones

    DEFF Research Database (Denmark)

    Borly, L; Anderson, I B; Bardram, Linda

    1999-01-01

    and sonography evaluated gallbladder motility, gallstones, and gallbladder volume. Preoperative variables in patients with or without postcholecystectomy pain were compared statistically, and significant variables were combined in a logistic regression model to predict the postoperative outcome. RESULTS: Eighty...... and by the absence of 'agonizing' pain and of symptoms coinciding with pain (P model 15 of 18 predicted patients had postoperative pain (PVpos = 0.83). Of 62 patients predicted as having no pain postoperatively, 56 were pain-free (PVneg = 0.90). Overall accuracy...... was 89%. CONCLUSION: From this prospective study a model based on preoperative symptoms was developed to predict postcholecystectomy pain. Since intrastudy reclassification may give too optimistic results, the model should be validated in future studies....

  17. Prediction of Chemical Function: Model Development and Application

    Science.gov (United States)

    The United States Environmental Protection Agency’s Exposure Forecaster (ExpoCast) project is developing both statistical and mechanism-based computational models for predicting exposures to thousands of chemicals, including those in consumer products. The high-throughput (...

  18. Linear regression crash prediction models : issues and proposed solutions.

    Science.gov (United States)

    2010-05-01

    The paper develops a linear regression model approach that can be applied to : crash data to predict vehicle crashes. The proposed approach involves novice data aggregation : to satisfy linear regression assumptions; namely error structure normality ...

  19. FPGA implementation of predictive degradation model for engine oil lifetime

    Science.gov (United States)

    Idros, M. F. M.; Razak, A. H. A.; Junid, S. A. M. Al; Suliman, S. I.; Halim, A. K.

    2018-03-01

    This paper presents the implementation of linear regression model for degradation prediction on Register Transfer Logic (RTL) using QuartusII. A stationary model had been identified in the degradation trend for the engine oil in a vehicle in time series method. As for RTL implementation, the degradation model is written in Verilog HDL and the data input are taken at a certain time. Clock divider had been designed to support the timing sequence of input data. At every five data, a regression analysis is adapted for slope variation determination and prediction calculation. Here, only the negative value are taken as the consideration for the prediction purposes for less number of logic gate. Least Square Method is adapted to get the best linear model based on the mean values of time series data. The coded algorithm has been implemented on FPGA for validation purposes. The result shows the prediction time to change the engine oil.

  20. Predictive Modeling: A New Paradigm for Managing Endometrial Cancer.

    Science.gov (United States)

    Bendifallah, Sofiane; Daraï, Emile; Ballester, Marcos

    2016-03-01

    With the abundance of new options in diagnostic and treatment modalities, a shift in the medical decision process for endometrial cancer (EC) has been observed. The emergence of individualized medicine and the increasing complexity of available medical data has lead to the development of several prediction models. In EC, those clinical models (algorithms, nomograms, and risk scoring systems) have been reported, especially for stratifying and subgrouping patients, with various unanswered questions regarding such things as the optimal surgical staging for lymph node metastasis as well as the assessment of recurrence and survival outcomes. In this review, we highlight existing prognostic and predictive models in EC, with a specific focus on their clinical applicability. We also discuss the methodologic aspects of the development of such predictive models and the steps that are required to integrate these tools into clinical decision making. In the future, the emerging field of molecular or biochemical markers research may substantially improve predictive and treatment approaches.

  1. 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...

  2. Predictive modeling in catalysis - from dream to reality

    NARCIS (Netherlands)

    Maldonado, A.G.; Rothenberg, G.

    2009-01-01

    In silico catalyst optimization is the ultimate application of computers in catalysis. This article provides an overview of the basic concepts of predictive modeling and describes how this technique can be used in catalyst and reaction design.

  3. Fuzzy model predictive control algorithm applied in nuclear power plant

    International Nuclear Information System (INIS)

    Zuheir, Ahmad

    2006-01-01

    The aim of this paper is to design a predictive controller based on a fuzzy model. The Takagi-Sugeno fuzzy model with an Adaptive B-splines neuro-fuzzy implementation is used and incorporated as a predictor in a predictive controller. An optimization approach with a simplified gradient technique is used to calculate predictions of the future control actions. In this approach, adaptation of the fuzzy model using dynamic process information is carried out to build the predictive controller. The easy description of the fuzzy model and the easy computation of the gradient sector during the optimization procedure are the main advantages of the computation algorithm. The algorithm is applied to the control of a U-tube steam generation unit (UTSG) used for electricity generation. (author)

  4. 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.

  5. Predictive Modeling of Partitioned Systems: Implementation and Applications

    OpenAIRE

    Latten, Christine

    2014-01-01

    A general mathematical methodology for predictive modeling of coupled multi-physics systems is implemented and has been applied without change to an illustrative heat conduction example and reactor physics benchmarks.

  6. A new, accurate predictive model for incident hypertension

    DEFF Research Database (Denmark)

    Völzke, Henry; Fung, Glenn; Ittermann, Till

    2013-01-01

    Data mining represents an alternative approach to identify new predictors of multifactorial diseases. This work aimed at building an accurate predictive model for incident hypertension using data mining procedures....

  7. Model Predictive Control for Ethanol Steam Reformers

    OpenAIRE

    Li, Mingming

    2014-01-01

    This thesis firstly proposes a new approach of modelling an ethanol steam reformer (ESR) for producing pure hydrogen. Hydrogen has obvious benefits as an alternative for feeding the proton exchange membrane fuel cells (PEMFCs) to produce electricity. However, an important drawback is that the hydrogen distribution and storage have high cost. So the ESR is regarded as a way to overcome these difficulties. Ethanol is currently considered as a promising energy source under the res...

  8. 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.

  9. Wireless model predictive control: Application to water-level system

    Directory of Open Access Journals (Sweden)

    Ramdane Hedjar

    2016-04-01

    Full Text Available This article deals with wireless model predictive control of a water-level control system. The objective of the model predictive control algorithm is to constrain the control signal inside saturation limits and maintain the water level around the desired level. Linear modeling of any nonlinear plant leads to parameter uncertainties and non-modeled dynamics in the linearized mathematical model. These uncertainties induce a steady-state error in the output response of the water level. To eliminate this steady-state error and increase the robustness of the control algorithm, an integral action is included in the closed loop. To control the water-level system remotely, the communication between the controller and the process is performed using radio channel. To validate the proposed scheme, simulation and real-time implementation of the algorithm have been conducted, and the results show the effectiveness of wireless model predictive control with integral action.

  10. Aqua/Aura Updated Inclination Adjust Maneuver Performance Prediction Model

    Science.gov (United States)

    Boone, Spencer

    2017-01-01

    This presentation will discuss the updated Inclination Adjust Maneuver (IAM) performance prediction model that was developed for Aqua and Aura following the 2017 IAM series. This updated model uses statistical regression methods to identify potential long-term trends in maneuver parameters, yielding improved predictions when re-planning past maneuvers. The presentation has been reviewed and approved by Eric Moyer, ESMO Deputy Project Manager.

  11. Approximating prediction uncertainty for random forest regression models

    Science.gov (United States)

    John W. Coulston; Christine E. Blinn; Valerie A. Thomas; Randolph H. Wynne

    2016-01-01

    Machine learning approaches such as random forest have increased for the spatial modeling and mapping of continuous variables. Random forest is a non-parametric ensemble approach, and unlike traditional regression approaches there is no direct quantification of prediction error. Understanding prediction uncertainty is important when using model-based continuous maps as...

  12. 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.

  13. The Next Page Access Prediction Using Makov Model

    OpenAIRE

    Deepti Razdan

    2011-01-01

    Predicting the next page to be accessed by the Webusers has attracted a large amount of research. In this paper, anew web usage mining approach is proposed to predict next pageaccess. It is proposed to identify similar access patterns from weblog using K-mean clustering and then Markov model is used forprediction for next page accesses. The tightness of clusters isimproved by setting similarity threshold while forming clusters.In traditional recommendation models, clustering by nonsequentiald...

  14. 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.

  15. Working Towards a Risk Prediction Model for Neural Tube Defects

    Science.gov (United States)

    Agopian, A.J.; Lupo, Philip J.; Tinker, Sarah C.; Canfield, Mark A.; Mitchell, Laura E.

    2015-01-01

    BACKGROUND Several risk factors have been consistently associated with neural tube defects (NTDs). However, the predictive ability of these risk factors in combination has not been evaluated. METHODS To assess the predictive ability of established risk factors for NTDs, we built predictive models using data from the National Birth Defects Prevention Study, which is a large, population-based study of nonsyndromic birth defects. Cases with spina bifida or anencephaly, or both (n = 1239), and controls (n = 8494) were randomly divided into separate training (75% of cases and controls) and validation (remaining 25%) samples. Multivariable logistic regression models were constructed with the training samples. The predictive ability of these models was evaluated in the validation samples by assessing the area under the receiver operator characteristic curves. An ordinal predictive risk index was also constructed and evaluated. In addition, the ability of classification and regression tree (CART) analysis to identify subgroups of women at increased risk for NTDs in offspring was evaluated. RESULTS The predictive ability of the multivariable models was poor (area under the receiver operating curve: 0.55 for spina bifida only, 0.59 for anencephaly only, and 0.56 for anencephaly and spina bifida combined). The predictive abilities of the ordinal risk indexes and CART models were also low. CONCLUSION Current established risk factors for NTDs are insufficient for population-level prediction of a women’s risk for having affected offspring. Identification of genetic risk factors and novel nongenetic risk factors will be critical to establishing models, with good predictive ability, for NTDs. PMID:22253139

  16. Predictive QSAR Models for the Toxicity of Disinfection Byproducts.

    Science.gov (United States)

    Qin, Litang; Zhang, Xin; Chen, Yuhan; Mo, Lingyun; Zeng, Honghu; Liang, Yanpeng

    2017-10-09

    Several hundred disinfection byproducts (DBPs) in drinking water have been identified, and are known to have potentially adverse health effects. There are toxicological data gaps for most DBPs, and the predictive method may provide an effective way to address this. The development of an in-silico model of toxicology endpoints of DBPs is rarely studied. The main aim of the present study is to develop predictive quantitative structure-activity relationship (QSAR) models for the reactive toxicities of 50 DBPs in the five bioassays of X-Microtox, GSH+, GSH-, DNA+ and DNA-. All-subset regression was used to select the optimal descriptors, and multiple linear-regression models were built. The developed QSAR models for five endpoints satisfied the internal and external validation criteria: coefficient of determination ( R ²) > 0.7, explained variance in leave-one-out prediction ( Q ² LOO ) and in leave-many-out prediction ( Q ² LMO ) > 0.6, variance explained in external prediction ( Q ² F1 , Q ² F2 , and Q ² F3 ) > 0.7, and concordance correlation coefficient ( CCC ) > 0.85. The application domains and the meaning of the selective descriptors for the QSAR models were discussed. The obtained QSAR models can be used in predicting the toxicities of the 50 DBPs.

