Appropriatie spatial scales to achieve model output uncertainty goals
Booij, Martijn J.; Melching, Charles S.; Chen, Xiaohong; Chen, Yongqin; Xia, Jun; Zhang, Hailun
2008-01-01
Appropriate spatial scales of hydrological variables were determined using an existing methodology based on a balance in uncertainties from model inputs and parameters extended with a criterion based on a maximum model output uncertainty. The original methodology uses different relationships between
Global sensitivity analysis for models with spatially dependent outputs
International Nuclear Information System (INIS)
Iooss, B.; Marrel, A.; Jullien, M.; Laurent, B.
2011-01-01
The global sensitivity analysis of a complex numerical model often calls for the estimation of variance-based importance measures, named Sobol' indices. Meta-model-based techniques have been developed in order to replace the CPU time-expensive computer code with an inexpensive mathematical function, which predicts the computer code output. The common meta-model-based sensitivity analysis methods are well suited for computer codes with scalar outputs. However, in the environmental domain, as in many areas of application, the numerical model outputs are often spatial maps, which may also vary with time. In this paper, we introduce an innovative method to obtain a spatial map of Sobol' indices with a minimal number of numerical model computations. It is based upon the functional decomposition of the spatial output onto a wavelet basis and the meta-modeling of the wavelet coefficients by the Gaussian process. An analytical example is presented to clarify the various steps of our methodology. This technique is then applied to a real hydrogeological case: for each model input variable, a spatial map of Sobol' indices is thus obtained. (authors)
Naufan, Ihsan; Sivakumar, Bellie; Woldemeskel, Fitsum M.; Raghavan, Srivatsan V.; Vu, Minh Tue; Liong, Shie-Yui
2018-01-01
Understanding the spatial and temporal variability of rainfall has always been a great challenge, and the impacts of climate change further complicate this issue. The present study employs the concepts of complex networks to study the spatial connections in rainfall, with emphasis on climate change and rainfall scaling. Rainfall outputs (during 1961-1990) from a regional climate model (i.e. Weather Research and Forecasting (WRF) model that downscaled the European Centre for Medium-range Weather Forecasts, ECMWF ERA-40 reanalyses) over Southeast Asia are studied, and data corresponding to eight different temporal scales (6-hr, 12-hr, daily, 2-day, 4-day, weekly, biweekly, and monthly) are analyzed. Two network-based methods are applied to examine the connections in rainfall: clustering coefficient (a measure of the network's local density) and degree distribution (a measure of the network's spread). The influence of rainfall correlation threshold (T) on spatial connections is also investigated by considering seven different threshold levels (ranging from 0.5 to 0.8). The results indicate that: (1) rainfall networks corresponding to much coarser temporal scales exhibit properties similar to that of small-world networks, regardless of the threshold; (2) rainfall networks corresponding to much finer temporal scales may be classified as either small-world networks or scale-free networks, depending upon the threshold; and (3) rainfall spatial connections exhibit a transition phase at intermediate temporal scales, especially at high thresholds. These results suggest that the most appropriate model for studying spatial connections may often be different at different temporal scales, and that a combination of small-world and scale-free network models might be more appropriate for rainfall upscaling/downscaling across all scales, in the strict sense of scale-invariance. The results also suggest that spatial connections in the studied rainfall networks in Southeast Asia are
Jacquin, A. P.
2012-04-01
This study analyses the effect of precipitation spatial distribution uncertainty on the uncertainty bounds of a snowmelt runoff model's discharge estimates. Prediction uncertainty bounds are derived using the Generalized Likelihood Uncertainty Estimation (GLUE) methodology. The model analysed is a conceptual watershed model operating at a monthly time step. The model divides the catchment into five elevation zones, where the fifth zone corresponds to the catchment glaciers. Precipitation amounts at each elevation zone i are estimated as the product between observed precipitation (at a single station within the catchment) and a precipitation factor FPi. Thus, these factors provide a simplified representation of the spatial variation of precipitation, specifically the shape of the functional relationship between precipitation and height. In the absence of information about appropriate values of the precipitation factors FPi, these are estimated through standard calibration procedures. The catchment case study is Aconcagua River at Chacabuquito, located in the Andean region of Central Chile. Monte Carlo samples of the model output are obtained by randomly varying the model parameters within their feasible ranges. In the first experiment, the precipitation factors FPi are considered unknown and thus included in the sampling process. The total number of unknown parameters in this case is 16. In the second experiment, precipitation factors FPi are estimated a priori, by means of a long term water balance between observed discharge at the catchment outlet, evapotranspiration estimates and observed precipitation. In this case, the number of unknown parameters reduces to 11. The feasible ranges assigned to the precipitation factors in the first experiment are slightly wider than the range of fixed precipitation factors used in the second experiment. The mean squared error of the Box-Cox transformed discharge during the calibration period is used for the evaluation of the
U.S. Environmental Protection Agency — This dataset contains WRF model output. There are three months of data: July 2012, July 2013, and January 2013. For each month, several simulations were made: A...
U.S. Environmental Protection Agency — CMAQ and CMAQ-VBS model output. This dataset is not publicly accessible because: Files too large. It can be accessed through the following means: via EPA's NCC tape...
Model output: fact or artefact?
Melsen, Lieke
2015-04-01
As a third-year PhD-student, I relatively recently entered the wonderful world of scientific Hydrology. A science that has many pillars that directly impact society, for example with the prediction of hydrological extremes (both floods and drought), climate change, applications in agriculture, nature conservation, drinking water supply, etcetera. Despite its demonstrable societal relevance, hydrology is often seen as a science between two stools. Like Klemeš (1986) stated: "By their academic background, hydrologists are foresters, geographers, electrical engineers, geologists, system analysts, physicists, mathematicians, botanists, and most often civil engineers." Sometimes it seems that the engineering genes are still present in current hydrological sciences, and this results in pragmatic rather than scientific approaches for some of the current problems and challenges we have in hydrology. Here, I refer to the uncertainty in hydrological modelling that is often neglected. For over thirty years, uncertainty in hydrological models has been extensively discussed and studied. But it is not difficult to find peer-reviewed articles in which it is implicitly assumed that model simulations represent the truth rather than a conceptualization of reality. For instance in trend studies, where data is extrapolated 100 years ahead. Of course one can use different forcing datasets to estimate the uncertainty of the input data, but how to prevent that the output is not a model artefact, caused by the model structure? Or how about impact studies, e.g. of a dam impacting river flow. Measurements are often available for the period after dam construction, so models are used to simulate river flow before dam construction. Both are compared in order to qualify the effect of the dam. But on what basis can we tell that the model tells us the truth? Model validation is common nowadays, but validation only (comparing observations with model output) is not sufficient to assume that a
Modelling Analysis of Forestry Input-Output Elasticity in China
Directory of Open Access Journals (Sweden)
Guofeng Wang
2016-01-01
Full Text Available Based on an extended economic model and space econometrics, this essay analyzed the spatial distributions and interdependent relationships of the production of forestry in China; also the input-output elasticity of forestry production were calculated. Results figure out there exists significant spatial correlation in forestry production in China. Spatial distribution is mainly manifested as spatial agglomeration. The output elasticity of labor force is equal to 0.6649, and that of capital is equal to 0.8412. The contribution of land is significantly negative. Labor and capital are the main determinants for the province-level forestry production in China. Thus, research on the province-level forestry production should not ignore the spatial effect. The policy-making process should take into consideration the effects between provinces on the production of forestry. This study provides some scientific technical support for forestry production.
Problems in Modelling Charge Output Accelerometers
Directory of Open Access Journals (Sweden)
Tomczyk Krzysztof
2016-12-01
Full Text Available The paper presents major issues associated with the problem of modelling change output accelerometers. The presented solutions are based on the weighted least squares (WLS method using transformation of the complex frequency response of the sensors. The main assumptions of the WLS method and a mathematical model of charge output accelerometers are presented in first two sections of this paper. In the next sections applying the WLS method to estimation of the accelerometer model parameters is discussed and the associated uncertainties are determined. Finally, the results of modelling a PCB357B73 charge output accelerometer are analysed in the last section of this paper. All calculations were executed using the MathCad software program. The main stages of these calculations are presented in Appendices A−E.
Multi-model MPC with output feedback
Directory of Open Access Journals (Sweden)
J. M. Perez
2014-03-01
Full Text Available In this work, a new formulation is presented for the model predictive control (MPC of a process system that is represented by a finite set of models, each one corresponding to a different operating point. The general case is considered of systems with stable and integrating outputs in closed-loop with output feedback. For this purpose, the controller is based on a non-minimal order model where the state is built with the measured outputs and the manipulated inputs of the control system. Therefore, the state can be considered as perfectly known and, consequently, there is no need to include a state observer in the control loop. This property of the proposed modeling approach is convenient to extend previous stability results of the closed loop system with robust MPC controllers based on state feedback. The controller proposed here is based on the solution of two optimization problems that are solved sequentially at the same time step. The method is illustrated with a simulated example of the process industry. The rigorous simulation of the control of an adiabatic flash of a multi-component hydrocarbon mixture illustrates the application of the robust controller. The dynamic simulation of this process is performed using EMSO - Environment Model Simulation and Optimization. Finally, a comparison with a linear MPC using a single model is presented.
Möller, M.; Obleitner, F.; Reijmer, C.H.; Pohjola, V.A.; Glowacki, P.; Kohler, J.
2016-01-01
Large-scale modeling of glacier mass balance relies often on the output from regional climate models (RCMs). However, the limited accuracy and spatial resolution of RCM output pose limitations on mass balance simulations at subregional or local scales. Moreover, RCM output is still rarely available
Validation of models with multivariate output
International Nuclear Information System (INIS)
Rebba, Ramesh; Mahadevan, Sankaran
2006-01-01
This paper develops metrics for validating computational models with experimental data, considering uncertainties in both. A computational model may generate multiple response quantities and the validation experiment might yield corresponding measured values. Alternatively, a single response quantity may be predicted and observed at different spatial and temporal points. Model validation in such cases involves comparison of multiple correlated quantities. Multiple univariate comparisons may give conflicting inferences. Therefore, aggregate validation metrics are developed in this paper. Both classical and Bayesian hypothesis testing are investigated for this purpose, using multivariate analysis. Since, commonly used statistical significance tests are based on normality assumptions, appropriate transformations are investigated in the case of non-normal data. The methodology is implemented to validate an empirical model for energy dissipation in lap joints under dynamic loading
Statistical Compression for Climate Model Output
Hammerling, D.; Guinness, J.; Soh, Y. J.
2017-12-01
Numerical climate model simulations run at high spatial and temporal resolutions generate massive quantities of data. As our computing capabilities continue to increase, storing all of the data is not sustainable, and thus is it important to develop methods for representing the full datasets by smaller compressed versions. We propose a statistical compression and decompression algorithm based on storing a set of summary statistics as well as a statistical model describing the conditional distribution of the full dataset given the summary statistics. We decompress the data by computing conditional expectations and conditional simulations from the model given the summary statistics. Conditional expectations represent our best estimate of the original data but are subject to oversmoothing in space and time. Conditional simulations introduce realistic small-scale noise so that the decompressed fields are neither too smooth nor too rough compared with the original data. Considerable attention is paid to accurately modeling the original dataset-one year of daily mean temperature data-particularly with regard to the inherent spatial nonstationarity in global fields, and to determining the statistics to be stored, so that the variation in the original data can be closely captured, while allowing for fast decompression and conditional emulation on modest computers.
Lawson, Andrew B
2002-01-01
Research has generated a number of advances in methods for spatial cluster modelling in recent years, particularly in the area of Bayesian cluster modelling. Along with these advances has come an explosion of interest in the potential applications of this work, especially in epidemiology and genome research. In one integrated volume, this book reviews the state-of-the-art in spatial clustering and spatial cluster modelling, bringing together research and applications previously scattered throughout the literature. It begins with an overview of the field, then presents a series of chapters that illuminate the nature and purpose of cluster modelling within different application areas, including astrophysics, epidemiology, ecology, and imaging. The focus then shifts to methods, with discussions on point and object process modelling, perfect sampling of cluster processes, partitioning in space and space-time, spatial and spatio-temporal process modelling, nonparametric methods for clustering, and spatio-temporal ...
Spatial data quality and coastal spill modelling
International Nuclear Information System (INIS)
Li, Y.; Brimicombe, A.J.; Ralphs, M.P.
1998-01-01
Issues of spatial data quality are central to the whole oil spill modelling process. Both model and data quality performance issues should be considered as indispensable parts of a complete oil spill model specification and testing procedure. This paper presents initial results of research that will emphasise to modeler and manager alike the practical issues of spatial data quality for coastal oil spill modelling. It is centred around a case study of Jiao Zhou Bay in the People's Republic of China. The implications for coastal oil spill modelling are discussed and some strategies for managing the effects of spatial data quality in the outputs of oil spill modelling are explored. (author)
Prell, Christina; Sun, Laixiang; Feng, Kuishuang; He, Jiaying; Hubacek, Klaus
2017-05-15
Land-use change is increasingly driven by global trade. The term "telecoupling" has been gaining ground as a means to describe how human actions in one part of the world can have spatially distant impacts on land and land-use in another. These interactions can, over time, create both direct and spatially distant feedback loops, in which human activity and land use mutually impact one another over great expanses. In this paper, we develop an analytical framework to clarify spatially distant feedbacks in the case of land use and global trade. We use an innovative mix of multi-regional input-output (MRIO) analysis and stochastic actor-oriented models (SAOMs) for analyzing the co-evolution of changes in trade network patterns with those of land use, as embodied in trade. Our results indicate that the formation of trade ties and changes in embodied land use mutually impact one another, and further, that these changes are linked to disparities in countries' wealth. Through identifying this feedback loop, our results support ongoing discussions about the unequal trade patterns between rich and poor countries that result in uneven distributions of negative environmental impacts. Finally, evidence for this feedback loop is present even when controlling for a number of underlying mechanisms, such as countries' land endowments, their geographical distance from one another, and a number of endogenous network tendencies. Copyright © 2017 Elsevier B.V. All rights reserved.
Measuring power output intermittency and unsteady loading in a micro wind farm model
Bossuyt, Juliaan; Howland, Michael; Meneveau, Charles; Meyers, Johan
2016-01-01
In this study porous disc models are used as a turbine model for a wind-tunnel wind farm experiment, allowing the measurement of the power output, thrust force and spatially averaged incoming velocity for every turbine. The model's capabilities for studying the unsteady turbine loading, wind farm power output intermittency and spatio temporal correlations between wind turbines are demonstrated on an aligned wind farm, consisting of 100 wind turbine models.
Model output statistics applied to wind power prediction
Energy Technology Data Exchange (ETDEWEB)
Joensen, A; Giebel, G; Landberg, L [Risoe National Lab., Roskilde (Denmark); Madsen, H; Nielsen, H A [The Technical Univ. of Denmark, Dept. of Mathematical Modelling, Lyngby (Denmark)
1999-03-01
Being able to predict the output of a wind farm online for a day or two in advance has significant advantages for utilities, such as better possibility to schedule fossil fuelled power plants and a better position on electricity spot markets. In this paper prediction methods based on Numerical Weather Prediction (NWP) models are considered. The spatial resolution used in NWP models implies that these predictions are not valid locally at a specific wind farm. Furthermore, due to the non-stationary nature and complexity of the processes in the atmosphere, and occasional changes of NWP models, the deviation between the predicted and the measured wind will be time dependent. If observational data is available, and if the deviation between the predictions and the observations exhibits systematic behavior, this should be corrected for; if statistical methods are used, this approaches is usually referred to as MOS (Model Output Statistics). The influence of atmospheric turbulence intensity, topography, prediction horizon length and auto-correlation of wind speed and power is considered, and to take the time-variations into account, adaptive estimation methods are applied. Three estimation techniques are considered and compared, Extended Kalman Filtering, recursive least squares and a new modified recursive least squares algorithm. (au) EU-JOULE-3. 11 refs.
Control of spatial discretisation in coastal oil spill modelling
Li, Yang
2007-01-01
Spatial discretisation plays an important role in many numerical environmental models. This paper studies the control of spatial discretisation in coastal oil spill modelling with a view to assure the quality of modelling outputs for given spatial data inputs. Spatial data analysis techniques are effective for investigating and improving the spatial discretisation in different phases of the modelling. Proposed methods are implemented and tested with experimental models. A new “Automatic Searc...
Downscaling climate model output for water resources impacts assessment (Invited)
Maurer, E. P.; Pierce, D. W.; Cayan, D. R.
2013-12-01
Water agencies in the U.S. and around the globe are beginning to wrap climate change projections into their planning procedures, recognizing that ongoing human-induced changes to hydrology can affect water management in significant ways. Future hydrology changes are derived using global climate model (GCM) projections, though their output is at a spatial scale that is too coarse to meet the needs of those concerned with local and regional impacts. Those investigating local impacts have employed a range of techniques for downscaling, the process of translating GCM output to a more locally-relevant spatial scale. Recent projects have produced libraries of publicly-available downscaled climate projections, enabling managers, researchers and others to focus on impacts studies, drawing from a shared pool of fine-scale climate data. Besides the obvious advantage to data users, who no longer need to develop expertise in downscaling prior to examining impacts, the use of the downscaled data by hundreds of people has allowed a crowdsourcing approach to examining the data. The wide variety of applications employed by different users has revealed characteristics not discovered during the initial data set production. This has led to a deeper look at the downscaling methods, including the assumptions and effect of bias correction of GCM output. Here new findings are presented related to the assumption of stationarity in the relationships between large- and fine-scale climate, as well as the impact of quantile mapping bias correction on precipitation trends. The validity of these assumptions can influence the interpretations of impacts studies using data derived using these standard statistical methods and help point the way to improved methods.
A two stage data envelopment analysis model with undesirable output
Shariff Adli Aminuddin, Adam; Izzati Jaini, Nur; Mat Kasim, Maznah; Nawawi, Mohd Kamal Mohd
2017-09-01
The dependent relationship among the decision making units (DMU) is usually assumed to be non-existent in the development of Data Envelopment Analysis (DEA) model. The dependency can be represented by the multi-stage DEA model, where the outputs from the precedent stage will be the inputs for the latter stage. The multi-stage DEA model evaluate both the efficiency score for each stages and the overall efficiency of the whole process. The existing multi stage DEA models do not focus on the integration with the undesirable output, in which the higher input will generate lower output unlike the normal desirable output. This research attempts to address the inclusion of such undesirable output and investigate the theoretical implication and potential application towards the development of multi-stage DEA model.
NACP Site: Terrestrial Biosphere Model Output Data in Original Format
National Aeronautics and Space Administration — ABSTRACT: This data set contains the original model output data submissions from the 24 terrestrial biosphere models (TBM) that participated in the North American...
NACP Site: Terrestrial Biosphere Model Output Data in Original Format
National Aeronautics and Space Administration — This data set contains the original model output data submissions from the 24 terrestrial biosphere models (TBM) that participated in the North American Carbon...
Logistics flows and enterprise input-output models: aggregate and disaggregate analysis
Albino, V.; Yazan, Devrim; Messeni Petruzzelli, A.; Okogbaa, O.G.
2011-01-01
In the present paper, we propose the use of enterprise input-output (EIO) models to describe and analyse the logistics flows considering spatial issues and related environmental effects associated with production and transportation processes. In particular, transportation is modelled as a specific
Modelling Waste Output from Trout Farms
DEFF Research Database (Denmark)
Frier, J. O.; From, J.; Larsen, Torben
1995-01-01
to calculate waste discharge from existing and planned aquaculture activities. A special purpose is simulating outcome of waste water treatment and altered feeding programmes. Different submodels must be applied for P, N, and organics, as well as for the different phases of food and waste treatment. Altogether...... this calls for an array of co-operating submodels for a sufficient coverage of the options. In all the required fields there is some scientific background for numerical model approaches, and some submodels have been proposed. Because of its multidisciplinary character a synthesized approach is still lacking...
Input-output analysis of CO2 emissions embodied in trade. The effects of spatial aggregation
International Nuclear Information System (INIS)
Su, Bin; Ang, B.W.
