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Sample records for model predicting environmental

  1. Predictions of models for environmental radiological assessment

    Energy Technology Data Exchange (ETDEWEB)

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

    2011-07-01

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

  2. Critical conceptualism in environmental modeling and prediction.

    Science.gov (United States)

    Christakos, G

    2003-10-15

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

  3. Improving Environmental Model Calibration and Prediction

    Science.gov (United States)

    2011-01-18

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

  4. 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.

  5. Application of a predictive Bayesian model to environmental accounting.

    Science.gov (United States)

    Anex, R P; Englehardt, J D

    2001-03-30

    Environmental accounting techniques are intended to capture important environmental costs and benefits that are often overlooked in standard accounting practices. Environmental accounting methods themselves often ignore or inadequately represent large but highly uncertain environmental costs and costs conditioned by specific prior events. Use of a predictive Bayesian model is demonstrated for the assessment of such highly uncertain environmental and contingent costs. The predictive Bayesian approach presented generates probability distributions for the quantity of interest (rather than parameters thereof). A spreadsheet implementation of a previously proposed predictive Bayesian model, extended to represent contingent costs, is described and used to evaluate whether a firm should undertake an accelerated phase-out of its PCB containing transformers. Variability and uncertainty (due to lack of information) in transformer accident frequency and severity are assessed simultaneously using a combination of historical accident data, engineering model-based cost estimates, and subjective judgement. Model results are compared using several different risk measures. Use of the model for incorporation of environmental risk management into a company's overall risk management strategy is discussed.

  6. Predicting People's Environmental Behaviour: Theory of Planned Behaviour and Model of Responsible Environmental Behaviour

    Science.gov (United States)

    Chao, Yu-Long

    2012-01-01

    Using different measures of self-reported and other-reported environmental behaviour (EB), two important theoretical models explaining EB--Hines, Hungerford and Tomera's model of responsible environmental behaviour (REB) and Ajzen's theory of planned behaviour (TPB)--were compared regarding the fit between model and data, predictive ability,…

  7. Predicting People's Environmental Behaviour: Theory of Planned Behaviour and Model of Responsible Environmental Behaviour

    Science.gov (United States)

    Chao, Yu-Long

    2012-01-01

    Using different measures of self-reported and other-reported environmental behaviour (EB), two important theoretical models explaining EB--Hines, Hungerford and Tomera's model of responsible environmental behaviour (REB) and Ajzen's theory of planned behaviour (TPB)--were compared regarding the fit between model and data, predictive ability,…

  8. A Prediction Model of MF Radiation in Environmental Assessment

    Institute of Scientific and Technical Information of China (English)

    HE-SHAN GE; YAN-FENG HONG

    2006-01-01

    Objective To predict the impact of MF radiation on human health.Methods The vertical distribution of field intensity was estimated by analogism on the basis of measured values from simulation measurement. Results A kind of analogism on the basis of geometric proportion decay pattern is put forward in the essay. It showed that with increasing of height the field intensity increased according to geometric proportion law. Conclusion This geometric proportion prediction model can be used to estimate the impact of MF radiation on inhabited environment, and can act as a reference pattern in predicting the environmental impact level of MF radiation.

  9. Cybernetic modeling of adaptive prediction of environmental changes by microorganisms.

    Science.gov (United States)

    Mandli, Aravinda R; Modak, Jayant M

    2014-02-01

    Microorganisms exhibit varied regulatory strategies such as direct regulation, symmetric anticipatory regulation, asymmetric anticipatory regulation, etc. Current mathematical modeling frameworks for the growth of microorganisms either do not incorporate regulation or assume that the microorganisms utilize the direct regulation strategy. In the present study, we extend the cybernetic modeling framework to account for asymmetric anticipatory regulation strategy. The extended model accurately captures various experimental observations. We use the developed model to explore the fitness advantage provided by the asymmetric anticipatory regulation strategy and observe that the optimal extent of asymmetric regulation depends on the selective pressure that the microorganisms experience. We also explore the importance of timing the response in anticipatory regulation and find that there is an optimal time, dependent on the extent of asymmetric regulation, at which microorganisms should respond anticipatorily to maximize their fitness. We then discuss the advantages offered by the cybernetic modeling framework over other modeling frameworks in modeling the asymmetric anticipatory regulation strategy.

  10. Integrating Environmental Optics into Multidisciplinary, Predictive Models of Ocean Dynamics

    Science.gov (United States)

    2011-09-30

    development has been based on decades of published research, our depth-integrated, spectral model of photosynthesis and the absorption -based model of...color, chlorophyll fluorescence, or spectral absorption coefficients. We extend the approach to include additional biological properties such as...of laboratory experiments in which photosynthesis , fluorescence and optical properties of phytoplankton are measured under a range of conditions

  11. Group contribution modelling for the prediction of safety-related and environmental properties

    DEFF Research Database (Denmark)

    Frutiger, Jerome; Abildskov, Jens; Sin, Gürkan

    We present a new set of property prediction models based on group contributions to predict major safety-related and environmental properties for organic compounds. The predicted list of properties includes lower and upper flammability limits, heat of combustion, auto ignition temperature, global...... models like group contribution (GC) models can estimate data. However, the estimation needs to be accurate, reliable and as little time-consuming as possible so that the models can be used on the fly. In this study the Marrero and Gani group contribution (MR GC) method has been used to develop the models...... for safety-related and environmental properties. The method considers the group contribution in three levels: The contributions from a specific functional group (1st order parameters), from poly-functional (2nd order parameters) as well as from structural groups (3rd order parameters). The latter two classes...

  12. Improving Hurricane Power Outage Prediction Models Through the Inclusion of Local Environmental Factors.

    Science.gov (United States)

    McRoberts, D Brent; Quiring, Steven M; Guikema, Seth D

    2016-10-25

    Tropical cyclones can significantly damage the electrical power system, so an accurate spatiotemporal forecast of outages prior to landfall can help utilities to optimize the power restoration process. The purpose of this article is to enhance the predictive accuracy of the Spatially Generalized Hurricane Outage Prediction Model (SGHOPM) developed by Guikema et al. (2014). In this version of the SGHOPM, we introduce a new two-step prediction procedure and increase the number of predictor variables. The first model step predicts whether or not outages will occur in each location and the second step predicts the number of outages. The SGHOPM environmental variables of Guikema et al. (2014) were limited to the wind characteristics (speed and duration of strong winds) of the tropical cyclones. This version of the model adds elevation, land cover, soil, precipitation, and vegetation characteristics in each location. Our results demonstrate that the use of a new two-step outage prediction model and the inclusion of these additional environmental variables increase the overall accuracy of the SGHOPM by approximately 17%.

  13. A General, Synthetic Model for Predicting Biodiversity Gradients from Environmental Geometry.

    Science.gov (United States)

    Gross, Kevin; Snyder-Beattie, Andrew

    2016-10-01

    Latitudinal and elevational biodiversity gradients fascinate ecologists, and have inspired dozens of explanations. The geometry of the abiotic environment is sometimes thought to contribute to these gradients, yet evaluations of geometric explanations are limited by a fragmented understanding of the diversity patterns they predict. This article presents a mathematical model that synthesizes multiple pathways by which environmental geometry can drive diversity gradients. The model characterizes species ranges by their environmental niches and limits on range sizes and places those ranges onto the simplified geometries of a sphere or cone. The model predicts nuanced and realistic species-richness gradients, including latitudinal diversity gradients with tropical plateaus and mid-latitude inflection points and elevational diversity gradients with low-elevation diversity maxima. The model also illustrates the importance of a mid-environment effect that augments species richness at locations with intermediate environments. Model predictions match multiple empirical biodiversity gradients, depend on ecological traits in a testable fashion, and formally synthesize elements of several geometric models. Together, these results suggest that previous assessments of geometric hypotheses should be reconsidered and that environmental geometry may play a deeper role in driving biodiversity gradients than is currently appreciated.

  14. Model-based Pedestrian Trajectory Prediction using Environmental Sensor for Mobile Robots Navigation

    Directory of Open Access Journals (Sweden)

    Haruka Tonoki

    2017-02-01

    Full Text Available Safety is the most important to the mobile robots that coexist with human. There are many studies that investigate obstacle detection and collision avoidance by predicting obstacles’ trajectories several seconds into the future using mounted sensors such as cameras and laser range finder (LRF for the safe behavior control of robots. In environments such as crossing roads where blind areas occur because of visual barriers like walls, obstacle detection might be delayed and collisions might be difficult to avoid. Using environmental sensors to detect obstacles is effective in such environments. When crossing roads, there are several passages pedestrian might move and it is difficult to depict going each passage in the same movement model. Therefore, we hypothesize that a more effective way to predict pedestrian movement is by predicting passages pedestrian might move and estimating the trajectories to the passages. We acquire pedestrian trajectory data using an environmental LRF with an extended Kalman filter (EKF and construct pedestrian movement models using vector auto regressive (VAR models, which pedestrian state is consisting of the position, speed and direction. Then, we test the validity of the constructed pedestrian movement models using experimental data. We narrow down the selection of a pedestrian movement model by comparing the prediction error for each path between the estimated pedestrian state using an EKF, and the predicted state using each movement model. We predict the trajectory using the selected movement model. Finally, we confirm that an appropriate path model that a pedestrian can actually move through is selected before the crossing area and that only the appropriate model is selected near the crossing area.

  15. Time-Series Modeling and Prediction of Global Monthly Absolute Temperature for Environmental Decision Making

    Institute of Scientific and Technical Information of China (English)

    YE Liming; YANG Guixia; Eric VAN RANST; TANG Huajun

    2013-01-01

    A generalized,structural,time series modeling framework was developed to analyze the monthly records of absolute surface temperature,one of the most important environmental parameters,using a deterministicstochastic combined (DSC) approach.Although the development of the framework was based on the characterization of the variation patterns of a global dataset,the methodology could be applied to any monthly absolute temperature record.Deterministic processes were used to characterize the variation patterns of the global trend and the cyclic oscillations of the temperature signal,involving polynomial functions and the Fourier method,respectively,while stochastic processes were employed to account for any remaining patterns in the temperature signal,involving seasonal autoregressive integrated moving average (SARIMA) models.A prediction of the monthly global surface temperature during the second decade of the 21st century using the DSC model shows that the global temperature will likely continue to rise at twice the average rate of the past 150 years.The evaluation of prediction accuracy shows that DSC models perform systematically well against selected models of other authors,suggesting that DSC models,when coupled with other ecoenvironmental models,can be used as a supplemental tool for short-term (~10-year) environmental planning and decision making.

  16. Environmental Modeling, Technology, and Communication for Land Falling Tropical Cyclone/Hurricane Prediction

    Directory of Open Access Journals (Sweden)

    Paul Tchounwou

    2010-04-01

    Full Text Available Katrina (a tropical cyclone/hurricane began to strengthen reaching a Category 5 storm on 28th August, 2005 and its winds reached peak intensity of 175 mph and pressure levels as low as 902 mb. Katrina eventually weakened to a category 3 storm and made a landfall in Plaquemines Parish, Louisiana, Gulf of Mexico, south of Buras on 29th August 2005. We investigate the time series intensity change of the hurricane Katrina using environmental modeling and technology tools to develop an early and advanced warning and prediction system. Environmental Mesoscale Model (Weather Research Forecast, WRF simulations are used for prediction of intensity change and track of the hurricane Katrina. The model is run on a doubly nested domain centered over the central Gulf of Mexico, with grid spacing of 90 km and 30 km for 6 h periods, from August 28th to August 30th. The model results are in good agreement with the observations suggesting that the model is capable of simulating the surface features, intensity change and track and precipitation associated with hurricane Katrina. We computed the maximum vertical velocities (Wmax using Convective Available Kinetic Energy (CAPE obtained at the equilibrium level (EL, from atmospheric soundings over the Gulf Coast stations during the hurricane land falling for the period August 21–30, 2005. The large vertical atmospheric motions associated with the land falling hurricane Katrina produced severe weather including thunderstorms and tornadoes 2–3 days before landfall. The environmental modeling simulations in combination with sounding data show that the tools may be used as an advanced prediction and communication system (APCS for land falling tropical cyclones/hurricanes.

  17. Environmental modeling, technology, and communication for land falling tropical cyclone/hurricane prediction.

    Science.gov (United States)

    Tuluri, Francis; Reddy, R Suseela; Anjaneyulu, Y; Colonias, John; Tchounwou, Paul

    2010-05-01

    Katrina (a tropical cyclone/hurricane) began to strengthen reaching a Category 5 storm on 28th August, 2005 and its winds reached peak intensity of 175 mph and pressure levels as low as 902 mb. Katrina eventually weakened to a category 3 storm and made a landfall in Plaquemines Parish, Louisiana, Gulf of Mexico, south of Buras on 29th August 2005. We investigate the time series intensity change of the hurricane Katrina using environmental modeling and technology tools to develop an early and advanced warning and prediction system. Environmental Mesoscale Model (Weather Research Forecast, WRF) simulations are used for prediction of intensity change and track of the hurricane Katrina. The model is run on a doubly nested domain centered over the central Gulf of Mexico, with grid spacing of 90 km and 30 km for 6 h periods, from August 28th to August 30th. The model results are in good agreement with the observations suggesting that the model is capable of simulating the surface features, intensity change and track and precipitation associated with hurricane Katrina. We computed the maximum vertical velocities (W(max)) using Convective Available Kinetic Energy (CAPE) obtained at the equilibrium level (EL), from atmospheric soundings over the Gulf Coast stations during the hurricane land falling for the period August 21-30, 2005. The large vertical atmospheric motions associated with the land falling hurricane Katrina produced severe weather including thunderstorms and tornadoes 2-3 days before landfall. The environmental modeling simulations in combination with sounding data show that the tools may be used as an advanced prediction and communication system (APCS) for land falling tropical cyclones/hurricanes.

  18. Spatial characterization and prediction of Neanderthal sites based on environmental information and stochastic modelling

    Science.gov (United States)

    Maerker, Michael; Bolus, Michael

    2014-05-01

    We present a unique spatial dataset of Neanderthal sites in Europe that was used to train a set of stochastic models to reveal the correlations between the site locations and environmental indices. In order to assess the relations between the Neanderthal sites and environmental variables as described above we applied a boosted regression tree approach (TREENET) a statistical mechanics approach (MAXENT) and support vector machines. The stochastic models employ a learning algorithm to identify a model that best fits the relationship between the attribute set (predictor variables (environmental variables) and the classified response variable which is in this case the types of Neanderthal sites. A quantitative evaluation of model performance was done by determining the suitability of the model for the geo-archaeological applications and by helping to identify those aspects of the methodology that need improvements. The models' predictive performances were assessed by constructing the Receiver Operating Characteristics (ROC) curves for each Neanderthal class, both for training and test data. In a ROC curve the Sensitivity is plotted over the False Positive Rate (1-Specificity) for all possible cut-off points. The quality of a ROC curve is quantified by the measure of the parameter area under the ROC curve. The dependent variable or target variable in this study are the locations of Neanderthal sites described by latitude and longitude. The information on the site location was collected from literature and own research. All sites were checked for site accuracy using high resolution maps and google earth. The study illustrates that the models show a distinct ranking in model performance with TREENET outperforming the other approaches. Moreover Pre-Neanderthals, Early Neanderthals and Classic Neanderthals show a specific spatial distribution. However, all models show a wide correspondence in the selection of the most important predictor variables generally showing less

  19. A Modelling Study for Predicting Life of Downhole Tubes Considering Service Environmental Parameters and Stress

    Directory of Open Access Journals (Sweden)

    Tianliang Zhao

    2016-09-01

    Full Text Available A modelling effort was made to try to predict the life of downhole tubes or casings, synthetically considering the effect of service influencing factors on corrosion rate. Based on the discussed corrosion mechanism and corrosion processes of downhole tubes, a mathematic model was established. For downhole tubes, the influencing factors are environmental parameters and stress, which vary with service duration. Stress and the environmental parameters including water content, partial pressure of H2S and CO2, pH value, total pressure and temperature, were considered to be time-dependent. Based on the model, life-span of an L80 downhole tube in oilfield Halfaya, an oilfield in Iraq, was predicted. The results show that life-span of the L80 downhole tube in Halfaya is 247 months (approximately 20 years under initial stress of 0.1 yield strength and 641 months (approximately 53 years under no initial stress, which indicates that an initial stress of 0.1 yield strength will reduce the life-span by more than half.

  20. Toxicity challenges in environmental chemicals: Prediction of human plasma protein binding through quantitative structure-activity relationship (QSAR) models

    Science.gov (United States)

    The present study explores the merit of utilizing available pharmaceutical data to construct a quantitative structure-activity relationship (QSAR) for prediction of the fraction of a chemical unbound to plasma protein (Fub) in environmentally relevant compounds. Independent model...

  1. Predicting biological parameters of estuarine benthic communities using models based on environmental data

    Directory of Open Access Journals (Sweden)

    José Souto Rosa-Filho

    2004-08-01

    Full Text Available This study aimed to predict the biological parameters (species composition, abundance, richness, diversity and evenness of benthic assemblages in southern Brazil estuaries using models based on environmental data (sediment characteristics, salinity, air and water temperature and depth. Samples were collected seasonally from five estuaries between the winter of 1996 and the summer of 1998. At each estuary, samples were taken in unpolluted areas with similar characteristics related to presence or absence of vegetation, depth and distance from the mouth. In order to obtain predictive models, two methods were used, the first one based on Multiple Discriminant Analysis (MDA, and the second based on Multiple Linear Regression (MLR. Models using MDA had better results than those based on linear regression. The best results using MLR were obtained for diversity and richness. It could be concluded that the use predictions models based on environmental data would be very useful in environmental monitoring studies in estuaries.Este trabalho objetivou predizer parâmetros da estrutura de associações macrobentônicas (composição específica, abundância, riqueza, diversidade e equitatividade em estuários do Sul do Brasil, utilizando modelos baseados em dados ambientais (características dos sedimentos, salinidade, temperaturas do ar e da água, e profundidade. As amostragens foram realizadas sazonalmente em cinco estuários entre o inverno de 1996 e o verão de 1998. Em cada estuário as amostras foram coletadas em áreas não poluídas, com características semelhantes quanto a presença ou ausência de vegetação, profundidade e distância da desenbocadura. Para a obtenção dos modelos de predição, foram utilizados dois métodos: o primeiro baseado em Análise Discriminante Múltipla (ADM e o segundo em Regressão Linear Múltipla (RLM. Os modelos baseados em ADM apresentaram resultados melhores do que os baseados em regressão linear. Os melhores

  2. A mixed modeling approach to predict the effect of environmental modification on species distributions.

    Directory of Open Access Journals (Sweden)

    Francesco Cozzoli

    Full Text Available Human infrastructures can modify ecosystems, thereby affecting the occurrence and spatial distribution of organisms, as well as ecosystem functionality. Sustainable development requires the ability to predict responses of species to anthropogenic pressures. We investigated the large scale, long term effect of important human alterations of benthic habitats with an integrated approach combining engineering and ecological modelling. We focused our analysis on the Oosterschelde basin (The Netherlands, which was partially embanked by a storm surge barrier (Oosterscheldekering, 1986. We made use of 1 a prognostic (numerical environmental (hydrodynamic model and 2 a novel application of quantile regression to Species Distribution Modeling (SDM to simulate both the realized and potential (habitat suitability abundance of four macrozoobenthic species: Scoloplos armiger, Peringia ulvae, Cerastoderma edule and Lanice conchilega. The analysis shows that part of the fluctuations in macrozoobenthic biomass stocks during the last decades is related to the effect of the coastal defense infrastructures on the basin morphology and hydrodynamics. The methodological framework we propose is particularly suitable for the analysis of large abundance datasets combined with high-resolution environmental data. Our analysis provides useful information on future changes in ecosystem functionality induced by human activities.

  3. Indoor environmental quality (IEQ) and building energy optimization through model predictive control (MPC)

    Science.gov (United States)

    Woldekidan, Korbaga

    This dissertation aims at developing a novel and systematic approach to apply Model Predictive Control (MPC) to improve energy efficiency and indoor environmental quality in office buildings. Model predictive control is one of the advanced optimal control approaches that use models to predict the behavior of the process beyond the current time to optimize the system operation at the present time. In building system, MPC helps to exploit buildings' thermal storage capacity and to use the information on future disturbances like weather and internal heat gains to estimate optimal control inputs ahead of time. In this research the major challenges of applying MPC to building systems are addressed. A systematic framework has been developed for ease of implementation. New methods are proposed to develop simple and yet reasonably accurate models that can minimize the MPC development effort as well as computational time. The developed MPC is used to control a detailed building model represented by whole building performance simulation tool, EnergyPlus. A co-simulation strategy is used to communicate the MPC control developed in Matlab platform with the case building model in EnergyPlus. The co-simulation tool used (MLE+) also has the ability to talk to actual building management systems that support the BACnet communication protocol which makes it easy to implement the developed MPC control in actual buildings. A building that features an integrated lighting and window control and HVAC system with a dedicated outdoor air system and ceiling radiant panels was used as a case building. Though this study is specifically focused on the case building, the framework developed can be applied to any building type. The performance of the developed MPC was compared against a baseline control strategy using Proportional Integral and Derivative (PID) control. Various conventional and advanced thermal comfort as well as ventilation strategies were considered for the comparison. These

  4. Determining the validity of exposure models for environmental epidemiology : predicting electromagnetic fields from mobile phone base stations

    NARCIS (Netherlands)

    Beekhuizen, Johan

    2014-01-01

    One of the key challenges in environmental epidemiology is the exposure assessment of large populations. Spatial exposure models have been developed that predict exposure to the pollutant of interest for large study sizes. However, the validity of these exposure models is often unknown. In this thes

  5. Development and Application of In Vitro Models for Screening Drugs and Environmental Chemicals that Predict Toxicity in Animals and Humans

    Science.gov (United States)

    Development and Application of In Vitro Models for Screening Drugs and Environmental Chemicals that Predict Toxicity in Animals and Humans (Presented by James McKim, Ph.D., DABT, Founder and Chief Science Officer, CeeTox) (5/25/2012)

  6. Report: Physics Constrained Stochastic Statistical Models for Extended Range Environmental Prediction

    Science.gov (United States)

    2013-09-30

    for Extended Range Environmental Prediction Andrew J. Majda New York University Courant Institute of Mathematical Sciences 251 Mercer Street...UCLA), and Dimitri Giannakis ( Courant Institute) with their post docs and Ph.D. students: A. Reemergence Mechanisms for North Pacific Sea Ice...ES) New York University, Courant Institute of Mathematical Sciences,251 Mercer Street,New York,NY,10012 8. PERFORMING ORGANIZATION REPORT NUMBER 9

  7. Predicting environmental suitability for a rare and threatened species (Lao newt, Laotriton laoensis using validated species distribution models.

    Directory of Open Access Journals (Sweden)

    Amanda J Chunco

    Full Text Available The Lao newt (Laotriton laoensis is a recently described species currently known only from northern Laos. Little is known about the species, but it is threatened as a result of overharvesting. We integrated field survey results with climate and altitude data to predict the geographic distribution of this species using the niche modeling program Maxent, and we validated these predictions by using interviews with local residents to confirm model predictions of presence and absence. The results of the validated Maxent models were then used to characterize the environmental conditions of areas predicted suitable for L. laoensis. Finally, we overlaid the resulting model with a map of current national protected areas in Laos to determine whether or not any land predicted to be suitable for this species is coincident with a national protected area. We found that both area under the curve (AUC values and interview data provided strong support for the predictive power of these models, and we suggest that interview data could be used more widely in species distribution niche modeling. Our results further indicated that this species is mostly likely geographically restricted to high altitude regions (i.e., over 1,000 m elevation in northern Laos and that only a minute fraction of suitable habitat is currently protected. This work thus emphasizes that increased protection efforts, including listing this species as endangered and the establishment of protected areas in the region predicted to be suitable for L. laoensis, are urgently needed.

  8. Predicting the resilience and recovery of aquatic systems: A framework for model evolution within environmental observatories

    Science.gov (United States)

    Hipsey, Matthew R.; Hamilton, David P.; Hanson, Paul C.; Carey, Cayelan C.; Coletti, Janaine Z.; Read, Jordan S.; Ibelings, Bas W.; Valesini, Fiona J.; Brookes, Justin D.

    2015-09-01

    Maintaining the health of aquatic systems is an essential component of sustainable catchment management, however, degradation of water quality and aquatic habitat continues to challenge scientists and policy-makers. To support management and restoration efforts aquatic system models are required that are able to capture the often complex trajectories that these systems display in response to multiple stressors. This paper explores the abilities and limitations of current model approaches in meeting this challenge, and outlines a strategy based on integration of flexible model libraries and data from observation networks, within a learning framework, as a means to improve the accuracy and scope of model predictions. The framework is comprised of a data assimilation component that utilizes diverse data streams from sensor networks, and a second component whereby model structural evolution can occur once the model is assessed against theoretically relevant metrics of system function. Given the scale and transdisciplinary nature of the prediction challenge, network science initiatives are identified as a means to develop and integrate diverse model libraries and workflows, and to obtain consensus on diagnostic approaches to model assessment that can guide model adaptation. We outline how such a framework can help us explore the theory of how aquatic systems respond to change by bridging bottom-up and top-down lines of enquiry, and, in doing so, also advance the role of prediction in aquatic ecosystem management.

  9. Chemometric Methods and Theoretical Molecular Descriptors in Predictive QSAR Modeling of the Environmental Behavior of Organic Pollutants

    Science.gov (United States)

    Gramatica, Paola

    This chapter surveys the QSAR modeling approaches (developed by the author's research group) for the validated prediction of environmental properties of organic pollutants. Various chemometric methods, based on different theoretical molecular descriptors, have been applied: explorative techniques (such as PCA for ranking, SOM for similarity analysis), modeling approaches by multiple-linear regression (MLR, in particular OLS), and classification methods (mainly k-NN, CART, CP-ANN). The focus of this review is on the main topics of environmental chemistry and ecotoxicology, related to the physico-chemical properties, the reactivity, and biological activity of chemicals of high environmental concern. Thus, the review deals with atmospheric degradation reactions of VOCs by tropospheric oxidants, persistence and long-range transport of POPs, sorption behavior of pesticides (Koc and leaching), bioconcentration, toxicity (acute aquatic toxicity, mutagenicity of PAHs, estrogen binding activity for endocrine disruptors compounds (EDCs)), and finally persistent bioaccumulative and toxic (PBT) behavior for the screening and prioritization of organic pollutants. Common to all the proposed models is the attention paid to model validation for predictive ability (not only internal, but also external for chemicals not participating in the model development) and checking of the chemical domain of applicability. Adherence to such a policy, requested also by the OECD principles, ensures the production of reliable predicted data, useful also in the new European regulation of chemicals, REACH.

  10. A Rat α-Fetoprotein Binding Activity Prediction Model to Facilitate Assessment of the Endocrine Disruption Potential of Environmental Chemicals.

    Science.gov (United States)

    Hong, Huixiao; Shen, Jie; Ng, Hui Wen; Sakkiah, Sugunadevi; Ye, Hao; Ge, Weigong; Gong, Ping; Xiao, Wenming; Tong, Weida

    2016-03-25

    Endocrine disruptors such as polychlorinated biphenyls (PCBs), diethylstilbestrol (DES) and dichlorodiphenyltrichloroethane (DDT) are agents that interfere with the endocrine system and cause adverse health effects. Huge public health concern about endocrine disruptors has arisen. One of the mechanisms of endocrine disruption is through binding of endocrine disruptors with the hormone receptors in the target cells. Entrance of endocrine disruptors into target cells is the precondition of endocrine disruption. The binding capability of a chemical with proteins in the blood affects its entrance into the target cells and, thus, is very informative for the assessment of potential endocrine disruption of chemicals. α-fetoprotein is one of the major serum proteins that binds to a variety of chemicals such as estrogens. To better facilitate assessment of endocrine disruption of environmental chemicals, we developed a model for α-fetoprotein binding activity prediction using the novel pattern recognition method (Decision Forest) and the molecular descriptors calculated from two-dimensional structures by Mold² software. The predictive capability of the model has been evaluated through internal validation using 125 training chemicals (average balanced accuracy of 69%) and external validations using 22 chemicals (balanced accuracy of 71%). Prediction confidence analysis revealed the model performed much better at high prediction confidence. Our results indicate that the model is useful (when predictions are in high confidence) in endocrine disruption risk assessment of environmental chemicals though improvement by increasing number of training chemicals is needed.

  11. A Rat α-Fetoprotein Binding Activity Prediction Model to Facilitate Assessment of the Endocrine Disruption Potential of Environmental Chemicals

    Directory of Open Access Journals (Sweden)

    Huixiao Hong

    2016-03-01

    Full Text Available Endocrine disruptors such as polychlorinated biphenyls (PCBs, diethylstilbestrol (DES and dichlorodiphenyltrichloroethane (DDT are agents that interfere with the endocrine system and cause adverse health effects. Huge public health concern about endocrine disruptors has arisen. One of the mechanisms of endocrine disruption is through binding of endocrine disruptors with the hormone receptors in the target cells. Entrance of endocrine disruptors into target cells is the precondition of endocrine disruption. The binding capability of a chemical with proteins in the blood affects its entrance into the target cells and, thus, is very informative for the assessment of potential endocrine disruption of chemicals. α-fetoprotein is one of the major serum proteins that binds to a variety of chemicals such as estrogens. To better facilitate assessment of endocrine disruption of environmental chemicals, we developed a model for α-fetoprotein binding activity prediction using the novel pattern recognition method (Decision Forest and the molecular descriptors calculated from two-dimensional structures by Mold2 software. The predictive capability of the model has been evaluated through internal validation using 125 training chemicals (average balanced accuracy of 69% and external validations using 22 chemicals (balanced accuracy of 71%. Prediction confidence analysis revealed the model performed much better at high prediction confidence. Our results indicate that the model is useful (when predictions are in high confidence in endocrine disruption risk assessment of environmental chemicals though improvement by increasing number of training chemicals is needed.

  12. Predicting the resilience and recovery of aquatic systems: a framework for model evolution within environmental observatories

    Science.gov (United States)

    Hipsey, Matthew R.; Hamilton, David P.; Hanson, Paul C.; Carey, Cayelan C.; Coletti, Janaine Z; Read, Jordan S.; Ibelings, Bas W; Valensini, Fiona J; Brookes, Justin D

    2015-01-01

    Maintaining the health of aquatic systems is an essential component of sustainable catchmentmanagement, however, degradation of water quality and aquatic habitat continues to challenge scientistsand policy-makers. To support management and restoration efforts aquatic system models are requiredthat are able to capture the often complex trajectories that these systems display in response to multiplestressors. This paper explores the abilities and limitations of current model approaches in meeting this chal-lenge, and outlines a strategy based on integration of flexible model libraries and data from observationnetworks, within a learning framework, as a means to improve the accuracy and scope of model predictions.The framework is comprised of a data assimilation component that utilizes diverse data streams from sensornetworks, and a second component whereby model structural evolution can occur once the model isassessed against theoretically relevant metrics of system function. Given the scale and transdisciplinarynature of the prediction challenge, network science initiatives are identified as a means to develop and inte-grate diverse model libraries and workflows, and to obtain consensus on diagnostic approaches to modelassessment that can guide model adaptation. We outline how such a framework can help us explore thetheory of how aquatic systems respond to change by bridging bottom-up and top-down lines of enquiry,and, in doing so, also advance the role of prediction in aquatic ecosystem management.

  13. Coal Extraction - Environmental Prediction

    Science.gov (United States)

    Cecil, C. Blaine; Tewalt, Susan J.

    2002-01-01

    Coal from the Appalachian region has supplied energy to the Nation for more than 200 years. Appalachian coal fueled America through a civil war and helped win two world wars. Appalachian coal has also provided fuel for keeping America warm in the winter and cool in the summer and has served as the basis for the steel, automobile, organic chemicals, chlorine, and aluminum industries. These benefits have not come without environmental costs, however. Coal extraction and utilization have had significant environmental impacts.

  14. Environmental impact prediction using remote sensing images

    Institute of Scientific and Technical Information of China (English)

    Pezhman ROUDGARMI; Masoud MONAVARI; Jahangir FEGHHI; Jafar NOURI; Nematollah KHORASANI

    2008-01-01

    Environmental impact prediction is an important step in many environmental studies. Awide variety of methods have been developed in this concern. During this study, remote sensing images were used for environmental impact prediction in Robatkarim area, Iran, during the years of 2005~2007. It was assumed that environmental impact could be predicted using time series satellite imageries. Natural vegetation cover was chosen as a main environmental element and a case study. Environmental impacts of the regional development on natural vegetation of the area were investigated considering the changes occurred on the extent of natural vegetation cover and the amount of biomass. Vegetation data, land use and land cover classes (as activity factors) within several years were prepared using satellite images. The amount ofbiomass was measured by Soil-adjusted Vegetation Index (SAVI) and Normalized Difference Vegetation Index (NDVI) based on satellite images. The resulted biomass estimates were tested by the paired samples t-test method. No significant difference was observed between the average biomass of estimated and control samples at the 5% significance level. Finally, regression models were used for the environmental impacts prediction. All obtained regression models for prediction of impacts on natural vegetation cover show values over 0.9 for both correlation coefficient and R-squared. According to the resulted methodology, the prediction models of projects and plans impacts can also be developed for other environmental elements which may be derived using time series remote sensing images.

  15. Calcareous Bio-Concretions in the Northern Adriatic Sea: Habitat Types, Environmental Factors that Influence Habitat Distributions, and Predictive Modeling.