  17. Predictive QSAR Models for the Toxicity of Disinfection Byproducts

    Directory of Open Access Journals (Sweden)

    Litang Qin

    2017-10-01

    Full Text Available Several hundred disinfection byproducts (DBPs in drinking water have been identified, and are known to have potentially adverse health effects. There are toxicological data gaps for most DBPs, and the predictive method may provide an effective way to address this. The development of an in-silico model of toxicology endpoints of DBPs is rarely studied. The main aim of the present study is to develop predictive quantitative structure–activity relationship (QSAR models for the reactive toxicities of 50 DBPs in the five bioassays of X-Microtox, GSH+, GSH−, DNA+ and DNA−. All-subset regression was used to select the optimal descriptors, and multiple linear-regression models were built. The developed QSAR models for five endpoints satisfied the internal and external validation criteria: coefficient of determination (R2 > 0.7, explained variance in leave-one-out prediction (Q2LOO and in leave-many-out prediction (Q2LMO > 0.6, variance explained in external prediction (Q2F1, Q2F2, and Q2F3 > 0.7, and concordance correlation coefficient (CCC > 0.85. The application domains and the meaning of the selective descriptors for the QSAR models were discussed. The obtained QSAR models can be used in predicting the toxicities of the 50 DBPs.

  18. Benthic macroinfaunal community structure, resource utilisation and trophic relationships in two Canadian Arctic Archipelago polynyas.

    Directory of Open Access Journals (Sweden)

    Anni Mäkelä

    Full Text Available Climate change driven alterations to patterns of Arctic marine primary production, with increasing phytoplankton- and decreasing ice algal production, have the potential to change the resource utilisation and trophic structure of the benthic communities relying on the algae for food. To predict the benthic responses to dietary changes, we studied the macroinfaunal community compositions, and used the faunal δ13C and δ15N signatures to investigate their main food sources and trophic positions in North Water (NOW and Lancaster Sound (LS polynyas in the Canadian Arctic Archipelago. Macroinfaunal density (10 952 ind. m-2 and biomass (3190 mg C m-2 recorded in NOW were higher than previously found in the Arctic at depths >500m, and significantly higher than in LS (8355 ind. m-2 and 2110 mg C m-2. This was attributed to higher particulate organic matter fluxes to seafloor in NOW. Polychaetes were significant taxa at both sites in terms of density and biomass, and in addition crustacean density in NOW and bivalve density in LS were high. Facultative filter and surface deposit feeders were highly prevalent at both sites, suggesting feeding plasticity is a successful strategy for accessing different food sources. The macrofaunal δ13C signatures reflected the signatures of pelagic particulate organic matter at the sites, and an isotope mixing model confirmed phytoplankton as the main food source for most taxa and feeding guilds. The food web length in LS was longer than in NOW (3.2 vs. 2.8 trophic levels. This was attributed to a larger reliance on reworked organic matter by the benthic community in LS, whereas the high export fluxes at the highly productive NOW resulted in higher rates of selective consumption of fresh algal matter. Despite studies suggesting that loss of ice algae from consumer diets in the Arctic might have a negative impact on the benthos, this study suggests that Arctic macrobenthic communities thrive using phytoplankton as their

  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

    function, however, is nonconvex due to the temperature dependence of thermodynamic efficiency. To handle this nonconvexity we propose a sequential convex optimization 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...... capacity associated with large penetration of intermittent renewable energy sources in a future smart grid....

  20. Impact of Human Activities on Nutrient and Trophic Status of some ...

    African Journals Online (AJOL)

    komla

    transparency, in predicting the trophic status of lagoons and coastal waters is a new and emerging scientific way of looking at lagoon ... The closed lagoons are fed by seasonal rivers and streams. They usually ... economy of some coastal inhabitants, especially during the off-season for marine fishing (Entsua-. Mensah et al.

  1. Investigation on trophic state index by artificial neural networks (case study: Dez Dam of Iran)

    Science.gov (United States)

    Saghi, H.; Karimi, L.; Javid, A. H.

    2015-06-01

    Dam construction and surface runoff control is one of the most common approaches for water-needs supply of human societies. However, the increasing development of social activities and hence the subsequent increase in environmental pollutants leads to deterioration of water quality in dam reservoirs and eutrophication process could be intensified. So, the water quality of reservoirs is now one of the key factors in operation and water quality management of reservoirs. Hence, maintaining the quality of the stored water and identification and examination of changes along time has been a constant concern of humans that involves the water authorities. Traditionally, empirical trophic state indices of dam reservoirs often defined based on changes in concentration of effective factors (nutrients) and its consequences (increase in chlorophyll a), have been used as an efficient tool in the definition of dam reservoirs quality. In recent years, modeling techniques such as artificial neural networks have enhanced the prediction capability and the accuracy of these studies. In this study, artificial neural networks have been applied to analyze eutrophication process in the Dez Dam reservoir in Iran. In this paper, feed forward neural network with one input layer, one hidden layer and one output layer was applied using MATLAB neural network toolbox for trophic state index (TSI) analysis in the Dez Dam reservoir. The input data of this network are effective parameters in the eutrophication: nitrogen cycle parameters and phosphorous cycle parameters and parameters that will be changed by eutrophication: Chl a, SD, DO and the output data is TSI. Based on the results from estimation of modified Carlson trophic state index, Dez Dam reservoir is considered to be eutrophic in the early July to mid-November and would be mesotrophic with decrease in temperature. Therefore, a decrease in water quality of the dam reservoir during the warm seasons is expectable. The results indicated that

  2. Maxent modelling for predicting the potential distribution of Thai Palms

    DEFF Research Database (Denmark)

    Tovaranonte, Jantrararuk; Barfod, Anders S.; Overgaard, Anne Blach

    2011-01-01

    Increasingly species distribution models are being used to address questions related to ecology, biogeography and species conservation on global and regional scales. We used the maximum entropy approach implemented in the MAXENT programme to build a habitat suitability model for Thai palms based...... overprediction of species distribution ranges. The models with the best predictive power were found by calculating the area under the curve (AUC) of receiver-operating characteristic (ROC). Here, we provide examples of contrasting predicted species distribution ranges as well as a map of modeled palm diversity...

  3. Validation of Fatigue Modeling Predictions in Aviation Operations

    Science.gov (United States)

    Gregory, Kevin; Martinez, Siera; Flynn-Evans, Erin

    2017-01-01

    Bio-mathematical fatigue models that predict levels of alertness and performance are one potential tool for use within integrated fatigue risk management approaches. A number of models have been developed that provide predictions based on acute and chronic sleep loss, circadian desynchronization, and sleep inertia. Some are publicly available and gaining traction in settings such as commercial aviation as a means of evaluating flight crew schedules for potential fatigue-related risks. Yet, most models have not been rigorously evaluated and independently validated for the operations to which they are being applied and many users are not fully aware of the limitations in which model results should be interpreted and applied.

  4. 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)

  5. 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.

  6. 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.

  7. Predictions for mt and MW in minimal supersymmetric models

    International Nuclear Information System (INIS)

    Buchmueller, O.; Ellis, J.R.; Flaecher, H.; Isidori, G.

    2009-12-01

    Using a frequentist analysis of experimental constraints within two versions of the minimal supersymmetric extension of the Standard Model, we derive the predictions for the top quark mass, m t , and the W boson mass, m W . We find that the supersymmetric predictions for both m t and m W , obtained by incorporating all the relevant experimental information and state-of-the-art theoretical predictions, are highly compatible with the experimental values with small remaining uncertainties, yielding an improvement compared to the case of the Standard Model. (orig.)

  8. 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

  9. Preprocedural Prediction Model for Contrast-Induced Nephropathy Patients.

    Science.gov (United States)

    Yin, Wen-Jun; Yi, Yi-Hu; Guan, Xiao-Feng; Zhou, Ling-Yun; Wang, Jiang-Lin; Li, Dai-Yang; Zuo, Xiao-Cong

    2017-02-03

    Several models have been developed for prediction of contrast-induced nephropathy (CIN); however, they only contain patients receiving intra-arterial contrast media for coronary angiographic procedures, which represent a small proportion of all contrast procedures. In addition, most of them evaluate radiological interventional procedure-related variables. So it is necessary for us to develop a model for prediction of CIN before radiological procedures among patients administered contrast media. A total of 8800 patients undergoing contrast administration were randomly assigned in a 4:1 ratio to development and validation data sets. CIN was defined as an increase of 25% and/or 0.5 mg/dL in serum creatinine within 72 hours above the baseline value. Preprocedural clinical variables were used to develop the prediction model from the training data set by the machine learning method of random forest, and 5-fold cross-validation was used to evaluate the prediction accuracies of the model. Finally we tested this model in the validation data set. The incidence of CIN was 13.38%. We built a prediction model with 13 preprocedural variables selected from 83 variables. The model obtained an area under the receiver-operating characteristic (ROC) curve (AUC) of 0.907 and gave prediction accuracy of 80.8%, sensitivity of 82.7%, specificity of 78.8%, and Matthews correlation coefficient of 61.5%. For the first time, 3 new factors are included in the model: the decreased sodium concentration, the INR value, and the preprocedural glucose level. The newly established model shows excellent predictive ability of CIN development and thereby provides preventative measures for CIN. © 2017 The Authors. Published on behalf of the American Heart Association, Inc., by Wiley Blackwell.

  10. 'Trophic' and 'source' amino acids in trophic estimation: a likely metabolic explanation.

    Science.gov (United States)

    O'Connell, T C

    2017-06-01

    Amino acid nitrogen isotopic analysis is a relatively new method for estimating trophic position. It uses the isotopic difference between an individual's 'trophic' and 'source' amino acids to determine its trophic position. So far, there is no accepted explanation for the mechanism by which the isotopic signals in 'trophic' and 'source' amino acids arise. Yet without a metabolic understanding, the utility of nitrogen isotopic analyses as a method for probing trophic relations, at either bulk tissue or amino acid level, is limited. I draw on isotopic tracer studies of protein metabolism, together with a consideration of amino acid metabolic pathways, to suggest that the 'trophic'/'source' groupings have a fundamental metabolic origin, to do with the cycling of amino-nitrogen between amino acids. 'Trophic' amino acids are those whose amino-nitrogens are interchangeable, part of a metabolic amino-nitrogen pool, and 'source' amino acids are those whose amino-nitrogens are not interchangeable with the metabolic pool. Nitrogen isotopic values of 'trophic' amino acids will reflect an averaged isotopic signal of all such dietary amino acids, offset by the integrated effect of isotopic fractionation from nitrogen cycling, and modulated by metabolic and physiological effects. Isotopic values of 'source' amino acids will be more closely linked to those of equivalent dietary amino acids, but also modulated by metabolism and physiology. The complexity of nitrogen cycling suggests that a single identifiable value for 'trophic discrimination factors' is unlikely to exist. Greater consideration of physiology and metabolism should help in better understanding observed patterns in nitrogen isotopic values.

  11. Risk Prediction Models for Oral Clefts Allowing for Phenotypic Heterogeneity

    Directory of Open Access Journals (Sweden)

    Yalu eWen

    2015-08-01

    Full Text Available Oral clefts are common birth defects that have a major impact on the affected individual, their family and society. World-wide, the incidence of oral clefts is 1/700 live births, making them the most common craniofacial birth defects. The successful prediction of oral clefts may help identify sub-population at high risk, and promote new diagnostic and therapeutic strategies. Nevertheless, developing a clinically useful oral clefts risk prediction model remains a great challenge. Compelling evidences suggest the etiologies of oral clefts are highly heterogeneous, and the development of a risk prediction model with consideration of phenotypic heterogeneity may potentially improve the accuracy of a risk prediction model. In this study, we applied a previously developed statistical method to investigate the risk prediction on sub-phenotypes of oral clefts. Our results suggested subtypes of cleft lip and palate have similar genetic etiologies (AUC=0.572 with subtypes of cleft lip only (AUC=0.589, while the subtypes of cleft palate only (CPO have heterogeneous underlying mechanisms (AUCs for soft CPO and hard CPO are 0.617 and 0.623, respectively. This highlighted the potential that the hard and soft forms of CPO have their own mechanisms despite sharing some of the genetic risk factors. Comparing with conventional methods for risk prediction modeling, our method considers phenotypic heterogeneity of a disease, which potentially improves the accuracy for predicting each sub-phenotype of oral clefts.