2010-01-01
Energy-related CO 2 emissions embodied in international trade have been widely studied by researchers using the environmental input-output analysis framework. It is well known that both sector aggregation and spatial aggregation affect the results obtained in such studies. With regard to the latter, past studies are often conducted at the national level irrespective of country or economy size. For a large economy with the needed data, studies may be conducted at different levels of spatial aggregation. We examine this problem analytically by extending the work of Su et al. ([Su, B., Huang, H.C., Ang, B.W., Zhou, P., 2010. Input-output analysis of CO 2 emissions embodied in trade: The effects of sector aggregation. Energy Economics 32 (1), 166-175.]) on sector aggregation. We present a numerical example using the data of China and by dividing the country into eight regions. It is found that the results are highly dependent on spatial aggregation. Our study shows that for a large country like China it is meaningful to look into the effect of spatial aggregation. (author)
The multi-factor energy input–output model
International Nuclear Information System (INIS)
Guevara, Zeus; Domingos, Tiago
2017-01-01
Energy input–output analysis (EIO analysis) is a noteworthy tool for the analysis of the role of energy in the economy. However, it has relied on models that provide a limited description of energy flows in the economic system and do not allow an adequate analysis of energy efficiency. This paper introduces a novel energy input–output model, the multi-factor energy input–output model (MF-EIO model), which is obtained from a partitioning of a hybrid-unit input–output system of the economy. This model improves on current models by describing the energy flows according to the processes of energy conversion and the levels of energy use in the economy. It characterizes the vector of total energy output as a function of seven factors: two energy efficiency indicators; two characteristics of end-use energy consumption; and three economic features of the rest of the economy. Moreover, it is consistent with the standard model for EIO analysis, i.e., the hybrid-unit model. This paper also introduces an approximate version of the MF-EIO model, which is equivalent to the former under equal energy prices for industries and final consumers, but requires less data processing. The latter is composed by two linked models: a model of the energy sector in physical units, and a model of the rest of the economy in monetary units. In conclusion, the proposed modelling framework improves EIO analysis and extends EIO applications to the accounting for energy efficiency of the economy. - Highlights: • A novel energy input–output model is introduced. • It allows a more adequate analysis of energy flows than current models. • It describes energy flows according to processes of energy conversion and use. • It can be used for other environmental applications (material use and emissions). • An approximate version of the model is introduced, simpler and less data intensive.
Alternative to Ritt's pseudodivision for finding the input-output equations of multi-output models.
Meshkat, Nicolette; Anderson, Chris; DiStefano, Joseph J
2012-09-01
Differential algebra approaches to structural identifiability analysis of a dynamic system model in many instances heavily depend upon Ritt's pseudodivision at an early step in analysis. The pseudodivision algorithm is used to find the characteristic set, of which a subset, the input-output equations, is used for identifiability analysis. A simpler algorithm is proposed for this step, using Gröbner Bases, along with a proof of the method that includes a reduced upper bound on derivative requirements. Efficacy of the new algorithm is illustrated with several biosystem model examples. Copyright © 2012 Elsevier Inc. All rights reserved.
The size and spatial distribution of microchannel plate output electron clouds
International Nuclear Information System (INIS)
Lapington, J.S.; Edgar, M.L.
1989-01-01
An experimental technique for measuring the spatial distribution of the output charge cloud from a microchannel plate (MCP), using a planar, charge-division-type anode is discussed. The anode simultaneously measures, for each charge cloud, both the position of the charge centroid and the fractional charge falling to one side of the split in the pattern. The measurements from several thousand events have been combined to calculate the average spatial distribution of the electron cloud and the dominant factors influencing the charge cloud distribution have been found to be the MCP gain and the MCP-anode accelerating field and geometry. Experimental research on the two dominant factors with respect to ranges of distribution is presented. 10 refs
Investigation of solar photovoltaic module power output by various models
International Nuclear Information System (INIS)
Jakhrani, A.Q.; Othman, A.K.; Rigit, A.R.H.; Baini, R.
2012-01-01
This paper aims to investigate the power output of a solar photovoltaic module by various models and to formulate a suitable model for predicting the performance of solar photovoltaic modules. The model was used to correct the configurations of solar photovoltaic systems for sustainable power supply. Different types of models namely the efficiency, power, fill factor and current-voltage characteristic curve models have been reviewed. It was found that the examined models predicted a 40% yield of the rated power in cloudy weather conditions and up to 80% in clear skies. The models performed well in terms of electrical efficiency in cloudy days if the influence of low irradiance were incorporated. Both analytical and numerical methods were employed in the formulation of improved model which gave +- 2% error when compared with the rated power output of solar photovoltaic module. The proposed model is more practical in terms of number of variables used and acceptable performance in humid atmospheres. Therefore, it could be useful for the estimation of power output of the solar photovoltaic systems in Sarawak region. (author)
Anikin, A. S.
2018-06-01
Conditional statistical characteristics of the phase difference are considered depending on the ratio of instantaneous output signal amplitudes of spatially separated weakly directional antennas for the normal field model for paths with radio-wave scattering. The dependences obtained are related to the physical processes on the radio-wave propagation path. The normal model parameters are established at which the statistical characteristics of the phase difference depend on the ratio of the instantaneous amplitudes and hence can be used to measure the phase difference. Using Shannon's formula, the amount of information on the phase difference of signals contained in the ratio of their amplitudes is calculated depending on the parameters of the normal field model. Approaches are suggested to reduce the shift of phase difference measured for paths with radio-wave scattering. A comparison with results of computer simulation by the Monte Carlo method is performed.
A model to predict the power output from wind farms
Energy Technology Data Exchange (ETDEWEB)
Landberg, L. [Riso National Lab., Roskilde (Denmark)
1997-12-31
This paper will describe a model that can predict the power output from wind farms. To give examples of input the model is applied to a wind farm in Texas. The predictions are generated from forecasts from the NGM model of NCEP. These predictions are made valid at individual sites (wind farms) by applying a matrix calculated by the sub-models of WASP (Wind Atlas Application and Analysis Program). The actual wind farm production is calculated using the Riso PARK model. Because of the preliminary nature of the results, they will not be given. However, similar results from Europe will be given.
System convergence in transport models: algorithms efficiency and output uncertainty
DEFF Research Database (Denmark)
Rich, Jeppe; Nielsen, Otto Anker
2015-01-01
of this paper is to analyse convergence performance for the external loop and to illustrate how an improper linkage between the converging parts can lead to substantial uncertainty in the final output. Although this loop is crucial for the performance of large-scale transport models it has not been analysed...... much in the literature. The paper first investigates several variants of the Method of Successive Averages (MSA) by simulation experiments on a toy-network. It is found that the simulation experiments produce support for a weighted MSA approach. The weighted MSA approach is then analysed on large......-scale in the Danish National Transport Model (DNTM). It is revealed that system convergence requires that either demand or supply is without random noise but not both. In that case, if MSA is applied to the model output with random noise, it will converge effectively as the random effects are gradually dampened...
Modeling the power output of piezoelectric energy harvesters
Al Ahmad, Mahmoud
2011-04-30
Design of experiments and multiphysics analyses were used to develop a parametric model for a d 33-based cantilever. The analysis revealed that the most significant parameters influencing the resonant frequency are the supporting layer thickness, piezoelectric layer thickness, and cantilever length. On the other hand, the most important factors affecting the charge output arethe piezoelectric thickness and the interdigitated electrode dimensions. The accuracy of the developed model was confirmed and showed less than 1% estimation error compared with a commercial simulation package. To estimate the power delivered to a load, the electric current output from the piezoelectric generator was calculated. A circuit model was built and used to estimate the power delivered to a load, which compared favorably to experimentally published power data on actual cantilevers of similar dimensions. © 2011 TMS.
Modeling the power output of piezoelectric energy harvesters
Al Ahmad, Mahmoud; Alshareef, Husam N.
2011-01-01
Design of experiments and multiphysics analyses were used to develop a parametric model for a d 33-based cantilever. The analysis revealed that the most significant parameters influencing the resonant frequency are the supporting layer thickness, piezoelectric layer thickness, and cantilever length. On the other hand, the most important factors affecting the charge output arethe piezoelectric thickness and the interdigitated electrode dimensions. The accuracy of the developed model was confirmed and showed less than 1% estimation error compared with a commercial simulation package. To estimate the power delivered to a load, the electric current output from the piezoelectric generator was calculated. A circuit model was built and used to estimate the power delivered to a load, which compared favorably to experimentally published power data on actual cantilevers of similar dimensions. © 2011 TMS.
Robust Output Model Predictive Control of an Unstable Rijke Tube
Directory of Open Access Journals (Sweden)
Fabian Jarmolowitz
2012-01-01
Full Text Available This work investigates the active control of an unstable Rijke tube using robust output model predictive control (RMPC. As internal model a polytopic linear system with constraints is assumed to account for uncertainties. For guaranteed stability, a linear state feedback controller is designed using linear matrix inequalities and used within a feedback formulation of the model predictive controller. For state estimation a robust gain-scheduled observer is developed. It is shown that the proposed RMPC ensures robust stability under constraints over the considered operating range.
Thermodynamic Model of Spatial Memory
Kaufman, Miron; Allen, P.
1998-03-01
We develop and test a thermodynamic model of spatial memory. Our model is an application of statistical thermodynamics to cognitive science. It is related to applications of the statistical mechanics framework in parallel distributed processes research. Our macroscopic model allows us to evaluate an entropy associated with spatial memory tasks. We find that older adults exhibit higher levels of entropy than younger adults. Thurstone's Law of Categorical Judgment, according to which the discriminal processes along the psychological continuum produced by presentations of a single stimulus are normally distributed, is explained by using a Hooke spring model of spatial memory. We have also analyzed a nonlinear modification of the ideal spring model of spatial memory. This work is supported by NIH/NIA grant AG09282-06.
A spatial Mankiw-Romer-Weil model: Theory and evidence
Fischer, Manfred M.
2009-01-01
This paper presents a theoretical growth model that extends the Mankiw-Romer-Weil [MRW] model by accounting for technological interdependence among regional economies. Interdependence is assumed to work through spatial externalities caused by disembodied knowledge diffusion. The transition from theory to econometrics leads to a reduced-form empirical spatial Durbin model specification that explains the variation in regional levels of per worker output at steady state. A system ...
Evacuation emergency response model coupling atmospheric release advisory capability output
International Nuclear Information System (INIS)
Rosen, L.C.; Lawver, B.S.; Buckley, D.W.; Finn, S.P.; Swenson, J.B.
1983-01-01
A Federal Emergency Management Agency (FEMA) sponsored project to develop a coupled set of models between those of the Lawrence Livermore National Laboratory (LLNL) Atmospheric Release Advisory Capability (ARAC) system and candidate evacuation models is discussed herein. This report describes the ARAC system and discusses the rapid computer code developed and the coupling with ARAC output. The computer code is adapted to the use of color graphics as a means to display and convey the dynamics of an emergency evacuation. The model is applied to a specific case of an emergency evacuation of individuals surrounding the Rancho Seco Nuclear Power Plant, located approximately 25 miles southeast of Sacramento, California. The graphics available to the model user for the Rancho Seco example are displayed and noted in detail. Suggestions for future, potential improvements to the emergency evacuation model are presented
National Oceanic and Atmospheric Administration, Department of Commerce — Weekly biophysical and fish model output of FEAST. Part of The Bering Sea Project, FEAST is a high resolution (~10km2) spatial model that uses a Regional Ocean...
Model validation and calibration based on component functions of model output
International Nuclear Information System (INIS)
Wu, Danqing; Lu, Zhenzhou; Wang, Yanping; Cheng, Lei
2015-01-01
The target in this work is to validate the component functions of model output between physical observation and computational model with the area metric. Based on the theory of high dimensional model representations (HDMR) of independent input variables, conditional expectations are component functions of model output, and the conditional expectations reflect partial information of model output. Therefore, the model validation of conditional expectations tells the discrepancy between the partial information of the computational model output and that of the observations. Then a calibration of the conditional expectations is carried out to reduce the value of model validation metric. After that, a recalculation of the model validation metric of model output is taken with the calibrated model parameters, and the result shows that a reduction of the discrepancy in the conditional expectations can help decrease the difference in model output. At last, several examples are employed to demonstrate the rationality and necessity of the methodology in case of both single validation site and multiple validation sites. - Highlights: • A validation metric of conditional expectations of model output is proposed. • HDRM explains the relationship of conditional expectations and model output. • An improved approach of parameter calibration updates the computational models. • Validation and calibration process are applied at single site and multiple sites. • Validation and calibration process show a superiority than existing methods
A PRODUCTIVITY EVALUATION MODEL BASED ON INPUT AND OUTPUT ORIENTATIONS
Directory of Open Access Journals (Sweden)
C.O. Anyaeche
2012-01-01
Full Text Available
ENGLISH ABSTRACT: Many productivity models evaluate either the input or the output performances using standalone techniques. This sometimes gives divergent views of the same system’s results. The work reported in this article, which simultaneously evaluated productivity from both orientations, was applied on real life data. The results showed losses in productivity (–2% and price recovery (–8% for the outputs; the inputs showed productivity gain (145% but price recovery loss (–63%. These imply losses in product performances but a productivity gain in inputs. The loss in the price recovery of inputs indicates a problem in the pricing policy. This model is applicable in product diversification.
AFRIKAANSE OPSOMMING: Die meeste produktiwiteitsmodelle evalueer of die inset- of die uitsetverrigting deur gebruik te maak van geïsoleerde tegnieke. Dit lei soms tot uiteenlopende perspektiewe van dieselfde sisteem se verrigting. Hierdie artikel evalueer verrigting uit beide perspektiewe en gebruik ware data. Die resultate toon ‘n afname in produktiwiteit (-2% en prysherwinning (-8% vir die uitsette. Die insette toon ‘n toename in produktiwiteit (145%, maar ‘n afname in prysherwinning (-63%. Dit impliseer ‘n afname in produkverrigting, maar ‘n produktiwiteitstoename in insette. Die afname in die prysherwinning van insette dui op ‘n problem in die prysvasstellingbeleid. Hierdie model is geskik vir produkdiversifikasie.
An improved robust model predictive control for linear parameter-varying input-output models
Abbas, H.S.; Hanema, J.; Tóth, R.; Mohammadpour, J.; Meskin, N.
2018-01-01
This paper describes a new robust model predictive control (MPC) scheme to control the discrete-time linear parameter-varying input-output models subject to input and output constraints. Closed-loop asymptotic stability is guaranteed by including a quadratic terminal cost and an ellipsoidal terminal
Competition in spatial location models
Webers, H.M.
1996-01-01
Models of spatial competition are designed and analyzed to describe the fact that space, by its very nature, is a source of market power. This field of research, lying at the interface of game theory and economics, has attracted much interest because location problems are related to many aspects of
Prioritizing Interdependent Production Processes using Leontief Input-Output Model
Directory of Open Access Journals (Sweden)
Masbad Jesah Grace
2016-03-01
Full Text Available This paper proposes a methodology in identifying key production processes in an interdependent production system. Previous approaches on this domain have drawbacks that may potentially affect the reliability of decision-making. The proposed approach adopts the Leontief input-output model (L-IOM which was proven successful in analyzing interdependent economic systems. The motivation behind such adoption lies in the strength of L-IOM in providing a rigorous quantitative framework in identifying key components of interdependent systems. In this proposed approach, the consumption and production flows of each process are represented respectively by the material inventory produced by the prior process and the material inventory produced by the current process, both in monetary values. A case study in a furniture production system located in central Philippines was carried out to elucidate the proposed approach. Results of the case were reported in this work
National Oceanic and Atmospheric Administration, Department of Commerce — This data contains output of a regional implementation of the Massachusetts Institute of Technology general circulation model (MITgcm) at a 1-km spatial resolution...
National Oceanic and Atmospheric Administration, Department of Commerce — This data contains output of a regional implementation of the Massachusetts Institute of Technology general circulation model (MITgcm) at a 1-km spatial resolution...
An analytical model for an input/output-subsystem
International Nuclear Information System (INIS)
Roemgens, J.
1983-05-01
An input/output-subsystem of one or several computers if formed by the external memory units and the peripheral units of a computer system. For these subsystems mathematical models are established, taking into account the special properties of the I/O-subsystems, in order to avoid planning errors and to allow for predictions of the capacity of such systems. Here an analytical model is presented for the magnetic discs of a I/O-subsystem, using analytical methods for the individual waiting queues or waiting queue networks. Only I/O-subsystems of IBM-computer configurations are considered, which can be controlled by the MVS operating system. After a description of the hardware and software components of these I/O-systems, possible solutions from the literature are presented and discussed with respect to their applicability in IBM-I/O-subsystems. Based on these models a special scheme is developed which combines the advantages of the literature models and avoids the disadvantages in part. (orig./RW) [de
A Markovian model of evolving world input-output network.
Directory of Open Access Journals (Sweden)
Vahid Moosavi
Full Text Available The initial theoretical connections between Leontief input-output models and Markov chains were established back in 1950s. However, considering the wide variety of mathematical properties of Markov chains, so far there has not been a full investigation of evolving world economic networks with Markov chain formalism. In this work, using the recently available world input-output database, we investigated the evolution of the world economic network from 1995 to 2011 through analysis of a time series of finite Markov chains. We assessed different aspects of this evolving system via different known properties of the Markov chains such as mixing time, Kemeny constant, steady state probabilities and perturbation analysis of the transition matrices. First, we showed how the time series of mixing times and Kemeny constants could be used as an aggregate index of globalization. Next, we focused on the steady state probabilities as a measure of structural power of the economies that are comparable to GDP shares of economies as the traditional index of economies welfare. Further, we introduced two measures of systemic risk, called systemic influence and systemic fragility, where the former is the ratio of number of influenced nodes to the total number of nodes, caused by a shock in the activity of a node, and the latter is based on the number of times a specific economic node is affected by a shock in the activity of any of the other nodes. Finally, focusing on Kemeny constant as a global indicator of monetary flow across the network, we showed that there is a paradoxical effect of a change in activity levels of economic nodes on the overall flow of the world economic network. While the economic slowdown of the majority of nodes with high structural power results to a slower average monetary flow over the network, there are some nodes, where their slowdowns improve the overall quality of the network in terms of connectivity and the average flow of the money.
A Markovian model of evolving world input-output network.
Moosavi, Vahid; Isacchini, Giulio
2017-01-01
The initial theoretical connections between Leontief input-output models and Markov chains were established back in 1950s. However, considering the wide variety of mathematical properties of Markov chains, so far there has not been a full investigation of evolving world economic networks with Markov chain formalism. In this work, using the recently available world input-output database, we investigated the evolution of the world economic network from 1995 to 2011 through analysis of a time series of finite Markov chains. We assessed different aspects of this evolving system via different known properties of the Markov chains such as mixing time, Kemeny constant, steady state probabilities and perturbation analysis of the transition matrices. First, we showed how the time series of mixing times and Kemeny constants could be used as an aggregate index of globalization. Next, we focused on the steady state probabilities as a measure of structural power of the economies that are comparable to GDP shares of economies as the traditional index of economies welfare. Further, we introduced two measures of systemic risk, called systemic influence and systemic fragility, where the former is the ratio of number of influenced nodes to the total number of nodes, caused by a shock in the activity of a node, and the latter is based on the number of times a specific economic node is affected by a shock in the activity of any of the other nodes. Finally, focusing on Kemeny constant as a global indicator of monetary flow across the network, we showed that there is a paradoxical effect of a change in activity levels of economic nodes on the overall flow of the world economic network. While the economic slowdown of the majority of nodes with high structural power results to a slower average monetary flow over the network, there are some nodes, where their slowdowns improve the overall quality of the network in terms of connectivity and the average flow of the money.
Ahmed, Kazi Farzan; Wang, Guiling; Silander, John; Wilson, Adam M.; Allen, Jenica M.; Horton, Radley; Anyah, Richard
2013-01-01
Statistical downscaling can be used to efficiently downscale a large number of General Circulation Model (GCM) outputs to a fine temporal and spatial scale. To facilitate regional impact assessments, this study statistically downscales (to 1/8deg spatial resolution) and corrects the bias of daily maximum and minimum temperature and daily precipitation data from six GCMs and four Regional Climate Models (RCMs) for the northeast United States (US) using the Statistical Downscaling and Bias Correction (SDBC) approach. Based on these downscaled data from multiple models, five extreme indices were analyzed for the future climate to quantify future changes of climate extremes. For a subset of models and indices, results based on raw and bias corrected model outputs for the present-day climate were compared with observations, which demonstrated that bias correction is important not only for GCM outputs, but also for RCM outputs. For future climate, bias correction led to a higher level of agreements among the models in predicting the magnitude and capturing the spatial pattern of the extreme climate indices. We found that the incorporation of dynamical downscaling as an intermediate step does not lead to considerable differences in the results of statistical downscaling for the study domain.