    Science.gov (United States)

    Falace, Annalisa; Kaleb, Sara; Curiel, Daniele; Miotti, Chiara; Galli, Giovanni; Querin, Stefano; Ballesteros, Enric; Solidoro, Cosimo; Bandelj, Vinko

    2015-01-01

    Habitat classifications provide guidelines for mapping and comparing marine resources across geographic regions. Calcareous bio-concretions and their associated biota have not been exhaustively categorized. Furthermore, for management and conservation purposes, species and habitat mapping is critical. Recently, several developments have occurred in the field of predictive habitat modeling, and multiple methods are available. In this study, we defined the habitats constituting northern Adriatic biogenic reefs and created a predictive habitat distribution model. We used an updated dataset of the epibenthic assemblages to define the habitats, which we verified using the fuzzy k-means (FKM) clustering method. Redundancy analysis was employed to model the relationships between the environmental descriptors and the FKM membership grades. Predictive modelling was carried out to map habitats across the basin. Habitat A (opportunistic macroalgae, encrusting Porifera, bioeroders) characterizes reefs closest to the coastline, which are affected by coastal currents and river inputs. Habitat B is distinguished by massive Porifera, erect Tunicata, and non-calcareous encrusting algae (Peyssonnelia spp.). Habitat C (non-articulated coralline, Polycitor adriaticus) is predicted in deeper areas. The onshore-offshore gradient explains the variability of the assemblages because of the influence of coastal freshwater, which is the main driver of nutrient dynamics. This model supports the interpretation of Habitat A and C as the extremes of a gradient that characterizes the epibenthic assemblages, while Habitat B demonstrates intermediate characteristics. Areas of transition are a natural feature of the marine environment and may include a mixture of habitats and species. The habitats proposed are easy to identify in the field, are related to different environmental features, and may be suitable for application in studies focused on other geographic areas. The habitat model outputs

  16. Calcareous Bio-Concretions in the Northern Adriatic Sea: Habitat Types, Environmental Factors that Influence Habitat Distributions, and Predictive Modeling.

    Directory of Open Access Journals (Sweden)

    Annalisa Falace

    Full Text Available Habitat classifications provide guidelines for mapping and comparing marine resources across geographic regions. Calcareous bio-concretions and their associated biota have not been exhaustively categorized. Furthermore, for management and conservation purposes, species and habitat mapping is critical. Recently, several developments have occurred in the field of predictive habitat modeling, and multiple methods are available. In this study, we defined the habitats constituting northern Adriatic biogenic reefs and created a predictive habitat distribution model. We used an updated dataset of the epibenthic assemblages to define the habitats, which we verified using the fuzzy k-means (FKM clustering method. Redundancy analysis was employed to model the relationships between the environmental descriptors and the FKM membership grades. Predictive modelling was carried out to map habitats across the basin. Habitat A (opportunistic macroalgae, encrusting Porifera, bioeroders characterizes reefs closest to the coastline, which are affected by coastal currents and river inputs. Habitat B is distinguished by massive Porifera, erect Tunicata, and non-calcareous encrusting algae (Peyssonnelia spp.. Habitat C (non-articulated coralline, Polycitor adriaticus is predicted in deeper areas. The onshore-offshore gradient explains the variability of the assemblages because of the influence of coastal freshwater, which is the main driver of nutrient dynamics. This model supports the interpretation of Habitat A and C as the extremes of a gradient that characterizes the epibenthic assemblages, while Habitat B demonstrates intermediate characteristics. Areas of transition are a natural feature of the marine environment and may include a mixture of habitats and species. The habitats proposed are easy to identify in the field, are related to different environmental features, and may be suitable for application in studies focused on other geographic areas. The habitat

  17. Predicting the potential environmental suitability for Theileria orientalis transmission in New Zealand cattle using maximum entropy niche modelling.

    Science.gov (United States)

    Lawrence, K E; Summers, S R; Heath, A C G; McFadden, A M J; Pulford, D J; Pomroy, W E

    2016-07-15

    The tick-borne haemoparasite Theileria orientalis is the most important infectious cause of anaemia in New Zealand cattle. Since 2012 a previously unrecorded type, T. orientalis type 2 (Ikeda), has been associated with disease outbreaks of anaemia, lethargy, jaundice and deaths on over 1000 New Zealand cattle farms, with most of the affected farms found in the upper North Island. The aim of this study was to model the relative environmental suitability for T. orientalis transmission throughout New Zealand, to predict the proportion of cattle farms potentially suitable for active T. orientalis infection by region, island and the whole of New Zealand and to estimate the average relative environmental suitability per farm by region, island and the whole of New Zealand. The relative environmental suitability for T. orientalis transmission was estimated using the Maxent (maximum entropy) modelling program. The Maxent model predicted that 99% of North Island cattle farms (n=36,257), 64% South Island cattle farms (n=15,542) and 89% of New Zealand cattle farms overall (n=51,799) could potentially be suitable for T. orientalis transmission. The average relative environmental suitability of T. orientalis transmission at the farm level was 0.34 in the North Island, 0.02 in the South Island and 0.24 overall. The study showed that the potential spatial distribution of T. orientalis environmental suitability was much greater than presumed in the early part of the Theileria associated bovine anaemia (TABA) epidemic. Maximum entropy offers a computer efficient method of modelling the probability of habitat suitability for an arthropod vectored disease. This model could help estimate the boundaries of the endemically stable and endemically unstable areas for T. orientalis transmission within New Zealand and be of considerable value in informing practitioner and farmer biosecurity decisions in these respective areas.

  18. Time series models of environmental exposures: Good predictions or good understanding.

    Science.gov (United States)

    Barnett, Adrian G; Stephen, Dimity; Huang, Cunrui; Wolkewitz, Martin

    2017-04-01

    Time series data are popular in environmental epidemiology as they make use of the natural experiment of how changes in exposure over time might impact on disease. Many published time series papers have used parameter-heavy models that fully explained the second order patterns in disease to give residuals that have no short-term autocorrelation or seasonality. This is often achieved by including predictors of past disease counts (autoregression) or seasonal splines with many degrees of freedom. These approaches give great residuals, but add little to our understanding of cause and effect. We argue that modelling approaches should rely more on good epidemiology and less on statistical tests. This includes thinking about causal pathways, making potential confounders explicit, fitting a limited number of models, and not over-fitting at the cost of under-estimating the true association between exposure and disease. Copyright © 2017 Elsevier Inc. All rights reserved.

  19. Integration of nitrogen dynamics into the Noah-MP land model v1.1 for climate and environmental predictions

    Directory of Open Access Journals (Sweden)

    X. Cai

    2015-05-01

    Full Text Available Climate and terrestrial biosphere models consider nitrogen an important factor in limiting plant carbon uptake, while operational environmental models view nitrogen as the leading pollutant causing eutrophication in water bodies. The community Noah land surface model with multi-parameterization options (Noah-MP is unique in that it is the next generation land surface model for the Weather Research and Forecasting meteorological model and for the operational weather/climate models in the National Centers for Environmental Prediction. In this study, we add capability to Noah-MP to simulate nitrogen dynamics by coupling the Fixation and Uptake of Nitrogen (FUN plant model and the Soil and Water Assessment Tool (SWAT soil nitrogen dynamics. This incorporates FUN's state-of-the-art concept of carbon cost theory and SWAT's strength in representing the impacts of agricultural management on the nitrogen cycle. Parameterizations for direct root and mycorrhizal-associated nitrogen uptake, leaf retranslocation, and symbiotic biological nitrogen fixation are employed from FUN, while parameterizations for nitrogen mineralization, nitrification, immobilization, volatilization, atmospheric deposition, and leaching are based on SWAT. The coupled model is then evaluated at the Kellogg Biological Station – a Long-term Ecological Research site within the U.S. Corn Belt. Results show that the model performs well in capturing the major nitrogen state/flux variables (e.g., soil nitrate and nitrate leaching. Furthermore, the addition of nitrogen dynamics improves the modeling of the carbon and water cycles (e.g., net primary productivity and evapotranspiration. The model improvement is expected to advance the capability of Noah-MP to simultaneously predict weather and water quality in fully coupled Earth system models.

  20. Microbial functional diversity enhances predictive models linking environmental parameters to ecosystem properties.

    Science.gov (United States)

    Powell, Jeff R; Welsh, Allana; Hallin, Sara

    2015-07-01

    Microorganisms drive biogeochemical processes, but linking these processes to real changes in microbial communities under field conditions is not trivial. Here, we present a model-based approach to estimate independent contributions of microbial community shifts to ecosystem properties. The approach was tested empirically, using denitrification potential as our model process, in a spatial survey of arable land encompassing a range of edaphic conditions and two agricultural production systems. Soil nitrate was the most important single predictor of denitrification potential (the change in Akaike's information criterion, corrected for sample size, ΔAIC(c) = 20.29); however, the inclusion of biotic variables (particularly the evenness and size of denitrifier communities [ΔAIC(c) = 12.02], and the abundance of one denitrifier genotype [ΔAIC(c) = 18.04]) had a substantial effect on model precision, comparable to the inclusion of abiotic variables (biotic R2 = 0.28, abiotic R2 = 0.50, biotic + abiotic R2 = 0.76). This approach provides a valuable tool for explicitly linking microbial communities to ecosystem functioning. By making this link, we have demonstrated that including aspects of microbial community structure and diversity in biogeochemical models can improve predictions of nutrient cycling in ecosystems and enhance our understanding of ecosystem functionality.

  1. Prediction of the environmental fate and aquatic ecological impact of nitrobenzene in the Songhua River using the modified AQUATOX model

    Institute of Scientific and Technical Information of China (English)

    LEI Bingli; HUANG Shengbiao; QIAO Min; LI Tianyun; WANG Zijian

    2008-01-01

    An accidental discharge of nitrobenzene happened in November 2005 in the Songhuajiang River, China. The AQUATOX model was modified and adapted to simulate the time-dependent nitrobenzene distribution in this multimedia aquatic system and its potential ecological impacts. Nitrobenzene concentrations in flowing water, sediment, and biota were predicted. Based on the initial concentrations of nitrobenzene observed on the field during the accidental discharge, that is, 0.167-1.47 mg/L at different river segments, the predicted water concentrations of nitrobenzene would decrease to 0.02 and 0.002 mg/L after twenty days and one month, respectively. Both model prediction and field observation were in good agreement. The predicted nitrobenzene concentrations in sediments and aquatic organisms would be lower than 0.025 and 0.002 mg/kg, respectively, after two months. Among environmental factors affecting nitrobenzene concentrations in water, inflow water dilution, water temperature, and initial concentration were the most important, by sensitivity analysis. Comparing the perturbed simulation and control simulation, the biomass changes for diatoms and mussel were significantly affected, whereas, no influence on other organisms could be predicted. Therefore the results indicated that nitrobenzene pollution in the Songhuajiang River should have a limited impact on the benthos community.

  2. Prediction of the environmental fate of chemicals.

    Science.gov (United States)

    Vighi, M; Calamari, D

    1993-01-01

    An overview is presented of the possibilities of applying multimedia compartmental evaluative models, and in particular the fugacity approach, to predict the environmental distribution and fate of organic chemicals. The use of this predictive approach for the evaluation of exposure to pollutants in the aquatic system is described, with reference to different environments or discharge patterns (surface and groundwaters, point and diffuse sources of pollution). The value and limitations of this approach are noted and the need for more research to improve predictive capability and practical usefulness is indicated. Finally some practical applications of evaluative models in the proposal of quantitative indices for ecotoxicological evaluation of risk from chemicals are described.

  3. Fully in Silico Calibration of Empirical Predictive Models for Environmental Fate Properties of Novel Munitions Compounds

    Science.gov (United States)

    2016-04-01

    susceptibilities to photodegradation [47]. Comparison of experimental FTIR spectra for CL-20 hydrolysis and DFT theorized spectra for 1,5- and 1,7...environmentally relevant oxidants ( ozone , chlorine dioxide, phosphate and carbonate radicals) by correlating relative rate constants (normalized to 4...available for manganese dioxide (MnO2) [100-103], chlorine dioxide (ClO2) [104], ozone (O3) [105], alkaline-activated persulphate [106], iron

  4. Predicted and actual indoor environmental quality: Verification of occupants' behaviour models in residential buildings

    DEFF Research Database (Denmark)

    Andersen, Rune Korsholm; Fabi, Valentina; Corgnati, Stefano P.

    2016-01-01

    performance using building energy performance simulations (BEPS). However, the validity of these models has only been sparsely tested. In this paper, stochastic models of occupants' behaviour from literature were tested against measurements in five apartments. In a monitoring campaign, measurements of indoor...... station close by. The stochastic models of window opening and heating set-point adjustments were implemented in the BEPS tool IDA ICE. Two apartments from the monitoring campaign were simulated using the implemented models and the measured weather data. The results were compared to measurements from...

  5. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 1998-03 (NODC Accession 0001531)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  6. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2005-12 (NODC Accession 0002659)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  7. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2005-06 (NODC Accession 0002406)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  8. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 1997-07 (NODC Accession 0001523)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  9. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 1997-09 (NODC Accession 0001525)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  10. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 1997-04 (NODC Accession 0001520)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  11. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 1997-06 (NODC Accession 0001522)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  12. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 1998-05 (NODC Accession 0001533)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  13. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 1997-03 (NODC Accession 0001519)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  14. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 1999-05 (NODC Accession 0001545)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  15. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2006-10 (NODC Accession 0043271)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  16. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2006-03 (NODC Accession 0002742)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  17. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 1998-04 (NODC Accession 0001532)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  18. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 1997-11 (NODC Accession 0001527)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  19. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2006-09 (NODC Accession 0043270)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  20. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 1999-10 (NODC Accession 0001550)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  1. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2006-12 (NODC Accession 0043273)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  2. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2000-02 (NODC Accession 0001554)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  3. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 1999-09 (NODC Accession 0001549)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  4. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 1999-02 (NODC Accession 0001542)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  5. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 1998-02 (NODC Accession 0001530)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  6. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2002-11 (NODC Accession 0001587)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  7. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2001-05 (NODC Accession 0001569)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  8. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 1997-08 (NODC Accession 0001524)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  9. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 1998-01 (NODC Accession 0001529)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  10. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2004-03 (NODC Accession 0001603)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  11. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2007-05 (NODC Accession 0043281)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  12. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2001-07 (NODC Accession 0001571)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  13. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 1997-05 (NODC Accession 0001521)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  14. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2001-04 (NODC Accession 0001568)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  15. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2000-07 (NODC Accession 0001559)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  16. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2003-03 (NODC Accession 0001591)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  17. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2007-08 (NODC Accession 0043284)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  18. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2000-10 (NODC Accession 0001562)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  19. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 1997-01 (NODC Accession 0001517)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  20. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2005-03 (NODC Accession 0002162)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  1. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2006-04 (NODC Accession 0043262)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  2. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2002-01 (NODC Accession 0001577)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  3. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2002-12 (NODC Accession 0001588)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  4. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2001-09 (NODC Accession 0001573)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  5. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2001-01 (NODC Accession 0001565)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  6. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2005-08 (NODC Accession 0002504)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  7. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2003-02 (NODC Accession 0001590)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  8. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2007-06 (NODC Accession 0043282)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  9. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2002-07 (NODC Accession 0001583)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  10. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2000-08 (NODC Accession 0001560)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  11. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2006-08 (NODC Accession 0043268)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  12. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2002-09 (NODC Accession 0001585)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  13. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2000-06 (NODC Accession 0001558)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  14. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2006-07 (NODC Accession 0043267)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  15. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2002-03 (NODC Accession 0001579)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  16. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2002-06 (NODC Accession 0001582)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  17. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2001-03 (NODC Accession 0001567)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  18. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 1999-11 (NODC Accession 0001551)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  19. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2006-01 (NODC Accession 0002660)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  20. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 1997-02 (NODC Accession 0001518)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  1. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2001-12 (NODC Accession 0001576)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  2. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2003-08 (NODC Accession 0001596)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  3. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2000-12 (NODC Accession 0001564)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  4. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2003-09 (NODC Accession 0001597)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  5. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2000-04 (NODC Accession 0001556)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  6. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2004-04 (NODC Accession 0001604)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  7. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2001-02 (NODC Accession 0001566)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  8. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2007-07 (NODC Accession 0043283)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  9. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2003-05 (NODC Accession 0001593)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  10. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2003-11 (NODC Accession 0001599)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  11. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2003-06 (NODC Accession 0001594)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  12. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2003-12 (NODC Accession 0001600)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  13. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2002-08 (NODC Accession 0001584)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  14. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2005-04 (NODC Accession 0002340)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  15. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2000-09 (NODC Accession 0001561)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  16. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2003-04 (NODC Accession 0001592)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  17. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2005-02 (NODC Accession 0002160)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  18. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2001-08 (NODC Accession 0001572)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  19. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2005-01 (NODC Accession 0002159)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  20. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2004-01 (NODC Accession 0001601)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  1. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2001-11 (NODC Accession 0001575)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  2. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2000-11 (NODC Accession 0001563)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  3. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2003-10 (NODC Accession 0001598)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  4. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2006-05 (NODC Accession 0043265)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  5. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2001-06 (NODC Accession 0001570)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  6. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2003-07 (NODC Accession 0001595)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  7. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 1998-07 (NODC Accession 0001535)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  8. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 1997-12 (NODC Accession 0001528)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  9. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2002-05 (NODC Accession 0001581)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  10. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2004-08 (NODC Accession 0002154)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  11. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2005-05 (NODC Accession 0002373)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  12. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2002-02 (NODC Accession 0001578)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  13. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2003-01 (NODC Accession 0001589)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  14. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2004-06 (NODC Accession 0002151)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  15. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2006-06 (NODC Accession 0043266)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  16. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2005-09 (NODC Accession 0002505)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  17. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 1997-10 (NODC Accession 0001526)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  18. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 1998-06 (NODC Accession 0001534)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  19. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 1999-08 (NODC Accession 0001548)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  20. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 1999-04 (NODC Accession 0001544)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  1. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 1999-07 (NODC Accession 0001547)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  2. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2004-02 (NODC Accession 0001602)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  3. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2002-10 (NODC Accession 0001586)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  4. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 1999-01 (NODC Accession 0001541)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  5. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 1998-12 (NODC Accession 0001540)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  6. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2002-04 (NODC Accession 0001580)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  7. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 1999-06 (NODC Accession 0001546)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  8. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 1999-03 (NODC Accession 0001543)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  9. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 1998-09 (NODC Accession 0001537)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  10. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2004-10 (NODC Accession 0002156)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  11. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2005-11 (NODC Accession 0002652)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  12. Interannual Fluctuations in Atmospheric Angular Momentum Simulated by the National Centers for Environmental Prediction Medium Range Forecast Model

    Science.gov (United States)

    Mo, Kingtse C.; Dickey, Jean O.; Marcus, Steven L.

    1997-01-01

    An earlier study established the existence of globally coherent interannual fluctuations in atmospheric angular momentum (AAM), associated with the El Nino-Southern Oscillation (ENSO) cycle. In this paper, we pursue the origin and the structure of these fluctuations using an ensemble of experiments generated by the National Centers for Environmental Prediction, medium range forecast model version 9. In the control experiments, where the observed sea surface temperatures (SSTs) were used as the lower boundary conditions, the model captures the characteristic V-like structure in time-latitude plots of zonally averaged AAM, while experiments with climatological SSTs and those with either perpetual warm or cold ENSO conditions superimposed on the climatological SSTs failed to reproduce this structure. The numerical results indicate that these AAM structures are related to SST variations associated with transitions between different phases of the ENSO cycle and have both propagating and standing components. The largest zonal wind contribution from the levels studied (850, 500, and 200 hPa) is at 200 hPa, where the tropical convective outflow is the strongest. Composites of zonal wind and geopotential height show a clear relationship between the stages of the global AAM oscillation and the ENSO cycle. The strong similarity between the simulated and observed AAM series attests to the model's ability to realistically simulate the interannual response of the atmosphere to ENSO SST anomalies.

  13. Physics Constrained Stochastic-Statistical Models for Extended Range Environmental Prediction

    Science.gov (United States)

    2014-09-30

    Ocean through a low- dimensional family of spatiotemporal modes extracted from global circulation model (GCM) output and satellite observations using...analysis in [1] to cover the whole of the Arctic , and to include both ocean and atmosphere variables [sea surface temperature (SST) and sea level...818) 393-3379 email: waliser@ucla.edu Informal CO-P. I. Dimitrios Giannakis Department of Mathematics and Center for Atmosphere Ocean Science

  14. A coupled carbon and plant hydraulic model to predict ecosystem carbon and water flux responses to disturbance and environmental change

    Science.gov (United States)

    Mackay, D. S.; Ewers, B. E.; Roberts, D. E.; McDowell, N. G.; Pendall, E.; Frank, J. M.; Reed, D. E.; Massman, W. J.; Mitra, B.

    2011-12-01

    Changing climate drivers including temperature, humidity, precipitation, and carbon dioxide (CO2) concentrations directly control land surface exchanges of CO2 and water. In a profound way these responses are modulated by disturbances that are driven by or exacerbated by climate change. Predicting these changes is challenging given that the feedbacks between environmental controls, disturbances, and fluxes are complex. Flux data in areas of bark beetle outbreaks in the western U.S.A. show differential declines in carbon and water flux in response to the occlusion of xylem by associated fungi. For example, bark beetle infestation at the GLEES AmeriFlux site manifested in a decline in summer water use efficiency to 60% in the year after peak infestation compared to previous years, and no recovery of carbon uptake following a period of high vapor pressure deficit. This points to complex feedbacks between disturbance and differential ecosystem reaction and relaxation responses. Theory based on plant hydraulics and extending to include links to carbon storage and exhaustion has potential for explaining these dynamics with simple, yet rigorous models. In this spirit we developed a coupled model that combines an existing model of canopy water and carbon flow, TREES [e.g., Loranty et al., 2010], with the Sperry et al., [1998] plant hydraulic model. The new model simultaneously solves carbon uptake and losses along with plant hydraulics, and allows for testing specific hypotheses on feedbacks between xylem dysfunction, stomatal and non-stomatal controls on photosynthesis and carbon allocation, and autotrophic and heterotrophic respiration. These are constrained through gas exchange, root vulnerability to cavitation, sap flux, and eddy covariance data in a novel model complexity-testing framework. Our analysis focuses on an ecosystem gradient spanning sagebrush to subalpine forests. Our modeling results support hypotheses on feedbacks between hydraulic dysfunction and 1) non

  15. Environmental Modeling Center

    Data.gov (United States)

    Federal Laboratory Consortium — The Environmental Modeling Center provides the computational tools to perform geostatistical analysis, to model ground water and atmospheric releases for comparison...

  16. The potential use of Chernobyl fallout data to test and evaluate the predictions of environmental radiological assessment models

    Energy Technology Data Exchange (ETDEWEB)

    Richmond, C.R.; Hoffman, F.O.; Blaylock, B.G.; Eckerman, K.F.; Lesslie, P.A.; Miller, C.W.; Ng, Y.C.; Till, J.E.

    1988-06-01

    The objectives of the Model Validation Committee were to collaborate with US and foreign scientists to collect, manage, and evaluate data for identifying critical research issues and data needs to support an integrated assessment of the Chernobyl nuclear accident; test environmental transport, human dosimetric, and health effects models against measured data to determine their efficacy in guiding decisions on protective actions and in estimating exposures to populations and individuals following a nuclear accident; and apply Chernobyl data to quantifications of key processes governing the environmental transport, fate and effects of radionuclides and other trace substances. 55 refs.

  17. Integration of nitrogen dynamics into the Noah-MP land surface model v1.1 for climate and environmental predictions

    Science.gov (United States)

    Cai, X.; Yang, Z.-L.; Fisher, J. B.; Zhang, X.; Barlage, M.; Chen, F.

    2016-01-01

    Climate and terrestrial biosphere models consider nitrogen an important factor in limiting plant carbon uptake, while operational environmental models view nitrogen as the leading pollutant causing eutrophication in water bodies. The community Noah land surface model with multi-parameterization options (Noah-MP) is unique in that it is the next-generation land surface model for the Weather Research and Forecasting meteorological model and for the operational weather/climate models in the National Centers for Environmental Prediction. In this study, we add a capability to Noah-MP to simulate nitrogen dynamics by coupling the Fixation and Uptake of Nitrogen (FUN) plant model and the Soil and Water Assessment Tool (SWAT) soil nitrogen dynamics. This model development incorporates FUN's state-of-the-art concept of carbon cost theory and SWAT's strength in representing the impacts of agricultural management on the nitrogen cycle. Parameterizations for direct root and mycorrhizal-associated nitrogen uptake, leaf retranslocation, and symbiotic biological nitrogen fixation are employed from FUN, while parameterizations for nitrogen mineralization, nitrification, immobilization, volatilization, atmospheric deposition, and leaching are based on SWAT. The coupled model is then evaluated at the Kellogg Biological Station - a Long Term Ecological Research site within the US Corn Belt. Results show that the model performs well in capturing the major nitrogen state/flux variables (e.g., soil nitrate and nitrate leaching). Furthermore, the addition of nitrogen dynamics improves the modeling of net primary productivity and evapotranspiration. The model improvement is expected to advance the capability of Noah-MP to simultaneously predict weather and water quality in fully coupled Earth system models.

  18. Scenario prediction of emerging coastal city using CA modeling under different environmental conditions: a case study of Lingang New City, China.

    Science.gov (United States)

    Feng, Yongjiu; Liu, Yan

    2016-09-01

    The world's coastal regions are experiencing rapid urbanization coupled with increased risk of ecological damage and storm surge related to global climate and sea level rising. This urban development issue is particularly important in China, where many emerging coastal cities are being developed. Lingang New City, southeast of Shanghai, is an excellent example of a coastal city that is increasingly vulnerable to environmental change. Sustainable urban development requires planning that classifies and allocates coastal lands using objective procedures that incorporate changing environmental conditions. In this paper, we applied cellular automata (CA) modeling based on self-adaptive genetic algorithm (SAGA) to predict future scenarios and explore sustainable urban development options for Lingang. The CA model was calibrated using the 2005 initial status, 2015 final status, and a set of spatial variables. We implemented specific ecological and environmental conditions as spatial constraints for the model and predicted four 2030 scenarios: (a) an urban planning-oriented Plan Scenario; (b) an ecosystem protection-oriented Eco Scenario; (c) a storm surge-affected Storm Scenario; and (d) a scenario incorporating both ecosystem protection and the effects of storm surge, called the Ecostorm Scenario. The Plan Scenario has been taken as the baseline, with the Lingang urban area increasing from 45.8 km(2) in 2015 to 66.8 km(2) in 2030, accounting for 23.9 % of the entire study area. The simulated urban land size of the Plan Scenario in 2030 was taken as the target to accommodate the projected population increase in this city, which was then applied in the remaining three development scenarios. We used CA modeling to reallocate the urban cells to other unconstrained areas in response to changing spatial constraints. Our predictions should be helpful not only in assessing and adjusting the urban planning schemes for Lingang but also for evaluating urban planning in coastal

  19. Mathematical Model Developed for Environmental Samples: Prediction of GC/MS Dioxin TEQ from XDS-CALUX Bioassay Data

    Science.gov (United States)

    Brown, David J.; Orelien, Jean; Gordon, John D.; Chu, Andrew C.; Chu, Michael D.; Nakamura, Masafumi; Handa, Hiroshi; Kayama, Fujio; Denison, Michael S.; Clark, George C.

    2010-01-01

    Remediation of hazardous waste sites requires efficient and cost-effective methods to assess the extent of contamination by toxic substances including dioxin-like chemicals. Traditionally, dioxin-like contamination has been assessed by gas chromatography/high-resolution mass spectrometry (GC/MS) analysis for specific polychlorinated dibenzo-p-dioxins, dibenzofurans, and biphenyl congeners. Toxic equivalency factors for these congeners are then used to estimate the overall dioxin toxic equivalency (TEQ) of complex mixtures found in samples. The XDS-CALUX bioassay estimates contamination by dioxin-like chemicals in a sample extract by measuring expression of a sensitive reporter gene in genetically engineered cells. The output of the XDS-CALUX assay is a CALUX-TEQ value, calibrated based on TCDD standards. Soil samples taken from a variety of hazardous waste sites were measured using the XDS-CALUX bioassay and GC/MS. TEQ and CALUX-TEQ from these methods were compared, and a mathematical model was developed describing the relationship between these two data sets: log(TEQ) = 0.654 × log(CALUX-TEQ) + 0.058-(log(CALUX-TEQ))2. Applying this equation to these samples showed that predicted and GC/MS measured TEQ values strongly correlate (R2 = 0.876) and that TEQ values predicted from CALUX-TEQ were on average nearly identical to the GC/MS-TEQ. The ability of XDS-CALUX bioassay data to predict GC/MS-derived TEQ data should make this procedure useful in risk assessment and management decisions. PMID:17626436

  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. Testing and validating environmental models

    Science.gov (United States)

    Kirchner, J.W.; Hooper, R.P.; Kendall, C.; Neal, C.; Leavesley, G.

    1996-01-01

    Generally accepted standards for testing and validating ecosystem models would benefit both modellers and model users. Universally applicable test procedures are difficult to prescribe, given the diversity of modelling approaches and the many uses for models. However, the generally accepted scientific principles of documentation and disclosure provide a useful framework for devising general standards for model evaluation. Adequately documenting model tests requires explicit performance criteria, and explicit benchmarks against which model performance is compared. A model's validity, reliability, and accuracy can be most meaningfully judged by explicit comparison against the available alternatives. In contrast, current practice is often characterized by vague, subjective claims that model predictions show 'acceptable' agreement with data; such claims provide little basis for choosing among alternative models. Strict model tests (those that invalid models are unlikely to pass) are the only ones capable of convincing rational skeptics that a model is probably valid. However, 'false positive' rates as low as 10% can substantially erode the power of validation tests, making them insufficiently strict to convince rational skeptics. Validation tests are often undermined by excessive parameter calibration and overuse of ad hoc model features. Tests are often also divorced from the conditions under which a model will be used, particularly when it is designed to forecast beyond the range of historical experience. In such situations, data from laboratory and field manipulation experiments can provide particularly effective tests, because one can create experimental conditions quite different from historical data, and because experimental data can provide a more precisely defined 'target' for the model to hit. We present a simple demonstration showing that the two most common methods for comparing model predictions to environmental time series (plotting model time series

  2. How well do cognitive and environmental variables predict active commuting?

    Directory of Open Access Journals (Sweden)

    Godin Gaston

    2009-03-01

    Full Text Available Abstract Background In recent years, there has been growing interest in theoretical studies integrating cognitions and environmental variables in the prediction of behaviour related to the obesity epidemic. This is the approach adopted in the present study in reference to the theory of planned behaviour. More precisely, the aim of this study was to determine the contribution of cognitive and environmental variables in the prediction of active commuting to get to and from work or school. Methods A prospective study was carried out with 130 undergraduate and graduate students (93 females; 37 males. Environmental, cognitive and socio-demographic variables were evaluated at baseline by questionnaire. Two weeks later, active commuting (walking/bicycling to get to and from work or school was self-reported by questionnaire. Hierarchical multiple regression analyses were performed to predict intention and behaviour. Results The model predicting behaviour based on cognitive variables explained more variance than the model based on environmental variables (37.4% versus 26.8%; Z = 3.86, p p p Conclusion The results showed that cognitive variables play a more important role than environmental variables in predicting and explaining active commuting. When environmental variables were significant, they were mediated by cognitive variables. Therefore, individual cognitions should remain one of the main focuses of interventions promoting active commuting among undergraduate and graduate students.

  3. Tox21Challenge to build predictive models of nuclear receptor and stress response pathways as mediated by exposure to environmental chemicals and drugs

    Directory of Open Access Journals (Sweden)

    Ruili eHuang

    2016-01-01

    Full Text Available Tens of thousands of chemicals with poorly understood biological properties are released into the environment each day. High-throughput screening (HTS is potentially a more efficient and cost-effective alternative to traditional toxicity tests. Using HTS, one can profile chemicals for potential adverse effects and prioritize a manageable number for more in-depth testing. Importantly, it can provide clues to mechanism of toxicity. The Tox21 program has generated >50 million quantitative high-throughput screening (qHTS data points. A library of several thousands of compounds, including environmental chemicals and drugs, is screened against a panel of nuclear receptor and stress response pathway assays. The National Center for Advancing Translational Sciences (NCATS has organized an International data challenge in order to crowd-source data and build predictive toxicity models. This Challenge asks a crowd of researchers to use these data to elucidate the extent to which the interference of biochemical and cellular pathways by compounds can be inferred from chemical structure data. The data generated against the Tox21 library served as the training set for this modeling Challenge. The competition attracted participants from 18 different countries to develop computational models aimed at better predicting chemical toxicity. The winning models from nearly 400 model submissions all achieved >80% accuracy. Several models exceeded 90% accuracy, which was measured by area under the receiver operating characteristic curve (AUC-ROC. Combining the winning models with the knowledge already gained from Tox21 screening data are expected to improve the community’s ability to prioritize novel chemicals with respect to potential human health concern.

  4. Environmental forecasting and turbulence modeling

    Science.gov (United States)

    Hunt, J. C. R.

    This review describes the fundamental assumptions and current methodologies of the two main kinds of environmental forecast; the first is valid for a limited period of time into the future and over a limited space-time ‘target’, and is largely determined by the initial and preceding state of the environment, such as the weather or pollution levels, up to the time when the forecast is issued and by its state at the edges of the region being considered; the second kind provides statistical information over long periods of time and/or over large space-time targets, so that they only depend on the statistical averages of the initial and ‘edge’ conditions. Environmental forecasts depend on the various ways that models are constructed. These range from those based on the ‘reductionist’ methodology (i.e., the combination of separate, scientifically based, models for the relevant processes) to those based on statistical methodologies, using a mixture of data and scientifically based empirical modeling. These are, as a rule, focused on specific quantities required for the forecast. The persistence and predictability of events associated with environmental and turbulent flows and the reasons for variation in the accuracy of their forecasts (of the first and second kinds) are now better understood and better modeled. This has partly resulted from using analogous results of disordered chaotic systems, and using the techniques of calculating ensembles of realizations, ideally involving several different models, so as to incorporate in the probabilistic forecasts a wider range of possible events. The rationale for such an approach needs to be developed. However, other insights have resulted from the recognition of the ordered, though randomly occurring, nature of the persistent motions in these flows, whose scales range from those of synoptic weather patterns (whether storms or ‘blocked’ anticyclones) to small scale vortices. These eigen states can be predicted

  5. Uncertainty quantification for environmental models

    Science.gov (United States)

    Hill, Mary C.; Lu, Dan; Kavetski, Dmitri; Clark, Martyn P.; Ye, Ming

    2012-01-01

    Environmental models are used to evaluate the fate of fertilizers in agricultural settings (including soil denitrification), the degradation of hydrocarbons at spill sites, and water supply for people and ecosystems in small to large basins and cities—to mention but a few applications of these models. They also play a role in understanding and diagnosing potential environmental impacts of global climate change. The models are typically mildly to extremely nonlinear. The persistent demand for enhanced dynamics and resolution to improve model realism [17] means that lengthy individual model execution times will remain common, notwithstanding continued enhancements in computer power. In addition, high-dimensional parameter spaces are often defined, which increases the number of model runs required to quantify uncertainty [2]. Some environmental modeling projects have access to extensive funding and computational resources; many do not. The many recent studies of uncertainty quantification in environmental model predictions have focused on uncertainties related to data error and sparsity of data, expert judgment expressed mathematically through prior information, poorly known parameter values, and model structure (see, for example, [1,7,9,10,13,18]). Approaches for quantifying uncertainty include frequentist (potentially with prior information [7,9]), Bayesian [13,18,19], and likelihood-based. A few of the numerous methods, including some sensitivity and inverse methods with consequences for understanding and quantifying uncertainty, are as follows: Bayesian hierarchical modeling and Bayesian model averaging; single-objective optimization with error-based weighting [7] and multi-objective optimization [3]; methods based on local derivatives [2,7,10]; screening methods like OAT (one at a time) and the method of Morris [14]; FAST (Fourier amplitude sensitivity testing) [14]; the Sobol' method [14]; randomized maximum likelihood [10]; Markov chain Monte Carlo (MCMC) [10

  6. Predictive models in urology.

    Science.gov (United States)

    Cestari, Andrea

    2013-01-01

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

  7. Building dynamic spatial environmental models

    NARCIS (Netherlands)

    Karssenberg, D.J.