  12. 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.

  13. Survival prediction model for postoperative hepatocellular carcinoma patients.

    Science.gov (United States)

    Ren, Zhihui; He, Shasha; Fan, Xiaotang; He, Fangping; Sang, Wei; Bao, Yongxing; Ren, Weixin; Zhao, Jinming; Ji, Xuewen; Wen, Hao

    2017-09-01

    This study is to establish a predictive index (PI) model of 5-year survival rate for patients with hepatocellular carcinoma (HCC) after radical resection and to evaluate its prediction sensitivity, specificity, and accuracy.Patients underwent HCC surgical resection were enrolled and randomly divided into prediction model group (101 patients) and model evaluation group (100 patients). Cox regression model was used for univariate and multivariate survival analysis. A PI model was established based on multivariate analysis and receiver operating characteristic (ROC) curve was drawn accordingly. The area under ROC (AUROC) and PI cutoff value was identified.Multiple Cox regression analysis of prediction model group showed that neutrophil to lymphocyte ratio, histological grade, microvascular invasion, positive resection margin, number of tumor, and postoperative transcatheter arterial chemoembolization treatment were the independent predictors for the 5-year survival rate for HCC patients. The model was PI = 0.377 × NLR + 0.554 × HG + 0.927 × PRM + 0.778 × MVI + 0.740 × NT - 0.831 × transcatheter arterial chemoembolization (TACE). In the prediction model group, AUROC was 0.832 and the PI cutoff value was 3.38. The sensitivity, specificity, and accuracy were 78.0%, 80%, and 79.2%, respectively. In model evaluation group, AUROC was 0.822, and the PI cutoff value was well corresponded to the prediction model group with sensitivity, specificity, and accuracy of 85.0%, 83.3%, and 84.0%, respectively.The PI model can quantify the mortality risk of hepatitis B related HCC with high sensitivity, specificity, and accuracy.

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

    International Nuclear Information System (INIS)

    Hauri, Dimitri D.; Huss, Anke; Zimmermann, Frank; Kuehni, Claudia E.; Röösli, Martin

    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 nationwide Swiss radon database collected between 1994 and 2004. Of these, 80% randomly selected measurements were used for model development and the remaining 20% for an independent model validation. A multivariable log-linear regression model was fitted and relevant predictors selected according to evidence from the literature, the adjusted R², the Akaike's information criterion (AIC), and the Bayesian information criterion (BIC). The prediction model was evaluated by calculating Spearman rank correlation between measured and predicted values. Additionally, the predicted values were categorised into three categories (50th, 50th–90th and 90th percentile) and compared with measured categories using a weighted Kappa statistic. The most relevant predictors for indoor radon levels were tectonic units and year of construction of the building, followed by soil texture, degree of urbanisation, floor of the building where the measurement was taken and housing type (P-values <0.001 for all). Mean predicted radon values (geometric mean) were 66 Bq/m³ (interquartile range 40–111 Bq/m³) in the lowest exposure category, 126 Bq/m³ (69–215 Bq/m³) in the medium category, and 219 Bq/m³ (108–427 Bq/m³) in the highest category. Spearman correlation between predictions and measurements was 0.45 (95%-CI: 0.44; 0.46) for the development dataset and 0.44 (95%-CI: 0.42; 0.46) for the validation dataset. Kappa coefficients were 0.31 for the development and 0.30 for the validation dataset, respectively. The model explained 20% overall variability (adjusted R²). In conclusion, this residential radon prediction model, based on a large number of measurements, was demonstrated to be

  15. Numerical modeling capabilities to predict repository performance

    International Nuclear Information System (INIS)

    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

  16. Model Predictive Control for Smart Energy Systems

    DEFF Research Database (Denmark)

    Halvgaard, Rasmus

    In this thesis, we consider control strategies for flexible distributed energy resources in the future intelligent energy system – the Smart Grid. The energy system is a large-scale complex network with many actors and objectives in different hierarchical layers. Specifically the power system must...... significantly. A Smart Grid calls for flexible consumers that can adjust their consumption based on the amount of green energy in the grid. This requires coordination through new large-scale control and optimization algorithms. Trading of flexibility is key to drive power consumption in a sustainable direction....... In Denmark, we expect that distributed energy resources such as heat pumps, and batteries in electric vehicles will mobilize part of the needed flexibility. Our primary objectives in the thesis were threefold: 1.Simulate the components in the power system based on simple models from literature (e.g. heat...

  17. Model Predictive Control of Wind Turbines

    DEFF Research Database (Denmark)

    Henriksen, Lars Christian

    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...... 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...

  18. 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.

  19. 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......'s are designed for each sea state using a model assuming a linear loss torque. The mean power results from two controllers are compared using both loss models. Simulation results show that MPC can outperform a reactive controller if a good model of the conversion losses is available....... 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...

  20. Impact of late glacial climate variations on stratification and trophic state of the meromictic lake Längsee (Austria: validation of a conceptual model by multi proxy studies

    Directory of Open Access Journals (Sweden)

    Jens MÜLLER

    2002-02-01

    Full Text Available Selected pigments, diatoms and diatom-inferred phosphorus (Di-TP concentrations of a late glacial sediment core section of the meromictic Längsee, Austria, were compared with tephra- and varve-dated pollen stratigraphic and geochemical results. A conceptual model was adopted for Längsee and evaluated using multi proxy data. During the unforested late Pleniglacial, a holomictic lake stage with low primary productivity prevailed. Subsequent to the Lateglacial Betula expansion, at about 14,300 cal. y BP, okenone and isorenieratene, pigments from purple and green sulphur bacteria, indicate the onset of anoxic conditions in the hypolimnion. The formation of laminae coincides with this anoxic, meromictic period with high, though fluctuating, amounts of okenone that persisted throughout the Lateglacial interstadial. The occurrence of unlaminated sediment sections of allochthonous origin, and concurrent low concentrations of okenone, were related to cool and wet climate fluctuations during this period, probably coupled with a complete mixing of the water column. Two of these oscillations of the Lateglacial interstadial have been correlated tentatively with the Aegelsee and Gerzensee oscillations in the Alps. The latter climate fluctuation divides a period of enhanced anoxia and primary productivity, correlated with the Alleröd chronozone. Continental climate conditions were assumed to be the main driving forces for meromictic stability during Alleröd times. In addition, calcite dissolution due to severe hypolimnetic anoxia, appear to have supported meromictic stability. Increased pigment concentrations, which are in contrast to low diatom-inferred total phosphorus (Di- TP, indicate the formation of a productive metalimnion during this period, probably due to a clear-water phase (low catchment erosion, increased temperatures, and a steep gradient between the phosphorus enriched hypolimnion and the oligotrophic epilimnion. Meltwater impacts from an

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

    Science.gov (United States)

    Taqi, Esmaeel; Wallace, Laurie E; de Heuvel, Elaine; Chelikani, Prasanth K; Zheng, Huiyuan; Berthoud, Hans-Rudolph; Holst, Jens J; Sigalet, David L

    2010-05-01

    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 systemic enteric hormones on intestinal adaptation in short bowel syndrome. Male rats (350-400 g; n = 8/group) underwent sham or GRYB with pair feeding and were observed for 14 days. Weight and serum hormonal levels (glucagon-like peptide-2 [GLP-2], PYY) were quantified. Adaptation was assessed by intestinal morphology and crypt cell kinetics in each intestinal limb of the bypass and the equivalent points in the sham intestine. Mucosal growth factors and expression of transporter proteins were measured in each limb of the model. The GRYB animals lost weight compared to controls and exhibited significant adaptive changes with increased bowel width, villus height, crypt depth, and proliferation indices in the alimentary and common intestinal limbs. Although the biliary limb did not adapt at the mucosa, it did show an increased bowel width and crypt cell proliferation rate. The bypass animals had elevated levels of systemic PYY and GLP-2. At the mucosal level, insulin-like growth factor-1 (IGF-1) and basic fibroblast growth factor (bFGF) increased in all limbs of the bypass animals, whereas keratinocyte growth factor (KGF) and epidermal growth factor (EGF) had variable responses. The expression of the passive transporter of glucose, GLUT-2, expression was increased, whereas GLUT-5 was unchanged in all limbs of the bypass groups. Expression of the active mucosal transporter of glucose, SGLT-1 was decreased in the alimentary limb. Adaptation occurred maximally in intestinal segments stimulated by nutrients. Partial adaptation in the biliary limb may reflect the effects of systemic hormones. Mucosal content of IGF-1, bFGF, and EGF appear to be stimulated by systemic hormones

  2. Review of Model Predictions for Extensive Air Showers

    Science.gov (United States)

    Pierog, Tanguy

    In detailed air shower simulations, the uncertainty in the prediction of shower observable for different primary particles and energies is currently dominated by differences between hadronic interaction models. With the results of the first run of the LHC, the difference between post-LHC model predictions has been reduced at the same level as experimental uncertainties of cosmic ray experiments. At the same time new types of air shower observables, like the muon production depth, have been measured, adding new constraints on hadronic models. Currently no model is able to reproduce consistently all mass composition measurements possible with the Pierre Auger Observatory for instance. We review the current model predictions for various particle production observables and their link with air shower observables and discuss the future possible improvements.

  3. Integrating predictive frameworks and cognitive models of face perception.

    Science.gov (United States)

    Trapp, Sabrina; Schweinberger, Stefan R; Hayward, William G; Kovács, Gyula

    2018-02-08

    The idea of a "predictive brain"-that is, the interpretation of internal and external information based on prior expectations-has been elaborated intensely over the past decade. Several domains in cognitive neuroscience have embraced this idea, including studies in perception, motor control, language, and affective, social, and clinical neuroscience. Despite the various studies that have used face stimuli to address questions related to predictive processing, there has been surprisingly little connection between this work and established cognitive models of face recognition. Here we suggest that the predictive framework can serve as an important complement of established cognitive face models. Conversely, the link to cognitive face models has the potential to shed light on issues that remain open in predictive frameworks.

  4. A model for predicting lung cancer response to therapy

    International Nuclear Information System (INIS)

    Seibert, Rebecca M.; Ramsey, Chester R.; Hines, J. Wesley; Kupelian, Patrick A.; Langen, Katja M.; Meeks, Sanford L.; Scaperoth, Daniel D.