Ichii, K.; Suzuki, T.; Kato, T.; Ito, A.; Hajima, T.; Ueyama, M.; Sasai, T.; Hirata, R.; Saigusa, N.; Ohtani, Y.; Takagi, K.
2009-08-01
Terrestrial biosphere models show large uncertainties when simulating carbon and water cycles, and reducing these uncertainties is a priority for developing more accurate estimates of both terrestrial ecosystem statuses and future climate changes. To reduce uncertainties and improve the understanding of these carbon budgets, we investigated the ability of flux datasets to improve model simulations and reduce variabilities among multi-model outputs of terrestrial biosphere models in Japan. Using 9 terrestrial biosphere models (Support Vector Machine-based regressions, TOPS, CASA, VISIT, Biome-BGC, DAYCENT, SEIB, LPJ, and TRIFFID), we conducted two simulations: (1) point simulations at four flux sites in Japan and (2) spatial simulations for Japan with a default model (based on original settings) and an improved model (based on calibration using flux observations). Generally, models using default model settings showed large deviations in model outputs from observation with large model-by-model variability. However, after we calibrated the model parameters using flux observations (GPP, RE and NEP), most models successfully simulated seasonal variations in the carbon cycle, with less variability among models. We also found that interannual variations in the carbon cycle are mostly consistent among models and observations. Spatial analysis also showed a large reduction in the variability among model outputs, and model calibration using flux observations significantly improved the model outputs. These results show that to reduce uncertainties among terrestrial biosphere models, we need to conduct careful validation and calibration with available flux observations. Flux observation data significantly improved terrestrial biosphere models, not only on a point scale but also on spatial scales.
Grouping influences output interference in short-term memory: a mixture modeling study
Directory of Open Access Journals (Sweden)
Min-Suk eKang
2016-05-01
Full Text Available Output interference is a source of forgetting induced by recalling. We investigated how grouping influences output interference in short-term memory. In Experiment 1, the participants were asked to remember four colored items. Those items were grouped by temporal coincidence as well as spatial alignment: two items were presented in the first memory array and two were presented in the second, and the items in both arrays were either vertically or horizontally aligned as well. The participants then performed two recall tasks in sequence by selecting a color presented at a cued location from a color wheel. In the same-group condition, the participants reported both items from the same memory array; however, in the different-group condition, the participants reported one item from each memory array. We analyzed participant responses with a mixture model, which yielded two measures: guess rate and precision of recalled memories. The guess rate in the second recall was higher for the different-group condition than for the same-group condition; however, the memory precisions obtained for both conditions were similarly degraded in the second recall. In Experiment 2, we varied the probability of the same- and different-group conditions with a ratio of 3 to 7. We expected output interference to be higher in the same-group condition than in the different-group condition. This is because items of the other group are more likely to be probed in the second recall phase and, thus, protecting those items during the first recall phase leads to a better performance. Nevertheless, the same pattern of results was robustly reproduced, suggesting grouping shields the grouped items from output interference because of the secured accessibility. We discussed how grouping influences output interference.
Linearised model for PV panel power output variation with changes ...
Indian Academy of Sciences (India)
PALLAVI BHARADWAJ
2017-10-26
Oct 26, 2017 ... change in system input, namely: irradiance and temperature, with its output, namely: array current and power. ... of a solar cell as shown in figure 1, with appropriate scaling according to ... measurement-based methods [8–13].
NACP Regional: Original Observation Data and Biosphere and Inverse Model Outputs
National Aeronautics and Space Administration — This data set contains the originally-submitted observation measurement data, terrestrial biosphere model output data, and inverse model simulations that various...
NACP Regional: Original Observation Data and Biosphere and Inverse Model Outputs
National Aeronautics and Space Administration — ABSTRACT: This data set contains the originally-submitted observation measurement data, terrestrial biosphere model output data, and inverse model simulations that...
Balancing Europe's wind power output through spatial deployment informed by weather regimes.
Grams, Christian M; Beerli, Remo; Pfenninger, Stefan; Staffell, Iain; Wernli, Heini
2017-08-01
As wind and solar power provide a growing share of Europe's electricity1, understanding and accommodating their variability on multiple timescales remains a critical problem. On weekly timescales, variability is related to long-lasting weather conditions, called weather regimes2-5, which can cause lulls with a loss of wind power across neighbouring countries6. Here we show that weather regimes provide a meteorological explanation for multi-day fluctuations in Europe's wind power and can help guide new deployment pathways which minimise this variability. Mean generation during different regimes currently ranges from 22 GW to 44 GW and is expected to triple by 2030 with current planning strategies. However, balancing future wind capacity across regions with contrasting inter-regime behaviour - specifically deploying in the Balkans instead of the North Sea - would almost eliminate these output variations, maintain mean generation, and increase fleet-wide minimum output. Solar photovoltaics could balance low-wind regimes locally, but only by expanding current capacity tenfold. New deployment strategies based on an understanding of continent-scale wind patterns and pan-European collaboration could enable a high share of wind energy whilst minimising the negative impacts of output variability.
Directory of Open Access Journals (Sweden)
Cheng-Xiang Wang
2007-02-01
Full Text Available The performance of multiple-input multiple-output (MIMO systems is greatly influenced by the spatial-temporal correlation properties of the underlying MIMO channels. This paper investigates the spatial-temporal correlation characteristics of the spatial channel model (SCM in the Third Generation Partnership Project (3GPP and the Kronecker-based stochastic model (KBSM at three levels, namely, the cluster level, link level, and system level. The KBSM has both the spatial separability and spatial-temporal separability at all the three levels. The spatial-temporal separability is observed for the SCM only at the system level, but not at the cluster and link levels. The SCM shows the spatial separability at the link and system levels, but not at the cluster level since its spatial correlation is related to the joint distribution of the angle of arrival (AoA and angle of departure (AoD. The KBSM with the Gaussian-shaped power azimuth spectrum (PAS is found to fit best the 3GPP SCM in terms of the spatial correlations. Despite its simplicity and analytical tractability, the KBSM is restricted to model only the average spatial-temporal behavior of MIMO channels. The SCM provides more insights of the variations of different MIMO channel realizations, but the implementation complexity is relatively high.
Continuous Spatial Process Models for Spatial Extreme Values
Sang, Huiyan; Gelfand, Alan E.
2010-01-01
process model for extreme values that provides mean square continuous realizations, where the behavior of the surface is driven by the spatial dependence which is unexplained under the latent spatio-temporal specification for the GEV parameters
Comparison of Laboratory Experimental Data to XBeach Numerical Model Output
Demirci, Ebru; Baykal, Cuneyt; Guler, Isikhan; Sogut, Erdinc
2016-04-01
generating data sets for testing and validation of sediment transport relationships for sand transport in the presence of waves and currents. In these series, there is no structure in the basin. The second and third series of experiments were designed to generate data sets for development of tombolos in the lee of detached 4m-long rubble mound breakwater that is 4 m from the initial shoreline. The fourth series of experiments are conducted to investigate tombolo development in the lee of a 4m-long T-head groin with the head section in the same location of the second and the third tests. The fifth series of experiments are used to investigate tombolo development in the lee of a 3-m-long rubble-mound breakwater positioned 1.5 m offshore of the initial shoreline. In this study, the data collected from the above mentioned five experiments are used to compare the results of the experimental data with XBeach numerical model results, both for the "no-structure" and "with-structure" cases regarding to sediment transport relationships in the presence of only waves and currents as well as the shoreline changes together with the detached breakwater and the T-groin. The main purpose is to investigate the similarities and differences between the laboratory experimental data behavior with XBeach numerical model outputs for these five cases. References: Baykal, C., Sogut, E., Ergin, A., Guler, I., Ozyurt, G.T., Guler, G., and Dogan, G.G. (2015). Modelling Long Term Morphological Changes with XBeach: Case Study of Kızılırmak River Mouth, Turkey, European Geosciences Union, General Assembly 2015, Vienna, Austria, 12-17 April 2015. Gravens, M.B. and Wang, P. (2007). "Data report: Laboratory testing of longshore sand transport by waves and currents; morphology change behind headland structures." Technical Report, ERDC/CHL TR-07-8, Coastal and Hydraulics Laboratory, US Army Engineer Research and Development Center, Vicksburg, MS. Roelvink, D., Reniers, A., van Dongeren, A., van Thiel de
Unleashing spatially distributed ecohydrology modeling using Big Data tools
Miles, B.; Idaszak, R.
2015-12-01
Physically based spatially distributed ecohydrology models are useful for answering science and management questions related to the hydrology and biogeochemistry of prairie, savanna, forested, as well as urbanized ecosystems. However, these models can produce hundreds of gigabytes of spatial output for a single model run over decadal time scales when run at regional spatial scales and moderate spatial resolutions (~100-km2+ at 30-m spatial resolution) or when run for small watersheds at high spatial resolutions (~1-km2 at 3-m spatial resolution). Numerical data formats such as HDF5 can store arbitrarily large datasets. However even in HPC environments, there are practical limits on the size of single files that can be stored and reliably backed up. Even when such large datasets can be stored, querying and analyzing these data can suffer from poor performance due to memory limitations and I/O bottlenecks, for example on single workstations where memory and bandwidth are limited, or in HPC environments where data are stored separately from computational nodes. The difficulty of storing and analyzing spatial data from ecohydrology models limits our ability to harness these powerful tools. Big Data tools such as distributed databases have the potential to surmount the data storage and analysis challenges inherent to large spatial datasets. Distributed databases solve these problems by storing data close to computational nodes while enabling horizontal scalability and fault tolerance. Here we present the architecture of and preliminary results from PatchDB, a distributed datastore for managing spatial output from the Regional Hydro-Ecological Simulation System (RHESSys). The initial version of PatchDB uses message queueing to asynchronously write RHESSys model output to an Apache Cassandra cluster. Once stored in the cluster, these data can be efficiently queried to quickly produce both spatial visualizations for a particular variable (e.g. maps and animations), as well
Towards systematic evaluation of crop model outputs for global land-use models
Leclere, David; Azevedo, Ligia B.; Skalský, Rastislav; Balkovič, Juraj; Havlík, Petr
2016-04-01
Land provides vital socioeconomic resources to the society, however at the cost of large environmental degradations. Global integrated models combining high resolution global gridded crop models (GGCMs) and global economic models (GEMs) are increasingly being used to inform sustainable solution for agricultural land-use. However, little effort has yet been done to evaluate and compare the accuracy of GGCM outputs. In addition, GGCM datasets require a large amount of parameters whose values and their variability across space are weakly constrained: increasing the accuracy of such dataset has a very high computing cost. Innovative evaluation methods are required both to ground credibility to the global integrated models, and to allow efficient parameter specification of GGCMs. We propose an evaluation strategy for GGCM datasets in the perspective of use in GEMs, illustrated with preliminary results from a novel dataset (the Hypercube) generated by the EPIC GGCM and used in the GLOBIOM land use GEM to inform on present-day crop yield, water and nutrient input needs for 16 crops x 15 management intensities, at a spatial resolution of 5 arc-minutes. We adopt the following principle: evaluation should provide a transparent diagnosis of model adequacy for its intended use. We briefly describe how the Hypercube data is generated and how it articulates with GLOBIOM in order to transparently identify the performances to be evaluated, as well as the main assumptions and data processing involved. Expected performances include adequately representing the sub-national heterogeneity in crop yield and input needs: i) in space, ii) across crop species, and iii) across management intensities. We will present and discuss measures of these expected performances and weight the relative contribution of crop model, input data and data processing steps in performances. We will also compare obtained yield gaps and main yield-limiting factors against the M3 dataset. Next steps include
Rasouli, K.; Pomeroy, J. W.; Hayashi, M.; Fang, X.; Gutmann, E. D.; Li, Y.
2017-12-01
The hydrology of mountainous cold regions has a large spatial variability that is driven both by climate variability and near-surface process variability associated with complex terrain and patterns of vegetation, soils, and hydrogeology. There is a need to downscale large-scale atmospheric circulations towards the fine scales that cold regions hydrological processes operate at to assess their spatial variability in complex terrain and quantify uncertainties by comparison to field observations. In this research, three high resolution numerical weather prediction models, namely, the Intermediate Complexity Atmosphere Research (ICAR), Weather Research and Forecasting (WRF), and Global Environmental Multiscale (GEM) models are used to represent spatial and temporal patterns of atmospheric conditions appropriate for hydrological modelling. An area covering high mountains and foothills of the Canadian Rockies was selected to assess and compare high resolution ICAR (1 km × 1 km), WRF (4 km × 4 km), and GEM (2.5 km × 2.5 km) model outputs with station-based meteorological measurements. ICAR with very low computational cost was run with different initial and boundary conditions and with finer spatial resolution, which allowed an assessment of modelling uncertainty and scaling that was difficult with WRF. Results show that ICAR, when compared with WRF and GEM, performs very well in precipitation and air temperature modelling in the Canadian Rockies, while all three models show a fair performance in simulating wind and humidity fields. Representation of local-scale atmospheric dynamics leading to realistic fields of temperature and precipitation by ICAR, WRF, and GEM makes these models suitable for high resolution cold regions hydrological predictions in complex terrain, which is a key factor in estimating water security in western Canada.
Semi-Automated Processing of Trajectory Simulator Output Files for Model Evaluation
2018-01-01
ARL-TR-8284 ● JAN 2018 US Army Research Laboratory Semi-Automated Processing of Trajectory Simulator Output Files for Model...Semi-Automated Processing of Trajectory Simulator Output Files for Model Evaluation 5a. CONTRACT NUMBER 5b. GRANT NUMBER 5c. PROGRAM ELEMENT...although some minor changes may be needed. The program processes a GTRAJ output text file that contains results from 2 or more simulations , where each
Specification and Aggregation Errors in Environmentally Extended Input-Output Models
Bouwmeester, Maaike C.; Oosterhaven, Jan
This article considers the specification and aggregation errors that arise from estimating embodied emissions and embodied water use with environmentally extended national input-output (IO) models, instead of with an environmentally extended international IO model. Model specification errors result
Model outputs - Developing end-to-end models of the Gulf of California
National Oceanic and Atmospheric Administration, Department of Commerce — The purpose of this project is to develop spatially discrete end-to-end models of the northern Gulf of California, linking oceanography, biogeochemistry, food web...
National Oceanic and Atmospheric Administration, Department of Commerce — The purpose of this project is to develop spatially discrete end-to-end models of the California Current LME, linking oceanography, biogeochemistry, food web...
Continuous Spatial Process Models for Spatial Extreme Values
Sang, Huiyan
2010-01-28
We propose a hierarchical modeling approach for explaining a collection of point-referenced extreme values. In particular, annual maxima over space and time are assumed to follow generalized extreme value (GEV) distributions, with parameters μ, σ, and ξ specified in the latent stage to reflect underlying spatio-temporal structure. The novelty here is that we relax the conditionally independence assumption in the first stage of the hierarchial model, an assumption which has been adopted in previous work. This assumption implies that realizations of the the surface of spatial maxima will be everywhere discontinuous. For many phenomena including, e. g., temperature and precipitation, this behavior is inappropriate. Instead, we offer a spatial process model for extreme values that provides mean square continuous realizations, where the behavior of the surface is driven by the spatial dependence which is unexplained under the latent spatio-temporal specification for the GEV parameters. In this sense, the first stage smoothing is viewed as fine scale or short range smoothing while the larger scale smoothing will be captured in the second stage of the modeling. In addition, as would be desired, we are able to implement spatial interpolation for extreme values based on this model. A simulation study and a study on actual annual maximum rainfall for a region in South Africa are used to illustrate the performance of the model. © 2009 International Biometric Society.
Panchromatic SED modelling of spatially resolved galaxies
Smith, Daniel J. B.; Hayward, Christopher C.
2018-05-01
We test the efficacy of the energy-balance spectral energy distribution (SED) fitting code MAGPHYS for recovering the spatially resolved properties of a simulated isolated disc galaxy, for which it was not designed. We perform 226 950 MAGPHYS SED fits to regions between 0.2 and 25 kpc in size across the galaxy's disc, viewed from three different sight-lines, to probe how well MAGPHYS can recover key galaxy properties based on 21 bands of UV-far-infrared model photometry. MAGPHYS yields statistically acceptable fits to >99 per cent of the pixels within the r-band effective radius and between 59 and 77 percent of pixels within 20 kpc of the nucleus. MAGPHYS is able to recover the distribution of stellar mass, star formation rate (SFR), specific SFR, dust luminosity, dust mass, and V-band attenuation reasonably well, especially when the pixel size is ≳ 1 kpc, whereas non-standard outputs (stellar metallicity and mass-weighted age) are recovered less well. Accurate recovery is more challenging in the smallest sub-regions of the disc (pixel scale ≲ 1 kpc), where the energy balance criterion becomes increasingly incorrect. Estimating integrated galaxy properties by summing the recovered pixel values, the true integrated values of all parameters considered except metallicity and age are well recovered at all spatial resolutions, ranging from 0.2 kpc to integrating across the disc, albeit with some evidence for resolution-dependent biases. These results must be considered when attempting to analyse the structure of real galaxies with actual observational data, for which the `ground truth' is unknown.
Wang, Huiqin; Wang, Xue; Cao, Minghua
2017-02-01
The spatial correlation extensively exists in the multiple-input multiple-output (MIMO) free space optical (FSO) communication systems due to the channel fading and the antenna space limitation. Wilkinson's method was utilized to investigate the impact of spatial correlation on the MIMO FSO communication system employing multipulse pulse-position modulation. Simulation results show that the existence of spatial correlation reduces the ergodic channel capacity, and the reception diversity is more competent to resist this kind of performance degradation.
Bayesian Spatial Modelling with R-INLA
Directory of Open Access Journals (Sweden)
Finn Lindgren
2015-02-01
Full Text Available The principles behind the interface to continuous domain spatial models in the R- INLA software package for R are described. The integrated nested Laplace approximation (INLA approach proposed by Rue, Martino, and Chopin (2009 is a computationally effective alternative to MCMC for Bayesian inference. INLA is designed for latent Gaussian models, a very wide and flexible class of models ranging from (generalized linear mixed to spatial and spatio-temporal models. Combined with the stochastic partial differential equation approach (SPDE, Lindgren, Rue, and Lindstrm 2011, one can accommodate all kinds of geographically referenced data, including areal and geostatistical ones, as well as spatial point process data. The implementation interface covers stationary spatial mod- els, non-stationary spatial models, and also spatio-temporal models, and is applicable in epidemiology, ecology, environmental risk assessment, as well as general geostatistics.
Sulis, M.; Paniconi, C.; Marrocu, M.; Huard, D.; Chaumont, D.
2012-12-01
General circulation models (GCMs) are the primary instruments for obtaining projections of future global climate change. Outputs from GCMs, aided by dynamical and/or statistical downscaling techniques, have long been used to simulate changes in regional climate systems over wide spatiotemporal scales. Numerous studies have acknowledged the disagreements between the various GCMs and between the different downscaling methods designed to compensate for the mismatch between climate model output and the spatial scale at which hydrological models are applied. Very little is known, however, about the importance of these differences once they have been input or assimilated by a nonlinear hydrological model. This issue is investigated here at the catchment scale using a process-based model of integrated surface and subsurface hydrologic response driven by outputs from 12 members of a multimodel climate ensemble. The data set consists of daily values of precipitation and min/max temperatures obtained by combining four regional climate models and five GCMs. The regional scenarios were downscaled using a quantile scaling bias-correction technique. The hydrologic response was simulated for the 690 km2des Anglais catchment in southwestern Quebec, Canada. The results show that different hydrological components (river discharge, aquifer recharge, and soil moisture storage) respond differently to precipitation and temperature anomalies in the multimodel climate output, with greater variability for annual discharge compared to recharge and soil moisture storage. We also find that runoff generation and extreme event-driven peak hydrograph flows are highly sensitive to any uncertainty in climate data. Finally, the results show the significant impact of changing sequences of rainy days on groundwater recharge fluxes and the influence of longer dry spells in modifying soil moisture spatial variability.