    2003-01-01

    An environmental model is a representation or imitation of complex natural phenomena that can be discerned by human cognitive processes. This thesis deals with the type of environmental models referred to as dynamic spatial environmental models. The word ‘spatial’ refers to the geographic domain whi

  8. MODEL PREDICTIVE CONTROL FUNDAMENTALS

    African Journals Online (AJOL)

    2012-07-02

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

  9. Environmental Light and Its Relationship with Electromagnetic Resonances of Biomolecular Interactions, as Predicted by the Resonant Recognition Model

    Directory of Open Access Journals (Sweden)

    Irena Cosic

    2016-06-01

    Full Text Available The meaning and influence of light to biomolecular interactions, and consequently to health, has been analyzed using the Resonant Recognition Model (RRM. The RRM proposes that biological processes/interactions are based on electromagnetic resonances between interacting biomolecules at specific electromagnetic frequencies within the infra-red, visible and ultra-violet frequency ranges, where each interaction can be identified by the certain frequency critical for resonant activation of specific biological activities of proteins and DNA. We found that: (1 the various biological interactions could be grouped according to their resonant frequency into super families of these functions, enabling simpler analyses of these interactions and consequently analyses of influence of electromagnetic frequencies to health; (2 the RRM spectrum of all analyzed biological functions/interactions is the same as the spectrum of the sun light on the Earth, which is in accordance with fact that life is sustained by the sun light; (3 the water is transparent to RRM frequencies, enabling proteins and DNA to interact without loss of energy; (4 the spectrum of some artificial sources of light, as opposed to the sun light, do not cover the whole RRM spectrum, causing concerns for disturbance to some biological functions and consequently we speculate that it can influence health.

  10. UK Environmental Prediction - integration and evaluation at the convective scale

    Science.gov (United States)

    Fallmann, Joachim; Lewis, Huw; Castillo, Juan Manuel; Pearson, David; Harris, Chris; Saulter, Andy; Bricheno, Lucy; Blyth, Eleanor

    2016-04-01

    It has long been understood that accurate prediction and warning of the impacts of severe weather requires an integrated approach to forecasting. For example, high impact weather is typically manifested through various interactions and feedbacks between different components of the Earth System. Damaging high winds can lead to significant damage from the large waves and storm surge along coastlines. The impact of intense rainfall can be translated through saturated soils and land surface processes, high river flows and flooding inland. The substantial impacts on individuals, businesses and infrastructure of such events indicate a pressing need to understand better the value that might be delivered through more integrated environmental prediction. To address this need, the Met Office, NERC Centre for Ecology & Hydrology and NERC National Oceanography Centre have begun to develop the foundations of a coupled high resolution probabilistic forecast system for the UK at km-scale. This links together existing model components of the atmosphere, coastal ocean, land surface and hydrology. Our initial focus has been on a 2-year Prototype project to demonstrate the UK coupled prediction concept in research mode. This presentation will provide an update on UK environmental prediction activities. We will present the results from the initial implementation of an atmosphere-land-ocean coupled system and discuss progress and initial results from further development to integrate wave interactions. We will discuss future directions and opportunities for collaboration in environmental prediction, and the challenges to realise the potential of integrated regional coupled forecasting for improving predictions and applications.

  11. Modeling and Simulating Environmental Effects

    OpenAIRE

    Guest, Peter S.; Murphree, Tom; Frederickson, Paul A.; Guest, Arlene A.

    2012-01-01

    MOVES Research & Education Systems Seminar: Presentation; Session 4: Collaborative NWDC/NPS M&S Research; Moderator: Curtis Blais; Modeling and Simulating Environmental Effects; speakers: Peter Guest, Paul Frederickson & Tom Murphree Environmental Effects Group

  12. Nominal model predictive control

    OpenAIRE

    Grüne, Lars

    2013-01-01

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

  13. Nominal Model Predictive Control

    OpenAIRE

    Grüne, Lars

    2014-01-01

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

  14. Candidate Prediction Models and Methods

    DEFF Research Database (Denmark)

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

    2005-01-01

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

  15. Application of Air Prediction Model and Online Monitoring in Environmental Air Quality Prediction%论大气预测模型与在线监测在环境空气质量预报中的应用

    Institute of Scientific and Technical Information of China (English)

    刘鲁新

    2013-01-01

    With the development of society and the development of industry, the worsening ecological environment poses a serious threat to human survival and development. Therefore, we must monitor the environment by certain means. The traditional environmental detection has not been able to meet the requirements for modernization and informatization. Therefore, the current monitoring system uses modern technology. This paper studies the present situation of research and development status and the problems in on-line environmental air quality monitoring at home and abroad. In the end, it presents some common atmospheric environment quality monitoring model. It puts forward the design scheme of atmospheric environmental quality prediction system by the third generation air forecasting model, combined with geographic information system software.%  随着社会以及工业的发展,日益恶化的生态环境严重威胁人类的生存和发展。因此,必须采用一定的手段对环境进行监测。传统的环境检测已经不能够满足现代化以及信息化的要求,因此,目前的监测系统都应用了现代化技术。本文研究了大气环境质量在线监测预报的国外发展现状、国内的研究现状及存在的问题,对一些常用的大气环境质量预测模型进行了介绍分析,最后选用第三代大气预测模型结合地理信息系统软件,提出了大气环境质量预报系统设计方案。

  16. 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.

  17. Predictions by the multimedia environmental fate model SimpleBox compared to field data: Intermedia concentration ratios of two phthalate esters

    NARCIS (Netherlands)

    Struijs J; Peijnenburg WJGM; ECO

    2003-01-01

    The multimedia environmental fate model SimpleBox is applied to compute steady-state concentration ratios with the aim to harmonize environmetal quality objectives of air, water, sediment and soil. In 1995 the Dutch Health Council recommended validation of the model. Several activities were initiate

  18. 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...

  19. UK Environmental Prediction - integration and evaluation at the convective scale

    Science.gov (United States)

    Fallmann, Joachim; Lewis, Huw; Castillo, Juan Manuel; Pearson, David; Harris, Chris; Saulter, Andy; Bricheno, Lucy; Blyth, Eleanor

    2016-04-01

    Traditionally, the simulation of regional ocean, wave and atmosphere components of the Earth System have been considered separately, with some information on other components provided by means of boundary or forcing conditions. More recently, the potential value of a more integrated approach, as required for global climate and Earth System prediction, for regional short-term applications has begun to gain increasing research effort. In the UK, this activity is motivated by an understanding that accurate prediction and warning of the impacts of severe weather requires an integrated approach to forecasting. The substantial impacts on individuals, businesses and infrastructure of such events indicate a pressing need to understand better the value that might be delivered through more integrated environmental prediction. To address this need, the Met Office, NERC Centre for Ecology & Hydrology and NERC National Oceanography Centre have begun to develop the foundations of a coupled high resolution probabilistic forecast system for the UK at km-scale. This links together existing model components of the atmosphere, coastal ocean, land surface and hydrology. Our initial focus has been on a 2-year Prototype project to demonstrate the UK coupled prediction concept in research mode. This presentation will provide an update on UK environmental prediction activities. We will present the results from the initial implementation of an atmosphere-land-ocean coupled system, including a new eddy-permitting resolution ocean component, and discuss progress and initial results from further development to integrate wave interactions in this relatively high resolution system. We will discuss future directions and opportunities for collaboration in environmental prediction, and the challenges to realise the potential of integrated regional coupled forecasting for improving predictions and applications.

  20. Risk terrain modeling predicts child maltreatment.

    Science.gov (United States)

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

    2016-12-01

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

  1. 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

  2. Deconstructing environmental predictability: seasonality, environmental colour and the biogeography of marine life histories.

    Science.gov (United States)

    Marshall, Dustin J; Burgess, Scott C

    2015-02-01

    Environmental predictability is predicted to shape the evolution of life histories. Two key types of environmental predictability, seasonality and environmental colour, may influence life-history evolution independently but formal considerations of both and how they relate to life history are exceedingly rare. Here, in a global biogeographical analysis of over 800 marine invertebrates, we explore the relationships between both forms of environmental predictability and three fundamental life-history traits: location of larval development (aplanktonic vs. planktonic), larval developmental mode (feeding vs. non-feeding) and offspring size. We found that both dispersal potential and offspring size related to environmental predictability, but the relationships depended on both the environmental factor as well as the type of predictability. Environments that were more seasonal in food availability had a higher prevalence of species with a planktonic larval stage. Future studies should consider both types of environmental predictability as each can strongly affect life-history evolution.

  3. A Maintainability Prediction Method Considering Environmental Impacts and Cost

    Institute of Scientific and Technical Information of China (English)

    2002-01-01

    Maintainability prediction is one kind of primary maintainability action. Design deficiency would be found through predicting maintainability parameters under certain conditions. Now a maintainability prediction method that mainly considers maintenance time or maintenance man-hour is a kind of prediction method with a single index. With increasing product complexity and people's environmental consciousness, more attention is paid to environment impacts and maintenance cost or resource consumption in the maintenance process. It is necessary for a maintainability prediction method that can predict maintenance cost and maintenance environmental impacts. A new maintainability prediction method is presented in this paper based on analyzing existing maintainability prediction methods.The method is MABTCE (maintenance activity based timing/costing/environment impact assessment )and can predict maintenance time, maintenance costing and maintenance environmental impacts andthen improve maintainability design with prediction results.

  4. Environmental relevance of laboratory-derived kinetic models to predict trace metal bioaccumulation in gammarids: Field experimentation at a large spatial scale (France).

    Science.gov (United States)

    Urien, N; Lebrun, J D; Fechner, L C; Uher, E; François, A; Quéau, H; Coquery, M; Chaumot, A; Geffard, O

    2016-05-15

    Kinetic models have become established tools for describing trace metal bioaccumulation in aquatic organisms and offer a promising approach for linking water contamination to trace metal bioaccumulation in biota. Nevertheless, models are based on laboratory-derived kinetic parameters, and the question of their relevance to predict trace metal bioaccumulation in the field is poorly addressed. In the present study, we propose to assess the capacity of kinetic models to predict trace metal bioaccumulation in gammarids in the field at a wide spatial scale. The field validation consisted of measuring dissolved Cd, Cu, Ni and Pb concentrations in the water column at 141 sites in France, running the models with laboratory-derived kinetic parameters, and comparing model predictions and measurements of trace metal concentrations in gammarids caged for 7 days to the same sites. We observed that gammarids poorly accumulated Cu showing the limited relevance of that species to monitor Cu contamination. Therefore, Cu was not considered for model predictions. In contrast, gammarids significantly accumulated Pb, Cd, and Ni over a wide range of exposure concentrations. These results highlight the relevance of using gammarids for active biomonitoring to detect spatial trends of bioavailable Pb, Cd, and Ni contamination in freshwaters. The best agreements between model predictions and field measurements were observed for Cd with 71% of good estimations (i.e. field measurements were predicted within a factor of two), which highlighted the potential for kinetic models to link Cd contamination to bioaccumulation in the field. The poorest agreements were observed for Ni and Pb (39% and 48% of good estimations, respectively). However, models developed for Ni, Pb, and to a lesser extent for Cd, globally underestimated bioaccumulation in caged gammarids. These results showed that the link between trace metal concentration in water and in biota remains complex, and underlined the limits of

  5. Modelling environmental dynamics. Advances in goematic solutions

    Energy Technology Data Exchange (ETDEWEB)

    Paegelow, Martin [Toulouse-2 Univ., 31 (France). GEODE UMR 5602 CNRS; Camacho Olmedo, Maria Teresa (eds.) [Granada Univ (Spain). Dpto. de Analisis Geografico Regional y Geografia Fisica

    2008-07-01

    Modelling environmental dynamics is critical to understanding and predicting the evolution of the environment in response to the large number of influences including urbanisation, climate change and deforestation. Simulation and modelling provide support for decision making in environmental management. The first chapter introduces terminology and provides an overview of methodological modelling approaches which may be applied to environmental and complex dynamics. Based on this introduction this book illustrates various models applied to a large variety of themes: deforestation in tropical regions, fire risk, natural reforestation in European mountains, agriculture, biodiversity, urbanism, climate change and land management for decision support, etc. These case studies, provided by a large international spectrum of researchers and presented in a uniform structure, focus particularly on methods and model validation so that this book is not only aimed at researchers and graduates but also at professionals. (orig.)

  6. Integrating hydrology within a fully coupled environmental prediction system

    Science.gov (United States)

    Best, Martin; Lewis, Huw; Ashton, Heather; Blyth, Eleanor; Martinez, Alberto

    2017-04-01

    Historically the hydrological community and the community developing the land surface component of atmospheric models have both been tasked with representing the terrestrial hydrological cycle, but have focused on different ends, namely streamflow and evaporation respectively. To date the lack of computational resources and representative observations have limited the integration of the skills within these two communities. However, this is no longer the case. In addition, the drive toward fully integrated high resolution environmental prediction systems, coupling atmosphere, land and ocean on regional domains, requires an accurate representation for all aspects of terrestrial hydrology. Hence a new focus is emerging to integrate improved hydrological processes within the land surface components of atmospheric models. The UK Environmental Prediction (UKEP) project is a research experiment aimed at understanding the potential benefits for detailed environmental forecasting from a fully coupled atmosphere/land/ocean system at km-scale resolution for the UK. The prototype model utilises the Joint UK Land Environment Simulator (JULES) as its land surface component, coupled to the RFM river flow model. Although JULES has been previously used for climate studies that close the global water cycle, the JULES/RFM system has not been comprehensively evaluated for its ability to simulate river discharge. In this study we attempt some initial evaluation of the JULES/RFM system for all aspects of the terrestrial hydrological cycle, including evaporation, soil moisture and streamflow. In addition, comparisons are made between the results from the fully coupled environmental prediction system and stand alone JULES/RFM simulations forced by atmospheric driving data from the UK weather forecasting model. This provides an opportunity to assess the impact of fully coupled versus a one way coupled response for terrestrial hydrology. Finally we consider the potential for coupling JULES

  7. Performance Evaluation of FAO Model for Prediction of Yield Production, Soil Water and Solute Balance under Environmental Stresses (Case Study Winter Wheat

    Directory of Open Access Journals (Sweden)

    V. Rezaverdinejad

    2014-11-01

    Full Text Available In this study, the FAO agro-hydrological model was investigated and evaluated to predict of yield production, soil water and solute balance by winter wheat field data under water and salt stresses. For this purpose, a field experimental was conducted with three salinity levels of irrigation water include: S1, S2 and S3 corresponding to 1.4, 4.5 and 9.6 dS/m, respectively, and four irrigation depth levels include: I1, I2, I3 and I4 corresponding to 50, 75, 100 and 125% of crop water requirement, respectively, for two varieties of winter wheat: Roshan and Ghods, with three replications in an experimental farm of Birjand University for 1384-85 period. Based on results, the mean relative error of the model in yield prediction for Roshan and Ghods were obtained 9.2 and 26.1%, respectively. The maximum error of yield prediction in both of the Roshan and Ghods varieties, were obtained for S1I1, S2I1 and S3I1 treatments. The relative error of Roshan yield prediction for S1I1, S2I1 and S3I1 were calculated 20.0, 28.1 and 26.6%, respectively and for Ghods variety were calculated 61, 94.5 and 99.9%, respectively, that indicated a significant over estimate error under higher water stress. The mean relative error of model for all treatments, in prediction of soil water depletion and electrical conductivity of soil saturation extract, were calculated 7.1 and 5.8%, respectively, that indicated proper accuracy of model in prediction of soil water content and soil salinity.

  8. Computer Model Locates Environmental Hazards

    Science.gov (United States)

    2008-01-01

    Catherine Huybrechts Burton founded San Francisco-based Endpoint Environmental (2E) LLC in 2005 while she was a student intern and project manager at Ames Research Center with NASA's DEVELOP program. The 2E team created the Tire Identification from Reflectance model, which algorithmically processes satellite images using turnkey technology to retain only the darkest parts of an image. This model allows 2E to locate piles of rubber tires, which often are stockpiled illegally and cause hazardous environmental conditions and fires.

  9. Model evaluation methodology applicable to environmental assessment models

    Energy Technology Data Exchange (ETDEWEB)

    Shaeffer, D.L.

    1979-08-01

    A model evaluation methodology is presented to provide a systematic framework within which the adequacy of environmental assessment models might be examined. The necessity for such a tool is motivated by the widespread use of models for predicting the environmental consequences of various human activities and by the reliance on these model predictions for deciding whether a particular activity requires the deployment of costly control measures. Consequently, the uncertainty associated with prediction must be established for the use of such models. The methodology presented here consists of six major tasks: model examination, algorithm examination, data evaluation, sensitivity analyses, validation studies, and code comparison. This methodology is presented in the form of a flowchart to show the logical interrelatedness of the various tasks. Emphasis has been placed on identifying those parameters which are most important in determining the predictive outputs of a model. Importance has been attached to the process of collecting quality data. A method has been developed for analyzing multiplicative chain models when the input parameters are statistically independent and lognormally distributed. Latin hypercube sampling has been offered as a promising candidate for doing sensitivity analyses. Several different ways of viewing the validity of a model have been presented. Criteria are presented for selecting models for environmental assessment purposes.

  10. Predictive Models for Music

    OpenAIRE

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

    2008-01-01

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

  11. Predicting effects of environmental change on a migratory herbivore

    Science.gov (United States)

    Stillman, R A; Wood, K A; Gilkerson, Whelan; Elkinton, E; Black, J. M.; Ward, David H.; Petrie, M.

    2015-01-01

    for which birds were disturbed. We discuss the consequences of these predictions for Black Brant conservation. A wide range of migratory species responses are expected in response to environmental change. Process-based models are potential tools to predict such responses and understand the mechanisms which underpin them.

  12. Integrated Environmental Assessment Modelling

    Energy Technology Data Exchange (ETDEWEB)

    Guardanz, R.; Gimeno, B. S.; Bermejo, V.; Elvira, S.; Martin, F.; Palacios, M.; Rodriguez, E.; Donaire, I. [Ciemat, Madrid (Spain)

    2000-07-01

    This report describes the results of the Spanish participation in the project Coupling CORINAIR data to cost-effect emission reduction strategies based on critical threshold. (EU/LIFE97/ENV/FIN/336). The subproject has focused on three tasks. Develop tools to improve knowledge on the spatial and temporal details of emissions of air pollutants in Spain. Exploit existing experimental information on plant response to air pollutants in temperate ecosystem and Integrate these findings in a modelling framework that can asses with more accuracy the impact of air pollutants to temperate ecosystems. The results obtained during the execution of this project have significantly improved the models of the impact of alternative emission control strategies on ecosystems and crops in the Iberian Peninsula. (Author) 375 refs.

  13. Advancing Environmental Prediction Capabilities for the Polar Regions and Beyond during The Year of Polar Prediction

    Science.gov (United States)

    Werner, Kirstin; Goessling, Helge; Hoke, Winfried; Kirchhoff, Katharina; Jung, Thomas

    2017-04-01

    Environmental changes in polar regions open up new opportunities for economic and societal operations such as vessel traffic related to scientific, fishery and tourism activities, and in the case of the Arctic also enhanced resource development. The availability of current and accurate weather and environmental information and forecasts will therefore play an increasingly important role in aiding risk reduction and safety management around the poles. The Year of Polar Prediction (YOPP) has been established by the World Meteorological Organization's World Weather Research Programme as the key activity of the ten-year Polar Prediction Project (PPP; see more on www.polarprediction.net). YOPP is an internationally coordinated initiative to significantly advance our environmental prediction capabilities for the polar regions and beyond, supporting improved weather and climate services. Scheduled to take place from mid-2017 to mid-2019, the YOPP core phase covers an extended period of intensive observing, modelling, prediction, verification, user-engagement and education activities in the Arctic and Antarctic, on a wide range of time scales from hours to seasons. The Year of Polar Prediction will entail periods of enhanced observational and modelling campaigns in both polar regions. With the purpose to close the gaps in the conventional polar observing systems in regions where the observation network is sparse, routine observations will be enhanced during Special Observing Periods for an extended period of time (several weeks) during YOPP. This will allow carrying out subsequent forecasting system experiments aimed at optimizing observing systems in the polar regions and providing insight into the impact of better polar observations on forecast skills in lower latitudes. With various activities and the involvement of a wide range of stakeholders, YOPP will contribute to the knowledge base needed to managing the opportunities and risks that come with polar climate change.

  14. Zephyr - the prediction models

    DEFF Research Database (Denmark)

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

    2001-01-01

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

  15. Environmental modeling framework invasiveness: analysis and implications

    Science.gov (United States)

    Environmental modeling frameworks support scientific model development by providing an Application Programming Interface (API) which model developers use to implement models. This paper presents results of an investigation on the framework invasiveness of environmental modeling frameworks. Invasiven...

  16. Modeling interfacial liquid layers on environmental ices

    Directory of Open Access Journals (Sweden)

    M. H. Kuo

    2011-09-01

    Full Text Available Interfacial layers on ice significantly influence air-ice chemical interactions. In solute-containing aqueous systems, a liquid brine may form upon freezing due to the exclusion of impurities from the ice crystal lattice coupled with freezing point depression in the concentrated brine. The brine may be segregated to the air-ice interface where it creates a surface layer, in micropockets, or at grain boundaries or triple junctions.

    We present a model for brines and their associated liquid layers in environmental ice systems that is valid over a wide range of temperatures and solute concentrations. The model is derived from fundamental equlibrium thermodynamics and takes into account nonideal solution behavior in the brine, partitioning of the solute into the ice matrix, and equilibration between the brine and the gas phase for volatile solutes. We find that these phenomena are important to consider when modeling brines in environmental ices, especially at low temperatures. We demonstrate its application for environmentally important volatile and nonvolatile solutes including NaCl, HCl, and HNO3. The model is compared to existing models and experimental data from literature where available. We also identify environmentally relevant regimes where brine is not predicted to exist, but the QLL may significantly impact air-ice chemical interactions. This model can be used to improve the representation of air-ice chemical interactions in polar atmospheric chemistry models.

  17. Alternative Testing Methods for Predicting Health Risk from Environmental Exposures

    Directory of Open Access Journals (Sweden)

    Annamaria Colacci

    2014-08-01

    Full Text Available Alternative methods to animal testing are considered as promising tools to support the prediction of toxicological risks from environmental exposure. Among the alternative testing methods, the cell transformation assay (CTA appears to be one of the most appropriate approaches to predict the carcinogenic properties of single chemicals, complex mixtures and environmental pollutants. The BALB/c 3T3 CTA shows a good degree of concordance with the in vivo rodent carcinogenesis tests. Whole-genome transcriptomic profiling is performed to identify genes that are transcriptionally regulated by different kinds of exposures. Its use in cell models representative of target organs may help in understanding the mode of action and predicting the risk for human health. Aiming at associating the environmental exposure to health-adverse outcomes, we used an integrated approach including the 3T3 CTA and transcriptomics on target cells, in order to evaluate the effects of airborne particulate matter (PM on toxicological complex endpoints. Organic extracts obtained from PM2.5 and PM1 samples were evaluated in the 3T3 CTA in order to identify effects possibly associated with different aerodynamic diameters or airborne chemical components. The effects of the PM2.5 extracts on human health were assessed by using whole-genome 44 K oligo-microarray slides. Statistical analysis by GeneSpring GX identified genes whose expression was modulated in response to the cell treatment. Then, modulated genes were associated with pathways, biological processes and diseases through an extensive biological analysis. Data derived from in vitro methods and omics techniques could be valuable for monitoring the exposure to toxicants, understanding the modes of action via exposure-associated gene expression patterns and to highlight the role of genes in key events related to adversity.

  18. Communication models in environmental health.

    Science.gov (United States)

    Guidotti, Tee L

    2013-01-01

    Communication models common in environmental health are not well represented in the literature on health communication. Risk communication is a systematic approach to conveying essential information about a specific environmental issue and a framework for thinking about community risk and the alternatives for dealing with it. Crisis communication is intended to provide essential information to people facing an emergency in order to mitigate its effects and to enable them to make appropriate decisions, and it is primarily used in emergency management. Corporate communication is intended to achieve a change in attitude or perception of an organization, and its role in environmental health is usually public relations or to rehabilitate a damaged reputation. Environmental health education is a more didactic approach to science education with respect to health and the environment. Social marketing uses conventional marketing methods to achieve a socially desirable purpose but is more heavily used in health promotion generally. Communication models and styles in environmental health are specialized to serve the needs of the field in communicating with the community. They are highly structured and executed in different ways but have in common a relative lack of emphasis on changing personal or lifestyle behavior compared with health promotion and public health in general and a tendency to emphasize content on specific environmental issues and decision frameworks for protecting oneself or the community through collective action.

  19. A predictive model of the effect of environmental factors on the occurrence of otters (Lutra lutra L. in Hungary

    Directory of Open Access Journals (Sweden)

    Ildikó Kemenes

    1995-12-01

    Full Text Available Abstract A survey of the distribution of otters (Lutra lutra L. in Hungary revealed that this species is common in most parts of the country where there appear to be suitable aquatic habitats. However, there were a large number of apparently "good" habitats where no otters were found. On the other hand, in some places where, based on a qualitative assessment, otters should not have been present, we still found signs of them. The only strictly and consistently limiting factor was heavy chemical pollution of the water which could not be assayed during the survey but was analysed based on data provided by the water authorities. These observations led us to employ a quantitative method which takes into account 3 scalable and 5 non-scalable variables of the environment and their relationships which might influence the occurrence of otters. The technique was based on a non-parametric multiple regression method specifically developed for use on PCs. This so called logistic regression model is useful for investigating the relationships between a binary dependent variable and a set of categorical independent variables. We recorded the presence (1 or absence (0 of signs of otters as well as the water depth, steepness of the bank, density of the bank vegetation and the presence or absence of various disturbance factors, such as agricultural use of the water bank, obvious signs of pollution of the water, etc., at 369 sites in Hungary. The three former environmental variables were scaled, whereas the disturbance factors were each assigned a value of either 0 or 1 (0 = absent, 1 = present. The analysis has shown that this method can be used to characterise particular combinations of factors at which otters were most likely to occur and even predictions can be made on the probability of finding otters at particular places with a known combination of these environmental factors. Besides its theoretical importance, this method is a very

  20. Confidence scores for prediction models

    DEFF Research Database (Denmark)

    Gerds, Thomas Alexander; van de Wiel, MA

    2011-01-01

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

  1. How the cerebral serotonin homeostasis predicts environmental changes: a model to explain seasonal changes of brain 5-HTT as intermediate phenotype of the 5-HTTLPR.

    Science.gov (United States)

    Kalbitzer, Jan; Kalbitzer, Urs; Knudsen, Gitte Moos; Cumming, Paul; Heinz, Andreas

    2013-12-01

    Molecular imaging studies with positron emission tomography have revealed that the availability of serotonin transporter (5-HTT) in the human brain fluctuates over the course of the year. This effect is most pronounced in carriers of the short allele of the 5-HTT promoter region (5-HTTLPR), which has in several previous studies been linked to an increased risk to develop mood disorders. We argue that long-lasting fluctuations in the cerebral serotonin transmission, which is regulated via the 5-HTT, are responsible for mediating responses to environmental changes based on an assessment of the expected "safety" of the environment; this response is obtained in part through serotonergic modulation of the hypothalamic-pituitary-adrenal (HPA) axis. We posit that the intermediate phenotype of the s-allele may properly be understood as mediating a trade-off, wherein increased responsiveness of cerebral serotonin transmission to seasonal and other forms of environmental change imparts greater behavioral flexibility, at the expense of increased vulnerability to stress. This model may explain the somewhat higher prevalence of the s-allele in some human populations dwelling at geographic latitudes with pronounced seasonal climatic changes, while this hypothesis does not rule out that genetic drift plays an additional or even exclusive role. We argue that s-allele manifests as an intermediate phenotype in terms of an increased responsiveness of the 5-HTT expression to number of daylight hours, which may serve as a stable surrogate marker of other environmental factors, such as availability of food and safety of the environment in populations that live closer to the geographic poles.

  2. Modeling Environmental Literacy of University Students

    Science.gov (United States)

    Teksoz, Gaye; Sahin, Elvan; Tekkaya-Oztekin, Ceren

    2012-01-01

    The present study proposed an Environmental Literacy Components Model to explain how environmental attitudes, environmental responsibility, environmental concern, and environmental knowledge as well as outdoor activities related to each other. A total of 1,345 university students responded to an environmental literacy survey (Kaplowitz and Levine…

  3. Challenges in Modelling of Environmental Semantics

    NARCIS (Netherlands)

    Athanasiadis, I.N.

    2015-01-01

    Modelling environmental semantics is a prerequisite for model and data interoperabilty and reuse, both essential for integrated modelling. This paper previews a landscape where integrated modelling activities are performed in a virtual environmental information space, and identifies challenges impos

  4. Modelling, controlling, predicting blackouts

    CERN Document Server

    Wang, Chengwei; Baptista, Murilo S

    2016-01-01

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

  5. 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.

  6. Quantifying the ability of environmental parameters to predict soil texture fractions using regression-tree model with GIS and LIDAR data

    DEFF Research Database (Denmark)

    Greve, Mogens Humlekrog; Bou Kheir, Rania; Greve, Mette Balslev

    2012-01-01

    sand, silt, and clay in soil determines its textural classification. This study used Geographic Information Systems (GIS) and regression-tree modeling to precisely quantify the relationships between the soil texture fractions and different environmental parameters on a national scale, and to detect...... precipitation, seasonal precipitation to statistically explain soil texture fractions field/laboratory measurements (45,224 sampling sites) in the area of interest (Denmark). The developed strongest relationships were associated with clay and silt, variance being equal to 60%, followed by coarse sand (54.......5%) and fine sand (52%) as the weakest relationship. This study also showed that parent materials (with a relative importance varying between 47% and 100%), geographic regions (31–100%) and landscape types (68–100%) considerably influenced all soil texture fractions, which is not the case for climate and DEM...

  7. Prediction models in complex terrain

    DEFF Research Database (Denmark)

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

    2001-01-01

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

  8. Environmental Observatories and Hydrologic Modeling

    Science.gov (United States)

    Hooper, R. P.; Duncan, J. M.

    2006-12-01

    During the past several years, the environmental sciences community has been attempting to design large- scale obsevatories that will transform the science. A watershed-based observatory has emerged as an effective landscape unit for a broad range of environmental sciences and engineering. For an effective observatory, modeling is a central requirement because models are precise statements of the hypothesized conceptual organization of watersheds and of the processes believed to be controlling hydrology of the watershed. Furthermore, models can serve to determine the value of existing data and the incremental value of any additional data to be collected. Given limited resources, such valuation is mandatory for an objective design of an observatory. Modeling is one part of a "digital watershed" that must be constructed for any observatory, a concept that has been developed by the CUAHSI Hydrologic Information Systems project. A digital watershed has three functions. First, it permits assembly of time series (such as stream discharge or precipitation measurements), static spatial coverages (such as topography), and dynamic fields (such as precipitation radar and other remotely sensed data). Second, based upon this common data description, a digital observatory permits multiple conceptualizations of the observatory to be created and to be stored. These conceptualizations could range from lumped box-and-arrow watershed models, to semi-distributed topographically based models, to three-dimensional finite element models. Finally, each conceptualization can lead to multiple models--that is, a set of equations that quantitatively describe hydrologic (or biogeochemical or geomorphologic) processes through libraries of tools that can be linked as workflow sequences. The advances in cyberinfrastructure that allow the storage of multiple conceptualizations and multiple model formulations of these conceptualizations promise to accelerate advances in environmental science both

  9. Toward seamless weather-climate and environmental prediction

    Science.gov (United States)

    Brunet, Gilbert

    2016-04-01

    Over the last decade or so, predicting the weather, climate and atmospheric composition has emerged as one of the most important areas of scientific endeavor. This is partly because the remarkable increase in skill of current weather forecasts has made society more and more dependent on them day to day for a whole range of decision making. And it is partly because climate change is now widely accepted and the realization is growing rapidly that it will affect every person in the world profoundly, either directly or indirectly. One of the important endeavors of our societies is to remain at the cutting-edge of modelling and predicting the evolution of the fully coupled environmental system: atmosphere (weather and composition), oceans, land surface (physical and biological), and cryosphere. This effort will provide an increasingly accurate and reliable service across all the socio-economic sectors that are vulnerable to the effects of adverse weather and climatic conditions, whether now or in the future. This emerging challenge was at the center of the World Weather Open Science Conference (Montreal, 2014).The outcomes of the conference are described in the World Meteorological Organization (WMO) book: Seamless Prediction of the Earth System: from Minutes to Months, (G. Brunet, S. Jones, P. Ruti Eds., WMO-No. 1156, 2015). It is freely available on line at the WMO website. We will discuss some of the outcomes of the conference for the WMO World Weather Research Programme (WWRP) and Global Atmospheric Watch (GAW) long term goals and provide examples of seamless modelling and prediction across a range of timescales at convective and sub-kilometer scales for regional coupled forecasting applications at Environment and Climate Change Canada (ECCC).