    2007-01-01

    Purpose: Volumetric computed tomography (CT) images acquired by image-guided radiation therapy (IGRT) systems can be used to measure tumor response over the course of treatment. Predictive adaptive therapy is a novel treatment technique that uses volumetric IGRT data to actively predict the future tumor response to therapy during the first few weeks of IGRT treatment. The goal of this study was to develop and test a model for predicting lung tumor response during IGRT treatment using serial megavoltage CT (MVCT). Methods and Materials: Tumor responses were measured for 20 lung cancer lesions in 17 patients that were imaged and treated with helical tomotherapy with doses ranging from 2.0 to 2.5 Gy per fraction. Five patients were treated with concurrent chemotherapy, and 1 patient was treated with neoadjuvant chemotherapy. Tumor response to treatment was retrospectively measured by contouring 480 serial MVCT images acquired before treatment. A nonparametric, memory-based locally weight regression (LWR) model was developed for predicting tumor response using the retrospective tumor response data. This model predicts future tumor volumes and the associated confidence intervals based on limited observations during the first 2 weeks of treatment. The predictive accuracy of the model was tested using a leave-one-out cross-validation technique with the measured tumor responses. Results: The predictive algorithm was used to compare predicted verse-measured tumor volume response for all 20 lesions. The average error for the predictions of the final tumor volume was 12%, with the true volumes always bounded by the 95% confidence interval. The greatest model uncertainty occurred near the middle of the course of treatment, in which the tumor response relationships were more complex, the model has less information, and the predictors were more varied. The optimal days for measuring the tumor response on the MVCT images were on elapsed Days 1, 2, 5, 9, 11, 12, 17, and 18 during

  5. Predictive modeling of coupled multi-physics systems: I. Theory

    International Nuclear Information System (INIS)

    Cacuci, Dan Gabriel

    2014-01-01

    Highlights: • We developed “predictive modeling of coupled multi-physics systems (PMCMPS)”. • PMCMPS reduces predicted uncertainties in predicted model responses and parameters. • PMCMPS treats efficiently very large coupled systems. - Abstract: This work presents an innovative mathematical methodology for “predictive modeling of coupled multi-physics systems (PMCMPS).” This methodology takes into account fully the coupling terms between the systems but requires only the computational resources that would be needed to perform predictive modeling on each system separately. The PMCMPS methodology uses the maximum entropy principle to construct an optimal approximation of the unknown a priori distribution based on a priori known mean values and uncertainties characterizing the parameters and responses for both multi-physics models. This “maximum entropy”-approximate a priori distribution is combined, using Bayes’ theorem, with the “likelihood” provided by the multi-physics simulation models. Subsequently, the posterior distribution thus obtained is evaluated using the saddle-point method to obtain analytical expressions for the optimally predicted values for the multi-physics models parameters and responses along with corresponding reduced uncertainties. Noteworthy, the predictive modeling methodology for the coupled systems is constructed such that the systems can be considered sequentially rather than simultaneously, while preserving exactly the same results as if the systems were treated simultaneously. Consequently, very large coupled systems, which could perhaps exceed available computational resources if treated simultaneously, can be treated with the PMCMPS methodology presented in this work sequentially and without any loss of generality or information, requiring just the resources that would be needed if the systems were treated sequentially

  6. 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.

  7. Comparison of Predictive Modeling Methods of Aircraft Landing Speed

    Science.gov (United States)

    Diallo, Ousmane H.

    2012-01-01

    Expected increases in air traffic demand have stimulated the development of air traffic control tools intended to assist the air traffic controller in accurately and precisely spacing aircraft landing at congested airports. Such tools will require an accurate landing-speed prediction to increase throughput while decreasing necessary controller interventions for avoiding separation violations. There are many practical challenges to developing an accurate landing-speed model that has acceptable prediction errors. This paper discusses the development of a near-term implementation, using readily available information, to estimate/model final approach speed from the top of the descent phase of flight to the landing runway. As a first approach, all variables found to contribute directly to the landing-speed prediction model are used to build a multi-regression technique of the response surface equation (RSE). Data obtained from operations of a major airlines for a passenger transport aircraft type to the Dallas/Fort Worth International Airport are used to predict the landing speed. The approach was promising because it decreased the standard deviation of the landing-speed error prediction by at least 18% from the standard deviation of the baseline error, depending on the gust condition at the airport. However, when the number of variables is reduced to the most likely obtainable at other major airports, the RSE model shows little improvement over the existing methods. Consequently, a neural network that relies on a nonlinear regression technique is utilized as an alternative modeling approach. For the reduced number of variables cases, the standard deviation of the neural network models errors represent over 5% reduction compared to the RSE model errors, and at least 10% reduction over the baseline predicted landing-speed error standard deviation. Overall, the constructed models predict the landing-speed more accurately and precisely than the current state-of-the-art.

  8. Importance of species interactions to community heritability: a genetic basis to trophic-level interactions.

    Science.gov (United States)

    Bailey, Joseph K; Wooley, Stuart C; Lindroth, Richard L; Whitham, Thomas G

    2006-01-01

    Recent community genetics studies have shown that specific genotypes of a host plant support distinct arthropod communities. Building upon these findings, we examined the hypothesis that a trophic community consisting of cottonwood trees, a galling herbivore and avian predators could also be related to the genetics of the host tree. We found genetic correlations among phytochemistry of individual tree genotypes, the density of a galling herbivore, and the intensity of avian predation on these herbivores. We detected significant broad-sense heritability of these interactions that range from H(B)2 = 0.70 to 0.83. The genetic basis of these interactions tended to increase across trophic levels suggesting that small genetic changes in the cottonwood phenotype could have major consequences at higher trophic levels affecting species interactions and energy flow. These findings show a heritable basis to trophic-level interactions indicating that there is a significant genetic basis to community composition and energy flow that is predictable by plant genotype. Our data clearly link plant genetics to patterns of avian foraging and show that species interactions are important components of community heritability and ecosystem processes. Overall, these data support the hypothesis that evolution of plant traits can alter trophic-level interactions and community composition.

  9. Model Predictive Control of Three Phase Inverter for PV Systems

    OpenAIRE

    Irtaza M. Syed; Kaamran Raahemifar

    2015-01-01

    This paper presents a model predictive control (MPC) of a utility interactive three phase inverter (TPI) for a photovoltaic (PV) system at commercial level. The proposed model uses phase locked loop (PLL) to synchronize the TPI with the power electric grid (PEG) and performs MPC control in a dq reference frame. TPI model consists of a boost converter (BC), maximum power point tracking (MPPT) control, and a three-leg voltage source inverter (VSI). The operational model of ...

  10. Spring diet and trophic partitioning in an alpine lizard community ...

    African Journals Online (AJOL)

    The influences of species interactions on habitat use, restrictions in trophic availability and evolutionary history as determinant factors are discussed. Keywords: trophic ecology, communities, pseudocommunity analysis, Lacerta perspicillata, Lacerta andreanszkyi, Podarcis vaucheri, Quedenfeldtia trachyblepharus, Morocco ...

  11. 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.

  12. Individualized prediction of perineural invasion in colorectal cancer: development and validation of a radiomics prediction model.

    Science.gov (United States)

    Huang, Yanqi; He, Lan; Dong, Di; Yang, Caiyun; Liang, Cuishan; Chen, Xin; Ma, Zelan; Huang, Xiaomei; Yao, Su; Liang, Changhong; Tian, Jie; Liu, Zaiyi

    2018-02-01

    To develop and validate a radiomics prediction model for individualized prediction of perineural invasion (PNI) in colorectal cancer (CRC). After computed tomography (CT) radiomics features extraction, a radiomics signature was constructed in derivation cohort (346 CRC patients). A prediction model was developed to integrate the radiomics signature and clinical candidate predictors [age, sex, tumor location, and carcinoembryonic antigen (CEA) level]. Apparent prediction performance was assessed. After internal validation, independent temporal validation (separate from the cohort used to build the model) was then conducted in 217 CRC patients. The final model was converted to an easy-to-use nomogram. The developed radiomics nomogram that integrated the radiomics signature and CEA level showed good calibration and discrimination performance [Harrell's concordance index (c-index): 0.817; 95% confidence interval (95% CI): 0.811-0.823]. Application of the nomogram in validation cohort gave a comparable calibration and discrimination (c-index: 0.803; 95% CI: 0.794-0.812). Integrating the radiomics signature and CEA level into a radiomics prediction model enables easy and effective risk assessment of PNI in CRC. This stratification of patients according to their PNI status may provide a basis for individualized auxiliary treatment.

  13. Fournier's gangrene: a model for early prediction.

    Science.gov (United States)

    Palvolgyi, Roland; Kaji, Amy H; Valeriano, Javier; Plurad, David; Rajfer, Jacob; de Virgilio, Christian

    2014-10-01

    Early diagnosis remains the cornerstone of management of Fournier's gangrene. As a result of variable progression of disease, identifying early predictors of necrosis becomes a diagnostic challenge. We present a scoring system based on objective admission criteria, which can help distinguish Fournier's gangrene from nonnecrotizing scrotal infections. Ninety-six patients were identified, 38 diagnosed with Fournier's gangrene and 58 diagnosed with scrotal cellulitis or abscess. Statistical analyses comparing admission vital signs, laboratory values, and imaging studies were performed and Classification and Regression Tree analysis was used to construct a scoring system. Admission heart rate greater than 110 beats/minute, serum sodium less than 135 mmol/L, blood urea nitrogen greater than 15 mg/dL, and white blood cell count greater than 15 × 10(3)/μL were significant predictors of Fournier's gangrene. Using a threshold score of two or greater, our model differentiates patients with Fournier's gangrene from those with nonnecrotizing infections with a sensitivity of 84.2 per cent. Only 34.2 per cent of patients with Fournier's gangrene had hard signs of necrotizing infection on admission, which were not observed in patients with nonnecrotizing infections. Objective admission criteria assist in distinguishing Fournier's gangrene from scrotal cellulitis or abscess. In situations in which results of the physical examination are ambiguous, this scoring system can heighten the index of suspicion for Fournier's gangrene and prompt rapid surgical intervention.

  14. 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

  15. BIOACCUMULATION DYNAMICS OF HEAVY METALS IN Oreochromis nilotycus: PREDICTED THROUGH A BIOACCUMULATION MODEL CONSTRUCTED BASED ON BIOTIC LIGAND MODEL (BLM

    Directory of Open Access Journals (Sweden)

    Sri Noegrohati

    2010-06-01

    Full Text Available In estuarine ecosystem, sediments are not only functioning as heavy metal scavenger, but also as one of potential sources for heavy metals to the ecosystem. Due the capability of aquatic organisms to accumulate heavy metals, there is possibility of heavy metals to exert their toxic effect towards the organisms and other organisms positioned in higher trophic level, such as fish, and further to human beings. To understand the different processes of heavy metal bioaccumulation in a dynamic manner, a bioaccumulation model is required. Since bioaccumulation starts with the uptake of chemical across a biological membrane, the bioaccumulation model was constructed based on Biotic Ligand Model (BLM. The input for the model was determined from laboratory scale simulated estuarine ecosystem of  sediment-brackish water (seawater:Aquaâ 1:1 for determining the heavy metal fractions in sediments; simulated Oreochromis nilotycus - brackish water (fish-water ecosystem for determining the rate constants; simulated fish-water-sediment ecosystem for evaluating the closeness between model-predicted and measured concentration, routes and distribution within specific internal organs. From these bioaccumulation studies, it was confirmed that the internalization of metals into the cells of gills and internal epithelias follows similar mechanisms, and governed mostly by the waterborne or hydrophilic heavy metals. The level of hydrophilic heavy metals are determined by desorption equilibrium coefficients, 1/KD, and influenced by salinity. Physiologically, the essential Cu and Zn body burden in O. nilotycus are tightly homeostasis regulated, shown as decreasing uptake efficiency factor, EW, at higher exposure concentrations, while non essential Cd and Hg were less or not regulated. From the distribution within specific internal organs, it was revealed that carcass was more relevant in describing the bioaccumulation condition than liver. It is clear that every heavy

  16. A deep auto-encoder model for gene expression prediction.

    Science.gov (United States)

    Xie, Rui; Wen, Jia; Quitadamo, Andrew; Cheng, Jianlin; Shi, Xinghua

    2017-11-17

    Gene expression is a key intermediate level that genotypes lead to a particular trait. Gene expression is affected by various factors including genotypes of genetic variants. With an aim of delineating the genetic impact on gene expression, we build a deep auto-encoder model to assess how good genetic variants will contribute to gene expression changes. This new deep learning model is a regression-based predictive model based on the MultiLayer Perceptron and Stacked Denoising Auto-encoder (MLP-SAE). The model is trained using a stacked denoising auto-encoder for feature selection and a multilayer perceptron framework for backpropagation. We further improve the model by introducing dropout to prevent overfitting and improve performance. To demonstrate the usage of this model, we apply MLP-SAE to a real genomic datasets with genotypes and gene expression profiles measured in yeast. Our results show that the MLP-SAE model with dropout outperforms other models including Lasso, Random Forests and the MLP-SAE model without dropout. Using the MLP-SAE model with dropout, we show that gene expression quantifications predicted by the model solely based on genotypes, align well with true gene expression patterns. We provide a deep auto-encoder model for predicting gene expression from SNP genotypes. This study demonstrates that deep learning is appropriate for tackling another genomic problem, i.e., building predictive models to understand genotypes' contribution to gene expression. With the emerging availability of richer genomic data, we anticipate that deep learning models play a bigger role in modeling and interpreting genomics.