DEFF Research Database (Denmark)
Fleskens, Luuk; Irvine, Brian; Kirkby, Mike
2012-01-01
Portuguese sites) a fire severity index under current conditions and under different technologies. The DESMICE model is informed by WB3 WOCAT database records, economic WB4 experimental results, additionally requested data on spatial variability of costs and benefits, and secondary data. It applies spatially...... multiple stakeholders in very different contexts into the modelling process, in order to enhance both the realism and relevance of outputs for policy and practice; b) site-selection modelling is being applied to land degradation mitigation to enable landscape-scale assessments of the most economically....... Biophysical models (e.g. PESERA) should be able to separate immediate and gradual aspects. Ongoing degradation in the without case is not yet implicitly considered. Analysis of robustness to climatic variability and prices is also essential. Finally, factors such as attitude towards conservation and risk...
Intelligent spatial ecosystem modeling using parallel processors
International Nuclear Information System (INIS)
Maxwell, T.; Costanza, R.
1993-01-01
Spatial modeling of ecosystems is essential if one's modeling goals include developing a relatively realistic description of past behavior and predictions of the impacts of alternative management policies on future ecosystem behavior. Development of these models has been limited in the past by the large amount of input data required and the difficulty of even large mainframe serial computers in dealing with large spatial arrays. These two limitations have begun to erode with the increasing availability of remote sensing data and GIS systems to manipulate it, and the development of parallel computer systems which allow computation of large, complex, spatial arrays. Although many forms of dynamic spatial modeling are highly amenable to parallel processing, the primary focus in this project is on process-based landscape models. These models simulate spatial structure by first compartmentalizing the landscape into some geometric design and then describing flows within compartments and spatial processes between compartments according to location-specific algorithms. The authors are currently building and running parallel spatial models at the regional scale for the Patuxent River region in Maryland, the Everglades in Florida, and Barataria Basin in Louisiana. The authors are also planning a project to construct a series of spatially explicit linked ecological and economic simulation models aimed at assessing the long-term potential impacts of global climate change
Dynamic spatial panels : models, methods, and inferences
Elhorst, J. Paul
This paper provides a survey of the existing literature on the specification and estimation of dynamic spatial panel data models, a collection of models for spatial panels extended to include one or more of the following variables and/or error terms: a dependent variable lagged in time, a dependent
Output-Feedback Model Predictive Control of a Pasteurization Pilot Plant based on an LPV model
Karimi Pour, Fatemeh; Ocampo-Martinez, Carlos; Puig, Vicenç
2017-01-01
This paper presents a model predictive control (MPC) of a pasteurization pilot plant based on an LPV model. Since not all the states are measured, an observer is also designed, which allows implementing an output-feedback MPC scheme. However, the model of the plant is not completely observable when augmented with the disturbance models. In order to solve this problem, the following strategies are used: (i) the whole system is decoupled into two subsystems, (ii) an inner state-feedback controller is implemented into the MPC control scheme. A real-time example based on the pasteurization pilot plant is simulated as a case study for testing the behavior of the approaches.
Energy Technology Data Exchange (ETDEWEB)
Khan, Sahubar Ali Mohd. Nadhar, E-mail: sahubar@uum.edu.my; Ramli, Razamin, E-mail: razamin@uum.edu.my; Baten, M. D. Azizul, E-mail: baten-math@yahoo.com [School of Quantitative Sciences, UUM College of Arts and Sciences, Universiti Utara Malaysia, 06010 Sintok, Kedah (Malaysia)
2015-12-11
Agricultural production process typically produces two types of outputs which are economic desirable as well as environmentally undesirable outputs (such as greenhouse gas emission, nitrate leaching, effects to human and organisms and water pollution). In efficiency analysis, this undesirable outputs cannot be ignored and need to be included in order to obtain the actual estimation of firms efficiency. Additionally, climatic factors as well as data uncertainty can significantly affect the efficiency analysis. There are a number of approaches that has been proposed in DEA literature to account for undesirable outputs. Many researchers has pointed that directional distance function (DDF) approach is the best as it allows for simultaneous increase in desirable outputs and reduction of undesirable outputs. Additionally, it has been found that interval data approach is the most suitable to account for data uncertainty as it is much simpler to model and need less information regarding its distribution and membership function. In this paper, an enhanced DEA model based on DDF approach that considers undesirable outputs as well as climatic factors and interval data is proposed. This model will be used to determine the efficiency of rice farmers who produces undesirable outputs and operates under uncertainty. It is hoped that the proposed model will provide a better estimate of rice farmers’ efficiency.
International Nuclear Information System (INIS)
Khan, Sahubar Ali Mohd. Nadhar; Ramli, Razamin; Baten, M. D. Azizul
2015-01-01
Agricultural production process typically produces two types of outputs which are economic desirable as well as environmentally undesirable outputs (such as greenhouse gas emission, nitrate leaching, effects to human and organisms and water pollution). In efficiency analysis, this undesirable outputs cannot be ignored and need to be included in order to obtain the actual estimation of firms efficiency. Additionally, climatic factors as well as data uncertainty can significantly affect the efficiency analysis. There are a number of approaches that has been proposed in DEA literature to account for undesirable outputs. Many researchers has pointed that directional distance function (DDF) approach is the best as it allows for simultaneous increase in desirable outputs and reduction of undesirable outputs. Additionally, it has been found that interval data approach is the most suitable to account for data uncertainty as it is much simpler to model and need less information regarding its distribution and membership function. In this paper, an enhanced DEA model based on DDF approach that considers undesirable outputs as well as climatic factors and interval data is proposed. This model will be used to determine the efficiency of rice farmers who produces undesirable outputs and operates under uncertainty. It is hoped that the proposed model will provide a better estimate of rice farmers’ efficiency
Khan, Sahubar Ali Mohd. Nadhar; Ramli, Razamin; Baten, M. D. Azizul
2015-12-01
Agricultural production process typically produces two types of outputs which are economic desirable as well as environmentally undesirable outputs (such as greenhouse gas emission, nitrate leaching, effects to human and organisms and water pollution). In efficiency analysis, this undesirable outputs cannot be ignored and need to be included in order to obtain the actual estimation of firms efficiency. Additionally, climatic factors as well as data uncertainty can significantly affect the efficiency analysis. There are a number of approaches that has been proposed in DEA literature to account for undesirable outputs. Many researchers has pointed that directional distance function (DDF) approach is the best as it allows for simultaneous increase in desirable outputs and reduction of undesirable outputs. Additionally, it has been found that interval data approach is the most suitable to account for data uncertainty as it is much simpler to model and need less information regarding its distribution and membership function. In this paper, an enhanced DEA model based on DDF approach that considers undesirable outputs as well as climatic factors and interval data is proposed. This model will be used to determine the efficiency of rice farmers who produces undesirable outputs and operates under uncertainty. It is hoped that the proposed model will provide a better estimate of rice farmers' efficiency.
Computer models and output, Spartan REM: Appendix B
Marlowe, D. S.; West, E. J.
1984-01-01
A computer model of the Spartan Release Engagement Mechanism (REM) is presented in a series of numerical charts and engineering drawings. A crack growth analysis code is used to predict the fracture mechanics of critical components.
COMBINING LONG MEMORY AND NONLINEAR MODEL OUTPUTS FOR INFLATION FORECAST
Heri Kuswanto; Irhamah Alimuhajin; Laylia Afidah
2014-01-01
Long memory and nonlinearity have been proven as two models that are easily to be mistaken. In other words, nonlinearity is a strong candidate of spurious long memory by introducing a certain degree of fractional integration that lies in the region of long memory. Indeed, nonlinear process belongs to short memory with zero integration order. The idea of the forecast is to obtain the future condition with minimum error. Some researches argued that no matter what the model is, the important thi...
Spherical Process Models for Global Spatial Statistics
Jeong, Jaehong; Jun, Mikyoung; Genton, Marc G.
2017-01-01
Statistical models used in geophysical, environmental, and climate science applications must reflect the curvature of the spatial domain in global data. Over the past few decades, statisticians have developed covariance models that capture
The Comparison of Point Data Models for the Output of WRF Hydro Model in the IDV
Ho, Y.; Weber, J.
2017-12-01
WRF Hydro netCDF output files contain streamflow, flow depth, longitude, latitude, altitude and stream order values for each forecast point. However, the data are not CF compliant. The total number of forecast points for the US CONUS is approximately 2.7 million and it is a big challenge for any visualization and analysis tool. The IDV point cloud display shows point data as a set of points colored by parameter. This display is very efficient compared to a standard point type display for rendering a large number of points. The one problem we have is that the data I/O can be a bottleneck issue when dealing with a large collection of point input files. In this presentation, we will experiment with different point data models and their APIs to access the same WRF Hydro model output. The results will help us construct a CF compliant netCDF point data format for the community.
Models of asthma: density-equalizing mapping and output benchmarking
Directory of Open Access Journals (Sweden)
Fischer Tanja C
2008-02-01
Full Text Available Abstract Despite the large amount of experimental studies already conducted on bronchial asthma, further insights into the molecular basics of the disease are required to establish new therapeutic approaches. As a basis for this research different animal models of asthma have been developed in the past years. However, precise bibliometric data on the use of different models do not exist so far. Therefore the present study was conducted to establish a data base of the existing experimental approaches. Density-equalizing algorithms were used and data was retrieved from a Thomson Institute for Scientific Information database. During the period from 1900 to 2006 a number of 3489 filed items were connected to animal models of asthma, the first being published in the year 1968. The studies were published by 52 countries with the US, Japan and the UK being the most productive suppliers, participating in 55.8% of all published items. Analyzing the average citation per item as an indicator for research quality Switzerland ranked first (30.54/item and New Zealand ranked second for countries with more than 10 published studies. The 10 most productive journals included 4 with a main focus allergy and immunology and 4 with a main focus on the respiratory system. Two journals focussed on pharmacology or pharmacy. In all assigned subject categories examined for a relation to animal models of asthma, immunology ranked first. Assessing numbers of published items in relation to animal species it was found that mice were the preferred species followed by guinea pigs. In summary it can be concluded from density-equalizing calculations that the use of animal models of asthma is restricted to a relatively small number of countries. There are also differences in the use of species. These differences are based on variations in the research focus as assessed by subject category analysis.
Including model uncertainty in the model predictive control with output feedback
Directory of Open Access Journals (Sweden)
Rodrigues M.A.
2002-01-01
Full Text Available This paper addresses the development of an efficient numerical output feedback robust model predictive controller for open-loop stable systems. Stability of the closed loop is guaranteed by using an infinite horizon predictive controller and a stable state observer. The performance and the computational burden of this approach are compared to a robust predictive controller from the literature. The case used for this study is based on an industrial gasoline debutanizer column.
Spatial Data Web Services Pricing Model Infrastructure
Ozmus, L.; Erkek, B.; Colak, S.; Cankurt, I.; Bakıcı, S.
2013-08-01
most important law with related NSDI is the establishment of General Directorate of Geographic Information System under the Ministry of Environment and Urbanism. due to; to do or to have do works and activities with related to the establishment of National Geographic Information Systems (NGIS), usage of NGIS and improvements of NGIS. Outputs of these projects are served to not only public administration but also to Turkish society. Today for example, TAKBIS data (cadastre services) are shared more than 50 institutions by Web services, Tusaga-Aktif system has more than 3800 users who are having real-time GPS data correction, Orthophoto WMS services has been started for two years as a charge of free. Today there is great discussion about data pricing among the institutions. Some of them think that the pricing is storage of the data. Some of them think that the pricing is value of data itself. There is no certain rule about pricing. On this paper firstly, pricing of data storage and later on spatial data pricing models in different countries are investigated to improve institutional understanding in Turkey.
Output fields from the NOAA WAVEWATCH IIIÂ® wave model monthly hindcasts
National Oceanic and Atmospheric Administration, Department of Commerce — The NOAA WAVEWATCH IIIÂ® hindcast dataset comprises output fields from the monthly WAVEWATCH IIIÂ® hindcast model runs conducted at the National Centers for...
International Nuclear Information System (INIS)
Balagyra, V.S.; Ryabka, P.M.
1999-01-01
For measuring the charged particle energy calculations of mean square angles of electron beam multiple Coulomb scattering at output combined accelerator target were undertaken according to seven theoretical models. Mollier method showed the best agreement with experiments
Investigation on the integral output power model of a large-scale wind farm
Institute of Scientific and Technical Information of China (English)
BAO Nengsheng; MA Xiuqian; NI Weidou
2007-01-01
The integral output power model of a large-scale wind farm is needed when estimating the wind farm's output over a period of time in the future.The actual wind speed power model and calculation method of a wind farm made up of many wind turbine units are discussed.After analyzing the incoming wind flow characteristics and their energy distributions,and after considering the multi-effects among the wind turbine units and certain assumptions,the incoming wind flow model of multi-units is built.The calculation algorithms and steps of the integral output power model of a large-scale wind farm are provided.Finally,an actual power output of the wind farm is calculated and analyzed by using the practical measurement wind speed data.The characteristics of a large-scale wind farm are also discussed.
Conditions for Model Matching of Switched Asynchronous Sequential Machines with Output Feedback
Jung–Min Yang
2016-01-01
Solvability of the model matching problem for input/output switched asynchronous sequential machines is discussed in this paper. The control objective is to determine the existence condition and design algorithm for a corrective controller that can match the stable-state behavior of the closed-loop system to that of a reference model. Switching operations and correction procedures are incorporated using output feedback so that the controlled switched machine can show the ...
Hierarchical modeling and analysis for spatial data
Banerjee, Sudipto; Gelfand, Alan E
2003-01-01
Among the many uses of hierarchical modeling, their application to the statistical analysis of spatial and spatio-temporal data from areas such as epidemiology And environmental science has proven particularly fruitful. Yet to date, the few books that address the subject have been either too narrowly focused on specific aspects of spatial analysis, or written at a level often inaccessible to those lacking a strong background in mathematical statistics.Hierarchical Modeling and Analysis for Spatial Data is the first accessible, self-contained treatment of hierarchical methods, modeling, and dat
Location Aggregation of Spatial Population CTMC Models
Directory of Open Access Journals (Sweden)
Luca Bortolussi
2016-10-01
Full Text Available In this paper we focus on spatial Markov population models, describing the stochastic evolution of populations of agents, explicitly modelling their spatial distribution, representing space as a discrete, finite graph. More specifically, we present a heuristic approach to aggregating spatial locations, which is designed to preserve the dynamical behaviour of the model whilst reducing the computational cost of analysis. Our approach combines stochastic approximation ideas (moment closure, linear noise, with computational statistics (spectral clustering to obtain an efficient aggregation, which is experimentally shown to be reasonably accurate on two case studies: an instance of epidemic spreading and a London bike sharing scenario.
Pandemic recovery analysis using the dynamic inoperability input-output model.
Santos, Joost R; Orsi, Mark J; Bond, Erik J
2009-12-01
Economists have long conceptualized and modeled the inherent interdependent relationships among different sectors of the economy. This concept paved the way for input-output modeling, a methodology that accounts for sector interdependencies governing the magnitude and extent of ripple effects due to changes in the economic structure of a region or nation. Recent extensions to input-output modeling have enhanced the model's capabilities to account for the impact of an economic perturbation; two such examples are the inoperability input-output model((1,2)) and the dynamic inoperability input-output model (DIIM).((3)) These models introduced sector inoperability, or the inability to satisfy as-planned production levels, into input-output modeling. While these models provide insights for understanding the impacts of inoperability, there are several aspects of the current formulation that do not account for complexities associated with certain disasters, such as a pandemic. This article proposes further enhancements to the DIIM to account for economic productivity losses resulting primarily from workforce disruptions. A pandemic is a unique disaster because the majority of its direct impacts are workforce related. The article develops a modeling framework to account for workforce inoperability and recovery factors. The proposed workforce-explicit enhancements to the DIIM are demonstrated in a case study to simulate a pandemic scenario in the Commonwealth of Virginia.
Towards Measures to Establish the Relevance of Climate Model Output for Decision Support
Clarke, L.; Smith, L. A.
2007-12-01
to weight climate model output in the decision process; one obvious example is the question of over what spatial and time averages modelers expect information in current climate distributions to be robust. The IPCC itself suggests continental/seasonal, while distributions over 10's of kilometers/hourly is on offer. Our aim here is not to resolve this discrepancy, but to develop methods with which it can be addressed. This is illustrated in the context of using another physically based, imperfect model setting: using Newton's laws in an actual case of NASA hazard evaluation. Our aim is to develop transparent standards of good practice managing expectations, which will allow model improvements over the next decades to be seen as progress by the users of climate science.
Mathematical model of accelerator output characteristics and their calculation on a computer
International Nuclear Information System (INIS)
Mishulina, O.A.; Ul'yanina, M.N.; Kornilova, T.V.
1975-01-01
A mathematical model is described of output characteristics of a linear accelerator. The model is a system of differential equations. Presence of phase limitations is a specific feature of setting the problem which makes it possible to ensure higher simulation accuracy and determine a capture coefficient. An algorithm is elaborated of computing output characteristics based upon the mathematical model suggested. A capture coefficient, coordinate expectation characterizing an average phase value of the beam particles, coordinate expectation characterizing an average value of the reverse relative velocity of the beam particles as well as dispersion of these coordinates are output characteristics of the accelerator. Calculation methods of the accelerator output characteristics are described in detail. The computations have been performed on the BESM-6 computer, the characteristics computing time being 2 min 20 sec. Relative error of parameter computation averages 10 -2
Spatial occupancy models for large data sets
Johnson, Devin S.; Conn, Paul B.; Hooten, Mevin B.; Ray, Justina C.; Pond, Bruce A.
2013-01-01
Since its development, occupancy modeling has become a popular and useful tool for ecologists wishing to learn about the dynamics of species occurrence over time and space. Such models require presence–absence data to be collected at spatially indexed survey units. However, only recently have researchers recognized the need to correct for spatially induced overdisperison by explicitly accounting for spatial autocorrelation in occupancy probability. Previous efforts to incorporate such autocorrelation have largely focused on logit-normal formulations for occupancy, with spatial autocorrelation induced by a random effect within a hierarchical modeling framework. Although useful, computational time generally limits such an approach to relatively small data sets, and there are often problems with algorithm instability, yielding unsatisfactory results. Further, recent research has revealed a hidden form of multicollinearity in such applications, which may lead to parameter bias if not explicitly addressed. Combining several techniques, we present a unifying hierarchical spatial occupancy model specification that is particularly effective over large spatial extents. This approach employs a probit mixture framework for occupancy and can easily accommodate a reduced-dimensional spatial process to resolve issues with multicollinearity and spatial confounding while improving algorithm convergence. Using open-source software, we demonstrate this new model specification using a case study involving occupancy of caribou (Rangifer tarandus) over a set of 1080 survey units spanning a large contiguous region (108 000 km2) in northern Ontario, Canada. Overall, the combination of a more efficient specification and open-source software allows for a facile and stable implementation of spatial occupancy models for large data sets.
Evaluating spatial patterns in hydrological modelling
DEFF Research Database (Denmark)
Koch, Julian
the contiguous United Sates (10^6 km2). To this end, the thesis at hand applies a set of spatial performance metrics on various hydrological variables, namely land-surface-temperature (LST), evapotranspiration (ET) and soil moisture. The inspiration for the applied metrics is found in related fields...... is not fully exploited by current modelling frameworks due to the lack of suitable spatial performance metrics. Furthermore, the traditional model evaluation using discharge is found unsuitable to lay confidence on the predicted catchment inherent spatial variability of hydrological processes in a fully...