  10. Development of computer program ENMASK for prediction of residual environmental masking-noise spectra, from any three independent environmental parameters

    Energy Technology Data Exchange (ETDEWEB)

    Chang, Y.-S.; Liebich, R. E.; Chun, K. C.

    2000-03-31

    Residual environmental sound can mask intrusive4 (unwanted) sound. It is a factor that can affect noise impacts and must be considered both in noise-impact studies and in noise-mitigation designs. Models for quantitative prediction of sensation level (audibility) and psychological effects of intrusive noise require an input with 1/3 octave-band spectral resolution of environmental masking noise. However, the majority of published residual environmental masking-noise data are given with either octave-band frequency resolution or only single A-weighted decibel values. A model has been developed that enables estimation of 1/3 octave-band residual environmental masking-noise spectra and relates certain environmental parameters to A-weighted sound level. This model provides a correlation among three environmental conditions: measured residual A-weighted sound-pressure level, proximity to a major roadway, and population density. Cited field-study data were used to compute the most probable 1/3 octave-band sound-pressure spectrum corresponding to any selected one of these three inputs. In turn, such spectra can be used as an input to models for prediction of noise impacts. This paper discusses specific algorithms included in the newly developed computer program ENMASK. In addition, the relative audibility of the environmental masking-noise spectra at different A-weighted sound levels is discussed, which is determined by using the methodology of program ENAUDIBL.

  11. 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...

  12. Predicting the risk of toxic blooms of golden alga from cell abundance and environmental covariates

    Science.gov (United States)

    Patino, Reynaldo; VanLandeghem, Matthew M.; Denny, Shawn

    2016-01-01

    Golden alga (Prymnesium parvum) is a toxic haptophyte that has caused considerable ecological damage to marine and inland aquatic ecosystems worldwide. Studies focused primarily on laboratory cultures have indicated that toxicity is poorly correlated with the abundance of golden alga cells. This relationship, however, has not been rigorously evaluated in the field where environmental conditions are much different. The ability to predict toxicity using readily measured environmental variables and golden alga abundance would allow managers rapid assessments of ichthyotoxicity potential without laboratory bioassay confirmation, which requires additional resources to accomplish. To assess the potential utility of these relationships, several a priori models relating lethal levels of golden alga ichthyotoxicity to golden alga abundance and environmental covariates were constructed. Model parameters were estimated using archived data from four river basins in Texas and New Mexico (Colorado, Brazos, Red, Pecos). Model predictive ability was quantified using cross-validation, sensitivity, and specificity, and the relative ranking of environmental covariate models was determined by Akaike Information Criterion values and Akaike weights. Overall, abundance was a generally good predictor of ichthyotoxicity as cross validation of golden alga abundance-only models ranged from ∼ 80% to ∼ 90% (leave-one-out cross-validation). Environmental covariates improved predictions, especially the ability to predict lethally toxic events (i.e., increased sensitivity), and top-ranked environmental covariate models differed among the four basins. These associations may be useful for monitoring as well as understanding the abiotic factors that influence toxicity during blooms.

  13. Predictive models of forest dynamics.

    Science.gov (United States)

    Purves, Drew; Pacala, Stephen

    2008-06-13

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

  14. Spatiotemporal Interpolation for Environmental Modelling

    Directory of Open Access Journals (Sweden)

    Ferry Susanto

    2016-08-01

    Full Text Available A variation of the reduction-based approach to spatiotemporal interpolation (STI, in which time is treated independently from the spatial dimensions, is proposed in this paper. We reviewed and compared three widely-used spatial interpolation techniques: ordinary kriging, inverse distance weighting and the triangular irregular network. We also proposed a new distribution-based distance weighting (DDW spatial interpolation method. In this study, we utilised one year of Tasmania’s South Esk Hydrology model developed by CSIRO. Root mean squared error statistical methods were performed for performance evaluations. Our results show that the proposed reduction approach is superior to the extension approach to STI. However, the proposed DDW provides little benefit compared to the conventional inverse distance weighting (IDW method. We suggest that the improved IDW technique, with the reduction approach used for the temporal dimension, is the optimal combination for large-scale spatiotemporal interpolation within environmental modelling applications.

  15. Spatiotemporal Interpolation for Environmental Modelling.

    Science.gov (United States)

    Susanto, Ferry; de Souza, Paulo; He, Jing

    2016-08-06

    A variation of the reduction-based approach to spatiotemporal interpolation (STI), in which time is treated independently from the spatial dimensions, is proposed in this paper. We reviewed and compared three widely-used spatial interpolation techniques: ordinary kriging, inverse distance weighting and the triangular irregular network. We also proposed a new distribution-based distance weighting (DDW) spatial interpolation method. In this study, we utilised one year of Tasmania's South Esk Hydrology model developed by CSIRO. Root mean squared error statistical methods were performed for performance evaluations. Our results show that the proposed reduction approach is superior to the extension approach to STI. However, the proposed DDW provides little benefit compared to the conventional inverse distance weighting (IDW) method. We suggest that the improved IDW technique, with the reduction approach used for the temporal dimension, is the optimal combination for large-scale spatiotemporal interpolation within environmental modelling applications.

  16. Environmental model for a capital city

    Directory of Open Access Journals (Sweden)

    Claudia Eugenia Toca Torres

    2013-06-01

    Full Text Available From a review of the various options for modeling a sustainable development in its environmental dimension, this research proposes a model of environmental impact for Bogota, using the Vensim PLE software to model the pollution, the pollution load and soil contamination. The model includes a limited number of endogenous variables, as well as a greater number of exogenous variables. This modeling allows us to anticipate the environmental situation in the capital, in order to support public policies for addressing issues such as economic sanctions and moral regulations on emissions, discharges and waste, environmental measures and environmentally friendly practices

  17. Prediction of phylogeographic endemism in an environmentally complex biome.

    Science.gov (United States)

    Carnaval, Ana Carolina; Waltari, Eric; Rodrigues, Miguel T; Rosauer, Dan; VanDerWal, Jeremy; Damasceno, Roberta; Prates, Ivan; Strangas, Maria; Spanos, Zoe; Rivera, Danielle; Pie, Marcio R; Firkowski, Carina R; Bornschein, Marcos R; Ribeiro, Luiz F; Moritz, Craig

    2014-10-07

    Phylogeographic endemism, the degree to which the history of recently evolved lineages is spatially restricted, reflects fundamental evolutionary processes such as cryptic divergence, adaptation and biological responses to environmental heterogeneity. Attempts to explain the extraordinary diversity of the tropics, which often includes deep phylogeographic structure, frequently invoke interactions of climate variability across space, time and topography. To evaluate historical versus contemporary drivers of phylogeographic endemism in a tropical system, we analyse the effects of current and past climatic variation on the genetic diversity of 25 vertebrates in the Brazilian Atlantic rainforest. We identify two divergent bioclimatic domains within the forest and high turnover around the Rio Doce. Independent modelling of these domains demonstrates that endemism patterns are subject to different climatic drivers. Past climate dynamics, specifically areas of relative stability, predict phylogeographic endemism in the north. Conversely, contemporary climatic heterogeneity better explains endemism in the south. These results accord with recent speleothem and fossil pollen studies, suggesting that climatic variability through the last 250 kyr impacted the northern and the southern forests differently. Incorporating sub-regional differences in climate dynamics will enhance our ability to understand those processes shaping high phylogeographic and species endemism, in the Neotropics and beyond.

  18. A kinetic model for predicting biodegradation.

    Science.gov (United States)

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

    2007-01-01

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

  19. Uncertainties in environmental radiological assessment models and their implications

    Energy Technology Data Exchange (ETDEWEB)

    Hoffman, F.O.; Miller, C.W.

    1983-01-01

    Environmental radiological assessments rely heavily on the use of mathematical models. The predictions of these models are inherently uncertain because these models are inexact representations of real systems. The major sources of this uncertainty are related to biases in model formulation and parameter estimation. The best approach for estimating the actual extent of over- or underprediction is model validation, a procedure that requires testing over the range of the intended realm of model application. Other approaches discussed are the use of screening procedures, sensitivity and stochastic analyses, and model comparison. The magnitude of uncertainty in model predictions is a function of the questions asked of the model and the specific radionuclides and exposure pathways of dominant importance. Estimates are made of the relative magnitude of uncertainty for situations requiring predictions of individual and collective risks for both chronic and acute releases of radionuclides. It is concluded that models developed as research tools should be distinguished from models developed for assessment applications. Furthermore, increased model complexity does not necessarily guarantee increased accuracy. To improve the realism of assessment modeling, stochastic procedures are recommended that translate uncertain parameter estimates into a distribution of predicted values. These procedures also permit the importance of model parameters to be ranked according to their relative contribution to the overall predicted uncertainty. Although confidence in model predictions can be improved through site-specific parameter estimation and increased model validation, risk factors and internal dosimetry models will probably remain important contributors to the amount of uncertainty that is irreducible.

  20. Environmental problems indicator under environmental modeling toward sustainable development

    Directory of Open Access Journals (Sweden)

    P. Sutthichaimethee

    2015-09-01

    Full Text Available This research aims to apply a model to the study and analysis of environmental and natural resource costs created in supply chains of goods and services produced in Thailand, and propose indicators for environmental problem management, caused by goods and services production, based on concepts of sustainable production and consumer behavior. The research showed that the highest environmental cost in terms of Natural Resource Materials was from pipelines and gas distribution, while the lowest was for farming coconuts. The highest environmental cost in terms of Energy and Transportation was for iron and steel. The highest environmental cost in the category of Fertilizer and Pesticides was for oil palm. For Sanitation Services, the highest environmental cost was movie theaters. Overall, the lowest environmental cost for all categories, except Natural Resource Materials, was for petroleum and refineries. Based on the cost index, coconut farming gained the highest Real Benefit to the farm owner, while pipelines and gas distribution had the lowest Real Benefit. If Thailand were to use a similar environmental problem indicator, it could be applied to formulate efficient policy and strategy for the country in three areas, namely social, economic, and environmental development.

  1. National Centers for Environmental Prediction-Department of Energy (NCEP-DOE) Atmospheric Model Intercomparison Project (AMIP)-II Reanalysis (Reanalysis-2)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The NCEP-DOE Atmospheric Model Intercomparison Project (AMIP-II) reanalysis is a follow-on project to the "50-year" (1948-present) NCEP-NCAR Reanalysis Project....

  2. Multimedia Environmental Fate and Speciation of Engineered Nanoparticles: A Probabilistic Modeling Approach

    NARCIS (Netherlands)

    Meesters, J.; Quik, J.T.K.; Koelmans, A.A.; Hendriks, A.J.; Meent, van de D.

    2016-01-01

    The robustness of novel multimedia fate models in environmental exposure estimation of engineered nanoparticles (ENPs) remains unclear, because of uncertainties in the emission, physicochemical properties and natural variability in environmental systems. Here, we evaluate the uncertainty in predicte

  3. Using integrated environmental modeling to automate a process-based Quantitative Microbial Risk Assessment

    Science.gov (United States)

    Integrated Environmental Modeling (IEM) organizes multidisciplinary knowledge that explains and predicts environmental-system response to stressors. A Quantitative Microbial Risk Assessment (QMRA) is an approach integrating a range of disparate data (fate/transport, exposure, and human health effect...

  4. Modeling Environmental Concern: Theory and Application.

    Science.gov (United States)

    Hackett, Paul M. W.

    1993-01-01

    Human concern for the quality and protection of the natural environment forms the basis of successful environmental conservation activities. Considers environmental concern research and proposes a model that incorporates the multiple dimensions of research through which environmental concern may be evaluated. (MDH)

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

    NARCIS (Netherlands)

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

    2016-01-01

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

  6. Environmental Factors Affecting Asthma and Allergies: Predicting and Simulating Downwind Exposure to Airborne Pollen

    Science.gov (United States)

    Luvall, Jeffrey; Estes, Sue; Sprigg, William A.; Nickovic, Slobodan; Huete, Alfredo; Solano, Ramon; Ratana, Piyachat; Jiang, Zhangyan; Flowers, Len; Zelicoff, Alan

    2009-01-01

    This slide presentation reviews the environmental factors that affect asthma and allergies and work to predict and simulate the downwind exposure to airborne pollen. Using a modification of Dust REgional Atmosphere Model (DREAM) that incorporates phenology (i.e. PREAM) the aim was to predict concentrations of pollen in time and space. The strategy for using the model to simulate downwind pollen dispersal, and evaluate the results. Using MODerate-resolution Imaging Spectroradiometer (MODIS), to get seasonal sampling of Juniper, the pollen chosen for the study, land cover on a near daily basis. The results of the model are reviewed.

  7. Dissolved Organic Matter: a Master Variable for Predicting and Modeling the Effects of Climatic and Environmental Change on Mercury Transport and Reactivity

    Science.gov (United States)

    Aiken, G.

    2013-12-01

    It is known that dissolved organic matter (DOM) exerts strong controls on the transport and reactivity of Hg in aquatic systems. Our research has demonstrated that DOM binds Hg strongly, interacts with nanoparticulate HgS to stabilize and enhance reactivity, and controls, in part, the availability of Hg for methylation by micro-organisms. In many rivers and streams, DOM and dissolved Hg concentrations are strongly positively correlated and DOM optical properties have been shown to be excellent proxies for Hg concentration. Of particular importance is the hydrophobic acid fraction of DOM that contains primarily terrestrially derived aquatic humic substances. This fraction is derived, in large part, from watershed soils and plant litter, is chromophore-rich, and strongly influences DOM optical properties, such as ultraviolet (UV) absorbance, fluorescence, and specific UV absorbance (SUVA - an indicator of DOM aromaticity). In most rivers and streams studied by our group, the relationships between total dissolved Hg concentration and hydrophobic organic acid (HPOA) content are often stronger than those observed between dissolved Hg and DOM. These results and those of lab studies support the hypothesis that interactions between Hg and the HPOA fraction are important drivers for the transport and reactivity of dissolved Hg in aquatic systems. Therefore, understanding how climate or land use related changes may influence DOM and HPOA export and yield within a particular watershed is key to predicting in the fate and bioaccumulation of Hg in that system. Watershed hydrology, the nature of source materials, and biogeochemical processes throughout the entire ecosystem drive DOM composition. In particular, the abundance of wetlands within a river basin is an excellent indicator of DOM concentration, DOM optical properties, and the concentration of HPOA. For instance, for 17 major North American rivers we found significant positive correlations between basin wetland-cover and

  8. Predictive Modeling of Cardiac Ischemia

    Science.gov (United States)

    Anderson, Gary T.

    1996-01-01

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

  9. Water Quality, Cyanobacteria, and Environmental Factors and Their Relations to Microcystin Concentrations for Use in Predictive Models at Ohio Lake Erie and Inland Lake Recreational Sites, 2013-14

    Science.gov (United States)

    Francy, Donna S.; Graham, Jennifer L.; Stelzer, Erin A.; Ecker, Christopher D.; Brady, Amie M G.; Pam Struffolino,; Loftin, Keith A.

    2015-11-06

    Harmful cyanobacterial “algal” blooms (cyanoHABs) and associated toxins, such as microcystin, are a major water-quality issue for Lake Erie and inland lakes in Ohio. Predicting when and where a bloom may occur is important to protect the public that uses and consumes a water resource; however, predictions are complicated and likely site specific because of the many factors affecting toxin production. Monitoring for a variety of environmental and water-quality factors, for concentrations of cyanobacteria by molecular methods, and for algal pigments such as chlorophyll and phycocyanin by using optical sensors may provide data that can be used to predict the occurrence of cyanoHABs.

  10. Shuttle sonic boom - Technology and predictions. [environmental impact

    Science.gov (United States)

    Holloway, P. F.; Wilhold, G. A.; Jones, J. H.; Garcia, F., Jr.; Hicks, R. M.

    1973-01-01

    Because the shuttle differs significantly in both geometric and operational characteristics from conventional supersonic aircraft, estimation of sonic boom characteristics required a new technology base. The prediction procedures thus developed are reviewed. Flight measurements obtained for both the ascent and entry phases of the Apollo 15 and 16 and for the ascent phase only of the Apollo 17 missions are presented which verify the techniques established for application to shuttle. Results of extensive analysis of the sonic boom overpressure characteristics completed to date are presented which indicate that this factor of the shuttle's environmental impact is predictable, localized, of short duration and acceptable. Efforts are continuing to define the shuttle sonic boom characteristics to a fine level of detail based on the final system design.

  11. Hierarchical modelling for the environmental sciences statistical methods and applications

    CERN Document Server

    Clark, James S

    2006-01-01

    New statistical tools are changing the way in which scientists analyze and interpret data and models. Hierarchical Bayes and Markov Chain Monte Carlo methods for analysis provide a consistent framework for inference and prediction where information is heterogeneous and uncertain, processes are complicated, and responses depend on scale. Nowhere are these methods more promising than in the environmental sciences.

  12. Numerical weather prediction model tuning via ensemble prediction system

    Science.gov (United States)

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

    2011-12-01

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

  13. Predictive modeling of coral disease distribution within a reef system.

    Directory of Open Access Journals (Sweden)

    Gareth J Williams

    Full Text Available Diseases often display complex and distinct associations with their environment due to differences in etiology, modes of transmission between hosts, and the shifting balance between pathogen virulence and host resistance. Statistical modeling has been underutilized in coral disease research to explore the spatial patterns that result from this triad of interactions. We tested the hypotheses that: 1 coral diseases show distinct associations with multiple environmental factors, 2 incorporating interactions (synergistic collinearities among environmental variables is important when predicting coral disease spatial patterns, and 3 modeling overall coral disease prevalence (the prevalence of multiple diseases as a single proportion value will increase predictive error relative to modeling the same diseases independently. Four coral diseases: Porites growth anomalies (PorGA, Porites tissue loss (PorTL, Porites trematodiasis (PorTrem, and Montipora white syndrome (MWS, and their interactions with 17 predictor variables were modeled using boosted regression trees (BRT within a reef system in Hawaii. Each disease showed distinct associations with the predictors. Environmental predictors showing the strongest overall associations with the coral diseases were both biotic and abiotic. PorGA was optimally predicted by a negative association with turbidity, PorTL and MWS by declines in butterflyfish and juvenile parrotfish abundance respectively, and PorTrem by a modal relationship with Porites host cover. Incorporating interactions among predictor variables contributed to the predictive power of our models, particularly for PorTrem. Combining diseases (using overall disease prevalence as the model response, led to an average six-fold increase in cross-validation predictive deviance over modeling the diseases individually. We therefore recommend coral diseases to be modeled separately, unless known to have etiologies that respond in a similar manner to

  14. Model-based uncertainty in species range prediction

    DEFF Research Database (Denmark)

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

    2006-01-01

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

  15. Modeling Of Construction Noise For Environmental Impact Assessment

    Directory of Open Access Journals (Sweden)

    Mohamed F. Hamoda

    2008-06-01

    Full Text Available This study measured the noise levels generated at different construction sites in reference to the stage of construction and the equipment used, and examined the methods to predict such noise in order to assess the environmental impact of noise. It included 33 construction sites in Kuwait and used artificial neural networks (ANNs for the prediction of noise. A back-propagation neural network (BPNN model was compared with a general regression neural network (GRNN model. The results obtained indicated that the mean equivalent noise level was 78.7 dBA which exceeds the threshold limit. The GRNN model was superior to the BPNN model in its accuracy of predicting construction noise due to its ability to train quickly on sparse data sets. Over 93% of the predictions were within 5% of the observed values. The mean absolute error between the predicted and observed data was only 2 dBA. The ANN modeling proved to be a useful technique for noise predictions required in the assessment of environmental impact of construction activities.

  16. 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...

  17. 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.

  18. 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.

  19. Predictive Model Assessment for Count Data

    Science.gov (United States)

    2007-09-05

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

  20. Predicting the distribution of canine leishmaniasis in western Europe based on environmental variables.

    Science.gov (United States)

    Franco, Ana O; Davies, Clive R; Mylne, Adrian; Dedet, Jean-Pierre; Gállego, Montserrat; Ballart, Cristina; Gramiccia, Marina; Gradoni, Luigi; Molina, Ricardo; Gálvez, Rosa; Morillas-Márquez, Francisco; Barón-López, Sergio; Pires, Carlos Alves; Afonso, Maria Odete; Ready, Paul D; Cox, Jonathan

    2011-12-01

    The domestic dog is the reservoir host of Leishmania infantum, the causative agent of zoonotic visceral leishmaniasis endemic in Mediterranean Europe. Targeted control requires predictive risk maps of canine leishmaniasis (CanL), which are now explored. We databased 2187 published and unpublished surveys of CanL in southern Europe. A total of 947 western surveys met inclusion criteria for analysis, including serological identification of infection (504, 369 dogs tested 1971-2006). Seroprevalence was 23 2% overall (median 10%). Logistic regression models within a GIS framework identified the main environmental predictors of CanL seroprevalence in Portugal, Spain, France and Italy, or in France alone. A 10-fold cross-validation approach determined model capacity to predict point-values of seroprevalence and the correct seroprevalence class (20%). Both the four-country and France-only models performed reasonably well for predicting correctly the 20% seroprevalence classes (AUC >0 70). However, the France-only model performed much better for France than the four-country model. The four-country model adequately predicted regions of CanL emergence in northern Italy (<5% seroprevalence). Both models poorly predicted intermediate point seroprevalences (5-20%) within regional foci, because surveys were biased towards known rural foci and Mediterranean bioclimates. Our recommendations for standardizing surveys would permit higher-resolution risk mapping.

  1. Nonlinear chaotic model for predicting storm surges

    Directory of Open Access Journals (Sweden)

    M. Siek

    2010-09-01

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

  2. Nonlinear chaotic model for predicting storm surges

    NARCIS (Netherlands)

    Siek, M.; Solomatine, D.P.

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

  3. National environmental/economic infrastructure system model

    Energy Technology Data Exchange (ETDEWEB)

    Drake, R.H.; Hardie, R.W.; Loose, V.W.; Booth, S.R.

    1997-08-01

    This is the final report for a one-year Laboratory Directed Research and Development (LDRD) project at the Los Alamos National Laboratory (LANL). The ultimate goal was to develop a new methodology for macroeconomic modeling applied to national environmental and economic problems. A modeling demonstration and briefings were produced, and significant internal technical support and program interest has been generated. External contacts with DOE`s Office of Environmental Management (DOE-EM), US State Department, and the US intelligence community were established. As a result of DOE-EM interest and requests for further development, this research has been redirected to national environmental simulations as a new LDRD project.

  4. EFFICIENT PREDICTIVE MODELLING FOR ARCHAEOLOGICAL RESEARCH

    OpenAIRE

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

    2014-01-01

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

  5. Prediction of environmental impacts of quarry blasting operation using fuzzy logic.

    Science.gov (United States)

    Fişne, Abdullah; Kuzu, Cengiz; Hüdaverdi, Türker

    2011-03-01

    Blast-induced ground vibration is one of the most important environmental impacts of blasting operations because it may cause severe damage to structures and plants in nearby environment. Estimation of ground vibration levels induced by blasting has vital importance for restricting the environmental effects of blasting operations. Several predictor equations have been proposed by various researchers to predict ground vibration prior to blasting, but these are site specific and not generally applicable beyond the specific conditions. In this study, an attempt has been made to predict the peak particle velocity (PPV) with the help of fuzzy logic approach using parameters of distance from blast face to vibration monitoring point and charge weight per delay. The PPV and charge weight per delay were recorded for 33 blast events at various distances and used for the validation of the proposed fuzzy model. The results of the fuzzy model were also compared with the values obtained from classical regression analysis. The root mean square error estimated for fuzzy-based model was 5.31, whereas it was 11.32 for classical regression-based model. Finally, the relationship between the measured and predicted values of PPV showed that the correlation coefficient for fuzzy model (0.96) is higher than that for regression model (0.82).

  6. How to Establish Clinical Prediction Models

    Directory of Open Access Journals (Sweden)

    Yong-ho Lee

    2016-03-01

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

  7. Comparison of Prediction-Error-Modelling Criteria

    DEFF Research Database (Denmark)

    Jørgensen, John Bagterp; Jørgensen, Sten Bay

    2007-01-01

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

  8. The integrated environmental control model

    Energy Technology Data Exchange (ETDEWEB)

    Rubin, E.S.; Berkenpas, M.B.; Kalagnanam, J.R. [Carnegie Mellon Univ., Pittsburgh, PA (United States)

    1995-11-01

    The capability to estimate the performance and cost of emission control systems is critical to a variety of planning and analysis requirements faced by utilities, regulators, researchers and analysts in the public and private sectors. The computer model described in this paper has been developed for DOe to provide an up-to-date capability for analyzing a variety of pre-combustion, combustion, and post-combustion options in an integrated framework. A unique capability allows performance and costs to be modeled probabilistically, which allows explicit characterization of uncertainties and risks.

  9. Multimedia Environmental Fate and Speciation of Engineered Nanoparticles: A Probabilistic Modeling Approach

    OpenAIRE

    Meesters, J.A.J.; Quik, J.T.K.; Koelmans, A.A.; Hendriks, A.J.; Meent, D. van de

    2016-01-01

    The robustness of novel multimedia fate models in environmental exposure estimation of engineered nanoparticles (ENPs) remains unclear, because of uncertainties in the emission, physicochemical properties and natural variability in environmental systems. Here, we evaluate the uncertainty in predicted environmental concentrations (PECs) by using the SimpleBox4nano (SB4N) model. Monte Carlo (MC) simulations were performed on the environmental fate, concentrations and speciation of nano-CeO2, -T...

  10. To predict the niche, model colonization and extinction

    Science.gov (United States)

    Yackulic, Charles B.; Nichols, James D.; Reid, Janice; Der, Ricky

    2015-01-01

    Ecologists frequently try to predict the future geographic distributions of species. Most studies assume that the current distribution of a species reflects its environmental requirements (i.e., the species' niche). However, the current distributions of many species are unlikely to be at equilibrium with the current distribution of environmental conditions, both because of ongoing invasions and because the distribution of suitable environmental conditions is always changing. This mismatch between the equilibrium assumptions inherent in many analyses and the disequilibrium conditions in the real world leads to inaccurate predictions of species' geographic distributions and suggests the need for theory and analytical tools that avoid equilibrium assumptions. Here, we develop a general theory of environmental associations during periods of transient dynamics. We show that time-invariant relationships between environmental conditions and rates of local colonization and extinction can produce substantial temporal variation in occupancy–environment relationships. We then estimate occupancy–environment relationships during three avian invasions. Changes in occupancy–environment relationships over time differ among species but are predicted by dynamic occupancy models. Since estimates of the occupancy–environment relationships themselves are frequently poor predictors of future occupancy patterns, research should increasingly focus on characterizing how rates of local colonization and extinction vary with environmental conditions.

  11. Modeling crop responses to environmental change

    Science.gov (United States)

    Rosenzweig, Cynthia

    1993-01-01

    Potential biophysical responses of crops to climate change are studied focusing on the primary environmental variables which define the limits to agricultural crop growth and production, and the principal methods for predicting climate change impacts on crop geography and production. It is concluded that the principal uncertainties in the prediction of the impacts of climate change on agriculture reside in the contribution of the direct effects of increasing CO2, in potential changes inclimate variability, and the effects of adjustments mechanisms in the context of climatic changes.

  12. Case studies in archaeological predictive modelling

    NARCIS (Netherlands)

    Verhagen, Jacobus Wilhelmus Hermanus Philippus

    2007-01-01

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

  13. Childhood asthma prediction models: a systematic review.

    Science.gov (United States)

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

    2015-12-01

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

  14. Mechanism-Based Modeling of Hydrogen Environment Assisted Cracking (HEAC) in High Strength Alloys for Marine Applications: Prediction of Monel K-500 HEAC for Select Environmental and Mechanical Conditions

    Science.gov (United States)

    2012-10-15

    activation energies (-40 kJ/mol). Moreover, possible trapping sites will differ for precipitation hardened fee Ni-based alloys compared to ferrous ...FINAL REPORT Mechanism-Based Modeling of Hydrogen Environment Assisted Cracking (HEAC) in High Strength Alloys for Marine Applications: Prediction...the microscopic scale to enable threshold and crack growth rate predictions in Ni-based alloys which differ substantially from high strength steels

  15. Flexing the PECs: Predicting environmental concentrations of veterinary drugs in Canadian agricultural soils.

    Science.gov (United States)

    Kullik, Sigrun A; Belknap, Andrew M

    2017-03-01

    Veterinary drugs administered to food animals primarily enter ecosystems through the application of livestock waste to agricultural land. Although veterinary drugs are essential for protecting animal health, their entry into the environment may pose a risk for nontarget organisms. A means to predict environmental concentrations of new veterinary drug ingredients in soil is required to assess their environmental fate, distribution, and potential effects. The Canadian predicted environmental concentrations in soil (PECsoil) for new veterinary drug ingredients for use in intensively reared animals is based on the approach currently used by the European Medicines Agency for VICH Phase I environmental assessments. The calculation for the European Medicines Agency PECsoil can be adapted to account for regional animal husbandry and land use practices. Canadian agricultural practices for intensively reared cattle, pigs, and poultry differ substantially from those in the European Union. The development of PECsoil default values and livestock categories representative of typical Canadian animal production methods and nutrient management practices culminates several years of research and an extensive survey and analysis of the scientific literature, Canadian agricultural statistics, national and provincial management recommendations, veterinary product databases, and producers. A PECsoil can be used to rapidly identify new veterinary drugs intended for intensive livestock production that should undergo targeted ecotoxicity and fate testing. The Canadian PECsoil model is readily available, transparent, and requires minimal inputs to generate a screening level environmental assessment for veterinary drugs that can be refined if additional data are available. PECsoil values for a hypothetical veterinary drug dosage regimen are presented and discussed in an international context. Integr Environ Assess Manag 2017;13:331-341. © 2016 Her Majesty the Queen in Right of Canada

  16. 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...

  17. Dynamic Probabilistic Modeling of Environmental Emissions of Engineered Nanomaterials.

    Science.gov (United States)

    Sun, Tian Yin; Bornhöft, Nikolaus A; Hungerbühler, Konrad; Nowack, Bernd

    2016-05-03

    The need for an environmental risk assessment for engineered nanomaterials (ENM) necessitates the knowledge about their environmental concentrations. Despite significant advances in analytical methods, it is still not possible to measure the concentrations of ENM in natural systems. Material flow and environmental fate models have been used to provide predicted environmental concentrations. However, almost all current models are static and consider neither the rapid development of ENM production nor the fact that many ENM are entering an in-use stock and are released with a lag phase. Here we use dynamic probabilistic material flow modeling to predict the flows of four ENM (nano-TiO2, nano-ZnO, nano-Ag and CNT) to the environment and to quantify their amounts in (temporary) sinks such as the in-use stock and ("final") environmental sinks such as soil and sediment. Caused by the increase in production, the concentrations of all ENM in all compartments are increasing. Nano-TiO2 had far higher concentrations than the other three ENM. Sediment showed in our worst-case scenario concentrations ranging from 6.7 μg/kg (CNT) to about 40 000 μg/kg (nano-TiO2). In most cases the concentrations in waste incineration residues are at the "mg/kg" level. The flows to the environment that we provide will constitute the most accurate and reliable input of masses for environmental fate models which are using process-based descriptions of the fate and behavior of ENM in natural systems and rely on accurate mass input parameters.

  18. A model for navigational errors in complex environmental fields.

    Science.gov (United States)

    Postlethwaite, Claire M; Walker, Michael M

    2014-12-21

    Many animals are believed to navigate using environmental signals such as light, sound, odours and magnetic fields. However, animals rarely navigate directly to their target location, but instead make a series of navigational errors which are corrected during transit. In previous work, we introduced a model showing that differences between an animal׳s 'cognitive map' of the environmental signals used for navigation and the true nature of these signals caused a systematic pattern in orientation errors when navigation begins. The model successfully predicted the pattern of errors seen in previously collected data from homing pigeons, but underestimated the amplitude of the errors. In this paper, we extend our previous model to include more complicated distortions of the contour lines of the environmental signals. Specifically, we consider the occurrence of critical points in the fields describing the signals. We consider three scenarios and compute orientation errors as parameters are varied in each case. We show that the occurrence of critical points can be associated with large variations in initial orientation errors over a small geographic area. We discuss the implications that these results have on predicting how animals will behave when encountering complex distortions in any environmental signals they use to navigate.

  19. Community Environmental Education as a Model for Effective Environmental Programmes

    Science.gov (United States)

    Blair, Morag

    2008-01-01

    The benefits of community environmental education outlined in environmental education literature are supported by the findings and implications of a research study undertaken in New Zealand. Evidence from a two-case case study suggests that environmental programmes guided by the key principles and practices of community environmental education,…

  20. 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...

  1. Models and parameters for environmental radiological assessments

    Energy Technology Data Exchange (ETDEWEB)

    Miller, C W [ed.

    1984-01-01

    This book presents a unified compilation of models and parameters appropriate for assessing the impact of radioactive discharges to the environment. Models examined include those developed for the prediction of atmospheric and hydrologic transport and deposition, for terrestrial and aquatic food-chain bioaccumulation, and for internal and external dosimetry. Chapters have been entered separately into the data base. (ACR)

  2. Massive Predictive Modeling using Oracle R Enterprise

    CERN Document Server

    CERN. Geneva

    2014-01-01

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

  3. Prediction of Dominant Forest Tree Species Using QuickBird and Environmental Data

    Directory of Open Access Journals (Sweden)

    Azadeh Abdollahnejad

    2017-02-01

    Full Text Available Modelling the spatial distribution of plants is one of the indirect methods for predicting the properties of plants and can be defined based on the relationship between the spatial distribution of vegetation and environmental variables. In this article, we introduce a new method for the spatial prediction of the dominant trees and species, through a combination of environmental and satellite data. Based on the basal area factor (BAF frequency for each tree species in a total of 518 sample plots, the dominant tree species were determined for each plot. Also, topographical maps of primary and secondary properties were prepared using the digital elevation model (DEM. Categories of soil and the climate maps database of the Doctor Bahramnia Forestry Plan were extracted as well. After pre-processing and processing of spectral data, the pixel values at the sample locations in all the independent factors such as spectral and non-spectral data, were extracted. The modelling rates of tree and shrub species diversity using data mining algorithms of 80% of the sampling plots were taken. Assessment of model accuracy was conducted using 20% of samples and evaluation criteria. Random forest (RF, support vector machine (SVM and k-nearest neighbor (k-NN algorithms were used for spatial distribution modelling of dominant species groups using environmental and spectral variables from 80% of the sample plots. Results showed physiographic factors, especially altitude in combination with soil and climate factors as the most important variables in the distribution of species, while the best model was created by the integration of physiographic factors (in combination with soil and climate with an overall accuracy of 63.85%. In addition, the results of the comparison between the algorithms, showed that the RF algorithm was the most accurate in modelling the diversity.