  17. Trophic diversity of Poznań Lakeland lakes

    OpenAIRE

    Dzieszko Piotr; Zwoliński Zbigniew

    2015-01-01

    The main goal of the presented work is to determine the current trophic state of 31 lakes located in Poznań Lakeland. These lakes are included in the lake monitoring programme executed by the Voivodship Environmental Protection Inspectorate in Poznań. The place in the trophic classification for investigated lakes was determined as well as the relationships between their trophic state indices. The trophic state of investigated lakes in the research area is poor. More than a half of the investi...

  18. 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 thickne...... and distribution were varied between SCC types. The results indicate that yield stress of SCC may be predicted using the model.......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...

  19. Predictive models of prolonged mechanical ventilation yield moderate accuracy.

    Science.gov (United States)

    Figueroa-Casas, Juan B; Dwivedi, Alok K; Connery, Sean M; Quansah, Raphael; Ellerbrook, Lowell; Galvis, Juan

    2015-06-01

    To develop a model to predict prolonged mechanical ventilation within 48 hours of its initiation. In 282 general intensive care unit patients, multiple variables from the first 2 days on mechanical ventilation and their total ventilation duration were prospectively collected. Three models accounting for early deaths were developed using different analyses: (a) multinomial logistic regression to predict duration > 7 days vs duration ≤ 7 days alive vs duration ≤ 7 days death; (b) binary logistic regression to predict duration > 7 days for the entire cohort and for survivors only, separately; and (c) Cox regression to predict time to being free of mechanical ventilation alive. Positive end-expiratory pressure, postoperative state (negatively), and Sequential Organ Failure Assessment score were independently associated with prolonged mechanical ventilation. The multinomial regression model yielded an accuracy (95% confidence interval) of 60% (53%-64%). The binary regression models yielded accuracies of 67% (61%-72%) and 69% (63%-75%) for the entire cohort and for survivors, respectively. The Cox regression model showed an equivalent to area under the curve of 0.67 (0.62-0.71). Different predictive models of prolonged mechanical ventilation in general intensive care unit patients achieve a moderate level of overall accuracy, likely insufficient to assist in clinical decisions. Copyright © 2015 Elsevier Inc. All rights reserved.

  20. Comparison of the models of financial distress prediction

    Directory of Open Access Journals (Sweden)

    Jiří Omelka

    2013-01-01

    Full Text Available Prediction of the financial distress is generally supposed as approximation if a business entity is closed on bankruptcy or at least on serious financial problems. Financial distress is defined as such a situation when a company is not able to satisfy its liabilities in any forms, or when its liabilities are higher than its assets. Classification of financial situation of business entities represents a multidisciplinary scientific issue that uses not only the economic theoretical bases but interacts to the statistical, respectively to econometric approaches as well.The first models of financial distress prediction have originated in the sixties of the 20th century. One of the most known is the Altman’s model followed by a range of others which are constructed on more or less conformable bases. In many existing models it is possible to find common elements which could be marked as elementary indicators of potential financial distress of a company. The objective of this article is, based on the comparison of existing models of prediction of financial distress, to define the set of basic indicators of company’s financial distress at conjoined identification of their critical aspects. The sample defined this way will be a background for future research focused on determination of one-dimensional model of financial distress prediction which would subsequently become a basis for construction of multi-dimensional prediction model.

  1. Plant water potential improves prediction of empirical stomatal models.

    Directory of Open Access Journals (Sweden)

    William R L Anderegg

    Full Text Available Climate change is expected to lead to increases in drought frequency and severity, with deleterious effects on many ecosystems. Stomatal responses to changing environmental conditions form the backbone of all ecosystem models, but are based on empirical relationships and are not well-tested during drought conditions. Here, we use a dataset of 34 woody plant species spanning global forest biomes to examine the effect of leaf water potential on stomatal conductance and test the predictive accuracy of three major stomatal models and a recently proposed model. We find that current leaf-level empirical models have consistent biases of over-prediction of stomatal conductance during dry conditions, particularly at low soil water potentials. Furthermore, the recently proposed stomatal conductance model yields increases in predictive capability compared to current models, and with particular improvement during drought conditions. Our results reveal that including stomatal sensitivity to declining water potential and consequent impairment of plant water transport will improve predictions during drought conditions and show that many biomes contain a diversity of plant stomatal strategies that range from risky to conservative stomatal regulation during water stress. Such improvements in stomatal simulation are greatly needed to help unravel and predict the response of ecosystems to future climate extremes.

  2. Predicting the ungauged basin: model validation and realism assessment

    NARCIS (Netherlands)

    van Emmerik, Tim; Mulder, Gert; Eilander, Dirk; Piet, Marijn; Savenije, Hubert

    2015-01-01

    The hydrological decade on Predictions in Ungauged Basins (PUB) led to many new insights in model development, calibration strategies, data acquisition and uncertainty analysis. Due to a limited amount of published studies on genuinely ungauged basins, model validation and realism assessment of

  3. Predicting the ungauged basin : Model validation and realism assessment

    NARCIS (Netherlands)

    van Emmerik, Tim; Mulder, Gert; Eilander, Dirk; Piet, Marijn; Savenije, Hubert

    2015-01-01

    The hydrological decade on Predictions in Ungauged Basins (PUB) led to many new insights in model development, calibration strategies, data acquisition and uncertainty analysis. Due to a limited amount of published studies on genuinely ungauged basins, model validation and realism assessment of

  4. A Mathematical Model for the Prediction of Injectivity Decline | Odeh ...

    African Journals Online (AJOL)

    Injectivity impairment due to invasion of solid suspensions has been studied by several investigators and some modelling approaches have also been reported. Worthy of note is the development of analytical models for internal and external filtration coupled with transition time concept for predicting the overall decline in ...

  5. Mathematical Model for Prediction of Flexural Strength of Mound ...

    African Journals Online (AJOL)

    The mound soil-cement blended proportions were mathematically optimized by using scheffe's approach and the optimization model developed. A computer program predicting the mix proportion for the model was written. The optimal proportion by the program was used prepare beam samples measuring 150mm x 150mm ...

  6. 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.

  7. Accident Prediction Models for Akure – Ondo Carriageway, Ondo ...

    African Journals Online (AJOL)

    FIRST LADY

    traffic exposure and intersection effects as independent variables. They suggested that the Poisson distribution allows for the relationship between exposure and crashes to be more accurately modeled as opposed to. Accident Prediction Models for Akure-Ondo Carriageway…Using Multiple Linear Regression ...

  8. Multi-model prediction of downward short-wave radiation

    Czech Academy of Sciences Publication Activity Database

    Eben, Kryštof; Resler, Jaroslav; Krč, Pavel; Juruš, Pavel; Pelikán, Emil

    2012-01-01

    Roč. 9, - (2012), EMS2012-384 [EMS Annual Meeting /12./ and European Conference on Applied Climatology /9./. 10.09.2012-14.09.2012, Lodz] Institutional support: RVO:67985807 Keywords : multi-model prediction * NWP * model postprocessing Subject RIV: DG - Athmosphere Sciences, Meteorology

  9. 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...

  10. Prediction Models and Decision Support: Chances and Challenges

    NARCIS (Netherlands)

    Kappen, T.H.

    2015-01-01

    A clinical prediction model can assist doctors in arriving at the most likely diagnosis or estimating the prognosis. By utilizing various patient- and disease-related properties, such models can yield objective estimations of the risk of a disease or the probability of a certain disease course for

  11. 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

  12. Predictive ability of boiler production models | Ogundu | Animal ...

    African Journals Online (AJOL)

    The weekly body weight measurements of a growing strain of Ross broiler were used to compare the of ability of three mathematical models (the multi, linear, quadratic and Exponential) to predict 8 week body weight from early body measurements at weeks I, II, III, IV, V, VI and VII. The results suggest that the three models ...

  13. Predictive modelling of noise level generated during sawing of rocks ...

    Indian Academy of Sciences (India)

    2016-08-26

    Aug 26, 2016 ... Influence of the operating variables and rock properties on the noise level are investigated and analysed. Statistical analyses are then employed and models are built for the prediction of noise levels depending on the operating variables and the rock properties. The derived models are validated through ...

  14. Modelling and prediction of non-stationary optical turbulence behaviour

    NARCIS (Netherlands)

    Doelman, N.J.; Osborn, J.

    2016-01-01

    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

  15. Inferential ecosystem models, from network data to prediction

    Science.gov (United States)

    James S. Clark; Pankaj Agarwal; David M. Bell; Paul G. Flikkema; Alan Gelfand; Xuanlong Nguyen; Eric Ward; Jun. Yang

    2011-01-01

    Recent developments suggest that predictive modeling could begin to play a larger role not only for data analysis, but also for data collection. We address the example of efficient wireless sensor networks, where inferential ecosystem models can be used to weigh the value of an observation against the cost of data collection. Transmission costs make observations ‘‘...

  16. Model prediction of maize yield responses to climate change in ...

    African Journals Online (AJOL)

    Observed data of the last three decades (1971 to 2000) from several climatological stations in north-eastern Zimbabwe and outputs from several global climate models were used. The downscaled model simulations consistently predicted a warming of between 1 and 2 ºC above the baseline period (1971-2000) at most of ...

  17. A theoretical model for predicting neutron fluxes for cyclic Neutron ...

    African Journals Online (AJOL)

    A theoretical model has been developed for prediction of thermal neutron fluxes required for cyclic irradiations of a sample to obtain the same activity previously used for the detection of any radionuclide of interest. The model is suitable for radiotracer production or for long-lived neutron activation products where the ...

  18. 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

  19. Model Predictive Control for Offset-Free Reference Tracking

    Czech Academy of Sciences Publication Activity Database

    Belda, Květoslav

    2016-01-01

    Roč. 5, č. 1 (2016), s. 8-13 ISSN 1805-3386 Institutional support: RVO:67985556 Keywords : offset-free reference tracking * predictive control * ARX model * state-space model * multi-input multi-output system * robotic system * mechatronic system Subject RIV: BC - Control Systems Theory http://library.utia.cas.cz/separaty/2016/AS/belda-0458355.pdf

  20. Multi-model ensemble schemes for predicting northeast monsoon ...

    Indian Academy of Sciences (India)

    An attempt has been made to improve the accuracy of predicted rainfall using three different multi-model ensemble (MME) schemes, viz., simple arithmetic mean of models (EM), principal component regression (PCR) and singular value decomposition based multiple linear regressions (SVD). It is found out that among ...