Input-output model for MACCS nuclear accident impacts estimation¹
Energy Technology Data Exchange (ETDEWEB)
Outkin, Alexander V. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Bixler, Nathan E. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Vargas, Vanessa N [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
2015-01-27
Since the original economic model for MACCS was developed, better quality economic data (as well as the tools to gather and process it) and better computational capabilities have become available. The update of the economic impacts component of the MACCS legacy model will provide improved estimates of business disruptions through the use of Input-Output based economic impact estimation. This paper presents an updated MACCS model, bases on Input-Output methodology, in which economic impacts are calculated using the Regional Economic Accounting analysis tool (REAcct) created at Sandia National Laboratories. This new GDP-based model allows quick and consistent estimation of gross domestic product (GDP) losses due to nuclear power plant accidents. This paper outlines the steps taken to combine the REAcct Input-Output-based model with the MACCS code, describes the GDP loss calculation, and discusses the parameters and modeling assumptions necessary for the estimation of long-term effects of nuclear power plant accidents.
A spatially explicit scenario-driven model of adaptive capacity to global change in Europe
Acosta, L.; Klein, R.J.T.; Reidsma, P.; Metzger, M.J.; Rounsevell, M.D.A.; Leemans, R.
2013-01-01
Traditional impact models combine exposure in the form of scenarios and sensitivity in the form of parameters, providing potential impacts of global change as model outputs. However, adaptive capacity is rarely addressed in these models. This paper presents the first spatially explicit
Crime Modeling using Spatial Regression Approach
Saleh Ahmar, Ansari; Adiatma; Kasim Aidid, M.
2018-01-01
Act of criminality in Indonesia increased both variety and quantity every year. As murder, rape, assault, vandalism, theft, fraud, fencing, and other cases that make people feel unsafe. Risk of society exposed to crime is the number of reported cases in the police institution. The higher of the number of reporter to the police institution then the number of crime in the region is increasing. In this research, modeling criminality in South Sulawesi, Indonesia with the dependent variable used is the society exposed to the risk of crime. Modelling done by area approach is the using Spatial Autoregressive (SAR) and Spatial Error Model (SEM) methods. The independent variable used is the population density, the number of poor population, GDP per capita, unemployment and the human development index (HDI). Based on the analysis using spatial regression can be shown that there are no dependencies spatial both lag or errors in South Sulawesi.
Chen, Jie; Li, Chao; Brissette, François P.; Chen, Hua; Wang, Mingna; Essou, Gilles R. C.
2018-05-01
Bias correction is usually implemented prior to using climate model outputs for impact studies. However, bias correction methods that are commonly used treat climate variables independently and often ignore inter-variable dependencies. The effects of ignoring such dependencies on impact studies need to be investigated. This study aims to assess the impacts of correcting the inter-variable correlation of climate model outputs on hydrological modeling. To this end, a joint bias correction (JBC) method which corrects the joint distribution of two variables as a whole is compared with an independent bias correction (IBC) method; this is considered in terms of correcting simulations of precipitation and temperature from 26 climate models for hydrological modeling over 12 watersheds located in various climate regimes. The results show that the simulated precipitation and temperature are considerably biased not only in the individual distributions, but also in their correlations, which in turn result in biased hydrological simulations. In addition to reducing the biases of the individual characteristics of precipitation and temperature, the JBC method can also reduce the bias in precipitation-temperature (P-T) correlations. In terms of hydrological modeling, the JBC method performs significantly better than the IBC method for 11 out of the 12 watersheds over the calibration period. For the validation period, the advantages of the JBC method are greatly reduced as the performance becomes dependent on the watershed, GCM and hydrological metric considered. For arid/tropical and snowfall-rainfall-mixed watersheds, JBC performs better than IBC. For snowfall- or rainfall-dominated watersheds, however, the two methods behave similarly, with IBC performing somewhat better than JBC. Overall, the results emphasize the advantages of correcting the P-T correlation when using climate model-simulated precipitation and temperature to assess the impact of climate change on watershed
Energy Technology Data Exchange (ETDEWEB)
Shi, Yan; Wu, Tiecheng; Cai, Maolin; Liu, Chong [Beihang University, Beijing (China)
2016-03-15
Hydropneumatic transformer (short for HP transformer) is used to pump pressurized hydraulic oil. Whereas, due to its insufficient usage of energy and low efficiency, a new kind of HP transformer: EEUHP transformer (Expansion energy used hydropneumatic transformer) was proposed. To illustrate the characteristics of the EEUHP transformer, a mathematical model was built. To verify the mathematical model, an experimental prototype was setup and studied. Through simulation and experimental study on the EEUHP transformer, the influence of five key parameters on the output flow of the EEUHP transformer were obtained, and some conclusions can be drawn. Firstly, the mathematical model was proved to be valid. Furthermore, the EEUHP transformer costs fewer of compressed air than the normal HP transformer when the output flow of the two kinds of transformers are almost same. Moreover, with an increase in the output pressure, the output flow decreases sharply. Finally, with an increase in the effective area of hydraulic output port, the output flow increases distinctly. This research can be referred to in the performance and design optimization of the EEUHP transformers.
National Research Council Canada - National Science Library
Hart, Kenneth
2003-01-01
The skill of a mesoscale model based Model Output Statistics (MOS) system that provided hourly forecasts for 18 sites over northern Utah during the 2002 Winter Olympic and Paralympic Games is evaluated...
Caliver: An R package for CALIbration and VERification of forest fire gridded model outputs.
Vitolo, Claudia; Di Giuseppe, Francesca; D'Andrea, Mirko
2018-01-01
The name caliver stands for CALIbration and VERification of forest fire gridded model outputs. This is a package developed for the R programming language and available under an APACHE-2 license from a public repository. In this paper we describe the functionalities of the package and give examples using publicly available datasets. Fire danger model outputs are taken from the modeling components of the European Forest Fire Information System (EFFIS) and observed burned areas from the Global Fire Emission Database (GFED). Complete documentation, including a vignette, is also available within the package.
Kumar, Sujay V.; Wang, Shugong; Mocko, David M.; Peters-Lidard, Christa D.; Xia, Youlong
2017-11-01
Multimodel ensembles are often used to produce ensemble mean estimates that tend to have increased simulation skill over any individual model output. If multimodel outputs are too similar, an individual LSM would add little additional information to the multimodel ensemble, whereas if the models are too dissimilar, it may be indicative of systematic errors in their formulations or configurations. The article presents a formal similarity assessment of the North American Land Data Assimilation System (NLDAS) multimodel ensemble outputs to assess their utility to the ensemble, using a confirmatory factor analysis. Outputs from four NLDAS Phase 2 models currently running in operations at NOAA/NCEP and four new/upgraded models that are under consideration for the next phase of NLDAS are employed in this study. The results show that the runoff estimates from the LSMs were most dissimilar whereas the models showed greater similarity for root zone soil moisture, snow water equivalent, and terrestrial water storage. Generally, the NLDAS operational models showed weaker association with the common factor of the ensemble and the newer versions of the LSMs showed stronger association with the common factor, with the model similarity increasing at longer time scales. Trade-offs between the similarity metrics and accuracy measures indicated that the NLDAS operational models demonstrate a larger span in the similarity-accuracy space compared to the new LSMs. The results of the article indicate that simultaneous consideration of model similarity and accuracy at the relevant time scales is necessary in the development of multimodel ensemble.
Multi input single output model predictive control of non-linear bio-polymerization process
Energy Technology Data Exchange (ETDEWEB)
Arumugasamy, Senthil Kumar; Ahmad, Z. [School of Chemical Engineering, Univerisiti Sains Malaysia, Engineering Campus, Seri Ampangan,14300 Nibong Tebal, Seberang Perai Selatan, Pulau Pinang (Malaysia)
2015-05-15
This paper focuses on Multi Input Single Output (MISO) Model Predictive Control of bio-polymerization process in which mechanistic model is developed and linked with the feedforward neural network model to obtain a hybrid model (Mechanistic-FANN) of lipase-catalyzed ring-opening polymerization of ε-caprolactone (ε-CL) for Poly (ε-caprolactone) production. In this research, state space model was used, in which the input to the model were the reactor temperatures and reactor impeller speeds and the output were the molecular weight of polymer (M{sub n}) and polymer polydispersity index. State space model for MISO created using System identification tool box of Matlab™. This state space model is used in MISO MPC. Model predictive control (MPC) has been applied to predict the molecular weight of the biopolymer and consequently control the molecular weight of biopolymer. The result shows that MPC is able to track reference trajectory and give optimum movement of manipulated variable.
Knoben, Wouter; Woods, Ross; Freer, Jim
2016-04-01
Conceptual hydrologic models consist of a certain arrangement of spatial and temporal dynamics consisting of stores, fluxes and transformation functions, depending on the modeller's choices and intended use. They have the advantages of being computationally efficient, being relatively easy model structures to reconfigure and having relatively low input data demands. This makes them well-suited for large-scale and large-sample hydrology, where appropriately representing the dominant hydrologic functions of a catchment is a main concern. Given these requirements, the number of parameters in the model cannot be too high, to avoid equifinality and identifiability issues. This limits the number and level of complexity of dominant hydrologic processes the model can represent. Specific purposes and places thus require a specific model and this has led to an abundance of conceptual hydrologic models. No structured overview of these models exists and there is no clear method to select appropriate model structures for different catchments. This study is a first step towards creating an overview of the elements that make up conceptual models, which may later assist a modeller in finding an appropriate model structure for a given catchment. To this end, this study brings together over 30 past and present conceptual models. The reviewed model structures are simply different configurations of three basic model elements (stores, fluxes and transformation functions), depending on the hydrologic processes the models are intended to represent. Differences also exist in the inner workings of the stores, fluxes and transformations, i.e. the mathematical formulations that describe each model element's intended behaviour. We investigate the hypothesis that different model structures can produce similar behavioural simulations. This can clarify the overview of model elements by grouping elements which are similar, which can improve model structure selection.
A simplified spatial model for BWR stability
International Nuclear Information System (INIS)
Berman, Y.; Lederer, Y.; Meron, E.
2012-01-01
A spatial reduced order model for the study of BWR stability, based on the phenomenological model of March-Leuba et al., is presented. As one dimensional spatial dependence of the neutron flux, fuel temperature and void fraction is introduced, it is possible to describe both global and regional oscillations of the reactor power. Both linear stability analysis and numerical analysis were applied in order to describe the parameters which govern the model stability. The results were found qualitatively similar to past results. Doppler reactivity feedback was found essential for the explanation of the different regions of the flow-power stability map. (authors)
Modeling the short-run effect of fiscal stimuli on GDP : A new semi-closed input-output model
Chen, Quanrun; Dietzenbacher, Erik; Los, Bart; Yang, Cuihong
In this study, we propose a new semi-closed input-output model, which reconciles input-output analysis with modern consumption theories. It can simulate changes in household consumption behavior when exogenous stimulus policies lead to higher disposable income levels. It is useful for quantifying
Modeling the short-run effect of fiscal stimuli on GDP : A new semi-closed input-output model
Chen, Quanrun; Dietzenbacher, Erik; Los, Bart; Yang, Cuihong
2016-01-01
In this study, we propose a new semi-closed input-output model, which reconciles input-output analysis with modern consumption theories. It can simulate changes in household consumption behavior when exogenous stimulus policies lead to higher disposable income levels. It is useful for quantifying
Regional disaster impact analysis: comparing Input-Output and Computable General Equilibrium models
Koks, E.E.; Carrera, L.; Jonkeren, O.; Aerts, J.C.J.H.; Husby, T.G.; Thissen, M.; Standardi, G.; Mysiak, J.
2016-01-01
A variety of models have been applied to assess the economic losses of disasters, of which the most common ones are input-output (IO) and computable general equilibrium (CGE) models. In addition, an increasing number of scholars have developed hybrid approaches: one that combines both or either of
DIMITRI 1.0: Beschrijving en toepassing van een dynamisch input-output model
Wilting HC; Blom WF; Thomas R; Idenburg AM; LAE
2001-01-01
DIMITRI, the Dynamic Input-Output Model to study the Impacts of Technology Related Innovations, was developed in the framework of the RIVM Environment and Economy project to answer questions about interrelationships between economy, technology and the environment. DIMITRI, a meso-economic model,
Modelling and Prediction of Photovoltaic Power Output Using Artificial Neural Networks
Directory of Open Access Journals (Sweden)
Aminmohammad Saberian
2014-01-01
Full Text Available This paper presents a solar power modelling method using artificial neural networks (ANNs. Two neural network structures, namely, general regression neural network (GRNN feedforward back propagation (FFBP, have been used to model a photovoltaic panel output power and approximate the generated power. Both neural networks have four inputs and one output. The inputs are maximum temperature, minimum temperature, mean temperature, and irradiance; the output is the power. The data used in this paper started from January 1, 2006, until December 31, 2010. The five years of data were split into two parts: 2006–2008 and 2009-2010; the first part was used for training and the second part was used for testing the neural networks. A mathematical equation is used to estimate the generated power. At the end, both of these networks have shown good modelling performance; however, FFBP has shown a better performance comparing with GRNN.
He, Yuning
2015-01-01
Safety of unmanned aerial systems (UAS) is paramount, but the large number of dynamically changing controller parameters makes it hard to determine if the system is currently stable, and the time before loss of control if not. We propose a hierarchical statistical model using Treed Gaussian Processes to predict (i) whether a flight will be stable (success) or become unstable (failure), (ii) the time-to-failure if unstable, and (iii) time series outputs for flight variables. We first classify the current flight input into success or failure types, and then use separate models for each class to predict the time-to-failure and time series outputs. As different inputs may cause failures at different times, we have to model variable length output curves. We use a basis representation for curves and learn the mappings from input to basis coefficients. We demonstrate the effectiveness of our prediction methods on a NASA neuro-adaptive flight control system.
Wolfensberger, D.; Gires, A.; Berne, A.; Tchiguirinskaia, I.; Schertzer, D. J. M.
2015-12-01
The resolution of operational numerical prediction models is typically of the order of a few kilometres meaning that small-scale features of precipitation can not be resolved explicitly. This creates the need for representative parametrizations of microphysical processes whose properties should be carefully analysed. In this study we will focus on the COSMO model which is a non-hydrostatic limited-area model, initially developed as the Lokal Model and used operationally in Switzerland and Germany. In its operational version, cloud microphysical processes are simulated with a one-moment bulk scheme where five hydrometeor classes are considered: cloud droplets, rain, ice crystals, snow, and graupel. A more sophisticated two-moment scheme is also available. The study will focus on two case studies: one in Payerne in western Switzerland in a relatively flat region and one in Davos in the eastern Swiss Alps in a more complex terrain.The objective of this work is to characterize the ability of the COSMO NWP model to reproduce the microphysics of precipitation across temporal and spatial scales as well as scaling variability. The characterization of COSMO outputs will rely on the Universal Multifractals framework, which allows to analyse and simulate geophysical fields extremely variabile over a wide range of scales with the help of a reduced number of parameters. First COSMO outputs are analysed; spatial multifractal analysis of 2D maps at various altitudes for each time steps are carried out for simulated solid, liquid, vapour and total water content. In general the fields exhibit a good quality of scaling on the whole range of available scales (2 km - 250 km), but some loss of scaling quality corresponding to the emergence of a scaling break are sometimes visible. This behaviour is not found at the same time or at the same altitude according to the water state and does not necessarily spread to the total water content. It is interpreted with the help of the underlying
Modeling and control of the output current of a Reformed Methanol Fuel Cell system
DEFF Research Database (Denmark)
Justesen, Kristian Kjær; Andreasen, Søren Juhl; Pasupathi, Sivakumar
2015-01-01
In this work, a dynamic Matlab SIMULINK model of the relationship between the fuel cell current set point of a Reformed Methanol Fuel Cell system and the output current of the system is developed. The model contains an estimated fuel cell model, based on a polarization curve and assumed first order...... dynamics, as well as a battery model based on an equivalent circuit model and a balance of plant power consumption model. The models are tuned with experimental data and verified using a verification data set. The model is used to develop an output current controller which can control the charge current...... of the battery. The controller is a PI controller with feedforward and anti-windup. The performance of the controller is tested and verified on the physical system....
Modeling of Output Characteristics of a UV Cu+ Ne-CuBr Laser
Directory of Open Access Journals (Sweden)
Snezhana Georgieva Gocheva-Ilieva
2012-01-01
Full Text Available This paper examines experiment data for a Ne-CuBr UV copper ion laser excited by longitudinal pulsed discharge emitting in multiline regime. The flexible multivariate adaptive regression splines (MARSs method has been used to develop nonparametric regression models describing the laser output power and service life of the devices. The models have been constructed as explicit functions of 9 basic input laser characteristics. The obtained models account for local nonlinearities of the relationships within the various multivariate subregions. The built best MARS models account for over 98% of data. The models are used to estimate the investigated output laser characteristics of existing UV lasers. The capabilities for using the models in predicting existing and future experiments have been demonstrated. Specific analyses have been presented comparing the models with actual experiments. The obtained results are applicable for guiding and planning the engineering experiment. The modeling methodology can be applied for a wide range of similar lasers and laser devices.
Khan, Sahubar Ali Mohd. Nadhar; Ramli, Razamin; Baten, M. D. Azizul
2017-11-01
In recent years eco-efficiency which considers the effect of production process on environment in determining the efficiency of firms have gained traction and a lot of attention. Rice farming is one of such production processes which typically produces two types of outputs which are economic desirable as well as environmentally undesirable. In efficiency analysis, these undesirable outputs cannot be ignored and need to be included in the model to obtain the actual estimation of firm's efficiency. There are numerous approaches that have been used in data envelopment analysis (DEA) literature to account for undesirable outputs of which directional distance function (DDF) approach is the most widely used as it allows for simultaneous increase in desirable outputs and reduction of undesirable outputs. Additionally, slack based DDF DEA approaches considers the output shortfalls and input excess in determining efficiency. In situations when data uncertainty is present, the deterministic DEA model is not suitable to be used as the effects of uncertain data will not be considered. In this case, it has been found that interval data approach is suitable to account for data uncertainty as it is much simpler to model and need less information regarding the underlying data distribution and membership function. The proposed model uses an enhanced DEA model which is based on DDF approach and incorporates slack based measure to determine efficiency in the presence of undesirable factors and data uncertainty. Interval data approach was used to estimate the values of inputs, undesirable outputs and desirable outputs. Two separate slack based interval DEA models were constructed for optimistic and pessimistic scenarios. The developed model was used to determine rice farmers efficiency from Kepala Batas, Kedah. The obtained results were later compared to the results obtained using a deterministic DDF DEA model. The study found that 15 out of 30 farmers are efficient in all cases. It
Spatial scale separation in regional climate modelling
Energy Technology Data Exchange (ETDEWEB)
Feser, F.
2005-07-01
In this thesis the concept of scale separation is introduced as a tool for first improving regional climate model simulations and, secondly, to explicitly detect and describe the added value obtained by regional modelling. The basic idea behind this is that global and regional climate models have their best performance at different spatial scales. Therefore the regional model should not alter the global model's results at large scales. The for this purpose designed concept of nudging of large scales controls the large scales within the regional model domain and keeps them close to the global forcing model whereby the regional scales are left unchanged. For ensemble simulations nudging of large scales strongly reduces the divergence of the different simulations compared to the standard approach ensemble that occasionally shows large differences for the individual realisations. For climate hindcasts this method leads to results which are on average closer to observed states than the standard approach. Also the analysis of the regional climate model simulation can be improved by separating the results into different spatial domains. This was done by developing and applying digital filters that perform the scale separation effectively without great computational effort. The separation of the results into different spatial scales simplifies model validation and process studies. The search for 'added value' can be conducted on the spatial scales the regional climate model was designed for giving clearer results than by analysing unfiltered meteorological fields. To examine the skill of the different simulations pattern correlation coefficients were calculated between the global reanalyses, the regional climate model simulation and, as a reference, of an operational regional weather analysis. The regional climate model simulation driven with large-scale constraints achieved a high increase in similarity to the operational analyses for medium-scale 2 meter
Nonparametric Bayesian models for a spatial covariance.