  4. Variation in Environmentalism among University Students: Majoring in Outdoor Recreation, Parks, and Tourism Predicts Environmental Concerns and Behaviors

    Science.gov (United States)

    Arnocky, Steven; Stroink, Mirella L.

    2011-01-01

    In a survey of Canadian university students (N = 205), the relationship between majoring in an outdoor recreation university program and environmental concern, cooperation, and behavior were examined. Stepwise linear regression indicated that enrollment in outdoor recreation was predictive of environmental behavior and ecological cooperation; and…

  5. Variation in Environmentalism among University Students: Majoring in Outdoor Recreation, Parks, and Tourism Predicts Environmental Concerns and Behaviors

    Science.gov (United States)

    Arnocky, Steven; Stroink, Mirella L.

    2011-01-01

    In a survey of Canadian university students (N = 205), the relationship between majoring in an outdoor recreation university program and environmental concern, cooperation, and behavior were examined. Stepwise linear regression indicated that enrollment in outdoor recreation was predictive of environmental behavior and ecological cooperation; and…

  6. Predicting Potential Habitat of Conifer and Broad-leaved Tree Using Environmental Variables and Seed Dispersal Ability

    Science.gov (United States)

    Heo, H. K.; Lee, D. K.; Mo, Y.; Kim, H. G.

    2016-12-01

    Research into predicting potential species distribution within forests is ongoing in relation to forest management. Conifer and broad-leaved tree, two main distinctive components in forests which are important concerning the management of forest, are used to predict potential forest distribution. Regarding prediction of potential tree species habitat distribution, environmental variables are commonly used to determine conditions that species can inhabit. However, seed dispersal ability was not used in species distribution model because it reflects succession process which is difficult to use.In this research, in addition to environmental variables, distance value was used to represent seed dispersal ability to predict tree distribution. Research was done in Namsan (Mt.) Sangju-si, Gyeongsangbuk-do, Korea, where few tree species exist according to detailed vegetation map, as a case study. To analyze the suitable environmental conditions and dispersal ability of conifer and broad-leaved trees, past distribution changing patterns were used. Past forest distribution maps (1984, 1995, 2005 and 2014) were used which was classified by Landsat images. Using these results, potential habitats of conifer and broad-leaved trees were predicted for 2024 and 2034. Furthermore, to quantify the uncertainty of prediction, monte carlo simulation was proceeded. As a result, it was possible to predict potential habitats using environmental variables and seed dispersal ability. Moreover, the dispersal ability turned out to be an important variable to predict change of potential habitat.

  7. 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.

  8. 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.

  9. 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.

  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. 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.

  12. 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.

  13. 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.

  14. 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.

  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. 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.

  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. 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.

  19. Streamlining environmental product declarations: a stage model

    Science.gov (United States)

    Lefebvre, Elisabeth; Lefebvre, Louis A.; Talbot, Stephane; Le Hen, Gael

    2001-02-01

    General public environmental awareness and education is increasing, therefore stimulating the demand for reliable, objective and comparable information about products' environmental performances. The recently published standard series ISO 14040 and ISO 14025 are normalizing the preparation of Environmental Product Declarations (EPDs) containing comprehensive information relevant to a product's environmental impact during its life cycle. So far, only a few environmentally leading manufacturing organizations have experimented the preparation of EPDs (mostly from Europe), demonstrating its great potential as a marketing weapon. However the preparation of EPDs is a complex process, requiring collection and analysis of massive amounts of information coming from disparate sources (suppliers, sub-contractors, etc.). In a foreseeable future, the streamlining of the EPD preparation process will require product manufacturers to adapt their information systems (ERP, MES, SCADA) in order to make them capable of gathering, and transmitting the appropriate environmental information. It also requires strong functional integration all along the product supply chain in order to ensure that all the information is made available in a standardized and timely manner. The goal of the present paper is two fold: first to propose a transitional model towards green supply chain management and EPD preparation; second to identify key technologies and methodologies allowing to streamline the EPD process and subsequently the transition toward sustainable product development

  20. Microbial forensics: predicting phenotypic characteristics and environmental conditions from large-scale gene expression profiles.

    Science.gov (United States)

    Kim, Minseung; Zorraquino, Violeta; Tagkopoulos, Ilias

    2015-03-01

    A tantalizing question in cellular physiology is whether the cellular state and environmental conditions can be inferred by the expression signature of an organism. To investigate this relationship, we created an extensive normalized gene expression compendium for the bacterium Escherichia coli that was further enriched with meta-information through an iterative learning procedure. We then constructed an ensemble method to predict environmental and cellular state, including strain, growth phase, medium, oxygen level, antibiotic and carbon source presence. Results show that gene expression is an excellent predictor of environmental structure, with multi-class ensemble models achieving balanced accuracy between 70.0% (±3.5%) to 98.3% (±2.3%) for the various characteristics. Interestingly, this performance can be significantly boosted when environmental and strain characteristics are simultaneously considered, as a composite classifier that captures the inter-dependencies of three characteristics (medium, phase and strain) achieved 10.6% (±1.0%) higher performance than any individual models. Contrary to expectations, only 59% of the top informative genes were also identified as differentially expressed under the respective conditions. Functional analysis of the respective genetic signatures implicates a wide spectrum of Gene Ontology terms and KEGG pathways with condition-specific information content, including iron transport, transferases, and enterobactin synthesis. Further experimental phenotypic-to-genotypic mapping that we conducted for knock-out mutants argues for the information content of top-ranked genes. This work demonstrates the degree at which genome-scale transcriptional information can be predictive of latent, heterogeneous and seemingly disparate phenotypic and environmental characteristics, with far-reaching applications.

  1. Microbial forensics: predicting phenotypic characteristics and environmental conditions from large-scale gene expression profiles.

    Directory of Open Access Journals (Sweden)

    Minseung Kim

    2015-03-01

    Full Text Available A tantalizing question in cellular physiology is whether the cellular state and environmental conditions can be inferred by the expression signature of an organism. To investigate this relationship, we created an extensive normalized gene expression compendium for the bacterium Escherichia coli that was further enriched with meta-information through an iterative learning procedure. We then constructed an ensemble method to predict environmental and cellular state, including strain, growth phase, medium, oxygen level, antibiotic and carbon source presence. Results show that gene expression is an excellent predictor of environmental structure, with multi-class ensemble models achieving balanced accuracy between 70.0% (±3.5% to 98.3% (±2.3% for the various characteristics. Interestingly, this performance can be significantly boosted when environmental and strain characteristics are simultaneously considered, as a composite classifier that captures the inter-dependencies of three characteristics (medium, phase and strain achieved 10.6% (±1.0% higher performance than any individual models. Contrary to expectations, only 59% of the top informative genes were also identified as differentially expressed under the respective conditions. Functional analysis of the respective genetic signatures implicates a wide spectrum of Gene Ontology terms and KEGG pathways with condition-specific information content, including iron transport, transferases, and enterobactin synthesis. Further experimental phenotypic-to-genotypic mapping that we conducted for knock-out mutants argues for the information content of top-ranked genes. This work demonstrates the degree at which genome-scale transcriptional information can be predictive of latent, heterogeneous and seemingly disparate phenotypic and environmental characteristics, with far-reaching applications.

  2. Simple models of assortment through environmental feedback.

    Science.gov (United States)

    Pepper, John W

    2007-01-01

    Social evolution depends critically on assortment, or segregation versus even mixing, between cooperators and noncooperators. Altruistic traits, which reduce the absolute fitness of their bearers, cannot evolve without positive assortment (excess segregation). The question of how positive assortment can arise has been controversial, but most evolutionary biologists believe that common descent is the only effective general mechanism. Here I investigate another recently proposed mechanism for generating nonrandom assortment, termed environmental feedback. This requires only that two forms of a trait affect the quality of the local environment differently in such a way that all individuals are more likely to leave low-quality locales. Experiments with simple computational models confirm that environmental feedback generates significant levels of genetic similarity among non-kin within locales. The mechanism is fairly general, and can under some conditions produce levels of genetic similarity comparable to those resulting from close genealogical relationship. Environmental feedback can also generate the negative assortment necessary for the evolution of spiteful traits. Environmental feedback is expected to create positive frequency-dependent selection, which thus favor any social trait that becomes common in the population. Results from this stylized model suggest that environmental feedback could be important in the evolution of both cooperation and spite, within as well as between species.

  3. 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…

  4. A Course in... Model Predictive Control.

    Science.gov (United States)

    Arkun, Yaman; And Others

    1988-01-01

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

  5. A case study predicting environmental impacts of urban transport planning in China.

    Science.gov (United States)

    Chen, Chong; Shao, Li-guo; Xu, Ling; Shang, Jin-cheng

    2009-10-01

    Predicting environmental impacts is essential when performing an environmental assessment on urban transport planning. System dynamics (SD) is usually used to solve complex nonlinear problems. In this study, we utilized system dynamics (SD) to evaluate the environmental impacts associated with urban transport planning in Jilin City, China with respect to the local economy, society, transport, the environment and resources. To accomplish this, we generated simulation models comprising interrelated subsystems designed to utilize changes in the economy, society, road construction, changes in the number of vehicles, the capacity of the road network capacity, nitrogen oxides emission, traffic noise, land used for road construction and fuel consumption associated with traffic to estimate dynamic trends in the environmental impacts associated with Jilin's transport planning. Two simulation scenarios were then analyzed comparatively. The results of this study indicated that implementation of Jilin transport planning would improve the current urban traffic conditions and boost the local economy and development while benefiting the environment in Jilin City. In addition, comparative analysis of the two scenarios provided additional information that can be used to aid in scientific decision-making regarding which aspects of the transport planning to implement in Jilin City. This study demonstrates that our application of the SD method, which is referred to as the Strategic Environmental Assessment (SEA), is feasible for use in urban transport planning.

  6. Equivalency and unbiasedness of grey prediction models

    Institute of Scientific and Technical Information of China (English)

    Bo Zeng; Chuan Li; Guo Chen; Xianjun Long

    2015-01-01

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

  7. 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.

  8. Prediction of blast-induced air overpressure: a hybrid AI-based predictive model.

    Science.gov (United States)

    Jahed Armaghani, Danial; Hajihassani, Mohsen; Marto, Aminaton; Shirani Faradonbeh, Roohollah; Mohamad, Edy Tonnizam

    2015-11-01

    Blast operations in the vicinity of residential areas usually produce significant environmental problems which may cause severe damage to the nearby areas. Blast-induced air overpressure (AOp) is one of the most important environmental impacts of blast operations which needs to be predicted to minimize the potential risk of damage. This paper presents an artificial neural network (ANN) optimized by the imperialist competitive algorithm (ICA) for the prediction of AOp induced by quarry blasting. For this purpose, 95 blasting operations were precisely monitored in a granite quarry site in Malaysia and AOp values were recorded in each operation. Furthermore, the most influential parameters on AOp, including the maximum charge per delay and the distance between the blast-face and monitoring point, were measured and used to train the ICA-ANN model. Based on the generalized predictor equation and considering the measured data from the granite quarry site, a new empirical equation was developed to predict AOp. For comparison purposes, conventional ANN models were developed and compared with the ICA-ANN results. The results demonstrated that the proposed ICA-ANN model is able to predict blast-induced AOp more accurately than other presented techniques.

  9. Predicting environmental mitigation requirements for hydropower projects through the integration of biophysical and socio-political geographies.

    Science.gov (United States)

    DeRolph, Christopher R; Schramm, Michael P; Bevelhimer, Mark S

    2016-10-01

    Uncertainty about environmental mitigation needs at existing and proposed hydropower projects makes it difficult for stakeholders to minimize environmental impacts. Hydropower developers and operators desire tools to better anticipate mitigation requirements, while natural resource managers and regulators need tools to evaluate different mitigation scenarios and order effective mitigation. Here we sought to examine the feasibility of using a suite of multi-faceted explanatory variables within a spatially explicit modeling framework to fit predictive models for future environmental mitigation requirements at hydropower projects across the conterminous U.S. Using a database comprised of mitigation requirements from more than 300 hydropower project licenses, we were able to successfully fit models for nearly 50 types of environmental mitigation and to apply the predictive models to a set of more than 500 non-powered dams identified as having hydropower potential. The results demonstrate that mitigation requirements are functions of a range of factors, from biophysical to socio-political. Project developers can use these models to inform cost projections and design considerations, while regulators can use the models to more quickly identify likely environmental issues and potential solutions, hopefully resulting in more timely and more effective decisions on environmental mitigation. Copyright © 2016 Elsevier B.V. All rights reserved.

  10. Energy-based models for environmental biotechnology.

    Science.gov (United States)

    Rodríguez, Jorge; Lema, Juan M; Kleerebezem, Robbert

    2008-07-01

    Environmental biotechnology is evolving. Current process objectives include the production of chemicals and/or energy carriers (biofuels) in addition to the traditional objective of removing pollutants from waste. To maximise product yields and minimise biomass production, future processes will rely on anaerobic microbial communities. Anaerobic processes are characterised by small Gibbs energy changes in the reactions catalysed, and this provides clear thermodynamic process boundaries. Here, a Gibbs-energy-based methodology is proposed for mathematical modelling of energy-limited anaerobic ecosystems. This methodology provides a basis for the description of microbial activities as a function of environmental factors, which will allow enhanced catalysis of specific reactions of interest for process development.

  11. Hybrid modeling and prediction of dynamical systems

    Science.gov (United States)

    Lloyd, Alun L.; Flores, Kevin B.

    2017-01-01

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

  12. Property predictions using microstructural modeling

    Energy Technology Data Exchange (ETDEWEB)

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

    2005-07-15

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

  13. 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.

  14. Modeling and Prediction Using Stochastic Differential Equations

    DEFF Research Database (Denmark)

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

    2016-01-01

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

  15. Precision Plate Plan View Pattern Predictive Model

    Institute of Scientific and Technical Information of China (English)

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

    2011-01-01

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

  16. The use of specialisation indices to predict vulnerability of coral-feeding butterflyfishes to environmental change

    KAUST Repository

    Lawton, Rebecca J.

    2011-07-14

    In the absence of detailed assessments of extinction risk, ecological specialisation is often used as a proxy of vulnerability to environmental disturbances and extinction risk. Numerous indices can be used to estimate specialisation; however, the utility of these different indices to predict vulnerability to future environmental change is unknown. Here we compare the performance of specialisation indices using coral-feeding butterflyfishes as a model group. Our aims were to 1) quantify the dietary preferences of three butterflyfish species across habitats with differing levels of resource availability; 2) investigate how estimates of dietary specialisation vary with the use of different specialisation indices; 3) determine which specialisation indices best inform predictions of vulnerability to environmental change; and 4) assess the utility of resource selection functions to inform predictions of vulnerability to environmental change. The relative level of dietary specialisation estimated for all three species varied when different specialisation indices were used, indicating that the choice of index can have a considerable impact upon estimates of specialisation. Specialisation indices that do not consider resource abundance may fail to distinguish species that primarily use common resources from species that actively target resources disproportionately more than they are available. Resource selection functions provided the greatest insights into the potential response of species to changes in resource availability. Examination of resource selection functions, in addition to specialisation indices, indicated that Chaetodon trifascialis was the most specialised feeder, with highly conserved dietary preferences across all sites, suggesting that this species is highly vulnerable to the impacts of climate-induced coral loss on reefs. Our results indicate that vulnerability assessments based on some specialisation indices may be misleading and the best estimates of

  17. NBC Hazard Prediction Model Capability Analysis

    Science.gov (United States)

    1999-09-01

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

  18. Data Aggregation, Curation and Modeling Approaches to Deliver Prediction Models to Support Computational Toxicology at the EPA (ACS Fall meeting)

    Science.gov (United States)

    The U.S. Environmental Protection Agency (EPA) Computational Toxicology Program develops and utilizes QSAR modeling approaches across a broad range of applications. In terms of physical chemistry we have a particular interest in the prediction of basic physicochemical parameters ...

  19. Physical and JIT Model Based Hybrid Modeling Approach for Building Thermal Load Prediction

    Science.gov (United States)

    Iino, Yutaka; Murai, Masahiko; Murayama, Dai; Motoyama, Ichiro

    Energy conservation in building fields is one of the key issues in environmental point of view as well as that of industrial, transportation and residential fields. The half of the total energy consumption in a building is occupied by HVAC (Heating, Ventilating and Air Conditioning) systems. In order to realize energy conservation of HVAC system, a thermal load prediction model for building is required. This paper propose a hybrid modeling approach with physical and Just-in-Time (JIT) model for building thermal load prediction. The proposed method has features and benefits such as, (1) it is applicable to the case in which past operation data for load prediction model learning is poor, (2) it has a self checking function, which always supervises if the data driven load prediction and the physical based one are consistent or not, so it can find if something is wrong in load prediction procedure, (3) it has ability to adjust load prediction in real-time against sudden change of model parameters and environmental conditions. The proposed method is evaluated with real operation data of an existing building, and the improvement of load prediction performance is illustrated.

  20. 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...... on presence data. The aim was to identify potential hot spot areas, assess the determinants of palm distribution ranges, and provide a firmer knowledge base for future conservation actions. We focused on a relatively small number of climatic, environmental and spatial variables in order to avoid...... 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...

  1. Corporate prediction models, ratios or regression analysis?

    NARCIS (Netherlands)

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

    1994-01-01

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

  2. Modelling Chemical Reasoning to Predict Reactions

    CERN Document Server

    Segler, Marwin H S

    2016-01-01

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

  3. Environmental dependence in the ellipsoidal collapse model

    CERN Document Server

    Desjacques, Vincent

    2007-01-01

    N-body simulations have demonstrated a correlation between the properties of haloes and their environment. In this paper, we assess whether the ellipsoidal collapse model can produce a similar dependence. First, we explore the statistical correlation that originates from Gaussian initial conditions. We derive analytic expressions for a number of joint statistics of the shear tensor and estimate the sensitivity of the local characteristics of the shear to the global geometry of the large scale environment. Next, we concentrate on the dynamical aspect of the environmental dependence using a simplified model that takes into account the interaction between a collapsing halo and its environment. We find that the tidal force exerted by the surrounding mass distribution causes haloes embedded in overdense regions to virialize earlier. An effective density threshold whose shape depends on the large scale density provides a good description of this environmental effect. We show that, using this approach, a correlation...

  4. Robust predictive modelling of water pollution using biomarker data.

    Science.gov (United States)

    Budka, Marcin; Gabrys, Bogdan; Ravagnan, Elisa

    2010-05-01

    This paper describes the methodology of building a predictive model for the purpose of marine pollution monitoring, based on low quality biomarker data. A step-by-step, systematic data analysis approach is presented, resulting in design of a purely data-driven model, able to accurately discriminate between various coastal water pollution levels. The environmental scientists often try to apply various machine learning techniques to their data without much success, mostly because of the lack of experience with different methods and required 'under the hood' knowledge. Thus this paper is a result of a collaboration between the machine learning and environmental science communities, presenting a predictive model development workflow, as well as discussing and addressing potential pitfalls and difficulties. The novelty of the modelling approach presented lays in successful application of machine learning techniques to high dimensional, incomplete biomarker data, which to our knowledge has not been done before and is the result of close collaboration between machine learning and environmental science communities.

  5. 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.

  6. TRANSGENIC FISH MODEL IN ENVIRONMENTAL TOXICOLOGY

    Directory of Open Access Journals (Sweden)

    Madhuri Sharma

    2012-05-01

    Full Text Available A number of experiments and the use of drugs have been performed in fish. The fish may be used as model organism in various biological experiments, including environmental toxicology. Aquatic animals are being engineered to increase aquaculture production, for medical and industrial research, and for ornamental reasons. Fish have been found to play an important role in assessing potential risks associated with exposure to toxic substances in aquatic environment. Hence, it has been thought that the development of transgenic fish can enhance the use of fish in environmental toxicology. India has developed experimental transgenics of rohu fish, zebra fish, cat fish and singhi fish. Genes, promoters and vectors of indigenous origin are now available for only two species namely rohu and singhi for engineering growth. Development of fish model carrying identical transgenes to those found in rodents is beneficial and has shown that several aspects of in vivo mutagenesis are similar between the two classes of vertebrates. Fish shows the frequencies of spontaneous mutations similar to rodents and respond to mutagen exposure consistent with known mutagenic mechanisms. The feasibility of in vivo mutation analysis using transgenic fish has been demonstrated and the potential value of transgenic fish as a comparative animal model has been illustrated. Therefore, the transgenic fish can give the significant contribution to study the environmental toxicity in animals as a whole.

  7. Predictive modeling of nanomaterial exposure effects in biological systems

    Directory of Open Access Journals (Sweden)

    Liu X

    2013-09-01

    Full Text Available Xiong Liu,1 Kaizhi Tang,1 Stacey Harper,2 Bryan Harper,2 Jeffery A Steevens,3 Roger Xu1 1Intelligent Automation, Inc., Rockville, MD, USA; 2Department of Environmental and Molecular Toxicology, School of Chemical, Biological, and Environmental Engineering, Oregon State University, Corvallis, OR, USA; 3ERDC Environmental Laboratory, Vicksburg, MS, USA Background: Predictive modeling of the biological effects of nanomaterials is critical for industry and policymakers to assess the potential hazards resulting from the application of engineered nanomaterials. Methods: We generated an experimental dataset on the toxic effects experienced by embryonic zebrafish due to exposure to nanomaterials. Several nanomaterials were studied, such as metal nanoparticles, dendrimer, metal oxide, and polymeric materials. The embryonic zebrafish metric (EZ Metric was used as a screening-level measurement representative of adverse effects. Using the dataset, we developed a data mining approach to model the toxic endpoints and the overall biological impact of nanomaterials. Data mining techniques, such as numerical prediction, can assist analysts in developing risk assessment models for nanomaterials. Results: We found several important attributes that contribute to the 24 hours post-fertilization (hpf mortality, such as dosage concentration, shell composition, and surface charge. These findings concur with previous studies on nanomaterial toxicity using embryonic zebrafish. We conducted case studies on modeling the overall effect/impact of nanomaterials and the specific toxic endpoints such as mortality, delayed development, and morphological malformations. The results show that we can achieve high prediction accuracy for certain biological effects, such as 24 hpf mortality, 120 hpf mortality, and 120 hpf heart malformation. The results also show that the weighting scheme for individual biological effects has a significant influence on modeling the overall impact of

  8. Life Prediction Issues in Thermal/Environmental Barrier Coatings in Ceramic Matrix Composites

    Science.gov (United States)

    Shah, Ashwin R.; Brewer, David N.; Murthy, Pappu L. N.

    2001-01-01

    Issues and design requirements for the environmental barrier coating (EBC)/thermal barrier coating (TBC) life that are general and those specific to the NASA Ultra-Efficient Engine Technology (UEET) development program have been described. The current state and trend of the research, methods in vogue related to the failure analysis, and long-term behavior and life prediction of EBCITBC systems are reported. Also, the perceived failure mechanisms, variables, and related uncertainties governing the EBCITBC system life are summarized. A combined heat transfer and structural analysis approach based on the oxidation kinetics using the Arrhenius theory is proposed to develop a life prediction model for the EBC/TBC systems. Stochastic process-based reliability approach that includes the physical variables such as gas pressure, temperature, velocity, moisture content, crack density, oxygen content, etc., is suggested. Benefits of the reliability-based approach are also discussed in the report.

  9. Genetic models of homosexuality: generating testable predictions

    OpenAIRE

    Gavrilets, Sergey; Rice, William R.

    2006-01-01

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

  10. Climate predictions: the chaos and complexity in climate models

    CERN Document Server

    Mihailović, Dragutin T; Arsenić, Ilija

    2013-01-01

    Some issues which are relevant for the recent state in climate modeling have been considered. A detailed overview of literature related to this subject is given. The concept in modeling of climate, as a complex system, seen through Godel's Theorem and Rosen's definition of complexity and predictability is discussed. It is pointed out to occurrence of chaos in computing the environmental interface temperature from the energy balance equation given in a difference form. A coupled system of equations, often used in climate models is analyzed. It is shown that the Lyapunov exponent mostly has positive values allowing presence of chaos in this systems. The horizontal energy exchange between environmental interfaces, which is described by the dynamics of driven coupled oscillators, is analyzed. Their behavior and synchronization, when a perturbation is introduced in the system, as a function of the coupling parameters, the logistic parameter and the parameter of exchange, was studied calculating the Lyapunov expone...

  11. Wind farm production prediction - The Zephyr model

    Energy Technology Data Exchange (ETDEWEB)

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

    2002-06-01

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

  12. Mathematical models for predicting indoor air quality from smoking activity.

    Science.gov (United States)

    Ott, W R

    1999-05-01

    Much progress has been made over four decades in developing, testing, and evaluating the performance of mathematical models for predicting pollutant concentrations from smoking in indoor settings. Although largely overlooked by the regulatory community, these models provide regulators and risk assessors with practical tools for quantitatively estimating the exposure level that people receive indoors for a given level of smoking activity. This article reviews the development of the mass balance model and its application to predicting indoor pollutant concentrations from cigarette smoke and derives the time-averaged version of the model from the basic laws of conservation of mass. A simple table is provided of computed respirable particulate concentrations for any indoor location for which the active smoking count, volume, and concentration decay rate (deposition rate combined with air exchange rate) are known. Using the indoor ventilatory air exchange rate causes slightly higher indoor concentrations and therefore errs on the side of protecting health, since it excludes particle deposition effects, whereas using the observed particle decay rate gives a more accurate prediction of indoor concentrations. This table permits easy comparisons of indoor concentrations with air quality guidelines and indoor standards for different combinations of active smoking counts and air exchange rates. The published literature on mathematical models of environmental tobacco smoke also is reviewed and indicates that these models generally give good agreement between predicted concentrations and actual indoor measurements.

  13. Integrated environmental modeling: a vision and roadmap for the future

    Science.gov (United States)

    Laniak, Gerard F.; Olchin, Gabriel; Goodall, Jonathan; Voinov, Alexey; Hill, Mary; Glynn, Pierre; Whelan, Gene; Geller, Gary; Quinn, Nigel; Blind, Michiel; Peckham, Scott; Reaney, Sim; Gaber, Noha; Kennedy, Philip R.; Hughes, Andrew

    2013-01-01

    Integrated environmental modeling (IEM) is inspired by modern environmental problems, decisions, and policies and enabled by transdisciplinary science and computer capabilities that allow the environment to be considered in a holistic way. The problems are characterized by the extent of the environmental system involved, dynamic and interdependent nature of stressors and their impacts, diversity of stakeholders, and integration of social, economic, and environmental considerations. IEM provides a science-based structure to develop and organize relevant knowledge and information and apply it to explain, explore, and predict the behavior of environmental systems in response to human and natural sources of stress. During the past several years a number of workshops were held that brought IEM practitioners together to share experiences and discuss future needs and directions. In this paper we organize and present the results of these discussions. IEM is presented as a landscape containing four interdependent elements: applications, science, technology, and community. The elements are described from the perspective of their role in the landscape, current practices, and challenges that must be addressed. Workshop participants envision a global scale IEM community that leverages modern technologies to streamline the movement of science-based knowledge from its sources in research, through its organization into databases and models, to its integration and application for problem solving purposes. Achieving this vision will require that the global community of IEM stakeholders transcend social, and organizational boundaries and pursue greater levels of collaboration. Among the highest priorities for community action are the development of standards for publishing IEM data and models in forms suitable for automated discovery, access, and integration; education of the next generation of environmental stakeholders, with a focus on transdisciplinary research, development, and

  14. Predictive model for segmented poly(urea

    Directory of Open Access Journals (Sweden)

    Frankl P.

    2012-08-01

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

  15. The NASA environmental models of Mars

    Science.gov (United States)

    Kaplan, D. I.

    1991-01-01

    NASA environmental models are discussed with particular attention given to the Mars Global Reference Atmospheric Model (Mars-GRAM) and the Mars Terrain simulator. The Mars-GRAM model takes into account seasonal, diurnal, and surface topography and dust storm effects upon the atmosphere. It is also capable of simulating appropriate random density perturbations along any trajectory path through the atmosphere. The Mars Terrain Simulator is a software program that builds pseudo-Martian terrains by layering the effects of geological processes upon one another. Output pictures of the constructed surfaces can be viewed from any vantage point under any illumination conditions. Attention is also given to the document 'Environment of Mars, 1988' in which scientific models of the Martian atmosphere and Martian surface are presented.

  16. 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.

  17. Predictive QSAR modeling of phosphodiesterase 4 inhibitors.

    Science.gov (United States)

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

    2012-02-01

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

  18. Crops Models for Varying Environmental Conditions

    Science.gov (United States)

    Jones, Harry; Cavazzoni, James; Keas, Paul

    2001-01-01

    New variable environment Modified Energy Cascade (MEC) crop models were developed for all the Advanced Life Support (ALS) candidate crops and implemented in SIMULINK. The MEC models are based on the Volk, Bugbee, and Wheeler Energy Cascade (EC) model and are derived from more recent Top-Level Energy Cascade (TLEC) models. The MEC models simulate crop plant responses to day-to-day changes in photosynthetic photon flux, photoperiod, carbon dioxide level, temperature, and relative humidity. The original EC model allows changes in light energy but uses a less accurate linear approximation. The simulation outputs of the new MEC models for constant nominal environmental conditions are very similar to those of earlier EC models that use parameters produced by the TLEC models. There are a few differences. The new MEC models allow setting the time for seed emergence, have realistic exponential canopy growth, and have corrected harvest dates for potato and tomato. The new MEC models indicate that the maximum edible biomass per meter squared per day is produced at the maximum allowed carbon dioxide level, the nominal temperatures, and the maximum light input. Reducing the carbon dioxide level from the maximum to the minimum allowed in the model reduces crop production significantly. Increasing temperature decreases production more than it decreases the time to harvest, so productivity in edible biomass per meter squared per day is greater at nominal than maximum temperatures, The productivity in edible biomass per meter squared per day is greatest at the maximum light energy input allowed in the model, but the edible biomass produced per light energy input unit is lower than at nominal light levels. Reducing light levels increases light and power use efficiency. The MEC models suggest we can adjust the light energy day-to- day to accommodate power shortages or Lise excess power while monitoring and controlling edible biomass production.

  19. Predictive vegetation modeling for conservation: impact of error propagation from digital elevation data.

    Science.gov (United States)

    Van Niel, Kimberly P; Austin, Mike P

    2007-01-01

    The effect of digital elevation model (DEM) error on environmental variables, and subsequently on predictive habitat models, has not been explored. Based on an error analysis of a DEM, multiple error realizations of the DEM were created and used to develop both direct and indirect environmental variables for input to predictive habitat models. The study explores the effects of DEM error and the resultant uncertainty of results on typical steps in the modeling procedure for prediction of vegetation species presence/absence. Results indicate that all of these steps and results, including the statistical significance of environmental variables, shapes of species response curves in generalized additive models (GAMs), stepwise model selection, coefficients and standard errors for generalized linear models (GLMs), prediction accuracy (Cohen's kappa and AUC), and spatial extent of predictions, were greatly affected by this type of error. Error in the DEM can affect the reliability of interpretations of model results and level of accuracy in predictions, as well as the spatial extent of the predictions. We suggest that the sensitivity of DEM-derived environmental variables to error in the DEM should be considered before including them in the modeling processes.

  20. Amphibians as models for studying environmental change.

    Science.gov (United States)

    Hopkins, William A

    2007-01-01

    The use of amphibians as models in ecological research has a rich history. From an early foundation in studies of amphibian natural history sprang generations of scientists who used amphibians as models to address fundamental questions in population and community ecology. More recently, in the wake of an environment that human disturbances rapidly altered, ecologists have adopted amphibians as models for studying applied ecological issues such as habitat loss, pollution, disease, and global climate change. Some of the characteristics of amphibians that make them useful models for studying these environmental problems are highlighted, including their trophic importance, environmental sensitivity, research tractability, and impending extinction. The article provides specific examples from the recent literature to illustrate how studies on amphibians have been instrumental in guiding scientific thought on a broad scale. Included are examples of how amphibian research has transformed scientific disciplines, generated new theories about global health, called into question widely accepted scientific paradigms, and raised awareness in the general public that our daily actions may have widespread repercussions. In addition, studies on amphibian declines have provided insight into the complexity in which multiple independent factors may interact with one another to produce catastrophic and sometimes unpredictable effects. Because of the complexity of these problems, amphibian ecologists have been among the strongest advocates for interdisciplinary research. Future studies of amphibians will be important not only for their conservation but also for the conservation of other species, critical habitats, and entire ecosystems.

  1. Modelling the predictive performance of credit scoring

    Directory of Open Access Journals (Sweden)

    Shi-Wei Shen

    2013-02-01

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

  2. Calibrated predictions for multivariate competing risks models.

    Science.gov (United States)

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

    2014-04-01

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

  3. 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.

  4. 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.

  5. Global Solar Dynamo Models: Simulations and Predictions

    Indian Academy of Sciences (India)

    Mausumi Dikpati; Peter A. Gilman

    2008-03-01

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

  6. Model Predictive Control of Sewer Networks

    Science.gov (United States)

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

    2017-01-01

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

  7. DKIST Polarization Modeling and Performance Predictions

    Science.gov (United States)

    Harrington, David

    2016-05-01

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

  8. Modelling Chemical Reasoning to Predict Reactions

    OpenAIRE

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

    2016-01-01

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

  9. Predictive Modeling of the CDRA 4BMS

    Science.gov (United States)

    Coker, Robert; Knox, James

    2016-01-01

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

  10. Raman Model Predicting Hardness of Covalent Crystals

    OpenAIRE

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

    2009-01-01

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

  11. Predictive Modelling of Mycotoxins in Cereals

    NARCIS (Netherlands)

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

    2015-01-01

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

  12. Unreachable Setpoints in Model Predictive Control

    DEFF Research Database (Denmark)

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

    2008-01-01

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

  13. Predictive Modelling of Mycotoxins in Cereals

    NARCIS (Netherlands)

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

    2015-01-01

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

  14. Prediction modelling for population conviction data

    NARCIS (Netherlands)

    Tollenaar, N.