  1. Supervisory Model Predictive Control of the Heat Integrated Distillation Column

    DEFF Research Database (Denmark)

    Meyer, Kristian; Bisgaard, Thomas; Huusom, Jakob Kjøbsted

    2017-01-01

    This paper benchmarks a centralized control system based on model predictive control for the operation of the heat integrated distillation column (HIDiC) against a fully decentralized control system using the most complete column model currently available in the literature. The centralized contro...

  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

  3. Three-model ensemble wind prediction in southern Italy

    Directory of Open Access Journals (Sweden)

    R. C. Torcasio

    2016-03-01

    Full Text Available 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.

  4. 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.

  5. The predictive performance and stability of six species distribution models.

    Science.gov (United States)

    Duan, Ren-Yan; Kong, Xiao-Quan; Huang, Min-Yi; Fan, Wei-Yi; Wang, Zhi-Gao

    2014-01-01

    Predicting species' potential geographical range by species distribution models (SDMs) is central to understand their ecological requirements. However, the effects of using different modeling techniques need further investigation. In order to improve the prediction effect, we need to assess the predictive performance and stability of different SDMs. We collected the distribution data of five common tree species (Pinus massoniana, Betula platyphylla, Quercus wutaishanica, Quercus mongolica and Quercus variabilis) and simulated their potential distribution area using 13 environmental variables and six widely used SDMs: BIOCLIM, DOMAIN, MAHAL, RF, MAXENT, and SVM. Each model run was repeated 100 times (trials). We compared the predictive performance by testing the consistency between observations and simulated distributions and assessed the stability by the standard deviation, coefficient of variation, and the 99% confidence interval of Kappa and AUC values. The mean values of AUC and Kappa from MAHAL, RF, MAXENT, and SVM trials were similar and significantly higher than those from BIOCLIM and DOMAIN trials (pSDMs (MAHAL, RF, MAXENT, and SVM) had higher prediction accuracy, smaller confidence intervals, and were more stable and less affected by the random variable (randomly selected pseudo-absence points). According to the prediction performance and stability of SDMs, we can divide these six SDMs into two categories: a high performance and stability group including MAHAL, RF, MAXENT, and SVM, and a low performance and stability group consisting of BIOCLIM, and DOMAIN. We highlight that choosing appropriate SDMs to address a specific problem is an important part of the modeling process.

  6. SHMF: Interest Prediction Model with Social Hub Matrix Factorization

    Directory of Open Access Journals (Sweden)

    Chaoyuan Cui

    2017-01-01

    Full Text Available With the development of social networks, microblog has become the major social communication tool. There is a lot of valuable information such as personal preference, public opinion, and marketing in microblog. Consequently, research on user interest prediction in microblog has a positive practical significance. In fact, how to extract information associated with user interest orientation from the constantly updated blog posts is not so easy. Existing prediction approaches based on probabilistic factor analysis use blog posts published by user to predict user interest. However, these methods are not very effective for the users who post less but browse more. In this paper, we propose a new prediction model, which is called SHMF, using social hub matrix factorization. SHMF constructs the interest prediction model by combining the information of blogs posts published by both user and direct neighbors in user’s social hub. Our proposed model predicts user interest by integrating user’s historical behavior and temporal factor as well as user’s friendships, thus achieving accurate forecasts of user’s future interests. The experimental results on Sina Weibo show the efficiency and effectiveness of our proposed model.

  7. Bayesian Genomic Prediction with Genotype × Environment Interaction Kernel Models

    Science.gov (United States)

    Cuevas, Jaime; Crossa, José; Montesinos-López, Osval A.; Burgueño, Juan; Pérez-Rodríguez, Paulino; de los Campos, Gustavo

    2016-01-01

    The phenomenon of genotype × environment (G × E) interaction in plant breeding decreases selection accuracy, thereby negatively affecting genetic gains. Several genomic prediction models incorporating G × E have been recently developed and used in genomic selection of plant breeding programs. Genomic prediction models for assessing multi-environment G × E interaction are extensions of a single-environment model, and have advantages and limitations. In this study, we propose two multi-environment Bayesian genomic models: the first model considers genetic effects (u) that can be assessed by the Kronecker product of variance–covariance matrices of genetic correlations between environments and genomic kernels through markers under two linear kernel methods, linear (genomic best linear unbiased predictors, GBLUP) and Gaussian (Gaussian kernel, GK). The other model has the same genetic component as the first model (u) plus an extra component, f, that captures random effects between environments that were not captured by the random effects u. We used five CIMMYT data sets (one maize and four wheat) that were previously used in different studies. Results show that models with G × E always have superior prediction ability than single-environment models, and the higher prediction ability of multi-environment models with u and f over the multi-environment model with only u occurred 85% of the time with GBLUP and 45% of the time with GK across the five data sets. The latter result indicated that including the random effect f is still beneficial for increasing prediction ability after adjusting by the random effect u. PMID:27793970

  8. Bayesian Genomic Prediction with Genotype × Environment Interaction Kernel Models

    Directory of Open Access Journals (Sweden)

    Jaime Cuevas

    2017-01-01

    Full Text Available The phenomenon of genotype × environment (G × E interaction in plant breeding decreases selection accuracy, thereby negatively affecting genetic gains. Several genomic prediction models incorporating G × E have been recently developed and used in genomic selection of plant breeding programs. Genomic prediction models for assessing multi-environment G × E interaction are extensions of a single-environment model, and have advantages and limitations. In this study, we propose two multi-environment Bayesian genomic models: the first model considers genetic effects ( u that can be assessed by the Kronecker product of variance–covariance matrices of genetic correlations between environments and genomic kernels through markers under two linear kernel methods, linear (genomic best linear unbiased predictors, GBLUP and Gaussian (Gaussian kernel, GK. The other model has the same genetic component as the first model ( u plus an extra component, f, that captures random effects between environments that were not captured by the random effects u . We used five CIMMYT data sets (one maize and four wheat that were previously used in different studies. Results show that models with G × E always have superior prediction ability than single-environment models, and the higher prediction ability of multi-environment models with u   and   f over the multi-environment model with only u occurred 85% of the time with GBLUP and 45% of the time with GK across the five data sets. The latter result indicated that including the random effect f is still beneficial for increasing prediction ability after adjusting by the random effect u .

  9. Bayesian Genomic Prediction with Genotype × Environment Interaction Kernel Models.

    Science.gov (United States)

    Cuevas, Jaime; Crossa, José; Montesinos-López, Osval A; Burgueño, Juan; Pérez-Rodríguez, Paulino; de Los Campos, Gustavo

    2017-01-05

    The phenomenon of genotype × environment (G × E) interaction in plant breeding decreases selection accuracy, thereby negatively affecting genetic gains. Several genomic prediction models incorporating G × E have been recently developed and used in genomic selection of plant breeding programs. Genomic prediction models for assessing multi-environment G × E interaction are extensions of a single-environment model, and have advantages and limitations. In this study, we propose two multi-environment Bayesian genomic models: the first model considers genetic effects [Formula: see text] that can be assessed by the Kronecker product of variance-covariance matrices of genetic correlations between environments and genomic kernels through markers under two linear kernel methods, linear (genomic best linear unbiased predictors, GBLUP) and Gaussian (Gaussian kernel, GK). The other model has the same genetic component as the first model [Formula: see text] plus an extra component, F: , that captures random effects between environments that were not captured by the random effects [Formula: see text] We used five CIMMYT data sets (one maize and four wheat) that were previously used in different studies. Results show that models with G × E always have superior prediction ability than single-environment models, and the higher prediction ability of multi-environment models with [Formula: see text] over the multi-environment model with only u occurred 85% of the time with GBLUP and 45% of the time with GK across the five data sets. The latter result indicated that including the random effect f is still beneficial for increasing prediction ability after adjusting by the random effect [Formula: see text]. Copyright © 2017 Cuevas et al.

  10. Stochastic models for predicting pitting corrosion damage of HLRW containers

    International Nuclear Information System (INIS)

    Henshall, G.A.

    1991-10-01

    Stochastic models for predicting aqueous pitting corrosion damage of high-level radioactive-waste containers are described. These models could be used to predict the time required for the first pit to penetrate a container and the increase in the number of breaches at later times, both of which would be useful in the repository system performance analysis. Monte Carlo implementations of the stochastic models are described, and predictions of induction time, survival probability and pit depth distributions are presented. These results suggest that the pit nucleation probability decreases with exposure time and that pit growth may be a stochastic process. The advantages and disadvantages of the stochastic approach, methods for modeling the effects of environment, and plans for future work are discussed

  11. Verification and improvement of a predictive model for radionuclide migration

    International Nuclear Information System (INIS)

    Miller, C.W.; Benson, L.V.; Carnahan, C.L.

    1982-01-01

    Prediction of the rates of migration of contaminant chemical species in groundwater flowing through toxic waste repositories is essential to the assessment of a repository's capability of meeting standards for release rates. A large number of chemical transport models, of varying degrees of complexity, have been devised for the purpose of providing this predictive capability. In general, the transport of dissolved chemical species through a water-saturated porous medium is influenced by convection, diffusion/dispersion, sorption, formation of complexes in the aqueous phase, and chemical precipitation. The reliability of predictions made with the models which omit certain of these processes is difficult to assess. A numerical model, CHEMTRN, has been developed to determine which chemical processes govern radionuclide migration. CHEMTRN builds on a model called MCCTM developed previously by Lichtner and Benson

  12. A novel Bayesian hierarchical model for road safety hotspot prediction.

    Science.gov (United States)

    Fawcett, Lee; Thorpe, Neil; Matthews, Joseph; Kremer, Karsten

    2017-02-01

    In this paper, we propose a Bayesian hierarchical model for predicting accident counts in future years at sites within a pool of potential road safety hotspots. The aim is to inform road safety practitioners of the location of likely future hotspots to enable a proactive, rather than reactive, approach to road safety scheme implementation. A feature of our model is the ability to rank sites according to their potential to exceed, in some future time period, a threshold accident count which may be used as a criterion for scheme implementation. Our model specification enables the classical empirical Bayes formulation - commonly used in before-and-after studies, wherein accident counts from a single before period are used to estimate counterfactual counts in the after period - to be extended to incorporate counts from multiple time periods. This allows site-specific variations in historical accident counts (e.g. locally-observed trends) to offset estimates of safety generated by a global accident prediction model (APM), which itself is used to help account for the effects of global trend and regression-to-mean (RTM). The Bayesian posterior predictive distribution is exploited to formulate predictions and to properly quantify our uncertainty in these predictions. The main contributions of our model include (i) the ability to allow accident counts from multiple time-points to inform predictions, with counts in more recent years lending more weight to predictions than counts from time-points further in the past; (ii) where appropriate, the ability to offset global estimates of trend by variations in accident counts observed locally, at a site-specific level; and (iii) the ability to account for unknown/unobserved site-specific factors which may affect accident counts. We illustrate our model with an application to accident counts at 734 potential hotspots in the German city of Halle; we also propose some simple diagnostics to validate the predictive capability of our

  13. Phytoplankton community and chlorophyll a as trophic state indices of Lake Skadar (Montenegro, Balkan).

    Science.gov (United States)

    Rakocevic-Nedovic, Jelena; Hollert, Henner

    2005-01-01

    Phytoplankton, as a first step in trophic cascades of lakes, can be a good indicator of trophic states, considering that every environmental change affects this community and many species of this community are sensitive to changes, and that they response very quickly. In this study, we tried to assess and predict the trophic state of Lake Skadar according to phytoplankton data. Water samples were collected using Ruttner sampling bottle. Temperature, dissolved oxygen, ph, conductivity and transparence were measured in situ using portable equipment. Nutrients and chlorophyll a were measured using standard spectrophotometric methods. A determination of phytoplankton species was performed using relevant keys and the counting of cells was performed using sedimentation methods. The species composition of Lake Skadar revealed 95 taxa, with Chlorophyceae and Bacillariophyceae being represented best. According to an average chlorophyll a concentration of 5.9 pg/l, Lake Skadar belongs to the mesotrophic level of the trophic scale. Developed prediction equation for chlorophyll a revealed a good prediction (R2 = 0.71) and the parameter Secchi depth was primarily correlated with chlorophyll a concentration. Trophic state indices derived from chlorophyll a and transparency, were close together, but both were below the phosphorous index. Values of trophic state indices rank the Lake Skadar as being mesotrophic. This study also showed that indices of diversity based on phytoplankton are weak indicators of trophic status and that they can well characterize only differences between assemblages and associations. According to calculated saprobic indices (ranging from 1.5 to 2.15), Lake Skadar is on betamesosaprobic level of saprobity, which means that it is moderately polluted with organic compounds. Total phosphorus is not the main limiting factor for the phytoplankton community in Lake Skadar. Disagreements between chlorophyll and the transparency index, on the one hand, and the

  14. Trophically available metal - A variable feast

    International Nuclear Information System (INIS)

    Rainbow, Philip S.; Luoma, Samuel N.; Wang Wenxiong

    2011-01-01

    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.