Reich, Brian J; Fuentes, Montserrat
2012-01-01
A crucial step in the analysis of spatial data is to estimate the spatial correlation function that determines the relationship between a spatial process at two locations. The standard approach to selecting the appropriate correlation function is to use prior knowledge or exploratory analysis, such as a variogram analysis, to select the correct parametric correlation function. Rather that selecting a particular parametric correlation function, we treat the covariance function as an unknown function to be estimated from the data. We propose a flexible prior for the correlation function to provide robustness to the choice of correlation function. We specify the prior for the correlation function using spectral methods and the Dirichlet process prior, which is a common prior for an unknown distribution function. Our model does not require Gaussian data or spatial locations on a regular grid. The approach is demonstrated using a simulation study as well as an analysis of California air pollution data.
An Improved Mathematical Model for Computing Power Output of Solar Photovoltaic Modules
Directory of Open Access Journals (Sweden)
Abdul Qayoom Jakhrani
2014-01-01
Full Text Available It is difficult to determine the input parameters values for equivalent circuit models of photovoltaic modules through analytical methods. Thus, the previous researchers preferred to use numerical methods. Since, the numerical methods are time consuming and need long term time series data which is not available in most developing countries, an improved mathematical model was formulated by combination of analytical and numerical methods to overcome the limitations of existing methods. The values of required model input parameters were computed analytically. The expression for output current of photovoltaic module was determined explicitly by Lambert W function and voltage was determined numerically by Newton-Raphson method. Moreover, the algebraic equations were derived for the shape factor which involves the ideality factor and the series resistance of a single diode photovoltaic module power output model. The formulated model results were validated with rated power output of a photovoltaic module provided by manufacturers using local meteorological data, which gave ±2% error. It was found that the proposed model is more practical in terms of precise estimations of photovoltaic module power output for any required location and number of variables used.
El Haimar, Amine; Santos, Joost R
2014-03-01
Influenza pandemic is a serious disaster that can pose significant disruptions to the workforce and associated economic sectors. This article examines the impact of influenza pandemic on workforce availability within an interdependent set of economic sectors. We introduce a simulation model based on the dynamic input-output model to capture the propagation of pandemic consequences through the National Capital Region (NCR). The analysis conducted in this article is based on the 2009 H1N1 pandemic data. Two metrics were used to assess the impacts of the influenza pandemic on the economic sectors: (i) inoperability, which measures the percentage gap between the as-planned output and the actual output of a sector, and (ii) economic loss, which quantifies the associated monetary value of the degraded output. The inoperability and economic loss metrics generate two different rankings of the critical economic sectors. Results show that most of the critical sectors in terms of inoperability are sectors that are related to hospitals and health-care providers. On the other hand, most of the sectors that are critically ranked in terms of economic loss are sectors with significant total production outputs in the NCR such as federal government agencies. Therefore, policy recommendations relating to potential mitigation and recovery strategies should take into account the balance between the inoperability and economic loss metrics. © 2013 Society for Risk Analysis.
About the use of rank transformation in sensitivity analysis of model output
International Nuclear Information System (INIS)
Saltelli, Andrea; Sobol', Ilya M
1995-01-01
Rank transformations are frequently employed in numerical experiments involving a computational model, especially in the context of sensitivity and uncertainty analyses. Response surface replacement and parameter screening are tasks which may benefit from a rank transformation. Ranks can cope with nonlinear (albeit monotonic) input-output distributions, allowing the use of linear regression techniques. Rank transformed statistics are more robust, and provide a useful solution in the presence of long tailed input and output distributions. As is known to practitioners, care must be employed when interpreting the results of such analyses, as any conclusion drawn using ranks does not translate easily to the original model. In the present note an heuristic approach is taken, to explore, by way of practical examples, the effect of a rank transformation on the outcome of a sensitivity analysis. An attempt is made to identify trends, and to correlate these effects to a model taxonomy. Employing sensitivity indices, whereby the total variance of the model output is decomposed into a sum of terms of increasing dimensionality, we show that the main effect of the rank transformation is to increase the relative weight of the first order terms (the 'main effects'), at the expense of the 'interactions' and 'higher order interactions'. As a result the influence of those parameters which influence the output mostly by way of interactions may be overlooked in an analysis based on the ranks. This difficulty increases with the dimensionality of the problem, and may lead to the failure of a rank based sensitivity analysis. We suggest that the models can be ranked, with respect to the complexity of their input-output relationship, by mean of an 'Association' index I y . I y may complement the usual model coefficient of determination R y 2 as a measure of model complexity for the purpose of uncertainty and sensitivity analysis
Landscape Modelling and Simulation Using Spatial Data
Directory of Open Access Journals (Sweden)
Amjed Naser Mohsin AL-Hameedawi
2017-08-01
Full Text Available In this paper a procedure was performed for engendering spatial model of landscape acclimated to reality simulation. This procedure based on combining spatial data and field measurements with computer graphics reproduced using Blender software. Thereafter that we are possible to form a 3D simulation based on VIS ALL packages. The objective was to make a model utilising GIS, including inputs to the feature attribute data. The objective of these efforts concentrated on coordinating a tolerable spatial prototype, circumscribing facilitation scheme and outlining the intended framework. Thus; the eventual result was utilized in simulation form. The performed procedure contains not only data gathering, fieldwork and paradigm providing, but extended to supply a new method necessary to provide the respective 3D simulation mapping production, which authorises the decision makers as well as investors to achieve permanent acceptance an independent navigation system for Geoscience applications.
Wismadi, Arif; Zuidgeest, Mark; Brussel, Mark; van Maarseveen, Martin
2014-01-01
To determine whether the inclusion of spatial neighbourhood comparison factors in Preference Modelling allows spatial decision support systems (SDSSs) to better address spatial equity, we introduce Spatial Preference Modelling (SPM). To evaluate the effectiveness of this model in addressing equity, various standardisation functions in both Non-Spatial Preference Modelling and SPM are compared. The evaluation involves applying the model to a resource location-allocation problem for transport infrastructure in the Special Province of Yogyakarta in Indonesia. We apply Amartya Sen's Capability Approach to define opportunity to mobility as a non-income indicator. Using the extended Moran's I interpretation for spatial equity, we evaluate the distribution output regarding, first, `the spatial distribution patterns of priority targeting for allocation' (SPT) and, second, `the effect of new distribution patterns after location-allocation' (ELA). The Moran's I index of the initial map and its comparison with six patterns for SPT as well as ELA consistently indicates that the SPM is more effective for addressing spatial equity. We conclude that the inclusion of spatial neighbourhood comparison factors in Preference Modelling improves the capability of SDSS to address spatial equity. This study thus proposes a new formal method for SDSS with specific attention on resource location-allocation to address spatial equity.
Input-output and energy demand models for Ireland: Data collection report. Part 1: EXPLOR
Energy Technology Data Exchange (ETDEWEB)
Henry, E W; Scott, S
1981-01-01
Data are presented in support of EXPLOR, an input-output economic model for Ireland. The data follow the listing of exogenous data-sets used by Batelle in document X11/515/77. Data are given for 1974, 1980, and 1985 and consist of household consumption, final demand-production, and commodity prices. (ACR)
From LCC to LCA Using a Hybrid Input Output Model – A Maritime Case Study
DEFF Research Database (Denmark)
Kjær, Louise Laumann; Pagoropoulos, Aris; Hauschild, Michael Zwicky
2015-01-01
As companies try to embrace life cycle thinking, Life Cycle Assessment (LCA) and Life Cycle Costing (LCC) have proven to be powerful tools. In this paper, an Environmental Input-Output model is used for analysis as it enables an LCA using the same economic input data as LCC. This approach helps...
Analyses of gust fronts by means of limited area NWP model outputs
Czech Academy of Sciences Publication Activity Database
Kašpar, Marek
67-68, - (2003), s. 559-572 ISSN 0169-8095 R&D Projects: GA ČR GA205/00/1451 Institutional research plan: CEZ:AV0Z3042911 Keywords : gust front * limited area NWP model * output Subject RIV: DG - Athmosphere Sciences, Meteorology Impact factor: 1.012, year: 2003
Input-Output model for waste management plan for Nigeria | Njoku ...
African Journals Online (AJOL)
An Input-Output Model for Waste Management Plan has been developed for Nigeria based on Leontief concept and life cycle analysis. Waste was considered as source of pollution, loss of resources, and emission of green house gasses from bio-chemical treatment and decomposition, with negative impact on the ...
The economic impact of multifunctional agriculture in Dutch regions: An input-output model
Heringa, P.W.; Heide, van der C.M.; Heijman, W.J.M.
2013-01-01
Multifunctional agriculture is a broad concept lacking a precise definition. Moreover, little is known about the societal importance of multifunctional agriculture. This paper is an empirical attempt to fill this gap. To this end, an input-output model was constructed for multifunctional agriculture
The economic impact of multifunctional agriculture in The Netherlands: A regional input-output model
Heringa, P.W.; Heide, van der C.M.; Heijman, W.J.M.
2012-01-01
Multifunctional agriculture is a broad concept lacking a precise and uniform definition. Moreover, little is known about the societal importance of multifunctional agriculture. This paper is an empirical attempt to fill this gap. To this end, an input-output model is constructed for multifunctional
a mathematical model for predicting output in an oilfield in the niger
African Journals Online (AJOL)
eobe
resultant model was found to have greater utility in predicting oil field output as it produced less residual. The ... decision making by the oilfield manager is facilitated by reliable ... Scaling laws from percolation theory was used to predict oilfield ...
texreg: Conversion of Statistical Model Output in R to LATEX and HTML Tables
Directory of Open Access Journals (Sweden)
Philip Leifeld
2013-11-01
Full Text Available A recurrent task in applied statistics is the (mostly manual preparation of model output for inclusion in LATEX, Microsoft Word, or HTML documents usually with more than one model presented in a single table along with several goodness-of-fit statistics. However, statistical models in R have diverse object structures and summary methods, which makes this process cumbersome. This article first develops a set of guidelines for converting statistical model output to LATEX and HTML tables, then assesses to what extent existing packages meet these requirements, and finally presents the texreg package as a solution that meets all of the criteria set out in the beginning. After providing various usage examples, a blueprint for writing custom model extensions is proposed.
Spatial Modeling for Resources Framework (SMRF)
Spatial Modeling for Resources Framework (SMRF) was developed by Dr. Scott Havens at the USDA Agricultural Research Service (ARS) in Boise, ID. SMRF was designed to increase the flexibility of taking measured weather data and distributing the point measurements across a watershed. SMRF was developed...
Input-output model of regional environmental and economic impacts of nuclear power plants
International Nuclear Information System (INIS)
Johnson, M.H.; Bennett, J.T.
1979-01-01
The costs of delayed licensing of nuclear power plants calls for a more-comprehensive method of quantifying the economic and environmental impacts on a region. A traditional input-output (I-O) analysis approach is extended to assess the effects of changes in output, income, employment, pollution, water consumption, and the costs and revenues of local government disaggregated among 23 industry sectors during the construction and operating phases. Unlike earlier studies, this model uses nonlinear environmental interactions and specifies environmental feedbacks to the economic sector. 20 references
The 3-D global spatial data model foundation of the spatial data infrastructure
Burkholder, Earl F
2008-01-01
Traditional methods for handling spatial data are encumbered by the assumption of separate origins for horizontal and vertical measurements. Modern measurement systems operate in a 3-D spatial environment. The 3-D Global Spatial Data Model: Foundation of the Spatial Data Infrastructure offers a new model for handling digital spatial data, the global spatial data model or GSDM. The GSDM preserves the integrity of three-dimensional spatial data while also providing additional benefits such as simpler equations, worldwide standardization, and the ability to track spatial data accuracy with greater specificity and convenience. This groundbreaking spatial model incorporates both a functional model and a stochastic model to connect the physical world to the ECEF rectangular system. Combining horizontal and vertical data into a single, three-dimensional database, this authoritative monograph provides a logical development of theoretical concepts and practical tools that can be used to handle spatial data mo...
Energy Technology Data Exchange (ETDEWEB)
Nillius, Peter, E-mail: nillius@mi.physics.kth.se; Klamra, Wlodek; Danielsson, Mats [Royal Institute of Technology (KTH), Stockholm SE-100 44 (Sweden); Sibczynski, Pawel [National Centre for Nuclear Research, Otwock 05-400 (Poland); Sharma, Diksha; Badano, Aldo [Division of Imaging, Diagnostics, and Software Reliability, Office of Science and Engineering Laboratories, Center for Devices and Radiological Health, FDA, Silver Spring, Maryland 20993 (United States)
2015-02-15
Purpose: The authors report on measurements of light output and spatial resolution of microcolumnar CsI:Tl scintillator detectors for x-ray imaging. In addition, the authors discuss the results of simulations aimed at analyzing the results of synchrotron and sealed-source exposures with respect to the contributions of light transport to the total light output. Methods: The authors measured light output from a 490-μm CsI:Tl scintillator screen using two setups. First, the authors used a photomultiplier tube (PMT) to measure the response of the scintillator to sealed-source exposures. Second, the authors performed imaging experiments with a 27-keV monoenergetic synchrotron beam and a slit to calculate the total signal generated in terms of optical photons per keV. The results of both methods are compared to simulations obtained with hybridMANTIS, a coupled x-ray, electron, and optical photon Monte Carlo transport package. The authors report line response (LR) and light output for a range of linear absorption coefficients and describe a model that fits at the same time the light output and the blur measurements. Comparing the experimental results with the simulations, the authors obtained an estimate of the absorption coefficient for the model that provides good agreement with the experimentally measured LR. Finally, the authors report light output simulation results and their dependence on scintillator thickness and reflectivity of the backing surface. Results: The slit images from the synchrotron were analyzed to obtain a total light output of 48 keV{sup −1} while measurements using the fast PMT instrument setup and sealed-sources reported a light output of 28 keV{sup −1}. The authors attribute the difference in light output estimates between the two methods to the difference in time constants between the camera and PMT measurements. Simulation structures were designed to match the light output measured with the camera while providing good agreement with the
International Nuclear Information System (INIS)
Nalegaev, S S; Petrov, N V; Bespalov, V G
2014-01-01
A numerical reconstruction of spatial distributions of optical radiation propagating through a volume of nonlinear medium at input and output planes of the medium was demonstrated using a scheme of digital holography. A nonlinear Schrodinger equation with Fourier Split-Step method was used as a tool to propagate wavefront in the volume of the medium. Time dependence of the refractive index change was not taken into account.
ANALYSIS OF THE BANDUNG CHANGES EXCELLENT POTENTIAL THROUGH INPUT-OUTPUT MODEL USING INDEX LE MASNE
Directory of Open Access Journals (Sweden)
Teti Sofia Yanti
2017-03-01
Full Text Available Input-Output Table is arranged to present an overview of the interrelationships and interdependence between units of activity (sector production in the whole economy. Therefore the input-output models are complete and comprehensive analytical tool. The usefulness of input-output tables is an analysis of the economic structure of the national/regional level which covers the structure of production and value-added (GDP of each sector. For the purposes of planning and evaluation of the outcomes of development that is comprehensive both national and smaller scale (district/city, a model for regional development planning approach can use the model input-output analysis. Analysis of Bandung Economic Structure did use Le Masne index, by comparing the coefficients of the technology in 2003 and 2008, of which nearly 50% change. The trade sector has grown very conspicuous than other areas, followed by the services of road transport and air transport services, the development priorities and investment Bandung should be directed to these areas, this is due to these areas can be thrust and be power attraction for the growth of other areas. The areas that experienced the highest decrease was Industrial Chemicals and Goods from Chemistry, followed by Oil and Refinery Industry Textile Industry Except For Garment.
Modelling innovation performance of European regions using multi-output neural networks.
Hajek, Petr; Henriques, Roberto
2017-01-01
Regional innovation performance is an important indicator for decision-making regarding the implementation of policies intended to support innovation. However, patterns in regional innovation structures are becoming increasingly diverse, complex and nonlinear. To address these issues, this study aims to develop a model based on a multi-output neural network. Both intra- and inter-regional determinants of innovation performance are empirically investigated using data from the 4th and 5th Community Innovation Surveys of NUTS 2 (Nomenclature of Territorial Units for Statistics) regions. The results suggest that specific innovation strategies must be developed based on the current state of input attributes in the region. Thus, it is possible to develop appropriate strategies and targeted interventions to improve regional innovation performance. We demonstrate that support of entrepreneurship is an effective instrument of innovation policy. We also provide empirical support that both business and government R&D activity have a sigmoidal effect, implying that the most effective R&D support should be directed to regions with below-average and average R&D activity. We further show that the multi-output neural network outperforms traditional statistical and machine learning regression models. In general, therefore, it seems that the proposed model can effectively reflect both the multiple-output nature of innovation performance and the interdependency of the output attributes.
Modelling innovation performance of European regions using multi-output neural networks.
Directory of Open Access Journals (Sweden)
Petr Hajek
Full Text Available Regional innovation performance is an important indicator for decision-making regarding the implementation of policies intended to support innovation. However, patterns in regional innovation structures are becoming increasingly diverse, complex and nonlinear. To address these issues, this study aims to develop a model based on a multi-output neural network. Both intra- and inter-regional determinants of innovation performance are empirically investigated using data from the 4th and 5th Community Innovation Surveys of NUTS 2 (Nomenclature of Territorial Units for Statistics regions. The results suggest that specific innovation strategies must be developed based on the current state of input attributes in the region. Thus, it is possible to develop appropriate strategies and targeted interventions to improve regional innovation performance. We demonstrate that support of entrepreneurship is an effective instrument of innovation policy. We also provide empirical support that both business and government R&D activity have a sigmoidal effect, implying that the most effective R&D support should be directed to regions with below-average and average R&D activity. We further show that the multi-output neural network outperforms traditional statistical and machine learning regression models. In general, therefore, it seems that the proposed model can effectively reflect both the multiple-output nature of innovation performance and the interdependency of the output attributes.
Using Weather Data and Climate Model Output in Economic Analyses of Climate Change
Energy Technology Data Exchange (ETDEWEB)
Auffhammer, M.; Hsiang, S. M.; Schlenker, W.; Sobel, A.
2013-06-28
Economists are increasingly using weather data and climate model output in analyses of the economic impacts of climate change. This article introduces a set of weather data sets and climate models that are frequently used, discusses the most common mistakes economists make in using these products, and identifies ways to avoid these pitfalls. We first provide an introduction to weather data, including a summary of the types of datasets available, and then discuss five common pitfalls that empirical researchers should be aware of when using historical weather data as explanatory variables in econometric applications. We then provide a brief overview of climate models and discuss two common and significant errors often made by economists when climate model output is used to simulate the future impacts of climate change on an economic outcome of interest.
Directory of Open Access Journals (Sweden)
Sie Long Kek
2015-01-01
Full Text Available A computational approach is proposed for solving the discrete time nonlinear stochastic optimal control problem. Our aim is to obtain the optimal output solution of the original optimal control problem through solving the simplified model-based optimal control problem iteratively. In our approach, the adjusted parameters are introduced into the model used such that the differences between the real system and the model used can be computed. Particularly, system optimization and parameter estimation are integrated interactively. On the other hand, the output is measured from the real plant and is fed back into the parameter estimation problem to establish a matching scheme. During the calculation procedure, the iterative solution is updated in order to approximate the true optimal solution of the original optimal control problem despite model-reality differences. For illustration, a wastewater treatment problem is studied and the results show the efficiency of the approach proposed.
Energy Technology Data Exchange (ETDEWEB)
Rice, P [Oak Ridge National Lab., TN; Smith, V K
1977-11-01
This paper describes a forty-two nonlinear equation model of the U.S. petroleum industry estimated over the period 1946 to 1973. The model specifies refinery outputs and prices as being simultaneously determined by market forces while the domestic output of crude oil is determined in a block-recursive segment of the model. The simultaneous behavioral equations are estimated with nonlinear two-stage least-squares adjusted to reflect the implications of autocorrelation for those equations where it appears to be a problem. A multi-period sample simulation, together with forecasts for 1974 and 1975 are used to evaluate the model's performance. Finally, it is used to forecast to 1985 under two scenarios and compared with the Federal Energy Administration's forecast for the same period. 2 figures, 8 tables, 38 references.
Turner, D.P.; Dodson, R.; Marks, D.