    2017-01-01

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

  15. A Predictive Model for MSSW Student Success

    Science.gov (United States)

    Napier, Angela Michele

    2011-01-01

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

  16. Predictability of extreme values in geophysical models

    NARCIS (Netherlands)

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

    2012-01-01

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

  17. A revised prediction model for natural conception

    NARCIS (Netherlands)

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

    2017-01-01

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

  18. Distributed Model Predictive Control via Dual Decomposition

    DEFF Research Database (Denmark)

    Biegel, Benjamin; Stoustrup, Jakob; Andersen, Palle

    2014-01-01

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

  19. Predictive Modelling of Mycotoxins in Cereals

    NARCIS (Netherlands)

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

    2015-01-01

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

  20. Leptogenesis in minimal predictive seesaw models

    CERN Document Server

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

    2015-01-01

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

  1. Prediction of gas chromatographic retention indices as classifier in non-target analysis of environmental samples.

    Science.gov (United States)

    Ulrich, Nadin; Schüürmann, Gerrit; Brack, Werner

    2013-04-12

    Kováts and Lee retention indices were predicted by the help of experimental and calculated boiling points and also by linear solvation energy relationship (LSER) models. The developed approaches should be applied as classifiers in non-target analysis of environmental samples to identify contaminants. To demonstrate the application as a classifier, an example of 14 isomers with empirical formula C12H10O2 was selected. Furthermore, seven compounds with different molecular composition were used to illustrate the application in non-target analysis, where progressive candidate exclusion is performed. The models help to reduce the number of potential candidates, and seem to be a useful addition to already existing classifiers.

  2. Innovative mathematical modeling in environmental remediation

    Energy Technology Data Exchange (ETDEWEB)

    Yeh, Gour T. [Taiwan Typhoon and Flood Research Institute (Taiwan); National Central Univ. (Taiwan); Univ. of Central Florida (United States); Gwo, Jin Ping [Nuclear Regulatory Commission (NRC), Rockville, MD (United States); Siegel, Malcolm D. [Sandia National Laboratories, Albuquerque, NM (United States); Li, Ming-Hsu [National Central Univ. (Taiwan); ; Fang, Yilin [Pacific Northwest National Laboratory (PNNL), Richland, WA (United States); Zhang, Fan [Inst. of Tibetan Plateau Research, Chinese Academy of Sciences (China); Luo, Wensui [Inst. of Tibetan Plateau Research, Chinese Academy of Sciences (China); Yabusaki, Steven B. [Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)

    2013-05-01

    There are two different ways to model reactive transport: ad hoc and innovative reaction-based approaches. The former, such as the Kd simplification of adsorption, has been widely employed by practitioners, while the latter has been mainly used in scientific communities for elucidating mechanisms of biogeochemical transport processes. It is believed that innovative mechanistic-based models could serve as protocols for environmental remediation as well. This paper reviews the development of a mechanistically coupled fluid flow, thermal transport, hydrologic transport, and reactive biogeochemical model and example-applications to environmental remediation problems. Theoretical bases are sufficiently described. Four example problems previously carried out are used to demonstrate how numerical experimentation can be used to evaluate the feasibility of different remediation approaches. The first one involved the application of a 56-species uranium tailing problem to the Melton Branch Subwatershed at Oak Ridge National Laboratory (ORNL) using the parallel version of the model. Simulations were made to demonstrate the potential mobilization of uranium and other chelating agents in the proposed waste disposal site. The second problem simulated laboratory-scale system to investigate the role of natural attenuation in potential off-site migration of uranium from uranium mill tailings after restoration. It showed inadequacy of using a single Kd even for a homogeneous medium. The third example simulated laboratory experiments involving extremely high concentrations of uranium, technetium, aluminum, nitrate, and toxic metals (e.g.,Ni, Cr, Co).The fourth example modeled microbially-mediated immobilization of uranium in an unconfined aquifer using acetate amendment in a field-scale experiment. The purposes of these modeling studies were to simulate various mechanisms of mobilization and immobilization of radioactive wastes and to illustrate how to apply reactive transport models

  3. Contrasting Water-Use Efficiency (WUE) Responses of a Potato Mapping Population and Capability of Modified Ball-Berry Model to Predict Stomatal Conductance and WUE Measured at Different Environmental Conditions

    DEFF Research Database (Denmark)

    Kaminski, K. P.; Sørensen, Kirsten Kørup; Kristensen, Kristian

    2015-01-01

    .001). The leaf chlorophyll content was lower in the high-WUE group indicating that the higher net photosynthesis rate was not due to higher leaf-N status. Less negative value of carbon isotope discrimination (δ13C) in the high-WUE group was only found in 2011. A modified Ball-Berry model was fitted to measured...... stomatal conductance (gs) under the systematically varied environmental conditions to identify parameter differences between the two groups, which could explain their contrasting WUE. Compared to the low-WUE group, the high-WUE group showed consistently lower values of the parameter m, which is inversely...... 0.5 to 3.5 kPa. The mapping population was normally distributed with respect to WUE suggesting a multigenic nature of this trait. The WUE groups identified can be further employed for quantitative trait loci (QTL) analysis by use of gene expression studies or genome resequencing. The differences...

  4. Specialized Language Models using Dialogue Predictions

    CERN Document Server

    Popovici, C; Popovici, Cosmin; Baggia, Paolo

    1996-01-01

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

  5. 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.

  6. 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

  7. 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...

  8. A COMPACT MODEL FOR PREDICTING ROAD TRAFFIC NOISE

    Directory of Open Access Journals (Sweden)

    R. Golmohammadi ، M. Abbaspour ، P. Nassiri ، H. Mahjub

    2009-07-01

    Full Text Available Noise is one of the most important sources of pollution in the metropolitan areas. The recognition of road traffic noise as one of the main sources of environmental pollution has led to develop models that enable us to predict noise level from fundamental variables. Traffic noise prediction models are required as aids in the design of roads and sometimes in the assessment of existing, or envisaged changes in, traffic noise conditions. The purpose of this study was to design a prediction road traffic noise model from traffic variables and conditions of transportation in Iran.This paper is the result of a research conducted in the city of Hamadan with the ultimate objective of setting up a traffic noise model based on the traffic conditions of Iranian cities. Noise levels and other variables have been measured in 282 samples to develop a statistical regression model based on A-weighted equivalent noise level for Iranian road condition. The results revealed that the average LAeq in all stations was 69.04± 4.25 dB(A, the average speed of vehicles was 44.57±11.46 km/h and average traffic load was 1231.9 ± 910.2 V/h.The developed model has seven explanatory entrance variables in order to achieve a high regression coefficient (R2=0.901. Comparing means of predicted and measuring equivalent sound pressure level (LAeq showed small difference less than -0.42 dB(A and -0.77 dB(A for Tehran and Hamadan cities, respectively. The suggested road traffic noise model can be effectively used as a decision support tool for predicting equivalent sound pressure level index in the cities of Iran.

  9. ENSO Prediction using Vector Autoregressive Models

    Science.gov (United States)

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

    2013-12-01

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

  10. 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.)

  11. Gas explosion prediction using CFD models

    Energy Technology Data Exchange (ETDEWEB)

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

    2006-07-15

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

  12. Genetic models of homosexuality: generating testable predictions.

    Science.gov (United States)

    Gavrilets, Sergey; Rice, William R

    2006-12-22

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

  13. 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.

  14. A constraint-based search algorithm for parameter identification of environmental models

    NARCIS (Netherlands)

    Gharari, S.; Shafiei, M.; Hrachowitz, M.; Kumar, R.; Fenicia, F.; Gupta, H.V.; Savenije, H.H.G.

    2014-01-01

    Many environmental systems models, such as conceptual rainfall-runoff models, rely on model calibration for parameter identification. For this, an observed output time series (such as runoff) is needed, but frequently not available (e.g., when making predictions in ungauged basins). In this study, w

  15. A Study On Distributed Model Predictive Consensus

    CERN Document Server

    Keviczky, Tamas

    2008-01-01

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

  16. NONLINEAR MODEL PREDICTIVE CONTROL OF CHEMICAL PROCESSES

    Directory of Open Access Journals (Sweden)

    R. G. SILVA

    1999-03-01

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

  17. Advancing an Information Model for Environmental Observations

    Science.gov (United States)

    Horsburgh, J. S.; Aufdenkampe, A. K.; Hooper, R. P.; Lehnert, K. A.; Schreuders, K.; Tarboton, D. G.; Valentine, D. W.; Zaslavsky, I.

    2011-12-01

    have been modified to support data management for the Critical Zone Observatories (CZOs). This paper will present limitations of the existing information model used by the CUAHSI HIS that have been uncovered through its deployment and use, as well as new advances to the information model, including: better representation of both in situ observations from field sensors and observations derived from environmental samples, extensibility in attributes used to describe observations, and observation provenance. These advances have been developed by the HIS team and the broader scientific community and will enable the information model to accommodate and better describe wider classes of environmental observations and to better meet the needs of the hydrologic science and CZO communities.

  18. Performance model to predict overall defect density

    Directory of Open Access Journals (Sweden)

    J Venkatesh

    2012-08-01

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

  19. 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.

  20. 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 (pMAXENT, and SVM. Compared to BIOCLIM and DOMAIN, other SDMs (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.

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

    Directory of Open Access Journals (Sweden)

    Ren-Yan Duan

    Full Text Available 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 (p<0.05, while the associated standard deviations and coefficients of variation were larger for BIOCLIM and DOMAIN trials (p<0.05, and the 99% confidence intervals for AUC and Kappa values were narrower for MAHAL, RF, MAXENT, and SVM. Compared to BIOCLIM and DOMAIN, other SDMs (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.

  2. Predicting Cereal Root Disease in Western Australia Using Soil DNA and Environmental Parameters.

    Science.gov (United States)

    Poole, Grant J; Harries, Martin; Hüberli, D; Miyan, S; MacLeod, W J; Lawes, Roger; McKay, A

    2015-08-01

    Root diseases have long been prevalent in Australian grain-growing regions, and most management decisions to reduce the risk of yield loss need to be implemented before the crop is sown. The levels of pathogens that cause the major root diseases can be measured using DNA-based services such as PreDicta B. Although these pathogens are often studied individually, in the field they often occur as mixed populations and their combined effect on crop production is likely to vary across diverse cropping environments. A 3-year survey was conducted covering most cropping regions in Western Australia, utilizing PreDicta B to determine soilborne pathogen levels and visual assessments to score root health and incidence of individual crop root diseases caused by the major root pathogens, including Rhizoctonia solani (anastomosis group [AG]-8), Gaeumannomyces graminis var. tritici (take-all), Fusarium pseudograminearum, and Pratylenchus spp. (root-lesion nematodes) on wheat roots for 115, 50, and 94 fields during 2010, 2011, and 2012, respectively. A predictive model was developed for root health utilizing autumn and summer rainfall and soil temperature parameters. The model showed that pathogen DNA explained 16, 5, and 2% of the variation in root health whereas environmental parameters explained 22, 11, and 1% of the variation in 2010, 2011, and 2012, respectively. Results showed that R. solani AG-8 soil pathogen DNA, environmental soil temperature, and rainfall parameters explained most of the variation in the root health. This research shows that interactions between environment and pathogen levels before seeding can be utilized in predictive models to improve assessment of risk from root diseases to assist growers to plan more profitable cropping programs.

  3. Pressure prediction model for compression garment design.

    Science.gov (United States)

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

    2010-01-01

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

  4. Statistical assessment of predictive modeling uncertainty

    Science.gov (United States)

    Barzaghi, Riccardo; Marotta, Anna Maria

    2017-04-01

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

  5. Use of Geochemical Indices in Environmental Assessment of Soil; the Predictable and the Predictably Unpredictable

    Science.gov (United States)

    Mikkonen, Hannah; Clarke, Bradley; van de Graaff, Robert; Reichman, Suzie

    2016-04-01

    Geochemical correlations between common contaminants (Pb, Ni, As, Cr, Co and Zn) and earth metals, Fe and Mn, have been recommended as empirical tools to estimate "background" concentrations of metals in soil. A limited number of studies indicate that geochemical ratios between Pb, Ni, As, Cr, Co, V and Zn with scavenger metals Fe or Mn, are consistent between soils collected from different regions (Hamon et al. 2004, Myers and Thorbjornsen 2004). These studies have resulted in the incorporation of geochemical indices into Australian guidance, for derivation of ecological investigation levels for Ni, Cr, Cu and Zn. However, little research has been undertaken to assess the variation of geochemical patterns between soils derived from different parent materials or different weathering environments. A survey of background soils derived from four different parent materials, across Victoria, Australia, was undertaken, comprising collection of samples (n=640) from the surface (0 to 0.1 m) and sub-surface (0.3 to 0.6 m). Soil samples were collected from urban and rural areas of low disturbance, away from point sources of contamination. Samples were analysed for metals/metalloids and soil physical and chemical properties. Statistical review of results included regression and multivariate analysis. The results of the soil survey were compared against geochemical relationships reported within Australia and internationally. Compilation of results from this study and international data sets, indicates that geochemical relationships for metals Cr and V (in the format of log[Cr] = alog[Fe] +c) are predictable, not only between soils derived from different parent materials, but also between soils of different continents. Conversely, relationships between Zn and Fe, Pb and Fe, Cu and Fe, Co and Mn are variable, particularly within soils derived from alluvial sediments, which may have undergone periods of reducing conditions, resulting in dissociation from metal oxides. Broad

  6. Seasonal Predictability in a Model Atmosphere.

    Science.gov (United States)

    Lin, Hai

    2001-07-01

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

  7. Seagrass Health Modeling and Prediction with NASA Science Data

    Science.gov (United States)

    Robinson, Harold D.; Easson, Greg; Slattery, Marc; Anderson, Daniel; Blonski, Slawomir; DeCurtins, Robert; Underwood, Lauren

    2010-01-01

    Previous research has demonstrated that MODIS data products can be used as inputs into the seagrass productivity model developed by Fong and Harwell (1994). To further explore this use to predict seagrass productivity, Moderate Resolution Imaging Spectroradiometer (MODIS) custom data products, including Sea Surface Temperature, Light Attenuation, and Chlorophyll-a have been created for use as model parameter inputs. Coastal researchers can use these MODIS data products and model results in conjunction with historical and daily assessment of seagrass conditions to assess variables that affect the productivity of the seagrass beds. Current monitoring practices involve manual data collection (typically on a quarterly basis) and the data is often insufficient for evaluating the dynamic events that influence seagrass beds. As part of a NASA-funded research grant, the University of Mississippi, is working with researchers at NASA and Radiance Technologies to develop methods to deliver MODIS derived model output for the northern Gulf of Mexico (GOM) to coastal and environmental managers. The result of the project will be a data portal that provides access to MODIS data products and model results from the past 5 years, that includes an automated process to incorporate new data as it becomes available. All model parameters and final output will be available through the use National Oceanic and Atmospheric Administration?s (NOAA) Environmental Research Divisions Data Access Program (ERDDAP) tools as well as viewable using Thematic Realtime Environmental Distributed Data Services (THREDDS) and the Integrated Data Viewer (IDV). These tools provide the ability to create raster-based time sequences of model output and parameters as well as create graphs of model parameters versus time. This tool will provide researchers and coastal managers the ability to analyze the model inputs so that the factors influencing a change in seagrass productivity can be determined over time.

  8. Thermal Residual Stress in Environmental Barrier Coated Silicon Nitride - Modeled

    Science.gov (United States)

    Ali, Abdul-Aziz; Bhatt, Ramakrishna T.

    2009-01-01

    When exposed to combustion environments containing moisture both un-reinforced and fiber reinforced silicon based ceramic materials tend to undergo surface recession. To avoid surface recession environmental barrier coating systems are required. However, due to differences in the elastic and thermal properties of the substrate and the environmental barrier coating, thermal residual stresses can be generated in the coated substrate. Depending on their magnitude and nature thermal residual stresses can have significant influence on the strength and fracture behavior of coated substrates. To determine the maximum residual stresses developed during deposition of the coatings, a finite element model (FEM) was developed. Using this model, the thermal residual stresses were predicted in silicon nitride substrates coated with three environmental coating systems namely barium strontium aluminum silicate (BSAS), rare earth mono silicate (REMS) and earth mono di-silicate (REDS). A parametric study was also conducted to determine the influence of coating layer thickness and material parameters on thermal residual stress. Results indicate that z-direction stresses in all three systems are small and negligible, but maximum in-plane stresses can be significant depending on the composition of the constituent layer and the distance from the substrate. The BSAS and REDS systems show much lower thermal residual stresses than REMS system. Parametric analysis indicates that in each system, the thermal residual stresses can be decreased with decreasing the modulus and thickness of the coating.

  9. 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

  10. 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...

  11. Evaluation of Sub-Zonal Airflow Models for the Prediction of Local Interior Boundary Conditions

    DEFF Research Database (Denmark)

    Steskens, Paul W. M. H.; Janssen, Hans; Rode, Carsten

    2013-01-01

    and applicability of the sub-zonal airflow model to predict the local indoor environmental conditions, as well as the local surface transfer coefficients near building components. Two test cases were analyzed for, respectively, natural and forced convection in a room. The simulation results predicted from the sub...

  12. 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.

  13. Probabilistic prediction models for aggregate quarry siting

    Science.gov (United States)

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

    2007-01-01

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

  14. APPLICATION OF ARTIFICIAL NEURAL NETWORKS FOR PREDICTION OF AIR POLLUTION LEVELS IN ENVIRONMENTAL MONITORING

    Directory of Open Access Journals (Sweden)

    Małgorzata Pawul

    2016-09-01

    Full Text Available Recently, a lot of attention was paid to the improvement of methods which are used to air quality forecasting. Artificial neural networks can be applied to model these problems. Their advantage is that they can solve the problem in the conditions of incomplete information, without the knowledge of the analytical relationship between the input and output data. In this paper we applied artificial neural networks to predict the PM 10 concentrations as factors determining the occurrence of smog phenomena. To create these networks we used meteorological data and concentrations of PM 10. The data were recorded in 2014 and 2015 at three measuring stations operating in Krakow under the State Environmental Monitoring. The best results were obtained by three-layer perceptron with back-propagation algorithm. The neural networks received a good fit in all cases.

  15. Predicting Footbridge Response using Stochastic Load Models

    DEFF Research Database (Denmark)

    Pedersen, Lars; Frier, Christian

    2013-01-01

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

  16. Nonconvex Model Predictive Control for Commercial Refrigeration

    DEFF Research Database (Denmark)

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

    2013-01-01

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

  17. Development of Prediction System for Environmental Burden for Machine Tool Operation

    Science.gov (United States)

    Narita, Hirohisa; Kawamura, Hiroshi; Norihisa, Takashi; Chen, Lian-Yi; Fujimoto, Hideo; Hasebe, Takao

    Recently, some activities for environmental protection have been attempted to reduce environmental burdens in many fields. The manufacturing field also requires such reduction. Hence, a prediction system for environmental burden for machining operation is proposed based on the Life Cycle Assessment (LCA) policy for the future manufacturing system in this research. This system enables the calculation of environmental burden (equivalent CO2 emission) due to the electric consumption of machine tool components, cutting tool status, coolant quantity, lubricant oil quantity and metal chip quantity, and provides accurate information of environmental burden of the machining process by considering some activities related to machine tool operation. In this paper, the development of the prediction system is described. As a case study, two Numerical Control (NC) programs that manufacture a simple shape are evaluated to show the feasibility of the proposed system.

  18. Predictive In Vivo Models for Oncology.

    Science.gov (United States)

    Behrens, Diana; Rolff, Jana; Hoffmann, Jens

    2016-01-01

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

  19. Constructing predictive models of human running.

    Science.gov (United States)

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

    2015-02-06

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

  20. Statistical Seasonal Sea Surface based Prediction Model

    Science.gov (United States)

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

    2014-05-01

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

  1. Proposing an Environmental Excellence Self-Assessment Model

    DEFF Research Database (Denmark)

    Meulengracht Jensen, Peter; Johansen, John; Wæhrens, Brian Vejrum

    2013-01-01

    This paper presents an Environmental Excellence Self-Assessment (EEA) model based on the structure of the European Foundation of Quality Management Business Excellence Framework. Four theoretical scenarios for deploying the model are presented as well as managerial implications, suggesting...... that the EEA model can be used in global organizations to differentiate environmental efforts depending on the maturity stage of the individual sites. Furthermore, the model can be used to support the decision-making process regarding when organizations should embark on more complex environmental efforts...... to continue to realize excellent environmental results. Finally, a development trajectory for environmental excellence is presented....

  2. Accommodating environmental variation in population models: metaphysiological biomass loss accounting.

    Science.gov (United States)

    Owen-Smith, Norman

    2011-07-01

    1. There is a pressing need for population models that can reliably predict responses to changing environmental conditions and diagnose the causes of variation in abundance in space as well as through time. In this 'how to' article, it is outlined how standard population models can be modified to accommodate environmental variation in a heuristically conducive way. This approach is based on metaphysiological modelling concepts linking populations within food web contexts and underlying behaviour governing resource selection. Using population biomass as the currency, population changes can be considered at fine temporal scales taking into account seasonal variation. Density feedbacks are generated through the seasonal depression of resources even in the absence of interference competition. 2. Examples described include (i) metaphysiological modifications of Lotka-Volterra equations for coupled consumer-resource dynamics, accommodating seasonal variation in resource quality as well as availability, resource-dependent mortality and additive predation, (ii) spatial variation in habitat suitability evident from the population abundance attained, taking into account resource heterogeneity and consumer choice using empirical data, (iii) accommodating population structure through the variable sensitivity of life-history stages to resource deficiencies, affecting susceptibility to oscillatory dynamics and (iv) expansion of density-dependent equations to accommodate various biomass losses reducing population growth rate below its potential, including reductions in reproductive outputs. Supporting computational code and parameter values are provided. 3. The essential features of metaphysiological population models include (i) the biomass currency enabling within-year dynamics to be represented appropriately, (ii) distinguishing various processes reducing population growth below its potential, (iii) structural consistency in the representation of interacting populations and

  3. t-GIS AND ENVIRONMENTAL DYNAMIC MODELS

    Institute of Scientific and Technical Information of China (English)

    2000-01-01

    Temporal geographic information system(t-GIS) is a kind of computer information system that can display,process and analyze the micro-format distribution of temporal-spatial information of real world.It includes both spatial geographic information and temporal information,and can analyze both the static geographic information and the dynamic geographic information.Three models based on t-GIS for environment dynamics,namely,the mechanism method,the experience method and the mixed method are given.t-GIS based on the environment dynamic model has more new functions than traditional GIS,such as fast I/O,inquiry,static/dynamic display and visible analysis of spatial and temporal sequence information,especially it can display the image of the evolution in the past,current and future environment through the extrapolation method within its defining region.The velocity for analogue display can be accelerated by setting up time-varying-area-function for position and attribute.Dynamic environmental information analysis systems based on t-GIS are applicable to almost all the fields related to management,display and analysis of local environmental dynamic information.For this reason,some considerations to construct distinct information systems have been enumerated in this paper,such as,the analysis information system for terrain evolution,the analysis information system for the condition of water and fertilizer of farmland,analysis and the evaluation information system for ecological environment,and the analysis information system for distribution changes of population of China,etc.

  4. Modelling of Malaria Risk Areas in Ghana by using Environmental ...

    African Journals Online (AJOL)

    Michael

    2015-12-02

    Dec 2, 2015 ... model of malaria using eight risk factors ranging from environmental to ... transmission. Keywords: Malaria, GIS, Analytical Hierarchy Process, Weighted Overlay ...... of Remote Sensing and GIS in Environmental Management,.

  5. Synergetic-bifurcated prediction model of slope occurrence and its application

    Institute of Scientific and Technical Information of China (English)

    HUANG Zhiquan; WANG Sijing

    2003-01-01

    Landslide prediction is one of the most important aspects of prevention and control for geological hazards and the environmental protection. In order to study the nonlinear methods for landslide prediction, the synergetic-bifurcated model of predicting the timing of slope failure is established by combining Synergetics with Bifurcation Theory based on single-state variable friction law in this paper. The synergetic effects and bifurcated process of the factors in the slope evolution can be characterized in the model. Taking the Xintan Landslide as an example, the prediction of landslide is carried out based on the model suggested.

  6. Predictive modeling by the cerebellum improves proprioception.

    Science.gov (United States)

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

    2013-09-04

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

  7. In search of greener pastures: Using satellite images to predict the effects of environmental change on zebra migration

    Science.gov (United States)

    Bartlam-Brooks, Hattie L. A.; Beck, Pieter S. A.; Bohrer, Gil; Harris, Stephen

    2013-12-01

    ungulate migrations occurred in most grassland and boreal woodland ecosystems, but many have been lost due to increasing habitat loss and fragmentation. With the rate of environmental change increasing, identifying and prioritizing migration routes for conservation has taken on a new urgency. Understanding the cues that drive long-distance animal movements is critical to predicting the fate of migrations under different environmental change scenarios and how large migratory herbivores will respond to increasing resource heterogeneity and anthropogenic influences. We used an individual-based modeling approach to investigate the influence of environmental conditions, monitored using satellite data, on departure date and movement speed of migrating zebras in Botswana. Daily zebra movements between dry and rainy season ranges were annotated with coincident observations of precipitation from the Tropical Rainfall Measuring Mission data set and Moderate Resolution Imaging Spectroradiometer-derived normalized difference vegetation index (NDVI). An array of increasingly complex movement models representing alternative hypotheses regarding the environmental cues and controls for movement was parameterized and tested. The best and most justified model predicted daily zebra movement as two linear functions of precipitation rate and NDVI and included a modeled departure date as a function of cumulative precipitation. The model was highly successful at replicating both the timing and pace of seven actual migrations observed using GPS telemetry (R2 = 0.914). It shows how zebras rapidly adjust their movement to changing environmental conditions during migration and are able to reverse migration to avoid adverse conditions or exploit renewed resource availability, a nomadic behavior which should lend them a degree of resilience to climate and environmental change. Our results demonstrate how competing individual-based migration models, informed by freely available satellite data

  8. 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.

  9. 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...

  10. 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.

  11. Ground Motion Prediction Models for Caucasus Region

    Science.gov (United States)

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

    2016-04-01

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

  12. Modeling and Prediction of Krueger Device Noise

    Science.gov (United States)

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

    2016-01-01

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

  13. 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

  14. Advancing environmentally explicit structured population models of plants

    DEFF Research Database (Denmark)

    Ehrlén, Johan; Morris, William; von Euler, Tove

    2016-01-01

    The relationship between the performance of individuals and the surrounding environment is fundamental in ecology and evolutionary biology. Assessing how abiotic and biotic environmental factors influence demographic processes is necessary to understand and predict population dynamics, as well as...

  15. Species richness and diversity in different functional groups across environmental stress gradients : a model for marine rocky shores

    NARCIS (Netherlands)

    Scrosati, Ricardo A.; van Genne, Barbara; Heaven, Christine S.; Watt, Cortney A.

    2011-01-01

    We present a model predicting how the species richness and diversity within benthic functional groups should vary across the full environmental stress gradient across which a regional biota from marine rocky shores can occur. Built upon previous models, our model makes predictions for sessile specie

  16. Optimal feedback scheduling of model predictive controllers

    Institute of Scientific and Technical Information of China (English)

    Pingfang ZHOU; Jianying XIE; Xiaolong DENG

    2006-01-01

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

  17. Objective calibration of numerical weather prediction models

    Science.gov (United States)

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

    2017-07-01

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

  18. Numerical daemons in hydrological modeling: Effects on uncertainty assessment, sensitivity analysis and model predictions

    Science.gov (United States)

    Kavetski, D.; Clark, M. P.; Fenicia, F.

    2011-12-01

    Hydrologists often face sources of uncertainty that dwarf those normally encountered in many engineering and scientific disciplines. Especially when representing large scale integrated systems, internal heterogeneities such as stream networks, preferential flowpaths, vegetation, etc, are necessarily represented with a considerable degree of lumping. The inputs to these models are themselves often the products of sparse observational networks. Given the simplifications inherent in environmental models, especially lumped conceptual models, does it really matter how they are implemented? At the same time, given the complexities usually found in the response surfaces of hydrological models, increasingly sophisticated analysis methodologies are being proposed for sensitivity analysis, parameter calibration and uncertainty assessment. Quite remarkably, rather than being caused by the model structure/equations themselves, in many cases model analysis complexities are consequences of seemingly trivial aspects of the model implementation - often, literally, whether the start-of-step or end-of-step fluxes are used! The extent of problems can be staggering, including (i) degraded performance of parameter optimization and uncertainty analysis algorithms, (ii) erroneous and/or misleading conclusions of sensitivity analysis, parameter inference and model interpretations and, finally, (iii) poor reliability of a calibrated model in predictive applications. While the often nontrivial behavior of numerical approximations has long been recognized in applied mathematics and in physically-oriented fields of environmental sciences, it remains a problematic issue in many environmental modeling applications. Perhaps detailed attention to numerics is only warranted for complicated engineering models? Would not numerical errors be an insignificant component of total uncertainty when typical data and model approximations are present? Is this really a serious issue beyond some rare isolated

  19. A software engineering perspective on environmental modeling framework design: The object modeling system

    Science.gov (United States)

    The environmental modeling community has historically been concerned with the proliferation of models and the effort associated with collective model development tasks (e.g., code generation, data provisioning and transformation, etc.). Environmental modeling frameworks (EMFs) have been developed to...

  20. Prediction models from CAD models of 3D objects

    Science.gov (United States)

    Camps, Octavia I.

    1992-11-01

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

  1. Influence of model selection on the predicted distribution of the seagrass Zostera marina

    Science.gov (United States)

    Downie, Anna-Leena; von Numers, Mikael; Boström, Christoffer

    2013-04-01

    There is an increasing need to model the distribution of species and habitats for effective conservation planning, but there is a paucity of models for the marine environment. We used presence (131) and absence (219) records of the marine angiosperm Zostera marina L. from the archipelago of SW Finland, northern Baltic Sea, to model its distribution in a 5400 km2 area. We used depth, slope, turbidity, wave exposure and distance to sandy shores as environmental predictors, and compared a presence-absence method: generalised additive model (GAM), with a presence only method: maximum entropy (Maxent). Models were validated using semi-independent data sets. Both models performed well and described the niche of Z. marina fairly consistently, although there were differences in the way the models weighted the environmental variables, and consequently the spatial predictions differed somewhat. A notable outcome from the process was that with relatively equal model performance, the area actually predicted in geographical space can vary by twofold. The area predicted as suitable for Z. marina by the ensemble was almost half of that predicted by the GAM model by itself. The ensemble of model predictions increased the model predictive capability marginally and clearly shifted the model towards a more conservative prediction, increasing specificity, but at the same time sacrificing sensitivity. The environmental predictors selected into the final models described the potential distribution of Z. marina well and showed that in the northern Baltic the species occupies a narrow niche, typically thriving in shallow and moderately exposed to exposed locations near sandy shores. We conclude that a prediction based on a combination of model results provides a more realistic estimate of the core area suitable for Z. marina and should be the modelling approach implemented in conservation planning and management.

  2. Model predictive control of MSMPR crystallizers

    Science.gov (United States)

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

    2005-02-01

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

  3. An Anisotropic Hardening Model for Springback Prediction

    Science.gov (United States)

    Zeng, Danielle; Xia, Z. Cedric

    2005-08-01

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

  4. Evaluation of Monticello Nuclear Power Plant, Environmental Impact Prediction, based on monitoring programs

    Energy Technology Data Exchange (ETDEWEB)

    Gore, K.L.; Thomas, J.M.; Kannberg, L.D.; Watson, D.G.

    1976-11-01

    This report evaluates quantitatively the nonradiological environmental monitoring programs at Monticello Nuclear Generating Plant. The general objective of the study is to assess the effectiveness of monitoring programs in the measurement of environmental impacts. Specific objectives include the following: (1) Assess the validity of environmental impact predictions made in the Environmental Statement by analysis of nonradiological monitoring data; (2) evaluate the general adequacy of environmental monitoring programs for detecting impacts and their responsiveness to Technical Specifications objectives; (3) assess the adequacy of preoperational monitoring programs in providing a sufficient data base for evaluating operational impacts; (4) identify possible impacts that were not predicted in the environmental statement and identify monitoring activities that need to be added, modified or deleted; and (5) assist in identifying environmental impacts, monitoring methods, and measurement problems that need additional research before quantitative predictions can be attempted. Preoperational as well as operational monitoring data were examined to test the usefulness of baseline information in evaluating impacts. This included an examination of the analytical methods used to measure ecological and physical parameters, and an assessment of sampling periodicity and sensitivity where appropriate data were available.

  5. Evaluation of Spatial Agreement of Distinct Landslide Prediction Models

    Science.gov (United States)

    Sterlacchini, Simone; Bordogna, Gloria; Frigerio, Ivan

    2013-04-01

    The aim of the study was to assess the degree of spatial agreement of different predicted patterns in a majority of coherent landslide prediction maps with almost similar success and prediction rate curves. If two or more models have a similar performance, the choice of the best one is not a trivial operation and cannot be based on success and prediction rate curves only. In fact, it may happen that two or more prediction maps with similar accuracy and predictive power do not have the same degree of agreement in terms of spatial predicted patterns. The selected study area is the high Valtellina valley, in North of Italy, covering a surface of about 450 km2 where mapping of historical landslides is available. In order to assess landslide susceptibility, we applied the Weights of Evidence (WofE) modeling technique implemented by USGS by means of ARC-SDM tool. WofE efficiently investigate the spatial relationships among past events and multiple predisposing factors, providing useful information to identify the most probable location of future landslide occurrences. We have carried out 13 distinct experiments by changing the number of morphometric and geo-environmental explanatory variables in each experiment with the same training set and thus generating distinct models of landslide prediction, computing probability degrees of occurrence of landslides in each pixel. Expert knowledge and previous results from indirect statistically-based methods suggested slope, land use, and geology the best "driving controlling factors". The Success Rate Curve (SRC) was used to estimate how much the results of each model fit the occurrence of landslides used for the training of the models. The Prediction Rate Curve (PRC) was used to estimate how much the model predict the occurrence of landslides in the validation set. We found that the performances were very similar for different models. Also the dendrogram of the Cohen's kappa statistic and Principal Component Analysis (PCA) were

  6. Integrated Environmental Modelling: human decisions, human challenges

    Science.gov (United States)

    Glynn, Pierre D.