  15. 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.

  16. 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.

  17. Trophic transfer of metal nanoparticles in freshwater ecosystems

    DEFF Research Database (Denmark)

    Tangaa, Stine Rosendal

    Metal-containing engineered nanoparticles (Me-ENPs) are used in a wide range of products, such as inks, plastics, consumer products, lubricants, electronics and bioactive coatings. Silver (Ag) ENPs are one of the most used Me-ENPs to date, primarily due to its antibacterial effects. When entering...... was examined. The biodynamic modelling approach was used to characterize Ag uptake from the two different uptake routes as well as to describe the elimination of Ag after waterborne exposures to the two Ag-forms. Thirdly, trophic transfer of silver Ag ENPs in a simple freshwater food web, including sediment...

  18. Numerical weather prediction (NWP) and hybrid ARMA/ANN model to predict global radiation

    International Nuclear Information System (INIS)

    Voyant, Cyril; Muselli, Marc; Paoli, Christophe; Nivet, Marie-Laure

    2012-01-01

    We propose in this paper an original technique to predict global radiation using a hybrid ARMA/ANN model and data issued from a numerical weather prediction model (NWP). We particularly look at the multi-layer perceptron (MLP). After optimizing our architecture with NWP and endogenous data previously made stationary and using an innovative pre-input layer selection method, we combined it to an ARMA model from a rule based on the analysis of hourly data series. This model has been used to forecast the hourly global radiation for five places in Mediterranean area. Our technique outperforms classical models for all the places. The nRMSE for our hybrid model MLP/ARMA is 14.9% compared to 26.2% for the naïve persistence predictor. Note that in the standalone ANN case the nRMSE is 18.4%. Finally, in order to discuss the reliability of the forecaster outputs, a complementary study concerning the confidence interval of each prediction is proposed. -- Highlights: ► Time series forecasting with hybrid method based on the use of ALADIN numerical weather model, ANN and ARMA. ► Innovative pre-input layer selection method. ► Combination of optimized MLP and ARMA model obtained from a rule based on the analysis of hourly data series. ► Stationarity process (method and control) for the global radiation time series.

  19. 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.

  20. A simplified building airflow model for agent concentration prediction.

    Science.gov (United States)

    Jacques, David R; Smith, David A

    2010-11-01

    A simplified building airflow model is presented that can be used to predict the spread of a contaminant agent from a chemical or biological attack. If the dominant means of agent transport throughout the building is an air-handling system operating at steady-state, a linear time-invariant (LTI) model can be constructed to predict the concentration in any room of the building as a result of either an internal or external release. While the model does not capture weather-driven and other temperature-driven effects, it is suitable for concentration predictions under average daily conditions. The model is easily constructed using information that should be accessible to a building manager, supplemented with assumptions based on building codes and standard air-handling system design practices. The results of the model are compared with a popular multi-zone model for a simple building and are demonstrated for building examples containing one or more air-handling systems. The model can be used for rapid concentration prediction to support low-cost placement strategies for chemical and biological detection sensors.

  1. Discrete fracture modelling for the Stripa tracer validation experiment predictions

    International Nuclear Information System (INIS)

    Dershowitz, W.; Wallmann, P.

    1992-02-01

    Groundwater flow and transport through three-dimensional networks of discrete fractures was modeled to predict the recovery of tracer from tracer injection experiments conducted during phase 3 of the Stripa site characterization and validation protect. Predictions were made on the basis of an updated version of the site scale discrete fracture conceptual model used for flow predictions and preliminary transport modelling. In this model, individual fractures were treated as stochastic features described by probability distributions of geometric and hydrologic properties. Fractures were divided into three populations: Fractures in fracture zones near the drift, non-fracture zone fractures within 31 m of the drift, and fractures in fracture zones over 31 meters from the drift axis. Fractures outside fracture zones are not modelled beyond 31 meters from the drift axis. Transport predictions were produced using the FracMan discrete fracture modelling package for each of five tracer experiments. Output was produced in the seven formats specified by the Stripa task force on fracture flow modelling. (au)

  2. Predicting nucleic acid binding interfaces from structural models of proteins.

    Science.gov (United States)

    Dror, Iris; Shazman, Shula; Mukherjee, Srayanta; Zhang, Yang; Glaser, Fabian; Mandel-Gutfreund, Yael

    2012-02-01

    The function of DNA- and RNA-binding proteins can be inferred from the characterization and accurate prediction of their binding interfaces. However, the main pitfall of various structure-based methods for predicting nucleic acid binding function is that they are all limited to a relatively small number of proteins for which high-resolution three-dimensional structures are available. In this study, we developed a pipeline for extracting functional electrostatic patches from surfaces of protein structural models, obtained using the I-TASSER protein structure predictor. The largest positive patches are extracted from the protein surface using the patchfinder algorithm. We show that functional electrostatic patches extracted from an ensemble of structural models highly overlap the patches extracted from high-resolution structures. Furthermore, by testing our pipeline on a set of 55 known nucleic acid binding proteins for which I-TASSER produces high-quality models, we show that the method accurately identifies the nucleic acids binding interface on structural models of proteins. Employing a combined patch approach we show that patches extracted from an ensemble of models better predicts the real nucleic acid binding interfaces compared with patches extracted from independent models. Overall, these results suggest that combining information from a collection of low-resolution structural models could be a valuable approach for functional annotation. We suggest that our method will be further applicable for predicting other functional surfaces of proteins with unknown structure. Copyright © 2011 Wiley Periodicals, Inc.

  3. Adaptive Gaussian Predictive Process Models for Large Spatial Datasets

    Science.gov (United States)

    Guhaniyogi, Rajarshi; Finley, Andrew O.; Banerjee, Sudipto; Gelfand, Alan E.

    2011-01-01

    Large point referenced datasets occur frequently in the environmental and natural sciences. Use of Bayesian hierarchical spatial models for analyzing these datasets is undermined by onerous computational burdens associated with parameter estimation. Low-rank spatial process models attempt to resolve this problem by projecting spatial effects to a lower-dimensional subspace. This subspace is determined by a judicious choice of “knots” or locations that are fixed a priori. One such representation yields a class of predictive process models (e.g., Banerjee et al., 2008) for spatial and spatial-temporal data. Our contribution here expands upon predictive process models with fixed knots to models that accommodate stochastic modeling of the knots. We view the knots as emerging from a point pattern and investigate how such adaptive specifications can yield more flexible hierarchical frameworks that lead to automated knot selection and substantial computational benefits. PMID:22298952

  4. Retrosynthetic Reaction Prediction Using Neural Sequence-to-Sequence Models.

    Science.gov (United States)

    Liu, Bowen; Ramsundar, Bharath; Kawthekar, Prasad; Shi, Jade; Gomes, Joseph; Luu Nguyen, Quang; Ho, Stephen; Sloane, Jack; Wender, Paul; Pande, Vijay

    2017-10-25

    We describe a fully data driven model that learns to perform a retrosynthetic reaction prediction task, which is treated as a sequence-to-sequence mapping problem. The end-to-end trained model has an encoder-decoder architecture that consists of two recurrent neural networks, which has previously shown great success in solving other sequence-to-sequence prediction tasks such as machine translation. The model is trained on 50,000 experimental reaction examples from the United States patent literature, which span 10 broad reaction types that are commonly used by medicinal chemists. We find that our model performs comparably with a rule-based expert system baseline model, and also overcomes certain limitations associated with rule-based expert systems and with any machine learning approach that contains a rule-based expert system component. Our model provides an important first step toward solving the challenging problem of computational retrosynthetic analysis.

  5. Coastal habitats as surrogates for taxonomic, functional and trophic structures of benthic faunal communities.

    Directory of Open Access Journals (Sweden)

    Anna Törnroos

    Full Text Available Due to human impact, there is extensive degradation and loss of marine habitats, which calls for measures that incorporate taxonomic as well as functional and trophic aspects of biodiversity. Since such data is less easily quantifiable in nature, the use of habitats as surrogates or proxies for biodiversity is on the rise in marine conservation and management. However, there is a critical gap in knowledge of whether pre-defined habitat units adequately represent the functional and trophic structure of communities. We also lack comparisons of different measures of community structure in terms of both between- (β and within-habitat (α variability when accounting for species densities. Thus, we evaluated a priori defined coastal habitats as surrogates for traditional taxonomic, functional and trophic zoobenthic community structure. We focused on four habitats (bare sand, canopy-forming algae, seagrass above- and belowground, all easily delineated in nature and defined through classification systems. We analyzed uni- and multivariate data on species and trait diversity as well as stable isotope ratios of benthic macrofauna. A good fit between habitat types and taxonomic and functional structure was found, although habitats were more similar functionally. This was attributed to within-habitat heterogeneity so when habitat divisions matched the taxonomic structure, only bare sand was functionally distinct. The pre-defined habitats did not meet the variability of trophic structure, which also proved to differentiate on a smaller spatial scale. The quantification of trophic structure using species density only identified an epi- and an infaunal unit. To summarize the results we present a conceptual model illustrating the match between pre-defined habitat types and the taxonomic, functional and trophic community structure. Our results show the importance of including functional and trophic aspects more comprehensively in marine management and spatial

  6. Coastal habitats as surrogates for taxonomic, functional and trophic structures of benthic faunal communities.

    Science.gov (United States)

    Törnroos, Anna; Nordström, Marie C; Bonsdorff, Erik

    2013-01-01

    Due to human impact, there is extensive degradation and loss of marine habitats, which calls for measures that incorporate taxonomic as well as functional and trophic aspects of biodiversity. Since such data is less easily quantifiable in nature, the use of habitats as surrogates or proxies for biodiversity is on the rise in marine conservation and management. However, there is a critical gap in knowledge of whether pre-defined habitat units adequately represent the functional and trophic structure of communities. We also lack comparisons of different measures of community structure in terms of both between- (β) and within-habitat (α) variability when accounting for species densities. Thus, we evaluated a priori defined coastal habitats as surrogates for traditional taxonomic, functional and trophic zoobenthic community structure. We focused on four habitats (bare sand, canopy-forming algae, seagrass above- and belowground), all easily delineated in nature and defined through classification systems. We analyzed uni- and multivariate data on species and trait diversity as well as stable isotope ratios of benthic macrofauna. A good fit between habitat types and taxonomic and functional structure was found, although habitats were more similar functionally. This was attributed to within-habitat heterogeneity so when habitat divisions matched the taxonomic structure, only bare sand was functionally distinct. The pre-defined habitats did not meet the variability of trophic structure, which also proved to differentiate on a smaller spatial scale. The quantification of trophic structure using species density only identified an epi- and an infaunal unit. To summarize the results we present a conceptual model illustrating the match between pre-defined habitat types and the taxonomic, functional and trophic community structure. Our results show the importance of including functional and trophic aspects more comprehensively in marine management and spatial planning.