1996-01-01
Spatially distributed biogeochemical models may be applied over grids at a range of spatial resolutions, however, evaluation of potential errors and loss of information at relatively coarse resolutions is rare. In this study, a georeferenced database at the 1-km spatial resolution was developed to initialize and drive a process-based model (Forest-BGC) of water and carbon balance over a gridded 54976 km2 area covering two river basins in mountainous western Oregon. Corresponding data sets were also prepared at 10-km and 50-km spatial resolutions using commonly employed aggregation schemes. Estimates were made at each grid cell for climate variables including daily solar radiation, air temperature, humidity, and precipitation. The topographic structure, water holding capacity, vegetation type and leaf area index were likewise estimated for initial conditions. The daily time series for the climatic drivers was developed from interpolations of meteorological station data for the water year 1990 (1 October 1989-30 September 1990). Model outputs at the 1-km resolution showed good agreement with observed patterns in runoff and productivity. The ranges for model inputs at the 10-km and 50-km resolutions tended to contract because of the smoothed topography. Estimates for mean evapotranspiration and runoff were relatively insensitive to changing the spatial resolution of the grid whereas estimates of mean annual net primary production varied by 11%. The designation of a vegetation type and leaf area at the 50-km resolution often subsumed significant heterogeneity in vegetation, and this factor accounted for much of the difference in the mean values for the carbon flux variables. Although area wide means for model outputs were generally similar across resolutions, difference maps often revealed large areas of disagreement. Relatively high spatial resolution analyses of biogeochemical cycling are desirable from several perspectives and may be particularly important in the
International Nuclear Information System (INIS)
Nagai, Haruyasu; Yamazawa, Hiromi
1995-03-01
This report describes the improvement of the mesoscale atmospheric dynamic model which is a part of the atmospheric dispersion calculation model PHYSIC. To introduce large-scale meteorological changes into the mesoscale atmospheric dynamic model, it is necessary to make the initial and boundary conditions of the model by using GPV (Grid Point Value) which is the output of the numerical weather prediction model of JMA (Japan Meteorological Agency). Therefore, the program which preprocesses the GPV data to make a input file to PHYSIC was developed and the input process and the methods of spatial and temporal interpolation were improved to correspond to the file. Moreover, the methods of calculating the cloud amount and ground surface moisture from GPV data were developed and added to the model code. As the example of calculation by the improved model, the wind field simulations of a north-west monsoon in winter and a sea breeze in summer in the Tokai area were also presented. (author)
Spatially explicit modeling in ecology: A review
DeAngelis, Donald L.; Yurek, Simeon
2017-01-01
The use of spatially explicit models (SEMs) in ecology has grown enormously in the past two decades. One major advancement has been that fine-scale details of landscapes, and of spatially dependent biological processes, such as dispersal and invasion, can now be simulated with great precision, due to improvements in computer technology. Many areas of modeling have shifted toward a focus on capturing these fine-scale details, to improve mechanistic understanding of ecosystems. However, spatially implicit models (SIMs) have played a dominant role in ecology, and arguments have been made that SIMs, which account for the effects of space without specifying spatial positions, have an advantage of being simpler and more broadly applicable, perhaps contributing more to understanding. We address this debate by comparing SEMs and SIMs in examples from the past few decades of modeling research. We argue that, although SIMs have been the dominant approach in the incorporation of space in theoretical ecology, SEMs have unique advantages for addressing pragmatic questions concerning species populations or communities in specific places, because local conditions, such as spatial heterogeneities, organism behaviors, and other contingencies, produce dynamics and patterns that usually cannot be incorporated into simpler SIMs. SEMs are also able to describe mechanisms at the local scale that can create amplifying positive feedbacks at that scale, creating emergent patterns at larger scales, and therefore are important to basic ecological theory. We review the use of SEMs at the level of populations, interacting populations, food webs, and ecosystems and argue that SEMs are not only essential in pragmatic issues, but must play a role in the understanding of causal relationships on landscapes.
Spatial stochastic regression modelling of urban land use
International Nuclear Information System (INIS)
Arshad, S H M; Jaafar, J; Abiden, M Z Z; Latif, Z A; Rasam, A R A
2014-01-01
Urbanization is very closely linked to industrialization, commercialization or overall economic growth and development. This results in innumerable benefits of the quantity and quality of the urban environment and lifestyle but on the other hand contributes to unbounded development, urban sprawl, overcrowding and decreasing standard of living. Regulation and observation of urban development activities is crucial. The understanding of urban systems that promotes urban growth are also essential for the purpose of policy making, formulating development strategies as well as development plan preparation. This study aims to compare two different stochastic regression modeling techniques for spatial structure models of urban growth in the same specific study area. Both techniques will utilize the same datasets and their results will be analyzed. The work starts by producing an urban growth model by using stochastic regression modeling techniques namely the Ordinary Least Square (OLS) and Geographically Weighted Regression (GWR). The two techniques are compared to and it is found that, GWR seems to be a more significant stochastic regression model compared to OLS, it gives a smaller AICc (Akaike's Information Corrected Criterion) value and its output is more spatially explainable
CONSTRUCTION OF A DYNAMIC INPUT-OUTPUT MODEL WITH A HUMAN CAPITAL BLOCK
Directory of Open Access Journals (Sweden)
Baranov A. O.
2017-03-01
Full Text Available The accumulation of human capital is an important factor of economic growth. It seems to be useful to include «human capital» as a factor of a macroeconomic model, as it helps to take into account the quality differentiation of the workforce. Most of the models usually distinguish labor force by the levels of education, while some of the factors remain unaccounted. Among them are health status and culture development level, which influence productivity level as well as gross product reproduction. Inclusion of the human capital block to the interindustry model can help to make it more reliable for economic development forecasting. The article presents a mathematical description of the extended dynamic input-output model (DIOM with a human capital block. The extended DIOM is based on the Input-Output Model from The KAMIN system (the System of Integrated Analyses of Interindustrial Information developed at the Institute of Economics and Industrial Engineering of the Siberian Branch of the Academy of Sciences of the Russian Federation and at the Novosibirsk State University. The extended input-output model can be used to analyze and forecast development of Russian economy.
Spatial Models and Networks of Living Systems
DEFF Research Database (Denmark)
Juul, Jeppe Søgaard
When studying the dynamics of living systems, insight can often be gained by developing a mathematical model that can predict future behaviour of the system or help classify system characteristics. However, in living cells, organisms, and especially groups of interacting individuals, a large number...... variables of the system. However, this approach disregards any spatial structure of the system, which may potentially change the behaviour drastically. An alternative approach is to construct a cellular automaton with nearest neighbour interactions, or even to model the system as a complex network...... with interactions defined by network topology. In this thesis I first describe three different biological models of ageing and cancer, in which spatial structure is important for the system dynamics. I then turn to describe characteristics of ecosystems consisting of three cyclically interacting species...
A nonlocal spatial model for Lyme disease
Yu, Xiao; Zhao, Xiao-Qiang
2016-07-01
This paper is devoted to the study of a nonlocal and time-delayed reaction-diffusion model for Lyme disease with a spatially heterogeneous structure. In the case of a bounded domain, we first prove the existence of the positive steady state and a threshold type result for the disease-free system, and then establish the global dynamics for the model system in terms of the basic reproduction number. In the case of an unbound domain, we obtain the existence of the disease spreading speed and its coincidence with the minimal wave speed. At last, we use numerical simulations to verify our analytic results and investigate the influence of model parameters and spatial heterogeneity on the disease infection risk.
Fundamental Frequency and Model Order Estimation Using Spatial Filtering
DEFF Research Database (Denmark)
Karimian-Azari, Sam; Jensen, Jesper Rindom; Christensen, Mads Græsbøll
2014-01-01
extend this procedure to account for inharmonicity using unconstrained model order estimation. The simulations show that beamforming improves the performance of the joint estimates of fundamental frequency and the number of harmonics in low signal to interference (SIR) levels, and an experiment......In signal processing applications of harmonic-structured signals, estimates of the fundamental frequency and number of harmonics are often necessary. In real scenarios, a desired signal is contaminated by different levels of noise and interferers, which complicate the estimation of the signal...... parameters. In this paper, we present an estimation procedure for harmonic-structured signals in situations with strong interference using spatial filtering, or beamforming. We jointly estimate the fundamental frequency and the constrained model order through the output of the beamformers. Besides that, we...
DEFF Research Database (Denmark)
Rasmussen, Bjarne D.; Jakobsen, Arne
1999-01-01
Mathematical models of refrigeration systems are often based on a coupling of component models forming a “closed loop” type of system model. In these models the coupling structure of the component models represents the actual flow path of refrigerant in the system. Very often numerical...... instabilities prevent the practical use of such a system model for more than one input/output combination and for other magnitudes of refrigerating capacities.A higher numerical robustness of system models can be achieved by making a model for the refrigeration cycle the core of the system model and by using...... variables with narrow definition intervals for the exchange of information between the cycle model and the component models.The advantages of the cycle-oriented method are illustrated by an example showing the refrigeration cycle similarities between two very different refrigeration systems....
Dynamic Modeling and Very Short-term Prediction of Wind Power Output Using Box-Cox Transformation
Urata, Kengo; Inoue, Masaki; Murayama, Dai; Adachi, Shuichi
2016-09-01
We propose a statistical modeling method of wind power output for very short-term prediction. The modeling method with a nonlinear model has cascade structure composed of two parts. One is a linear dynamic part that is driven by a Gaussian white noise and described by an autoregressive model. The other is a nonlinear static part that is driven by the output of the linear part. This nonlinear part is designed for output distribution matching: we shape the distribution of the model output to match with that of the wind power output. The constructed model is utilized for one-step ahead prediction of the wind power output. Furthermore, we study the relation between the prediction accuracy and the prediction horizon.
Linear and quadratic models of point process systems: contributions of patterned input to output.
Lindsay, K A; Rosenberg, J R
2012-08-01
In the 1880's Volterra characterised a nonlinear system using a functional series connecting continuous input and continuous output. Norbert Wiener, in the 1940's, circumvented problems associated with the application of Volterra series to physical problems by deriving from it a new series of terms that are mutually uncorrelated with respect to Gaussian processes. Subsequently, Brillinger, in the 1970's, introduced a point-process analogue of Volterra's series connecting point-process inputs to the instantaneous rate of point-process output. We derive here a new series from this analogue in which its terms are mutually uncorrelated with respect to Poisson processes. This new series expresses how patterned input in a spike train, represented by third-order cross-cumulants, is converted into the instantaneous rate of an output point-process. Given experimental records of suitable duration, the contribution of arbitrary patterned input to an output process can, in principle, be determined. Solutions for linear and quadratic point-process models with one and two inputs and a single output are investigated. Our theoretical results are applied to isolated muscle spindle data in which the spike trains from the primary and secondary endings from the same muscle spindle are recorded in response to stimulation of one and then two static fusimotor axons in the absence and presence of a random length change imposed on the parent muscle. For a fixed mean rate of input spikes, the analysis of the experimental data makes explicit which patterns of two input spikes contribute to an output spike. Copyright © 2012 Elsevier Ltd. All rights reserved.
Estimation of spatial uncertainties of tomographic velocity models
Energy Technology Data Exchange (ETDEWEB)
Jordan, M.; Du, Z.; Querendez, E. [SINTEF Petroleum Research, Trondheim (Norway)
2012-12-15
This research project aims to evaluate the possibility of assessing the spatial uncertainties in tomographic velocity model building in a quantitative way. The project is intended to serve as a test of whether accurate and specific uncertainty estimates (e.g., in meters) can be obtained. The project is based on Monte Carlo-type perturbations of the velocity model as obtained from the tomographic inversion guided by diagonal and off-diagonal elements of the resolution and the covariance matrices. The implementation and testing of this method was based on the SINTEF in-house stereotomography code, using small synthetic 2D data sets. To test the method the calculation and output of the covariance and resolution matrices was implemented, and software to perform the error estimation was created. The work included the creation of 2D synthetic data sets, the implementation and testing of the software to conduct the tests (output of the covariance and resolution matrices which are not implicitly provided by stereotomography), application to synthetic data sets, analysis of the test results, and creating the final report. The results show that this method can be used to estimate the spatial errors in tomographic images quantitatively. The results agree with' the known errors for our synthetic models. However, the method can only be applied to structures in the model, where the change of seismic velocity is larger than the predicted error of the velocity parameter amplitudes. In addition, the analysis is dependent on the tomographic method, e.g., regularization and parameterization. The conducted tests were very successful and we believe that this method could be developed further to be applied to third party tomographic images.
Application of a Linear Input/Output Model to Tankless Water Heaters
Energy Technology Data Exchange (ETDEWEB)
Butcher T.; Schoenbauer, B.
2011-12-31
In this study, the applicability of a linear input/output model to gas-fired, tankless water heaters has been evaluated. This simple model assumes that the relationship between input and output, averaged over both active draw and idle periods, is linear. This approach is being applied to boilers in other studies and offers the potential to make a small number of simple measurements to obtain the model parameters. These parameters can then be used to predict performance under complex load patterns. Both condensing and non-condensing water heaters have been tested under a very wide range of load conditions. It is shown that this approach can be used to reproduce performance metrics, such as the energy factor, and can be used to evaluate the impacts of alternative draw patterns and conditions.
Linking spatial and dynamic models for traffic maneuvers
DEFF Research Database (Denmark)
Olderog, Ernst-Rüdiger; Ravn, Anders Peter; Wisniewski, Rafal
2015-01-01
For traffic maneuvers of multiple vehicles on highways we build an abstract spatial and a concrete dynamic model. In the spatial model we show the safety (collision freedom) of lane-change maneuvers. By linking the spatial and dynamic model via suitable refinements of the spatial atoms to distance...
Developing a modelling for the spatial data infrastructure
CSIR Research Space (South Africa)
Hjelmager, J
2005-07-01
Full Text Available The Commission on Spatial Data Standards of the International Cartographic Association (ICA) is working on defining spatial models and technical characteristics of a Spatial Data Infrastructure (SDI). To date, this work has been restricted...
Factor Copula Models for Replicated Spatial Data
Krupskii, Pavel
2016-12-19
We propose a new copula model that can be used with replicated spatial data. Unlike the multivariate normal copula, the proposed copula is based on the assumption that a common factor exists and affects the joint dependence of all measurements of the process. Moreover, the proposed copula can model tail dependence and tail asymmetry. The model is parameterized in terms of a covariance function that may be chosen from the many models proposed in the literature, such as the Matérn model. For some choice of common factors, the joint copula density is given in closed form and therefore likelihood estimation is very fast. In the general case, one-dimensional numerical integration is needed to calculate the likelihood, but estimation is still reasonably fast even with large data sets. We use simulation studies to show the wide range of dependence structures that can be generated by the proposed model with different choices of common factors. We apply the proposed model to spatial temperature data and compare its performance with some popular geostatistics models.
Factor Copula Models for Replicated Spatial Data
Krupskii, Pavel; Huser, Raphaë l; Genton, Marc G.
2016-01-01
We propose a new copula model that can be used with replicated spatial data. Unlike the multivariate normal copula, the proposed copula is based on the assumption that a common factor exists and affects the joint dependence of all measurements of the process. Moreover, the proposed copula can model tail dependence and tail asymmetry. The model is parameterized in terms of a covariance function that may be chosen from the many models proposed in the literature, such as the Matérn model. For some choice of common factors, the joint copula density is given in closed form and therefore likelihood estimation is very fast. In the general case, one-dimensional numerical integration is needed to calculate the likelihood, but estimation is still reasonably fast even with large data sets. We use simulation studies to show the wide range of dependence structures that can be generated by the proposed model with different choices of common factors. We apply the proposed model to spatial temperature data and compare its performance with some popular geostatistics models.
Modeling Spatially Unrestricted Pedestrian Traffic on Footbridges
DEFF Research Database (Denmark)
Zivanovic, Stana; Pavic, Aleksandar; Ingólfsson, Einar Thór
2010-01-01
restricted movement of pedestrians, has kept attracting attention of researchers. However, it is the normal spatially unrestricted pedestrian traffic, and its vertical dynamic loading component, that are most relevant for vibration serviceability checks for most footbridges. Despite the existence of numerous...... design procedures concerned with this loading, the current confidence in its modelling is low due to lack of verification of the models on as-built structures. This is the motivation behind reviewing the existing design procedures for modelling normal pedestrian traffic in this paper and evaluating...
Towards Quantitative Spatial Models of Seabed Sediment Composition.
Directory of Open Access Journals (Sweden)
David Stephens
Full Text Available There is a need for fit-for-purpose maps for accurately depicting the types of seabed substrate and habitat and the properties of the seabed for the benefits of research, resource management, conservation and spatial planning. The aim of this study is to determine whether it is possible to predict substrate composition across a large area of seabed using legacy grain-size data and environmental predictors. The study area includes the North Sea up to approximately 58.44°N and the United Kingdom's parts of the English Channel and the Celtic Seas. The analysis combines outputs from hydrodynamic models as well as optical remote sensing data from satellite platforms and bathymetric variables, which are mainly derived from acoustic remote sensing. We build a statistical regression model to make quantitative predictions of sediment composition (fractions of mud, sand and gravel using the random forest algorithm. The compositional data is analysed on the additive log-ratio scale. An independent test set indicates that approximately 66% and 71% of the variability of the two log-ratio variables are explained by the predictive models. A EUNIS substrate model, derived from the predicted sediment composition, achieved an overall accuracy of 83% and a kappa coefficient of 0.60. We demonstrate that it is feasible to spatially predict the seabed sediment composition across a large area of continental shelf in a repeatable and validated way. We also highlight the potential for further improvements to the method.
Assessing fit in Bayesian models for spatial processes
Jun, M.
2014-09-16
© 2014 John Wiley & Sons, Ltd. Gaussian random fields are frequently used to model spatial and spatial-temporal data, particularly in geostatistical settings. As much of the attention of the statistics community has been focused on defining and estimating the mean and covariance functions of these processes, little effort has been devoted to developing goodness-of-fit tests to allow users to assess the models\\' adequacy. We describe a general goodness-of-fit test and related graphical diagnostics for assessing the fit of Bayesian Gaussian process models using pivotal discrepancy measures. Our method is applicable for both regularly and irregularly spaced observation locations on planar and spherical domains. The essential idea behind our method is to evaluate pivotal quantities defined for a realization of a Gaussian random field at parameter values drawn from the posterior distribution. Because the nominal distribution of the resulting pivotal discrepancy measures is known, it is possible to quantitatively assess model fit directly from the output of Markov chain Monte Carlo algorithms used to sample from the posterior distribution on the parameter space. We illustrate our method in a simulation study and in two applications.
Assessing fit in Bayesian models for spatial processes
Jun, M.; Katzfuss, M.; Hu, J.; Johnson, V. E.
2014-01-01
© 2014 John Wiley & Sons, Ltd. Gaussian random fields are frequently used to model spatial and spatial-temporal data, particularly in geostatistical settings. As much of the attention of the statistics community has been focused on defining and estimating the mean and covariance functions of these processes, little effort has been devoted to developing goodness-of-fit tests to allow users to assess the models' adequacy. We describe a general goodness-of-fit test and related graphical diagnostics for assessing the fit of Bayesian Gaussian process models using pivotal discrepancy measures. Our method is applicable for both regularly and irregularly spaced observation locations on planar and spherical domains. The essential idea behind our method is to evaluate pivotal quantities defined for a realization of a Gaussian random field at parameter values drawn from the posterior distribution. Because the nominal distribution of the resulting pivotal discrepancy measures is known, it is possible to quantitatively assess model fit directly from the output of Markov chain Monte Carlo algorithms used to sample from the posterior distribution on the parameter space. We illustrate our method in a simulation study and in two applications.
Human Plague Risk: Spatial-Temporal Models
Pinzon, Jorge E.
2010-01-01
This chpater reviews the use of spatial-temporal models in identifying potential risks of plague outbreaks into the human population. Using earth observations by satellites remote sensing there has been a systematic analysis and mapping of the close coupling between the vectors of the disease and climate variability. The overall result is that incidence of plague is correlated to positive El Nino/Southem Oscillation (ENSO).
Robin M. Reich; C. Aguirre-Bravo; M.S. Williams
2006-01-01
A statistical strategy for spatial estimation and modeling of natural and environmental resource variables and indicators is presented. This strategy is part of an inventory and monitoring pilot study that is being carried out in the Mexican states of Jalisco and Colima. Fine spatial resolution estimates of key variables and indicators are outputs that will allow the...