    2015-01-01

    Integrated Environmental Modelling (IEM) is an invaluable tool for understanding the complex, dynamic ecosystems that house our natural resources and control our environments. Human behaviour affects the ways in which the science of IEM is assembled and used for meaningful societal applications. In particular, human biases and heuristics reflect adaptation and experiential learning to issues with frequent, sharply distinguished, feedbacks. Unfortunately, human behaviour is not adapted to the more diffusely experienced problems that IEM typically seeks to address. Twelve biases are identified that affect IEM (and science in general). These biases are supported by personal observations and by the findings of behavioural scientists. A process for critical analysis is proposed that addresses some human challenges of IEM and solicits explicit description of (1) represented processes and information, (2) unrepresented processes and information, and (3) accounting for, and cognizance of, potential human biases. Several other suggestions are also made that generally complement maintaining attitudes of watchful humility, open-mindedness, honesty and transparent accountability. These suggestions include (1) creating a new area of study in the behavioural biogeosciences, (2) using structured processes for engaging the modelling and stakeholder communities in IEM, and (3) using ‘red teams’ to increase resilience of IEM constructs and use.

  7. Predictive modelling of ferroelectric tunnel junctions

    Science.gov (United States)

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

    2016-05-01

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

  8. Simple predictions from multifield inflationary models.

    Science.gov (United States)

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

    2014-04-25

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

  9. Temporal variations analyses and predictive modeling of microbiological seawater quality.

    Science.gov (United States)

    Lušić, Darija Vukić; Kranjčević, Lado; Maćešić, Senka; Lušić, Dražen; Jozić, Slaven; Linšak, Željko; Bilajac, Lovorka; Grbčić, Luka; Bilajac, Neiro

    2017-08-01

    Bathing water quality is a major public health issue, especially for tourism-oriented regions. Currently used methods within EU allow at least a 2.2 day period for obtaining the analytical results, making outdated the information forwarded to the public. Obtained results and beach assessment are influenced by the temporal and spatial characteristics of sample collection, and numerous environmental parameters, as well as by differences of official water standards. This paper examines the temporal variation of microbiological parameters during the day, as well as the influence of the sampling hour, on decision processes in the management of the beach. Apart from the fecal indicators stipulated by the EU Bathing Water Directive (E. coli and enterococci), additional fecal (C. perfringens) and non-fecal (S. aureus and P. aeriginosa) parameters were analyzed. Moreover, the effects of applying different evaluation criteria (national, EU and U.S. EPA) to beach ranking were studied, and the most common reasons for exceeding water-quality standards were investigated. In order to upgrade routine monitoring, a predictive statistical model was developed. The highest concentrations of fecal indicators were recorded early in the morning (6 AM) due to the lack of solar radiation during the night period. When compared to enterococci, E. coli criteria appears to be more stringent for the detection of fecal pollution. In comparison to EU and U.S. EPA criteria, Croatian national evaluation criteria provide stricter public health standards. Solar radiation and precipitation were the predominant environmental parameters affecting beach water quality, and these parameters were included in the predictive model setup. Predictive models revealed great potential for the monitoring of recreational water bodies, and with further development can become a useful tool for the improvement of public health protection. Copyright © 2017 Elsevier Ltd. All rights reserved.

  10. Predicting Protein Secondary Structure with Markov Models

    DEFF Research Database (Denmark)

    Fischer, Paul; Larsen, Simon; Thomsen, Claus

    2004-01-01

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

  11. A Modified Model Predictive Control Scheme

    Institute of Scientific and Technical Information of China (English)

    Xiao-Bing Hu; Wen-Hua Chen

    2005-01-01

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

  12. Hierarchical Model Predictive Control for Resource Distribution

    DEFF Research Database (Denmark)

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

    2010-01-01

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

  13. Explicit model predictive control accuracy analysis

    OpenAIRE

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

    2015-01-01

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

  14. Environmental prediction, risk assessment and extreme events: adaptation strategies for the developing world.

    Science.gov (United States)

    Webster, Peter J; Jian, Jun

    2011-12-13

    The uncertainty associated with predicting extreme weather events has serious implications for the developing world, owing to the greater societal vulnerability to such events. Continual exposure to unanticipated extreme events is a contributing factor for the descent into perpetual and structural rural poverty. We provide two examples of how probabilistic environmental prediction of extreme weather events can support dynamic adaptation. In the current climate era, we describe how short-term flood forecasts have been developed and implemented in Bangladesh. Forecasts of impending floods with horizons of 10 days are used to change agricultural practices and planning, store food and household items and evacuate those in peril. For the first time in Bangladesh, floods were anticipated in 2007 and 2008, with broad actions taking place in advance of the floods, grossing agricultural and household savings measured in units of annual income. We argue that probabilistic environmental forecasts disseminated to an informed user community can reduce poverty caused by exposure to unanticipated extreme events. Second, it is also realized that not all decisions in the future can be made at the village level and that grand plans for water resource management require extensive planning and funding. Based on imperfect models and scenarios of economic and population growth, we further suggest that flood frequency and intensity will increase in the Ganges, Brahmaputra and Yangtze catchments as greenhouse-gas concentrations increase. However, irrespective of the climate-change scenario chosen, the availability of fresh water in the latter half of the twenty-first century seems to be dominated by population increases that far outweigh climate-change effects. Paradoxically, fresh water availability may become more critical if there is no climate change.

  15. 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.

  16. Computational prediction of microRNA networks incorporating environmental toxicity and disease etiology

    Science.gov (United States)

    Li, Jie; Wu, Zengrui; Cheng, Feixiong; Li, Weihua; Liu, Guixia; Tang, Yun

    2014-07-01

    MicroRNAs (miRNAs) play important roles in multiple biological processes and have attracted much scientific attention recently. Their expression can be altered by environmental factors (EFs), which are associated with many diseases. Identification of the phenotype-genotype relationships among miRNAs, EFs, and diseases at the network level will help us to better understand toxicology mechanisms and disease etiologies. In this study, we developed a computational systems toxicology framework to predict new associations among EFs, miRNAs and diseases by integrating EF structure similarity and disease phenotypic similarity. Specifically, three comprehensive bipartite networks: EF-miRNA, EF-disease and miRNA-disease associations, were constructed to build predictive models. The areas under the receiver operating characteristic curves using 10-fold cross validation ranged from 0.686 to 0.910. Furthermore, we successfully inferred novel EF-miRNA-disease networks in two case studies for breast cancer and cigarette smoke. Collectively, our methods provide a reliable and useful tool for the study of chemical risk assessment and disease etiology involving miRNAs.

  17. Model Fusion Tool - the Open Environmental Modelling Platform Concept

    Science.gov (United States)

    Kessler, H.; Giles, J. R.

    2010-12-01

    The vision of an Open Environmental Modelling Platform - seamlessly linking geoscience data, concepts and models to aid decision making in times of environmental change. Governments and their executive agencies across the world are facing increasing pressure to make decisions about the management of resources in light of population growth and environmental change. In the UK for example, groundwater is becoming a scarce resource for large parts of its most densely populated areas. At the same time river and groundwater flooding resulting from high rainfall events are increasing in scale and frequency and sea level rise is threatening the defences of coastal cities. There is also a need for affordable housing, improved transport infrastructure and waste disposal as well as sources of renewable energy and sustainable food production. These challenges can only be resolved if solutions are based on sound scientific evidence. Although we have knowledge and understanding of many individual processes in the natural sciences it is clear that a single science discipline is unable to answer the questions and their inter-relationships. Modern science increasingly employs computer models to simulate the natural, economic and human system. Management and planning requires scenario modelling, forecasts and ‘predictions’. Although the outputs are often impressive in terms of apparent accuracy and visualisation, they are inherently not suited to simulate the response to feedbacks from other models of the earth system, such as the impact of human actions. Geological Survey Organisations (GSO) are increasingly employing advances in Information Technology to visualise and improve their understanding of geological systems. Instead of 2 dimensional paper maps and reports many GSOs now produce 3 dimensional geological framework models and groundwater flow models as their standard output. Additionally the British Geological Survey have developed standard routines to link geological

  18. Prediction of Environmental Properties for Chlorophenols with Posetic Quantitative Super-Structure/Property Relationships (QSSPR

    Directory of Open Access Journals (Sweden)

    Douglas J. Kleinc

    2006-09-01

    Full Text Available Due to their widespread use in bactericides, insecticides, herbicides, andfungicides, chlorophenols represent an important source of soil contaminants. Theenvironmental fate of these chemicals depends on their physico-chemical properties. In theabsence of experimental values for these physico-chemical properties, one can use predictedvalues computed with quantitative structure-property relationships (QSPR. As analternative to correlations to molecular structure we have studied the super-structure of areaction network, thereby developing three new QSSPR models (poset-average, cluster-expansion, and splinoid poset that can be applied to chemical compounds which can behierarchically ordered into a reaction network. In the present work we illustrate these posetQSSPR models for the correlation of the octanol/water partition coefficient (log Kow and thesoil sorption coefficient (log KOC of chlorophenols. Excellent results are obtained for allQSSPR poset models to yield: log Kow, r = 0.991, s = 0.107, with the cluster-expansionQSSPR; and log KOC, r = 0.938, s = 0.259, with the spline QSSPR. Thus, the poset QSSPRmodels predict environmentally important properties of chlorophenols.

  19. A Predictive Maintenance Model for Railway Tracks

    DEFF Research Database (Denmark)

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

    2015-01-01

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

  20. Predicting future glacial lakes in Austria using different modelling approaches

    Science.gov (United States)

    Otto, Jan-Christoph; Helfricht, Kay; Prasicek, Günther; Buckel, Johannes; Keuschnig, Markus

    2017-04-01

    Glacier retreat is one of the most apparent consequences of temperature rise in the 20th and 21th centuries in the European Alps. In Austria, more than 240 new lakes have formed in glacier forefields since the Little Ice Age. A similar signal is reported from many mountain areas worldwide. Glacial lakes can constitute important environmental and socio-economic impacts on high mountain systems including water resource management, sediment delivery, natural hazards, energy production and tourism. Their development significantly modifies the landscape configuration and visual appearance of high mountain areas. Knowledge on the location, number and extent of these future lakes can be used to assess potential impacts on high mountain geo-ecosystems and upland-lowland interactions. Information on new lakes is critical to appraise emerging threads and potentials for society. The recent development of regional ice thickness models and their combination with high resolution glacier surface data allows predicting the topography below current glaciers by subtracting ice thickness from glacier surface. Analyzing these modelled glacier bed surfaces reveals overdeepenings that represent potential locations for future lakes. In order to predict the location of future glacial lakes below recent glaciers in the Austrian Alps we apply different ice thickness models using high resolution terrain data and glacier outlines. The results are compared and validated with ice thickness data from geophysical surveys. Additionally, we run the models on three different glacier extents provided by the Austrian Glacier Inventories from 1969, 1998 and 2006. Results of this historical glacier extent modelling are compared to existing glacier lakes and discussed focusing on geomorphological impacts on lake evolution. We discuss model performance and observed differences in the results in order to assess the approach for a realistic prediction of future lake locations. The presentation delivers

  1. A predictive fitness model for influenza

    Science.gov (United States)

    Łuksza, Marta; Lässig, Michael

    2014-03-01

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

  2. Predictive Model of Radiative Neutrino Masses

    CERN Document Server

    Babu, K S

    2013-01-01

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

  3. 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...

  4. 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.

  5. Prediction and setup of phytoplankton statistical model of Qiandaohu Lake

    Institute of Scientific and Technical Information of China (English)

    严力蛟; 全为民; 赵晓慧

    2004-01-01

    This research considers the mathematical relationship between concentration of Chla and seven environmental factors, i.e. Lake water temperature (T), Secci-depth (SD), pH, DO, CODMn, Total Nitrogen (TN), Total Phosphorus (TP).Stepwise linear regression of 1997 to 1999 monitoring data at each sampling point of Qiandaohu Lake yielded the multivariate regression models presented in this paper. The concentration of Chla as simulation for the year 2000 by the regression model was similar to the observed value. The suggested mathematical relationship could be used to predict changes in the lakewater environment at any point in time. The results showed that SD, TP and pH were the most significant factors affecting Chla concentration.

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

    DEFF Research Database (Denmark)

    Jørgensen, John Bagterp; Jørgensen, Sten Bay

    2007-01-01

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

  7. Aquatic Exposure Predictions of Insecticide Field Concentrations Using a Multimedia Mass-Balance Model.

    Science.gov (United States)

    Knäbel, Anja; Scheringer, Martin; Stehle, Sebastian; Schulz, Ralf

    2016-04-05

    Highly complex process-driven mechanistic fate and transport models and multimedia mass balance models can be used for the exposure prediction of pesticides in different environmental compartments. Generally, both types of models differ in spatial and temporal resolution. Process-driven mechanistic fate models are very complex, and calculations are time-intensive. This type of model is currently used within the European regulatory pesticide registration (FOCUS). Multimedia mass-balance models require fewer input parameters to calculate concentration ranges and the partitioning between different environmental media. In this study, we used the fugacity-based small-region model (SRM) to calculate predicted environmental concentrations (PEC) for 466 cases of insecticide field concentrations measured in European surface waters. We were able to show that the PECs of the multimedia model are more protective in comparison to FOCUS. In addition, our results show that the multimedia model results have a higher predictive power to simulate varying field concentrations at a higher level of field relevance. The adaptation of the model scenario to actual field conditions suggests that the performance of the SRM increases when worst-case conditions are replaced by real field data. Therefore, this study shows that a less complex modeling approach than that used in the regulatory risk assessment exhibits a higher level of protectiveness and predictiveness and that there is a need to develop and evaluate new ecologically relevant scenarios in the context of pesticide exposure modeling.

  8. Predicting plant performance under simultaneously changing environmental conditions – the interplay between temperature, light and internode growth

    Directory of Open Access Journals (Sweden)

    Katrin eKahlen

    2015-12-01

    Full Text Available Plant performance is significantly influenced by prevailing light and temperature conditions during plant growth and development. For plants exposed to natural fluctuations in abiotic environmental conditions it is however laborious and cumbersome to experimentally assign any contribution of individual environmental factors to plant responses. This study aimed at analyzing the interplay between light, temperature and internode growth based on model approaches. We extended the light-sensitive virtual plant model L-Cucumber by implementing a common Arrhenius function for appearance rates, growth rates and growth durations. For two greenhouse experiments, the temperature-sensitive model approach resulted in a precise prediction of cucumber mean internode lengths and number of internodes, as well as in accurately predicted patterns of individual internode lengths along the main stem. In addition, a system’s analysis revealed that environmental data averaged over the experimental period were not necessarily related to internode performance. Finally, the need for a species-specific parameterization of the temperature response function and related aspects in modelling temperature effects on plant development and growth is discussed.

  9. Predicting Plant Performance Under Simultaneously Changing Environmental Conditions-The Interplay Between Temperature, Light, and Internode Growth.

    Science.gov (United States)

    Kahlen, Katrin; Chen, Tsu-Wei

    2015-01-01

    Plant performance is significantly influenced by prevailing light and temperature conditions during plant growth and development. For plants exposed to natural fluctuations in abiotic environmental conditions it is however laborious and cumbersome to experimentally assign any contribution of individual environmental factors to plant responses. This study aimed at analyzing the interplay between light, temperature and internode growth based on model approaches. We extended the light-sensitive virtual plant model L-Cucumber by implementing a common Arrhenius function for appearance rates, growth rates, and growth durations. For two greenhouse experiments, the temperature-sensitive model approach resulted in a precise prediction of cucumber mean internode lengths and number of internodes, as well as in accurately predicted patterns of individual internode lengths along the main stem. In addition, a system's analysis revealed that environmental data averaged over the experimental period were not necessarily related to internode performance. Finally, the need for a species-specific parameterization of the temperature response function and related aspects in modeling temperature effects on plant development and growth is discussed.

  10. Use of the Pathogen Modeling Program (PMP) and the Predictive Microbiology Information Portal (PMIP)

    Science.gov (United States)

    The Predictive Microbiology Program,(PMP)is based on the fact that most bacterial behaviors are reproducible and can be quantified by characterizing the environmental factors that affect growth, survival, and inactivation using mathematical modeling. The contents of PMP, a collection of models, are ...

  11. A Model For Predicting the Educational Use of Information and Communication Technologies.

    Science.gov (United States)

    Collis, Betty; Peters, Oscar; Pals, Nico

    2001-01-01

    This study of 550 participants was designed to test an integrated theoretical model (the 4-E Model) for predicting the likelihood of the use of telecommunications-related technological innovations in learning-related settings. The four "Es" are: environmental factors, effectiveness, ease of use, and (personal) engagement. Results, which supported…

  12. Modeling environmental noise exceedances using non-homogeneous Poisson processes.

    Science.gov (United States)

    Guarnaccia, Claudio; Quartieri, Joseph; Barrios, Juan M; Rodrigues, Eliane R

    2014-10-01

    In this work a non-homogeneous Poisson model is considered to study noise exposure. The Poisson process, counting the number of times that a sound level surpasses a threshold, is used to estimate the probability that a population is exposed to high levels of noise a certain number of times in a given time interval. The rate function of the Poisson process is assumed to be of a Weibull type. The presented model is applied to community noise data from Messina, Sicily (Italy). Four sets of data are used to estimate the parameters involved in the model. After the estimation and tuning are made, a way of estimating the probability that an environmental noise threshold is exceeded a certain number of times in a given time interval is presented. This estimation can be very useful in the study of noise exposure of a population and also to predict, given the current behavior of the data, the probability of occurrence of high levels of noise in the near future. One of the most important features of the model is that it implicitly takes into account different noise sources, which need to be treated separately when using usual models.

  13. Two criteria for evaluating risk prediction models.

    Science.gov (United States)

    Pfeiffer, R M; Gail, M H

    2011-09-01

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

  14. Methods for Handling Missing Variables in Risk Prediction Models

    NARCIS (Netherlands)

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

    2016-01-01

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

  15. Environmental modeling in data-sparse regions: Mozambique demonstrator case

    Science.gov (United States)

    Schumann, G.; Niebuhr, E.; Rashid, K.; Escobar, V. M.; Andreadis, K.; Njoku, E. G.; Neal, J. C.; Voisin, N.; Pappenberger, F.; Phanthuwongpakdee, N.; Bates, P. D.; Chao, Y.; Moller, D.; Paron, P.

    2014-12-01

    Long time-series computations of seasonal and flood event inundation volumes from archived forecast rainfall events for the Lower Zambezi basin (Mozambique), using a coupled hydrology-hydrodynamic model, are correlated and regressed with satellite soil moisture observations and NWP rainfall forecasts as predictors for inundation volumes. This dynamic library of volume predictions can then be re-projected onto the topography to generate the corresponding floodplain and wetland inundation dynamics, including periods of flood and low flows. Especially for data-poor regions, the application potential of such a library of data is invaluable as the modeling chain is greatly simplified and readily available. The library is flexible, portable and transitional. Furthermore, deriving environmental indicators from this dynamic look-up catalogue would be relatively straightforward. Application fields are various and here we present conceptually a few that we plan to research in more detail and on some of which we already collaborate with other scientists and international institutions, though at the moment largely on an unfunded basis. The primary application is to implement an early warning system for flood inundation relief operations and flood inundation mitigation and resilience. Having this flood inundation warning system set up adequately would also allow looking into long-term predictions of crop productivity and consequently food security. Another potentially high-impact application is to relate flood inundation dynamics to disease modeling for public health monitoring and prediction, in particular focusing on Malaria. Last but not least, the dynamic inundation library we are building can be validated and complemented with advanced airborne radar imagery of flooding and inundated wetlands to study changes in wetland ecology and biodiversity with unprecedented detail in data-poor regions, in this case in particular the important wetlands of the Zambezi Delta.

  16. Predicting the fate of biodiversity using species' distribution models: enhancing model comparability and repeatability.

    Directory of Open Access Journals (Sweden)

    Genoveva Rodríguez-Castañeda

    Full Text Available Species distribution modeling (SDM is an increasingly important tool to predict the geographic distribution of species. Even though many problems associated with this method have been highlighted and solutions have been proposed, little has been done to increase comparability among studies. We reviewed recent publications applying SDMs and found that seventy nine percent failed to report methods that ensure comparability among studies, such as disclosing the maximum probability range produced by the models and reporting on the number of species occurrences used. We modeled six species of Falco from northern Europe and demonstrate that model results are altered by (1 spatial bias in species' occurrence data, (2 differences in the geographic extent of the environmental data, and (3 the effects of transformation of model output to presence/absence data when applying thresholds. Depending on the modeling decisions, forecasts of the future geographic distribution of Falco ranged from range contraction in 80% of the species to no net loss in any species, with the best model predicting no net loss of habitat in Northern Europe. The fact that predictions of range changes in response to climate change in published studies may be influenced by decisions in the modeling process seriously hampers the possibility of making sound management recommendations. Thus, each of the decisions made in generating SDMs should be reported and evaluated to ensure conclusions and policies are based on the biology and ecology of the species being modeled.

  17. Predicting recycling behaviour: Comparison of a linear regression model and a fuzzy logic model.

    Science.gov (United States)

    Vesely, Stepan; Klöckner, Christian A; Dohnal, Mirko

    2016-03-01

    In this paper we demonstrate that fuzzy logic can provide a better tool for predicting recycling behaviour than the customarily used linear regression. To show this, we take a set of empirical data on recycling behaviour (N=664), which we randomly divide into two halves. The first half is used to estimate a linear regression model of recycling behaviour, and to develop a fuzzy logic model of recycling behaviour. As the first comparison, the fit of both models to the data included in estimation of the models (N=332) is evaluated. As the second comparison, predictive accuracy of both models for "new" cases (hold-out data not included in building the models, N=332) is assessed. In both cases, the fuzzy logic model significantly outperforms the regression model in terms of fit. To conclude, when accurate predictions of recycling and possibly other environmental behaviours are needed, fuzzy logic modelling seems to be a promising technique. Copyright © 2015 Elsevier Ltd. All rights reserved.

  18. Predicting the fate of biodiversity using species' distribution models: enhancing model comparability and repeatability.

    Science.gov (United States)

    Rodríguez-Castañeda, Genoveva; Hof, Anouschka R; Jansson, Roland; Harding, Larisa E

    2012-01-01

    Species distribution modeling (SDM) is an increasingly important tool to predict the geographic distribution of species. Even though many problems associated with this method have been highlighted and solutions have been proposed, little has been done to increase comparability among studies. We reviewed recent publications applying SDMs and found that seventy nine percent failed to report methods that ensure comparability among studies, such as disclosing the maximum probability range produced by the models and reporting on the number of species occurrences used. We modeled six species of Falco from northern Europe and demonstrate that model results are altered by (1) spatial bias in species' occurrence data, (2) differences in the geographic extent of the environmental data, and (3) the effects of transformation of model output to presence/absence data when applying thresholds. Depending on the modeling decisions, forecasts of the future geographic distribution of Falco ranged from range contraction in 80% of the species to no net loss in any species, with the best model predicting no net loss of habitat in Northern Europe. The fact that predictions of range changes in response to climate change in published studies may be influenced by decisions in the modeling process seriously hampers the possibility of making sound management recommendations. Thus, each of the decisions made in generating SDMs should be reported and evaluated to ensure conclusions and policies are based on the biology and ecology of the species being modeled.

  19. Bayesian model-based cluster analysis for predicting macrofaunal communities

    NARCIS (Netherlands)

    Braak, ter C.J.F.; Hoijtink, H.; Akkermans, W.; Verdonschot, P.F.M.

    2003-01-01

    To predict macrofaunal community composition from environmental data a two-step approach is often followed: (1) the water samples are clustered into groups on the basis of the macrofauna data and (2) the groups are related to the environmental data, e.g. by discriminant analysis. For the cluster ana

  20. Evaluation of various predictive methods for determining OTEC SKSS environmental loads. Revision A, Report No. 79-049-004A

    Energy Technology Data Exchange (ETDEWEB)

    1979-10-31

    The NOAA Office of Engineering is presently managing two contracts for the preliminary design of the OTEC stationkeeping system. Results presented by the contractors thus far (draft report stage in conceptual design) indicate significant differences in the predicted environmental loads. The discrepancies were caused by the use of different prediction methods and variations in the description of the environment. In order to reconcile these discrepancies, a review, comparison, and assessment of the mooring system load prediction methods used by SKSS contractors was presented in Giannotti and Associates, Inc., Report Nos. 78-049-001, 002, and 003. A search for existing model test results and full scale experiments were conducted in an effort to compare results. The present report documents a study on the state of technology for the prediction of environmental loads in general and drift force loads in particular. The outcome of this study should provide: (1) a consistent approach to be taken by SKSS contractors; (2) levels of confidence to be associated with the predicted loads; and (3) a set of recommendations to be used by NOAA to increase confidence in the OTEC SKSS designs and to reduce the risk attributed to those designs. (WHK)

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

    Science.gov (United States)

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

  2. Neighbourhood, Route and Workplace-Related Environmental Characteristics Predict Adults' Mode of Travel to Work.

    Directory of Open Access Journals (Sweden)

    Alice M Dalton

    Full Text Available Commuting provides opportunities for regular physical activity which can reduce the risk of chronic disease. Commuters' mode of travel may be shaped by their environment, but understanding of which specific environmental characteristics are most important and might form targets for intervention is limited. This study investigated associations between mode choice and a range of objectively assessed environmental characteristics.Participants in the Commuting and Health in Cambridge study reported where they lived and worked, their usual mode of travel to work and a variety of socio-demographic characteristics. Using geographic information system (GIS software, 30 exposure variables were produced capturing characteristics of areas around participants' homes and workplaces and their shortest modelled routes to work. Associations between usual mode of travel to work and personal and environmental characteristics were investigated using multinomial logistic regression.Of the 1124 respondents, 50% reported cycling or walking as their usual mode of travel to work. In adjusted analyses, home-work distance was strongly associated with mode choice, particularly for walking. Lower odds of walking or cycling rather than driving were associated with a less frequent bus service (highest versus lowest tertile: walking OR 0.61 [95% CI 0.20-1.85]; cycling OR 0.43 [95% CI 0.23-0.83], low street connectivity (OR 0.22, [0.07-0.67]; OR 0.48 [0.26-0.90] and free car parking at work (OR 0.24 [0.10-0.59]; OR 0.55 [0.32-0.95]. Participants were less likely to cycle if they had access to fewer destinations (leisure facilities, shops and schools close to work (OR 0.36 [0.21-0.62] and a railway station further from home (OR 0.53 [0.30-0.93]. Covariates strongly predicted travel mode (pseudo r-squared 0.74.Potentially modifiable environmental characteristics, including workplace car parking, street connectivity and access to public transport, are associated with travel mode

  3. Model comparisons and genetic and environmental parameter ...

    African Journals Online (AJOL)

    arc

    South African Journal of Animal Science 2005, 35 (1) ... Genetic and environmental parameters were estimated for pre- and post-weaning average daily gain ..... and BWT (and medium maternal genetic correlations) indicates that these traits ...

  4. Prediction of Catastrophes: an experimental model

    CERN Document Server

    Peters, Randall D; Pomeau, Yves

    2012-01-01

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

  5. Using CV-GLUE procedure in analysis of wetland model predictive uncertainty.

    Science.gov (United States)

    Huang, Chun-Wei; Lin, Yu-Pin; Chiang, Li-Chi; Wang, Yung-Chieh

    2014-07-01

    This study develops a procedure that is related to Generalized Likelihood Uncertainty Estimation (GLUE), called the CV-GLUE procedure, for assessing the predictive uncertainty that is associated with different model structures with varying degrees of complexity. The proposed procedure comprises model calibration, validation, and predictive uncertainty estimation in terms of a characteristic coefficient of variation (characteristic CV). The procedure first performed two-stage Monte-Carlo simulations to ensure predictive accuracy by obtaining behavior parameter sets, and then the estimation of CV-values of the model outcomes, which represent the predictive uncertainties for a model structure of interest with its associated behavior parameter sets. Three commonly used wetland models (the first-order K-C model, the plug flow with dispersion model, and the Wetland Water Quality Model; WWQM) were compared based on data that were collected from a free water surface constructed wetland with paddy cultivation in Taipei, Taiwan. The results show that the first-order K-C model, which is simpler than the other two models, has greater predictive uncertainty. This finding shows that predictive uncertainty does not necessarily increase with the complexity of the model structure because in this case, the more simplistic representation (first-order K-C model) of reality results in a higher uncertainty in the prediction made by the model. The CV-GLUE procedure is suggested to be a useful tool not only for designing constructed wetlands but also for other aspects of environmental management.

  6. Predictive modeling of low solubility semiconductor alloys

    Science.gov (United States)

    Rodriguez, Garrett V.; Millunchick, Joanna M.

    2016-09-01

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

  7. 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 ...

  8. Leptogenesis in minimal predictive seesaw models

    Science.gov (United States)

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

    2015-10-01

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

  9. Beyond size : Predicting engagement in environmental management practices of Dutch SMEs

    NARCIS (Netherlands)

    Uhlaner, L.M.; Berent-Braun, M.M.; Jeurissen, R.J.M.; Wit, de G.

    2012-01-01

    This study focuses on the prediction of the engagement of small- and medium-sized enterprises (SMEs) in environmental management practices, based on a random sample of 689 SMEs. The study finds that several endogenous factors, including tangibility of sector, firm size, innovative orientation, famil

  10. Green technical innovation,environmental expenditure,and the environmental Kuznets curve:a dynamics model

    Institute of Scientific and Technical Information of China (English)

    Cai Zhonghua

    2008-01-01

    The relationship between the emission of pollutant and economic growth has attracted a lot of attention in the environmental debate of the recent decades.Based on some theoretical and empirical research on environmental Kuznets curve(EKC),this paper introduces the environmental technical innovation and environmental investmen into Solow growth model to discuss the relationship between GDP per capital and the emission of pollutant.By the dvnamic simulation and parameters analysis,the results of the model indicate(1) when "green"technical progress and environmental investment are.fixed,the relationship between GDP per capital and the emission shows the linear elationship;(2)"green"technical progress can lead to the positive growth rates with a decreasing level of emision,which is compatible with an EKC;(3) the proportion of the environmental investment can lead the different growth rates and level of emission.These results can explain that developing countries are"too poor to be green".

  11. Data Sources Available for Modeling Environmental Exposures in Older Adults

    Science.gov (United States)

    This report, “Data Sources Available for Modeling Environmental Exposures in Older Adults,” focuses on information sources and data available for modeling environmental exposures in the older U.S. population, defined here to be people 60 years and older, with an emphasis on those...

  12. Unmix 6.0 Model for environmental data analyses

    Science.gov (United States)

    Unmix Model is a mathematical receptor model developed by EPA scientists that provides scientific support for the development and review of the air and water quality standards, exposure research, and environmental forensics.

  13. Modelling of Electrokinetic Processes in Civil and Environmental Engineering Applications

    DEFF Research Database (Denmark)

    Paz-Garcia, Juan Manuel; Johannesson, Björn; Ottosen, Lisbeth M.

    2011-01-01

    A mathematical model for the electrokinetic phenomena is described. Numerical simulations of different applications of electrokinetic techniques to the fields of civil and environmental engineering are included, showing the versatility and consistency of the model. The electrokinetics phenomena...

  14. National differences in environmental concern and performance are predicted by country age.

    Science.gov (United States)

    Hershfield, Hal E; Bang, H Min; Weber, Elke U

    2014-01-01

    There are obvious economic predictors of ability and willingness to invest in environmental sustainability. Yet, given that environmental decisions represent trade-offs between present sacrifices and uncertain future benefits, psychological factors may also play a role in country-level environmental behavior. Gott's principle suggests that citizens may use perceptions of their country's age to predict its future continuation, with longer pasts predicting longer futures. Using country- and individual-level analyses, we examined whether longer perceived pasts result in longer perceived futures, which in turn motivate concern for continued environmental quality. Study 1 found that older countries scored higher on an environmental performance index, even when the analysis controlled for country-level differences in gross domestic product and governance. Study 2 showed that when the United States was framed as an old country (vs. a young one), participants were willing to donate more money to an environmental organization. The findings suggest that framing a country as a long-standing entity may effectively prompt proenvironmental behavior.

  15. Semen parameters can be predicted from environmental factors and lifestyle using artificial intelligence methods.

    Science.gov (United States)

    Girela, Jose L; Gil, David; Johnsson, Magnus; Gomez-Torres, María José; De Juan, Joaquín

    2013-04-01

    Fertility rates have dramatically decreased in the last two decades, especially in men. It has been described that environmental factors as well as life habits may affect semen quality. In this paper we use artificial intelligence techniques in order to predict semen characteristics resulting from environmental factors, life habits, and health status, with these techniques constituting a possible decision support system that can help in the study of male fertility potential. A total of 123 young, healthy volunteers provided a semen sample that was analyzed according to the World Health Organization 2010 criteria. They also were asked to complete a validated questionnaire about life habits and health status. Sperm concentration and percentage of motile sperm were related to sociodemographic data, environmental factors, health status, and life habits in order to determine the predictive accuracy of a multilayer perceptron network, a type of artificial neural network. In conclusion, we have developed an artificial neural network that can predict the results of the semen analysis based on the data collected by the questionnaire. The semen parameter that is best predicted using this methodology is the sperm concentration. Although the accuracy for motility is slightly lower than that for concentration, it is possible to predict it with a significant degree of accuracy. This methodology can be a useful tool in early diagnosis of patients with seminal disorders or in the selection of candidates to become semen donors.

  16. Quantitative Structure-Use Relationship Model Predictions to evaluate Tox21 Chemicals as Functional Substitutes and Candidate Alternatives

    Data.gov (United States)

    U.S. Environmental Protection Agency — This dataset provides a prediction for all Tox21 chemicals with available QSUR descriptors across all 41 valid QSUR models developed with FUse. This dataset is...