  7. Pulsatile fluidic pump demonstration and predictive model application

    International Nuclear Information System (INIS)

    Morgan, J.G.; Holland, W.D.

    1986-04-01

    Pulsatile fluidic pumps were developed as a remotely controlled method of transferring or mixing feed solutions. A test in the Integrated Equipment Test facility demonstrated the performance of a critically safe geometry pump suitable for use in a 0.1-ton/d heavy metal (HM) fuel reprocessing plant. A predictive model was developed to calculate output flows under a wide range of external system conditions. Predictive and experimental flow rates are compared for both submerged and unsubmerged fluidic pump cases

  8. Cloud Based Metalearning System for Predictive Modeling of Biomedical Data

    Directory of Open Access Journals (Sweden)

    Milan Vukićević

    2014-01-01

    Full Text Available Rapid growth and storage of biomedical data enabled many opportunities for predictive modeling and improvement of healthcare processes. On the other side analysis of such large amounts of data is a difficult and computationally intensive task for most existing data mining algorithms. This problem is addressed by proposing a cloud based system that integrates metalearning framework for ranking and selection of best predictive algorithms for data at hand and open source big data technologies for analysis of biomedical data.

  9. Models for Predicting and Explaining Citation Count of Biomedical Articles

    OpenAIRE

    Fu, Lawrence D.; Aliferis, Constantin

    2008-01-01

    The single most important bibliometric criterion for judging the impact of biomedical papers and their authors’ work is the number of citations received which is commonly referred to as “citation count”. This metric however is unavailable until several years after publication time. In the present work, we build computer models that accurately predict citation counts of biomedical publications within a deep horizon of ten years using only predictive information available at publication time. O...

  10. Predictive Models of Li-ion Battery Lifetime

    Energy Technology Data Exchange (ETDEWEB)

    Smith, Kandler; Wood, Eric; Santhanagopalan, Shriram; Kim, Gi-heon; Shi, Ying; Pesaran, Ahmad

    2015-06-15

    It remains an open question how best to predict real-world battery lifetime based on accelerated calendar and cycle aging data from the laboratory. Multiple degradation mechanisms due to (electro)chemical, thermal, and mechanical coupled phenomena influence Li-ion battery lifetime, each with different dependence on time, cycling and thermal environment. The standardization of life predictive models would benefit the industry by reducing test time and streamlining development of system controls.

  11. 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

  12. Modelling personality, plasticity and predictability in shelter dogs

    Science.gov (United States)

    2017-01-01

    Behavioural assessments of shelter dogs (Canis lupus familiaris) typically comprise standardized test batteries conducted at one time point, but test batteries have shown inconsistent predictive validity. Longitudinal behavioural assessments offer an alternative. We modelled longitudinal observational data on shelter dog behaviour using the framework of behavioural reaction norms, partitioning variance into personality (i.e. inter-individual differences in behaviour), plasticity (i.e. inter-individual differences in average behaviour) and predictability (i.e. individual differences in residual intra-individual variation). We analysed data on interactions of 3263 dogs (n = 19 281) with unfamiliar people during their first month after arrival at the shelter. Accounting for personality, plasticity (linear and quadratic trends) and predictability improved the predictive accuracy of the analyses compared to models quantifying personality and/or plasticity only. While dogs were, on average, highly sociable with unfamiliar people and sociability increased over days since arrival, group averages were unrepresentative of all dogs and predictions made at the individual level entailed considerable uncertainty. Effects of demographic variables (e.g. age) on personality, plasticity and predictability were observed. Behavioural repeatability was higher one week after arrival compared to arrival day. Our results highlight the value of longitudinal assessments on shelter dogs and identify measures that could improve the predictive validity of behavioural assessments in shelters. PMID:28989764

  13. Prediction of type A behaviour: A structural equation model

    Directory of Open Access Journals (Sweden)

    René van Wyk

    2009-05-01

    Full Text Available The predictability of Type A behaviour was measured in a sample of 375 professionals with a shortened version of the Jenkins Activity Survey (JAS. Two structural equation models were constructed with the Type A behaviour achievement sub-scale and global (total Type A as the predictor variables. The indices showed a reasonable-to-promising fit with the data. Type A achievement was reasonably predicted by service-career orientation, internal locus of control, power self-concept and economic innovation. Type A global was also predicted by internal locus of control, power self-concept and the entrepreneurial attitude of achievement and personal control.

  14. 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...... in the estimated/predicted property values, how to assess the quality and reliability of the estimated/predicted property values? The paper will review a class of models for prediction of physical and thermodynamic properties of organic chemicals and their mixtures based on the combined group contribution – atom...

  15. 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.

  16. Predictive power of theoretical modelling of the nuclear mean field: examples of improving predictive capacities

    Science.gov (United States)

    Dedes, I.; Dudek, J.

    2018-03-01

    We examine the effects of the parametric correlations on the predictive capacities of the theoretical modelling keeping in mind the nuclear structure applications. The main purpose of this work is to illustrate the method of establishing the presence and determining the form of parametric correlations within a model as well as an algorithm of elimination by substitution (see text) of parametric correlations. We examine the effects of the elimination of the parametric correlations on the stabilisation of the model predictions further and further away from the fitting zone. It follows that the choice of the physics case and the selection of the associated model are of secondary importance in this case. Under these circumstances we give priority to the relative simplicity of the underlying mathematical algorithm, provided the model is realistic. Following such criteria, we focus specifically on an important but relatively simple case of doubly magic spherical nuclei. To profit from the algorithmic simplicity we chose working with the phenomenological spherically symmetric Woods–Saxon mean-field. We employ two variants of the underlying Hamiltonian, the traditional one involving both the central and the spin orbit potential in the Woods–Saxon form and the more advanced version with the self-consistent density-dependent spin–orbit interaction. We compare the effects of eliminating of various types of correlations and discuss the improvement of the quality of predictions (‘predictive power’) under realistic parameter adjustment conditions.

  17. GA-ARMA Model for Predicting IGS RTS Corrections

    Directory of Open Access Journals (Sweden)

    Mingyu Kim

    2017-01-01

    Full Text Available The global navigation satellite system (GNSS is widely used to estimate user positions. For precise positioning, users should correct for GNSS error components such as satellite orbit and clock errors as well as ionospheric delay. The international GNSS service (IGS real-time service (RTS can be used to correct orbit and clock errors in real-time. Since the IGS RTS provides real-time corrections via the Internet, intermittent data loss can occur due to software or hardware failures. We propose applying a genetic algorithm autoregressive moving average (GA-ARMA model to predict the IGS RTS corrections during data loss periods. The RTS orbit and clock corrections are predicted up to 900 s via the GA-ARMA model, and the prediction accuracies are compared with the results from a generic ARMA model. The orbit prediction performance of the GA-ARMA is nearly equivalent to that of ARMA, but GA-ARMA’s clock prediction performance is clearly better than that of ARMA, achieving a 32% error reduction. Predicted RTS corrections are applied to the broadcast ephemeris, and precise point positioning accuracies are compared. GA-ARMA shows a significant accuracy improvement over ARMA, particularly in terms of vertical positioning.

  18. Modeling the prediction of business intelligence system effectiveness.

    Science.gov (United States)

    Weng, Sung-Shun; Yang, Ming-Hsien; Koo, Tian-Lih; Hsiao, Pei-I

    2016-01-01

    Although business intelligence (BI) technologies are continually evolving, the capability to apply BI technologies has become an indispensable resource for enterprises running in today's complex, uncertain and dynamic business environment. This study performed pioneering work by constructing models and rules for the prediction of business intelligence system effectiveness (BISE) in relation to the implementation of BI solutions. For enterprises, effectively managing critical attributes that determine BISE to develop prediction models with a set of rules for self-evaluation of the effectiveness of BI solutions is necessary to improve BI implementation and ensure its success. The main study findings identified the critical prediction indicators of BISE that are important to forecasting BI performance and highlighted five classification and prediction rules of BISE derived from decision tree structures, as well as a refined regression prediction model with four critical prediction indicators constructed by logistic regression analysis that can enable enterprises to improve BISE while effectively managing BI solution implementation and catering to academics to whom theory is important.

  19. In silico modeling to predict drug-induced phospholipidosis

    International Nuclear Information System (INIS)

    Choi, Sydney S.; Kim, Jae S.; Valerio, Luis G.; Sadrieh, Nakissa

    2013-01-01

    Drug-induced phospholipidosis (DIPL) is a preclinical finding during pharmaceutical drug development that has implications on the course of drug development and regulatory safety review. A principal characteristic of drugs inducing DIPL is known to be a cationic amphiphilic structure. This provides evidence for a structure-based explanation and opportunity to analyze properties and structures of drugs with the histopathologic findings for DIPL. In previous work from the FDA, in silico quantitative structure–activity relationship (QSAR) modeling using machine learning approaches has shown promise with a large dataset of drugs but included unconfirmed data as well. In this study, we report the construction and validation of a battery of complementary in silico QSAR models using the FDA's updated database on phospholipidosis, new algorithms and predictive technologies, and in particular, we address high performance with a high-confidence dataset. The results of our modeling for DIPL include rigorous external validation tests showing 80–81% concordance. Furthermore, the predictive performance characteristics include models with high sensitivity and specificity, in most cases above ≥ 80% leading to desired high negative and positive predictivity. These models are intended to be utilized for regulatory toxicology applied science needs in screening new drugs for DIPL. - Highlights: • New in silico models for predicting drug-induced phospholipidosis (DIPL) are described. • The training set data in the models is derived from the FDA's phospholipidosis database. • We find excellent predictivity values of the models based on external validation. • The models can support drug screening and regulatory decision-making on DIPL

  20. Cardiopulmonary Circuit Models for Predicting Injury to the Heart

    Science.gov (United States)

    Ward, Richard; Wing, Sarah; Bassingthwaighte, James; Neal, Maxwell

    2004-11-01

    Circuit models have been used extensively in physiology to describe cardiopulmonary function. Such models are being used in the DARPA Virtual Soldier (VS) Project* to predict the response to injury or physiological stress. The most complex model consists of systemic circulation, pulmonary circulation, and a four-chamber heart sub-model. This model also includes baroreceptor feedback, airway mechanics, gas exchange, and pleural pressure influence on the circulation. As part of the VS Project, Oak Ridge National Laboratory has been evaluating various cardiopulmonary circuit models for predicting the effects of injury to the heart. We describe, from a physicist's perspective, the concept of building circuit models, discuss both unstressed and stressed models, and show how the stressed models are used to predict effects of specific wounds. *This work was supported by a grant from the DARPA, executed by the U.S. Army Medical Research and Materiel Command/TATRC Cooperative Agreement, Contract # W81XWH-04-2-0012. The submitted manuscript has been authored by the U.S. Department of Energy, Office of Science of the Oak Ridge National Laboratory, managed for the U.S. DOE by UT-Battelle, LLC, under contract No. DE-AC05-00OR22725. Accordingly, the U.S. Government retains a non-exclusive, royalty-free license to publish or reproduce the published form of this contribution, or allow others to do so, for U.S. Government purpose.