  17. Comparing model predictions for ecosystem-based management

    DEFF Research Database (Denmark)

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

    2016-01-01

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

  18. A mathematical model for environmental risk assessment in manufacturing industry

    Institute of Scientific and Technical Information of China (English)

    何莉萍; 徐盛明; 陈大川; 党创寅

    2002-01-01

    Environmental conscious manufacturing has become an important issue in industry because of market pressure and environmental regulations. An environmental risk assessment model was developed based on the network analytic method and fuzzy set theory. The "interval analysis method" was applied to deal with the on-site monitoring data as basic information for assessment. In addition, the fuzzy set theory was employed to allow uncertain, interactive and dynamic information to be effectively incorporated into the environmental risk assessment. This model is a simple, practical and effective tool for evaluating the environmental risk of manufacturing industry and for analyzing the relative impacts of emission wastes, which are hazardous to both human and ecosystem health. Furthermore, the model is considered useful for design engineers and decision-maker to design and select processes when the costs, environmental impacts and performances of a product are taken into consideration.

  19. Remaining Useful Lifetime (RUL - Probabilistic Predictive Model

    Directory of Open Access Journals (Sweden)

    Ephraim Suhir

    2011-01-01

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

  20. A Predictive Model of Geosynchronous Magnetopause Crossings

    CERN Document Server

    Dmitriev, A; Chao, J -K

    2013-01-01

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

  1. Predictive modeling for EBPC in EBDW

    Science.gov (United States)

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

    2009-10-01

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

  2. Quantum correlations dynamics under different non-markovian environmental models

    CERN Document Server

    Zhang, Ying-Jie; Shan, Chuan-Jia; Xia, Yun-Jie

    2011-01-01

    We investigate the roles of different environmental models on quantum correlation dynamics of two-qubit composite system interacting with two independent environments. The most common environmental models (the single-Lorentzian model, the squared-Lorentzian model, the two-Lorentzian model and band-gap model) are analyzed. First, we note that for the weak coupling regime, the monotonous decay speed of the quantum correlation is mainly determined by the spectral density functions of these different environments. Then, by considering the strong coupling regime we find that, contrary to what is stated in the weak coupling regime, the dynamics of quantum correlation depends on the non-Markovianity of the environmental models, and is independent of the environmental spectrum density functions.

  3. THE OBJECTIVE ANALOGUE PREDICTION MODEL FOR TROPICAL CYCLONE TRACK WITH COMPREHENSIVE ASSESSMENT OF THE ENVIRONMENT

    Institute of Scientific and Technical Information of China (English)

    钟元

    2002-01-01

    An objective analogue prediction model for tropical cyclone (TC) track is put forward that comprehensively assesses the environmental field. With the parameters of the tropical cyclone and environmental field at initial and future time, objective analogue criteria are set up in the model. Analogous samples are recognized by comprehensive assessment of historical TC cases for similarity with multivariate criteria, using non-linear analogue indexes especially defined for the purpose. When the historical tracks are coordinateconverted and weighted with reference to analogue indexes, forecast tracks are determined. As shown in model verification and forecast experiments, the model has forecasting skill.

  4. A review of mathematical models in economic environmental problems

    DEFF Research Database (Denmark)

    Nahorski, Z.; Ravn, H.F.

    2000-01-01

    The paper presents a review of mathematical models used,in economic analysis of environmental problems. This area of research combines macroeconomic models of growth, as dependent on capital, labour, resources, etc., with environmental models describing such phenomena like natural resources...... exhaustion or pollution accumulation and degradation. In simpler cases the models can be treated analytically and the utility function can be optimized using, e.g., such tools as the maximum principle. In more complicated cases calculation of the optimal environmental policies requires a computer solution....

  5. A predictive model of community assembly that incorporates intraspecific trait variation.

    Science.gov (United States)

    Laughlin, Daniel C; Joshi, Chaitanya; van Bodegom, Peter M; Bastow, Zachary A; Fulé, Peter Z

    2012-11-01

    Community assembly involves two antagonistic processes that select functional traits in opposite directions. Environmental filtering tends to increase the functional similarity of species within communities leading to trait convergence, whereas competition tends to limit the functional similarity of species within communities leading to trait divergence. Here, we introduce a new hierarchical Bayesian model that incorporates intraspecific trait variation into a predictive framework to unify classic coexistence theory and evolutionary biology with recent trait-based approaches. Model predictions exhibited a significant positive correlation (r = 0.66) with observed relative abundances along a 10 °C gradient in mean annual temperature. The model predicted the correct dominant species in half of the plots, and accurately reproduced species' temperature optimums. The framework is generalizable to any ecosystem as it can accommodate any species pool, any set of functional traits and multiple environmental gradients, and it eliminates some of the criticisms associated with recent trait-based community assembly models.

  6. 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

  7. Environmental fate of rice paddy pesticides in a model ecosystem.

    Science.gov (United States)

    Tomizawa, C; Kazano, H

    1979-01-01

    The distribution and metabolic fate of several rice paddy pesticides were evaluated in a modified model ecosystem. Among the three BHC isomers, beta-isomer was the most stable and bioconcentrated in all of the organisms. Alpha- and gamma-isomers were moderately persistent and degraded to some extent during the 33 day period. Disulfoton was relatively persistent due to the transformation to its oxidation products. Pyridaphenthion was fairly biodegradable. N-Phenyl maleic hydrazide derived from the hydrolysis of pyridaphenthion was not detected in the organisms though it was found in the aquarium water after 33 days. Cartap and edifenphos were considerably biodegradable, and the ratio of the conversion to water soluble metabolites was very high. There was a distinct difference in the persistence of Kitazin P and edifenphos in the aquarium water. It appeared that the hydrolysis rate of the pesticides affected their fate in the organisms. PCP appeared to be moderately biodegradable. CNP was considerably stable and stored in the organisms though the concentration in the aquarium water was relatively low. The persistence and distribution of the pesticides in the model ecosystem were dependent on their chemical structures. In spite of the limitation derived from short experimental period, the model ecosystem may be applicable for predicting the environmental fate of pesticides.

  8. A study to examine the role of environmental motivation and sensation seeking personality to predict behavioral intention in volunteer tourism

    Directory of Open Access Journals (Sweden)

    Usep Suhud

    2014-11-01

    Full Text Available The first, objective of this study is to understand the role of environmental motivation and sensation seeking personality in predicting intention to be involved in VT. The second objective is to examine whether the theory of reasoned action could be extended by adding two new variables - environmental motivation and sensation seeking personality. The last objective is to understand the difference intention in difference periods of time – within one, three, and five years; Volunteer tourism is about combination of volunteering and tourism activities that require participants to pay their own costs – transport, accommodation, meals, and even contribute to the project offered in a destination. Researchers claimed that there is an overlap between volunteer tourism and eco-tourism. This claim referred to two reasons: most of projects are relating to environment and part of motivations of the participants relate to environment. The author adapted the theory of reasoned action by adding new variables – sensation seeking personality and environmental motivation. The author considers that this is an experimental study as there has not been documented that a single environmental motivation has an influence on behavioral intention, particularly in the tourism field. The author collected data using an online survey and approached volunteers, tourists, and volunteer tourists to participate in an online survey conveniently. In total, 551 respondents participated, coming from developed and developing countries. Data were analyzed using exploratory and confirmatory factor analyses (structural equation model. Three fitted models were built representing intention within one year, three years, and five years. Almost all hypotheses in all models were accepted. This quantitative study proves that environmental motivation and sensation seeking personality have different influences on intention to be involved in VT in different periods of time. In addition, it

  9. 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.

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

    Science.gov (United States)

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

    2016-05-01

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

  11. Concentrations of antibiotics predicted to select for resistant bacteria: Proposed limits for environmental regulation.

    Science.gov (United States)

    Bengtsson-Palme, Johan; Larsson, D G Joakim

    2016-01-01

    There are concerns that selection pressure from antibiotics in the environment may accelerate the evolution and dissemination of antibiotic-resistant pathogens. Nevertheless, there is currently no regulatory system that takes such risks into account. In part, this is due to limited knowledge of environmental concentrations that might exert selection for resistant bacteria. To experimentally determine minimal selective concentrations in complex microbial ecosystems for all antibiotics would involve considerable effort. In this work, our aim was to estimate upper boundaries for selective concentrations for all common antibiotics, based on the assumption that selective concentrations a priori need to be lower than those completely inhibiting growth. Data on Minimal Inhibitory Concentrations (MICs) were obtained for 111 antibiotics from the public EUCAST database. The 1% lowest observed MICs were identified, and to compensate for limited species coverage, predicted lowest MICs adjusted for the number of tested species were extrapolated through modeling. Predicted No Effect Concentrations (PNECs) for resistance selection were then assessed using an assessment factor of 10 to account for differences between MICs and minimal selective concentrations. The resulting PNECs ranged from 8 ng/L to 64 μg/L. Furthermore, the link between taxonomic similarity between species and lowest MIC was weak. This work provides estimated upper boundaries for selective concentrations (lowest MICs) and PNECs for resistance selection for all common antibiotics. In most cases, PNECs for selection of resistance were below available PNECs for ecotoxicological effects. The generated PNECs can guide implementation of compound-specific emission limits that take into account risks for resistance promotion.

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

    NARCIS (Netherlands)

    Kappen, Teus H.; Peelen, Linda M.

    2016-01-01

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

  13. The application of DEA model in enterprise environmental performance auditing

    Science.gov (United States)

    Li, F.; Zhu, L. Y.; Zhang, J. D.; Liu, C. Y.; Qu, Z. G.; Xiao, M. S.

    2017-01-01

    As a part of society, enterprises have an inescapable responsibility for environmental protection and governance. This article discusses the feasibility and necessity of enterprises environmental performance auditing and uses DEA model calculate the environmental performance of Haier for example. The most of reference data are selected and sorted from Haier’s environmental reportspublished in 2008, 2009, 2011 and 2015, and some of the data from some published articles and fieldwork. All the calculation results are calculated by DEAP software andhave a high credibility. The analysis results of this article can give corporate managements an idea about using environmental performance auditing to adjust their corporate environmental investments capital quota and change their company’s environmental strategies.

  14. 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.

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

    Science.gov (United States)

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

    2017-03-01

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

  16. A new approach to predicting environmental transfer of radionuclides to wildlife: A demonstration for freshwater fish and caesium

    Energy Technology Data Exchange (ETDEWEB)

    Beresford, N.A., E-mail: nab@ceh.ac.uk [NERC Centre for Ecology and Hydrology, Lancaster Environment Centre, Library Av. Bailrigg, Lancaster LA1 4AP (United Kingdom); Yankovich, T.L. [Saskatchewan Research Council, Environment and Forestry, 125, 15 Innovation Blvd., Saskatoon, SK S7N 2X8 (Canada); Wood, M.D. [School of Environment and Life Sciences, Room 323, Peel Building, University of Salford, Manchester, M5 4WT (United Kingdom); Fesenko, S. [International Atomic Energy Agency, 1400 Vienna (Austria); Andersson, P. [Strålsäkerhetsnymdigheten, Swedish Radiation Safety Authority, SE-171 16 Stockholm (Sweden); Muikku, M. [STUK, P.O. Box 14, 00881 Helsinki (Finland); Willey, N.J. [Centre for Research in Biosciences, University of the West of England, Coldharbour Lane, Frenchay, Bristol BS16 1QY (United Kingdom)

    2013-10-01

    The application of the concentration ratio (CR) to predict radionuclide activity concentrations in wildlife from those in soil or water has become the widely accepted approach for environmental assessments. Recently both the ICRP and IAEA have produced compilations of CR values for application in environmental assessment. However, the CR approach has many limitations, most notably, that the transfer of most radionuclides is largely determined by site-specific factors (e.g. water or soil chemistry). Furthermore, there are few, if any, CR values for many radionuclide-organism combinations. In this paper, we propose an alternative approach and, as an example, demonstrate and test this for caesium and freshwater fish. Using a Residual Maximum Likelihood (REML) mixed-model regression we analysed a dataset comprising 597 entries for 53 freshwater fish species from 67 sites. The REML analysis generated a mean value for each species on a common scale after REML adjustment taking account of the effect of the inter-site variation. Using an independent dataset, we subsequently test the hypothesis that the REML model outputs can be used to predict radionuclide, in this case radiocaesium, activity concentrations in unknown species from the results of a species which has been sampled at a specific site. The outputs of the REML analysis accurately predicted {sup 137}Cs activity concentrations in different species of fish from 27 Finnish lakes; these data had not been used in our initial analyses. We recommend that this alternative approach be further investigated for other radionuclides and ecosystems. - Highlights: • An alternative approach to estimating radionuclide transfer to wildlife is presented. • Analysed a dataset comprising 53 freshwater fish species collected from 67 sites. • Residual Maximum Likelihood mixed model regression is used. • Model output takes account of the effect of inter-site variation. • Successfully predicted {sup 137}Cs concentrations in

  17. Nonconvex model predictive control for commercial refrigeration

    Science.gov (United States)

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

    2013-08-01

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

  18. Leptogenesis in minimal predictive seesaw models

    Energy Technology Data Exchange (ETDEWEB)

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

    2015-10-15

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

  19. QSPR Models for Octane Number Prediction

    Directory of Open Access Journals (Sweden)

    Jabir H. Al-Fahemi

    2014-01-01

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

  20. Climate modelling, uncertainty and responses to predictions of change

    Energy Technology Data Exchange (ETDEWEB)

    Henderson-Sellers, A. [Climatic Impacts Centre, Macquarie University, Sydney (Australia)

    1996-12-31

    Article 4.1(F) of the Framework Convention on Climate Change commits all parties to take climate change considerations into account, to the extent feasible, in relevant social, economic and environmental policies and actions and to employ methods such as impact assessments to minimize adverse effects of climate change. This could be achieved by, inter alia, incorporating climate change risk assessment into development planning processes, i.e. relating climatic change to issues of habitability and sustainability. Adaptation is an ubiquitous and beneficial natural and human strategy. Future adaptation (adjustment) to climate is inevitable at the least to decrease the vulnerability to current climatic impacts. An urgent issue is the mismatch between the predictions of global climatic change and the need for information on local to regional change in order to develop adaptation strategies. Mitigation efforts are essential since the more successful mitigation activities are, the less need there will be for adaptation responses. And, mitigation responses can be global (e.g. a uniform percentage reduction in greenhouse gas emissions) while adaptation responses will be local to regional in character and therefore depend upon confident predictions of regional climatic change. The dilemma facing policymakers is that scientists have considerable confidence in likely global climatic changes but virtually zero confidence in regional changes. Mitigation and adaptation strategies relevant to climatic change can most usefully be developed in the context of sound understanding of climate, especially the near-surface continental climate, permitting discussion of societally relevant issues. But, climate models can`t yet deliver this type of regionally and locationally specific prediction and some aspects of current research even seem to indicate increased uncertainty. These topics are explored in this paper using the specific example of the prediction of land-surface climate changes.

  1. Interpreting predictive maps of disease: highlighting the pitfalls of distribution models in epidemiology

    Directory of Open Access Journals (Sweden)

    Nicola A. Wardrop

    2014-11-01

    Full Text Available The application of spatial modelling to epidemiology has increased significantly over the past decade, delivering enhanced understanding of the environmental and climatic factors affecting disease distributions and providing spatially continuous representations of disease risk (predictive maps. These outputs provide significant information for disease control programmes, allowing spatial targeting and tailored interventions. However, several factors (e.g. sampling protocols or temporal disease spread can influence predictive mapping outputs. This paper proposes a conceptual framework which defines several scenarios and their potential impact on resulting predictive outputs, using simulated data to provide an exemplar. It is vital that researchers recognise these scenarios and their influence on predictive models and their outputs, as a failure to do so may lead to inaccurate interpretation of predictive maps. As long as these considerations are kept in mind, predictive mapping will continue to contribute significantly to epidemiological research and disease control planning.

  2. Air quality measurements versus model predictions: a case study for the Sugozu power plant

    Energy Technology Data Exchange (ETDEWEB)

    A. Korur; C. Derinoz; C. Yurteri [ENVY Energy and Environmental Investments Inc., Ankara (Turkey)

    2003-07-01

    Air quality modeling is one of the tools used in Environmental Impact Assessment (EIA) studies in order to predict the potential impacts of atmospheric emissions. The main advantage of air quality modeling is the simulation of the ground-level concentrations under different conditions (i.e., meteorological variations and other pollutant sources in the vicinity). The accuracy of model predictions, on the other hand, depends mainly on the quality of the input data reflecting meteorological and topographical conditions as well as emission sources. In this regard, the model predictions should be supported with monitoring data. In the paper, the predictions of Gaussian air dispersion model (Industrial Source Complex - ISC) for SO{sub 2} and NO{sub 2} carried out in the vicinity of the Sugozu Power Plant on the coast of Turkey are compared with the air quality monitoring results of the same region. 2 refs., 3 figs., 2 tabs.

  3. Predictive models for Escherichia coli concentrations at inland lake beaches and relationship of model variables to pathogen detection

    Science.gov (United States)

    Methods are needed improve the timeliness and accuracy of recreational water‐quality assessments. Traditional culture methods require 18–24 h to obtain results and may not reflect current conditions. Predictive models, based on environmental and water quality variables, have been...

  4. Toward Seamless Weather-Climate Prediction with a Global Cloud Resolving Model

    Science.gov (United States)

    2016-01-14

    distribution is unlimited. TOWARD SEAMLESS WEATHER- CLIMATE PREDICTION WITH A GLOBAL CLOUD RESOLVING MODEL PI: Tim Li IPRC/SOEST, University of Hawaii at...under global warming This study uses the MRI high-resolution Atmospheric Climate Model to determine whether environmental parameters that control...ENSO Amplitude under Global Warming in Four CMIP5 Models , J. Climate , 28 (8), 3250-3274. 6. Chung, P.-H., and T. Li, 2015: Characteristics of tropical

  5. Environmental Management Model for Road Maintenance Operation Involving Community Participation

    Science.gov (United States)

    Triyono, A. R. H.; Setyawan, A.; Sobriyah; Setiono, P.

    2017-07-01

    Public expectations of Central Java, which is very high on demand fulfillment, especially road infrastructure as outlined in the number of complaints and community expectations tweeter, Short Mail Massage (SMS), e-mail and public reports from various media, Highways Department of Central Java province requires development model of environmental management in the implementation of a routine way by involving the community in order to fulfill the conditions of a representative, may serve road users safely and comfortably. This study used survey method with SEM analysis and SWOT with Latent Independent Variable (X), namely; Public Participation in the regulation, development, construction and supervision of road (PSM); Public behavior in the utilization of the road (PMJ) Provincial Road Service (PJP); Safety in the Provincial Road (KJP); Integrated Management System (SMT) and latent dependent variable (Y) routine maintenance of the provincial road that is integrated with the environmental management system and involve the participation of the community (MML). The result showed the implementation of routine maintenance of road conditions in Central Java province has yet to implement an environmental management by involving the community; Therefore developed environmental management model with the results of H1: Community Participation (PSM) has positive influence on the Model of Environmental Management (MML); H2: Behavior Society in Jalan Utilization (PMJ) positive effect on Model Environmental Management (MML); H3: Provincial Road Service (PJP) positive effect on Model Environmental Management (MML); H4: Safety in the Provincial Road (KJP) positive effect on Model Environmental Management (MML); H5: Integrated Management System (SMT) has positive influence on the Model of Environmental Management (MML). From the analysis obtained formulation model describing the relationship / influence of the independent variables PSM, PMJ, PJP, KJP, and SMT on the dependent variable

  6. MEASURED CONCENTRATIONS OF HERBICIDES AND MODEL PREDICTIONS OF ATRAZINE FATE IN THE PATUXENT RIVER ESTUARY

    Science.gov (United States)

    McConnell, Laura L., Jennifer A. Harman-Fetcho and James D. Hagy, III. 2004. Measured Concentrations of Herbicides and Model Predictions of Atrazine Fate in the Patuxent River Estuary. J. Environ. Qual. 33(2):594-604. (ERL,GB X1051). The environmental fate of herbicides i...

  7. 1987 Oak Ridge model conference: Proceedings: Volume 2, Environmental protection

    Energy Technology Data Exchange (ETDEWEB)

    1987-01-01

    See the abstract for Volume I for general information on the conference. Topics discussed in Volume II include data management techiques for environmental protection efforts, the use of models in environmental auditing, in emergency plans, chemical accident emergency response, risk assessment, monitoring of waste sites, air and water monitoring of waste sites, and in training programs. (TEM)

  8. Modeling Environmental Literacy of Malaysian Pre-University Students

    Science.gov (United States)

    Shamuganathan, Sheila; Karpudewan, Mageswary

    2015-01-01

    In this study attempt was made to model the environmental literacy of Malaysian pre-university students enrolled in a matriculation college. Students enrolled in the matriculation colleges in Malaysia are the top notch students in the country. Environmental literacy of this group is perceived important because in the future these students will be…

  9. Application of Health Promotion Theories and Models for Environmental Health

    Science.gov (United States)

    Parker, Edith A.; Baldwin, Grant T.; Israel, Barbara; Salinas, Maria A.

    2004-01-01

    The field of environmental health promotion gained new prominence in recent years as awareness of physical environmental stressors and exposures increased in communities across the country and the world. Although many theories and conceptual models are used routinely to guide health promotion and health education interventions, they are rarely…

  10. Measuring physical activity-related environmental factors: reliability and predictive validity of the European environmental questionnaire ALPHA

    Directory of Open Access Journals (Sweden)

    Oppert Jean-Michel

    2010-05-01

    Full Text Available Abstract Background A questionnaire to assess physical activity related environmental factors in the European population (a 49-item and an 11-item version was created as part of the framework of the EU-funded project "Instruments for Assessing Levels of PHysical Activity and fitness (ALPHA". This paper reports on the development and assessment of the questionnaire's test-retest stability, predictive validity, and applicability to European adults. Methods The first pilot test was conducted in Belgium, France and the UK. In total 190 adults completed both forms of the ALPHA questionnaire twice with a one-week interval. Physical activity was concurrently measured (i by administration of the long version of the International Physical Activity Questionnaire (IPAQ by interview and (ii by accelerometry (Actigraph™ device. After adaptations, the second field test took place in Belgium, the UK and Austria; 166 adults completed the adapted questionnaire at two time points, with minimum one-week interval. In both field studies intraclass correlation coefficients (ICC and proportion of agreement were computed to assess the stability of the two test scores. Predictive validity was examined in the first field test by correlating the results of the questionnaires with physical activity data from accelerometry and long IPAQ-last 7 days. Results The reliability scores of the ALPHA questionnaire were moderate-to good in the first field testing (ICC range 0.66 - 0.86 and good in the second field testing (ICC range 0.71 - 0.87. The proportion of agreement for the ALPHA short increased significantly from the first (range 50 - 83% to the second field testing (range 85 - 95%. Environmental scales from both versions of the ALPHA questionnaire were significantly associated with self-reported minutes of transport-related walking, and objectively measured low intensity physical activity levels, particularly in women. Both versions were easily administered with an

  11. Environmental fate model for ultra-low-volume insecticide applications used for adult mosquito management.

    Science.gov (United States)

    Schleier, Jerome J; Peterson, Robert K D; Irvine, Kathryn M; Marshall, Lucy M; Weaver, David K; Preftakes, Collin J

    2012-11-01

    One of the more effective ways of managing high densities of adult mosquitoes that vector human and animal pathogens is ultra-low-volume (ULV) aerosol applications of insecticides. The U.S. Environmental Protection Agency uses models that are not validated for ULV insecticide applications and exposure assumptions to perform their human and ecological risk assessments. Currently, there is no validated model that can accurately predict deposition of insecticides applied using ULV technology for adult mosquito management. In addition, little is known about the deposition and drift of small droplets like those used under conditions encountered during ULV applications. The objective of this study was to perform field studies to measure environmental concentrations of insecticides and to develop a validated model to predict the deposition of ULV insecticides. The final regression model was selected by minimizing the Bayesian Information Criterion and its prediction performance was evaluated using k-fold cross validation. Density of the formulation and the density and CMD interaction coefficients were the largest in the model. The results showed that as density of the formulation decreases, deposition increases. The interaction of density and CMD showed that higher density formulations and larger droplets resulted in greater deposition. These results are supported by the aerosol physics literature. A k-fold cross validation demonstrated that the mean square error of the selected regression model is not biased, and the mean square error and mean square prediction error indicated good predictive ability.

  12. Environmental fate model for ultra-low-volume insecticide applications used for adult mosquito management

    Science.gov (United States)

    Schleier, Jerome J.; Peterson, Robert K.D.; Irvine, Kathryn M.; Marshall, Lucy M.; Weaver, David K.; Preftakes, Collin J.

    2012-01-01

    One of the more effective ways of managing high densities of adult mosquitoes that vector human and animal pathogens is ultra-low-volume (ULV) aerosol applications of insecticides. The U.S. Environmental Protection Agency uses models that are not validated for ULV insecticide applications and exposure assumptions to perform their human and ecological risk assessments. Currently, there is no validated model that can accurately predict deposition of insecticides applied using ULV technology for adult mosquito management. In addition, little is known about the deposition and drift of small droplets like those used under conditions encountered during ULV applications. The objective of this study was to perform field studies to measure environmental concentrations of insecticides and to develop a validated model to predict the deposition of ULV insecticides. The final regression model was selected by minimizing the Bayesian Information Criterion and its prediction performance was evaluated using k-fold cross validation. Density of the formulation and the density and CMD interaction coefficients were the largest in the model. The results showed that as density of the formulation decreases, deposition increases. The interaction of density and CMD showed that higher density formulations and larger droplets resulted in greater deposition. These results are supported by the aerosol physics literature. A k-fold cross validation demonstrated that the mean square error of the selected regression model is not biased, and the mean square error and mean square prediction error indicated good predictive ability.

  13. Predictability in models of the atmospheric circulation.

    NARCIS (Netherlands)

    Houtekamer, P.L.

    1992-01-01

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

  14. Addressing the complexity of water chemistry in environmental fate modeling for engineered nanoparticles.

    Science.gov (United States)

    Sani-Kast, Nicole; Scheringer, Martin; Slomberg, Danielle; Labille, Jérôme; Praetorius, Antonia; Ollivier, Patrick; Hungerbühler, Konrad

    2015-12-01

    Engineered nanoparticle (ENP) fate models developed to date - aimed at predicting ENP concentration in the aqueous environment - have limited applicability because they employ constant environmental conditions along the modeled system or a highly specific environmental representation; both approaches do not show the effects of spatial and/or temporal variability. To address this conceptual gap, we developed a novel modeling strategy that: 1) incorporates spatial variability in environmental conditions in an existing ENP fate model; and 2) analyzes the effect of a wide range of randomly sampled environmental conditions (representing variations in water chemistry). This approach was employed to investigate the transport of nano-TiO2 in the Lower Rhône River (France) under numerous sets of environmental conditions. The predicted spatial concentration profiles of nano-TiO2 were then grouped according to their similarity by using cluster analysis. The analysis resulted in a small number of clusters representing groups of spatial concentration profiles. All clusters show nano-TiO2 accumulation in the sediment layer, supporting results from previous studies. Analysis of the characteristic features of each cluster demonstrated a strong association between the water conditions in regions close to the ENP emission source and the cluster membership of the corresponding spatial concentration profiles. In particular, water compositions favoring heteroaggregation between the ENPs and suspended particulate matter resulted in clusters of low variability. These conditions are, therefore, reliable predictors of the eventual fate of the modeled ENPs. The conclusions from this study are also valid for ENP fate in other large river systems. Our results, therefore, shift the focus of future modeling and experimental research of ENP environmental fate to the water characteristic in regions near the expected ENP emission sources. Under conditions favoring heteroaggregation in these

  15. Sexual Recruitment in Zostera marina: Progress toward a Predictive Model.

    Science.gov (United States)

    Furman, Bradley T; Peterson, Bradley J

    2015-01-01

    Ecophysiological stress and physical disturbance are capable of structuring meadows through a combination of direct biomass removal and recruitment limitation; however, predicting these effects at landscape scales has rarely been successful. To model environmental influence on sexual recruitment in perennial Zostera marina, we selected a sub-tidal, light-replete study site with seasonal extremes in temperature and wave energy. During an 8-year observation period, areal coverage increased from 4.8 to 42.7%. Gains were stepwise in pattern, attributable to annual recruitment of patches followed by centrifugal growth and coalescence. Recruitment varied from 13 to 4,894 patches per year. Using a multiple linear regression approach, we examined the association between patch appearance and relative wave energy, atmospheric condition and water temperature. Two models were developed, one appropriate for the dispersal of naked seeds, and another for rafted flowers. Results indicated that both modes of sexual recruitment varied as functions of wind, temperature, rainfall and wave energy, with a regime shift in wind-wave energy corresponding to periods of rapid colonization within our site. Temporal correlations between sexual recruitment and time-lagged climatic summaries highlighted floral induction, seed bank and small patch development as periods of vulnerability. Given global losses in seagrass coverage, regions of recovery and re-colonization will become increasingly important. Lacking landscape-scale process models for seagrass recruitment, temporally explicit statistical approaches presented here could be used to forecast colonization trajectories and to provide managers with real-time estimates of future meadow performance; i.e., when to expect a good year in terms of seagrass expansion. To facilitate use as forecasting tools, we did not use statistical composites or normalized variables as our predictors. This study, therefore, represents a first step toward linking

  16. Sexual Recruitment in Zostera marina: Progress toward a Predictive Model.

    Directory of Open Access Journals (Sweden)

    Bradley T Furman

    Full Text Available Ecophysiological stress and physical disturbance are capable of structuring meadows through a combination of direct biomass removal and recruitment limitation; however, predicting these effects at landscape scales has rarely been successful. To model environmental influence on sexual recruitment in perennial Zostera marina, we selected a sub-tidal, light-replete study site with seasonal extremes in temperature and wave energy. During an 8-year observation period, areal coverage increased from 4.8 to 42.7%. Gains were stepwise in pattern, attributable to annual recruitment of patches followed by centrifugal growth and coalescence. Recruitment varied from 13 to 4,894 patches per year. Using a multiple linear regression approach, we examined the association between patch appearance and relative wave energy, atmospheric condition and water temperature. Two models were developed, one appropriate for the dispersal of naked seeds, and another for rafted flowers. Results indicated that both modes of sexual recruitment varied as functions of wind, temperature, rainfall and wave energy, with a regime shift in wind-wave energy corresponding to periods of rapid colonization within our site. Temporal correlations between sexual recruitment and time-lagged climatic summaries highlighted floral induction, seed bank and small patch development as periods of vulnerability. Given global losses in seagrass coverage, regions of recovery and re-colonization will become increasingly important. Lacking landscape-scale process models for seagrass recruitment, temporally explicit statistical approaches presented here could be used to forecast colonization trajectories and to provide managers with real-time estimates of future meadow performance; i.e., when to expect a good year in terms of seagrass expansion. To facilitate use as forecasting tools, we did not use statistical composites or normalized variables as our predictors. This study, therefore, represents a first

  17. Modelling land use change and environmental impact

    NARCIS (Netherlands)

    Veldkamp, A.; Verburg, P.H.

    2004-01-01

    Land use change models are tools for understanding and explaining the causes and consequences of land use dynamics. Recently, new models, combining knowledge and tools from biophysical and socio-economic sciences, have become available. This has resulted in spatially explicit models focussed on patt

  18. Thematic and spatial resolutions affect model-based predictions of tree species distribution.

    Directory of Open Access Journals (Sweden)

    Yu Liang

    Full Text Available Subjective decisions of thematic and spatial resolutions in characterizing environmental heterogeneity may affect the characterizations of spatial pattern and the simulation of occurrence and rate of ecological processes, and in turn, model-based tree species distribution. Thus, this study quantified the importance of thematic and spatial resolutions, and their interaction in predictions of tree species distribution (quantified by species abundance. We investigated how model-predicted species abundances changed and whether tree species with different ecological traits (e.g., seed dispersal distance, competitive capacity had different responses to varying thematic and spatial resolutions. We used the LANDIS forest landscape model to predict tree species distribution at the landscape scale and designed a series of scenarios with different thematic (different numbers of land types and spatial resolutions combinations, and then statistically examined the differences of species abundance among these scenarios. Results showed that both thematic and spatial resolutions affected model-based predictions of species distribution, but thematic resolution had a greater effect. Species ecological traits affected the predictions. For species with moderate dispersal distance and relatively abundant seed sources, predicted abundance increased as thematic resolution increased. However, for species with long seeding distance or high shade tolerance, thematic resolution had an inverse effect on predicted abundance. When seed sources and dispersal distance were not limiting, the predicted species abundance increased with spatial resolution and vice versa. Results from this study may provide insights into the choice of thematic and spatial resolutions for model-based predictions of tree species distribution.

  19. Thematic and spatial resolutions affect model-based predictions of tree species distribution.

    Science.gov (United States)

    Liang, Yu; He, Hong S; Fraser, Jacob S; Wu, ZhiWei

    2013-01-01

    Subjective decisions of thematic and spatial resolutions in characterizing environmental heterogeneity may affect the characterizations of spatial pattern and the simulation of occurrence and rate of ecological processes, and in turn, model-based tree species distribution. Thus, this study quantified the importance of thematic and spatial resolutions, and their interaction in predictions of tree species distribution (quantified by species abundance). We investigated how model-predicted species abundances changed and whether tree species with different ecological traits (e.g., seed dispersal distance, competitive capacity) had different responses to varying thematic and spatial resolutions. We used the LANDIS forest landscape model to predict tree species distribution at the landscape scale and designed a series of scenarios with different thematic (different numbers of land types) and spatial resolutions combinations, and then statistically examined the differences of species abundance among these scenarios. Results showed that both thematic and spatial resolutions affected model-based predictions of species distribution, but thematic resolution had a greater effect. Species ecological traits affected the predictions. For species with moderate dispersal distance and relatively abundant seed sources, predicted abundance increased as thematic resolution increased. However, for species with long seeding distance or high shade tolerance, thematic resolution had an inverse effect on predicted abundance. When seed sources and dispersal distance were not limiting, the predicted species abundance increased with spatial resolution and vice versa. Results from this study may provide insights into the choice of thematic and spatial resolutions for model-based predictions of tree species distribution.

  20. Allostasis: a model of predictive regulation.

    Science.gov (United States)

    Sterling, Peter

    2012-04-12

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