Wang, Hao; Chen, Cao-cao; Pan, Tao; Liu, Chun-lan; Chen, Long; Sun, Li
2014-09-01
Distinguishing product-based and consumption-based CO2 emissions in the open economic region is the basis for differentiating the emission responsibility, which is attracting increasing attention of decision-makers'attention. The spatial and temporal characteristics of product-based and consumption-based CO2 emissions, as well as carbon balance, in 1997, 2002 and 2007 of JING- JIN-JI region were analyzed by the Economic Input-Output-Life Cycle Assessment model. The results revealed that both the product- based and consumption-based CO2 emissions in the region have been increased by about 4% annually. The percentage of CO2 emissions embodied in trade was 30% -83% , to which the domestic trading added the most. The territorial and consumption-based CO2 emissions in Hebei province were the predominant emission in JING-JIN-JI region, and the increasing speed and emission intensity were stronger than those of Beijing and Tianjin. JING-JIN-JI region was a net inflow region of CO2 emissions, and parts of the emission responsibility were transferred. Beijing and Tianjin were the net importers of CO2 emissions, and Hebei was a net outflow area of CO2 emissions. The key CO2 emission departments in the region were concentrated, and the similarity was great. The inter-regional mechanisms could be set up for joint prevention and control work. - Production and distribution of electricity, gas and water and smelting and pressing of metals had the highest reliability on CO2 emissions, and took on the responsibility of other departments. The EIO-LCA model could be used to analyze the product-based and consumption-based CO2 emissions, which is helpful for the delicate management of regional CO2 emissions reduction and policies making, and stimulating the reduction cooperation at regional scale.
The quantitative modelling of human spatial habitability
Wise, James A.
1988-01-01
A theoretical model for evaluating human spatial habitability (HuSH) in the proposed U.S. Space Station is developed. Optimizing the fitness of the space station environment for human occupancy will help reduce environmental stress due to long-term isolation and confinement in its small habitable volume. The development of tools that operationalize the behavioral bases of spatial volume for visual kinesthetic, and social logic considerations is suggested. This report further calls for systematic scientific investigations of how much real and how much perceived volume people need in order to function normally and with minimal stress in space-based settings. The theoretical model presented in this report can be applied to any size or shape interior, at any scale of consideration, for the Space Station as a whole to an individual enclosure or work station. Using as a point of departure the Isovist model developed by Dr. Michael Benedikt of the U. of Texas, the report suggests that spatial habitability can become as amenable to careful assessment as engineering and life support concerns.
On the statistical comparison of climate model output and climate data
International Nuclear Information System (INIS)
Solow, A.R.
1991-01-01
Some broad issues arising in the statistical comparison of the output of climate models with the corresponding climate data are reviewed. Particular attention is paid to the question of detecting climate change. The purpose of this paper is to review some statistical approaches to the comparison of the output of climate models with climate data. There are many statistical issues arising in such a comparison. The author will focus on some of the broader issues, although some specific methodological questions will arise along the way. One important potential application of the approaches discussed in this paper is the detection of climate change. Although much of the discussion will be fairly general, he will try to point out the appropriate connections to the detection question. 9 refs
On the statistical comparison of climate model output and climate data
International Nuclear Information System (INIS)
Solow, A.R.
1990-01-01
Some broad issues arising in the statistical comparison of the output of climate models with the corresponding climate data are reviewed. Particular attention is paid to the question of detecting climate change. The purpose of this paper is to review some statistical approaches to the comparison of the output of climate models with climate data. There are many statistical issues arising in such a comparison. The author will focus on some of the broader issues, although some specific methodological questions will arise along the way. One important potential application of the approaches discussed in this paper is the detection of climate change. Although much of the discussion will be fairly general, he will try to point out the appropriate connections to the detection question
Directory of Open Access Journals (Sweden)
Mariana Santos Matos Cavalca
2012-01-01
Full Text Available One of the main advantages of predictive control approaches is the capability of dealing explicitly with constraints on the manipulated and output variables. However, if the predictive control formulation does not consider model uncertainties, then the constraint satisfaction may be compromised. A solution for this inconvenience is to use robust model predictive control (RMPC strategies based on linear matrix inequalities (LMIs. However, LMI-based RMPC formulations typically consider only symmetric constraints. This paper proposes a method based on pseudoreferences to treat asymmetric output constraints in integrating SISO systems. Such technique guarantees robust constraint satisfaction and convergence of the state to the desired equilibrium point. A case study using numerical simulation indicates that satisfactory results can be achieved.
The Canadian Defence Input-Output Model DIO Version 4.41
2011-09-01
Request to develop DND tailored Input/Output Model. Electronic communication from AllenWeldon to Team Leader, Defence Economics Team onMarch 12, 2011...and similar contain- ers 166 1440 Handbags, wallets and similar personal articles such as eyeglass and cigar cases and coin purses 167 1450 Cotton yarn...408 3600 Radar and radio navigation equipment 409 3619 Semi-conductors 410 3621 Printed circuits 411 3622 Integrated circuits 412 3623 Other electronic
Use of medium-range numerical weather prediction model output to produce forecasts of streamflow
Clark, M.P.; Hay, L.E.
2004-01-01
This paper examines an archive containing over 40 years of 8-day atmospheric forecasts over the contiguous United States from the NCEP reanalysis project to assess the possibilities for using medium-range numerical weather prediction model output for predictions of streamflow. This analysis shows the biases in the NCEP forecasts to be quite extreme. In many regions, systematic precipitation biases exceed 100% of the mean, with temperature biases exceeding 3??C. In some locations, biases are even higher. The accuracy of NCEP precipitation and 2-m maximum temperature forecasts is computed by interpolating the NCEP model output for each forecast day to the location of each station in the NWS cooperative network and computing the correlation with station observations. Results show that the accuracy of the NCEP forecasts is rather low in many areas of the country. Most apparent is the generally low skill in precipitation forecasts (particularly in July) and low skill in temperature forecasts in the western United States, the eastern seaboard, and the southern tier of states. These results outline a clear need for additional processing of the NCEP Medium-Range Forecast Model (MRF) output before it is used for hydrologic predictions. Techniques of model output statistics (MOS) are used in this paper to downscale the NCEP forecasts to station locations. Forecasted atmospheric variables (e.g., total column precipitable water, 2-m air temperature) are used as predictors in a forward screening multiple linear regression model to improve forecasts of precipitation and temperature for stations in the National Weather Service cooperative network. This procedure effectively removes all systematic biases in the raw NCEP precipitation and temperature forecasts. MOS guidance also results in substantial improvements in the accuracy of maximum and minimum temperature forecasts throughout the country. For precipitation, forecast improvements were less impressive. MOS guidance increases
A Synergistic Approach for Evaluating Climate Model Output for Ecological Applications
Directory of Open Access Journals (Sweden)
Rachel D. Cavanagh
2017-09-01
Full Text Available Increasing concern about the impacts of climate change on ecosystems is prompting ecologists and ecosystem managers to seek reliable projections of physical drivers of change. The use of global climate models in ecology is growing, although drawing ecologically meaningful conclusions can be problematic. The expertise required to access and interpret output from climate and earth system models is hampering progress in utilizing them most effectively to determine the wider implications of climate change. To address this issue, we present a joint approach between climate scientists and ecologists that explores key challenges and opportunities for progress. As an exemplar, our focus is the Southern Ocean, notable for significant change with global implications, and on sea ice, given its crucial role in this dynamic ecosystem. We combined perspectives to evaluate the representation of sea ice in global climate models. With an emphasis on ecologically-relevant criteria (sea ice extent and seasonality we selected a subset of eight models that reliably reproduce extant sea ice distributions. While the model subset shows a similar mean change to the full ensemble in sea ice extent (approximately 50% decline in winter and 30% decline in summer, there is a marked reduction in the range. This improved the precision of projected future sea ice distributions by approximately one third, and means they are more amenable to ecological interpretation. We conclude that careful multidisciplinary evaluation of climate models, in conjunction with ongoing modeling advances, should form an integral part of utilizing model output.
Czech Academy of Sciences Publication Activity Database
Paulescu, M.; Brabec, Marek; Boata, R.; Badescu, V.
2017-01-01
Roč. 121, 15 February (2017), s. 792-802 ISSN 0360-5442 Grant - others:European Cooperation in Science and Technology(XE) COST ES1002 Institutional support: RVO:67985807 Keywords : photovoltaic plant * output power * forecasting * fuzzy model * generalized additive model Subject RIV: BB - Applied Statistics, Operational Research OBOR OECD: Statistics and probability Impact factor: 4.520, year: 2016
An Optimized Grey Dynamic Model for Forecasting the Output of High-Tech Industry in China
Directory of Open Access Journals (Sweden)
Zheng-Xin Wang
2014-01-01
Full Text Available The grey dynamic model by convolution integral with the first-order derivative of the 1-AGO data and n series related, abbreviated as GDMC(1,n, performs well in modelling and forecasting of a grey system. To improve the modelling accuracy of GDMC(1,n, n interpolation coefficients (taken as unknown parameters are introduced into the background values of the n variables. The parameters optimization is formulated as a combinatorial optimization problem and is solved collectively using the particle swarm optimization algorithm. The optimized result has been verified by a case study of the economic output of high-tech industry in China. Comparisons of the obtained modelling results from the optimized GDMC(1,n model with the traditional one demonstrate that the optimal algorithm is a good alternative for parameters optimization of the GDMC(1,n model. The modelling results can assist the government in developing future policies regarding high-tech industry management.
Directory of Open Access Journals (Sweden)
Keller Alevtina
2017-01-01
Full Text Available The article considers the issue of allocation of depreciation costs in the dynamic inputoutput model of an industrial enterprise. Accounting the depreciation costs in such a model improves the policy of fixed assets management. It is particularly relevant to develop the algorithm for the allocation of depreciation costs in the construction of dynamic input-output model of an industrial enterprise, since such enterprises have a significant amount of fixed assets. Implementation of terms of the adequacy of such an algorithm itself allows: evaluating the appropriateness of investments in fixed assets, studying the final financial results of an industrial enterprise, depending on management decisions in the depreciation policy. It is necessary to note that the model in question for the enterprise is always degenerate. It is caused by the presence of zero rows in the matrix of capital expenditures by lines of structural elements unable to generate fixed assets (part of the service units, households, corporate consumers. The paper presents the algorithm for the allocation of depreciation costs for the model. This algorithm was developed by the authors and served as the basis for further development of the flowchart for subsequent implementation with use of software. The construction of such algorithm and its use for dynamic input-output models of industrial enterprises is actualized by international acceptance of the effectiveness of the use of input-output models for national and regional economic systems. This is what allows us to consider that the solutions discussed in the article are of interest to economists of various industrial enterprises.
The quantitative modelling of human spatial habitability
Wise, J. A.
1985-01-01
A model for the quantitative assessment of human spatial habitability is presented in the space station context. The visual aspect assesses how interior spaces appear to the inhabitants. This aspect concerns criteria such as sensed spaciousness and the affective (emotional) connotations of settings' appearances. The kinesthetic aspect evaluates the available space in terms of its suitability to accommodate human movement patterns, as well as the postural and anthrometric changes due to microgravity. Finally, social logic concerns how the volume and geometry of available space either affirms or contravenes established social and organizational expectations for spatial arrangements. Here, the criteria include privacy, status, social power, and proxemics (the uses of space as a medium of social communication).
Modeling mental spatial reasoning about cardinal directions.
Schultheis, Holger; Bertel, Sven; Barkowsky, Thomas
2014-01-01
This article presents research into human mental spatial reasoning with orientation knowledge. In particular, we look at reasoning problems about cardinal directions that possess multiple valid solutions (i.e., are spatially underdetermined), at human preferences for some of these solutions, and at representational and procedural factors that lead to such preferences. The article presents, first, a discussion of existing, related conceptual and computational approaches; second, results of empirical research into the solution preferences that human reasoners actually have; and, third, a novel computational model that relies on a parsimonious and flexible spatio-analogical knowledge representation structure to robustly reproduce the behavior observed with human reasoners. Copyright © 2014 Cognitive Science Society, Inc.
Input vs. Output Taxation—A DSGE Approach to Modelling Resource Decoupling
Directory of Open Access Journals (Sweden)
Marek Antosiewicz
2016-04-01
Full Text Available Environmental taxes constitute a crucial instrument aimed at reducing resource use through lower production losses, resource-leaner products, and more resource-efficient production processes. In this paper we focus on material use and apply a multi-sector dynamic stochastic general equilibrium (DSGE model to study two types of taxation: tax on material inputs used by industry, energy, construction, and transport sectors, and tax on output of these sectors. We allow for endogenous adoption of resource-saving technologies. We calibrate the model for the EU27 area using an IO matrix. We consider taxation introduced from 2021 and simulate its impact until 2050. We compare the taxes along their ability to induce reduction in material use and raise revenue. We also consider the effect of spending this revenue on reduction of labour taxation. We find that input and output taxation create contrasting incentives and have opposite effects on resource efficiency. The material input tax induces investment in efficiency-improving technology which, in the long term, results in GDP and employment by 15%–20% higher than in the case of a comparable output tax. We also find that using revenues to reduce taxes on labour has stronger beneficial effects for the input tax.
Accessing National Water Model Output for Research and Application: An R package
Johnson, M.; Coll, J.
2017-12-01
With the National Water Model becoming operational in August of 2016, the need for a open source way to translate a huge amount of data into actionable intelligence and innovative research is apparent. The first step in doing this is to provide a package for accessing, managing, and writing data in a way that is both interpretable, portable, and useful to the end user in both the R environment, and other applications. This can be as simple as subsetting the outputs and writing to a CSV, but can also include converting discharge output to more meaningful statistics and measurements, and methods to visualize data in ways that are meaningful to a wider audience. The NWM R package presented here aims to serve this need through a suite of functions fit for researchers, first responders, and average citizens. A vignette of how this package can be applied to real-time flood mapping will be demonstrated.
Modelling health and output at business cycle horizons for the USA.
Narayan, Paresh Kumar
2010-07-01
In this paper we employ a theoretical framework - a simple macro model augmented with health - that draws guidance from the Keynesian view of business cycles to examine the relative importance of permanent and transitory shocks in explaining variations in health expenditure and output at business cycle horizons for the USA. The variance decomposition analysis of shocks reveals that at business cycle horizons permanent shocks explain the bulk of the variations in output, while transitory shocks explain the bulk of the variations in health expenditures. We undertake a shock decomposition analysis for private health expenditures versus public health expenditures and interestingly find that while transitory shocks are more important for private sector expenditures, permanent shocks dominate public health expenditures. Copyright (c) 2009 John Wiley & Sons, Ltd.
Usefulness of non-linear input-output models for economic impact analyses in tourism and recreation
Klijs, J.; Peerlings, J.H.M.; Heijman, W.J.M.
2015-01-01
In tourism and recreation management it is still common practice to apply traditional input–output (IO) economic impact models, despite their well-known limitations. In this study the authors analyse the usefulness of applying a non-linear input–output (NLIO) model, in which price-induced input
International Nuclear Information System (INIS)
Kirste, D.
2008-01-01
A geochemical model was used to predict the short-term and long-term behaviour of carbon dioxide (CO 2 ), formation water, and reservoir mineralogy at a carbon sequestration site. Data requirements for the geochemical model included detailed mineral petrography; formation water chemistry; thermodynamic and kinetic data for mineral phases; and rock and reservoir physical characteristics. The model was used to determine the types of outputs expected for potential CO 2 storage sites and natural analogues. Reaction path modelling was conducted to determine the total reactivity or CO 2 storage capability of the rock by applying static equilibrium and kinetic simulations. Potential product phases were identified using the modelling technique, which also enabled the identification of the chemical evolution of the system. Results of the modelling study demonstrated that changes in porosity and permeability over time should be considered during the site selection process.
Helbich, M; Griffith, D
2016-01-01
Real estate policies in urban areas require the recognition of spatial heterogeneity in housing prices to account for local settings. In response to the growing number of spatially varying coefficient models in housing applications, this study evaluated four models in terms of their spatial patterns
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.
A Computational Model of Spatial Development
Hiraki, Kazuo; Sashima, Akio; Phillips, Steven
Psychological experiments on children's development of spatial knowledge suggest experience at self-locomotion with visual tracking as important factors. Yet, the mechanism underlying development is unknown. We propose a robot that learns to mentally track a target object (i.e., maintaining a representation of an object's position when outside the field-of-view) as a model for spatial development. Mental tracking is considered as prediction of an object's position given the previous environmental state and motor commands, and the current environment state resulting from movement. Following Jordan & Rumelhart's (1992) forward modeling architecture the system consists of two components: an inverse model of sensory input to desired motor commands; and a forward model of motor commands to desired sensory input (goals). The robot was tested on the `three cups' paradigm (where children are required to select the cup containing the hidden object under various movement conditions). Consistent with child development, without the capacity for self-locomotion the robot's errors are self-center based. When given the ability of self-locomotion the robot responds allocentrically.
Spherical Process Models for Global Spatial Statistics
Jeong, Jaehong
2017-11-28
Statistical models used in geophysical, environmental, and climate science applications must reflect the curvature of the spatial domain in global data. Over the past few decades, statisticians have developed covariance models that capture the spatial and temporal behavior of these global data sets. Though the geodesic distance is the most natural metric for measuring distance on the surface of a sphere, mathematical limitations have compelled statisticians to use the chordal distance to compute the covariance matrix in many applications instead, which may cause physically unrealistic distortions. Therefore, covariance functions directly defined on a sphere using the geodesic distance are needed. We discuss the issues that arise when dealing with spherical data sets on a global scale and provide references to recent literature. We review the current approaches to building process models on spheres, including the differential operator, the stochastic partial differential equation, the kernel convolution, and the deformation approaches. We illustrate realizations obtained from Gaussian processes with different covariance structures and the use of isotropic and nonstationary covariance models through deformations and geographical indicators for global surface temperature data. To assess the suitability of each method, we compare their log-likelihood values and prediction scores, and we end with a discussion of related research problems.
Latent spatial models and sampling design for landscape genetics
Hanks, Ephraim M.; Hooten, Mevin B.; Knick, Steven T.; Oyler-McCance, Sara J.; Fike, Jennifer A.; Cross, Todd B.; Schwartz, Michael K.
2016-01-01
We propose a spatially-explicit approach for modeling genetic variation across space and illustrate how this approach can be used to optimize spatial prediction and sampling design for landscape genetic data. We propose a multinomial data model for categorical microsatellite allele data commonly used in landscape genetic studies, and introduce a latent spatial random effect to allow for spatial correlation between genetic observations. We illustrate how modern dimension reduction approaches to spatial statistics can allow for efficient computation in landscape genetic statistical models covering large spatial domains. We apply our approach to propose a retrospective spatial sampling design for greater sage-grouse (Centrocercus urophasianus) population genetics in the western United States.
DEFF Research Database (Denmark)
Bøvith, Thomas; Nielsen, Allan Aasbjerg; Hansen, Lars Kai
2006-01-01
A method for detecting clutter in weather radar images by information fusion is presented. Radar data, satellite images, and output from a numerical weather prediction model are combined and the radar echoes are classified using supervised classification. The presented method uses indirect...... information on precipitation in the atmosphere from Meteosat-8 multispectral images and near-surface temperature estimates from the DMI-HIRLAM-S05 numerical weather prediction model. Alternatively, an operational nowcasting product called 'Precipitating Clouds' based on Meteosat-8 input is used. A scale...
Including spatial data in nutrient balance modelling on dairy farms
van Leeuwen, Maricke; van Middelaar, Corina; Stoof, Cathelijne; Oenema, Jouke; Stoorvogel, Jetse; de Boer, Imke
2017-04-01
The Annual Nutrient Cycle Assessment (ANCA) calculates the nitrogen (N) and phosphorus (P) balance at a dairy farm, while taking into account the subsequent nutrient cycles of the herd, manure, soil and crop components. Since January 2016, Dutch dairy farmers are required to use ANCA in order to increase understanding of nutrient flows and to minimize nutrient losses to the environment. A nutrient balance calculates the difference between nutrient inputs and outputs. Nutrients enter the farm via purchased feed, fertilizers, deposition and fixation by legumes (nitrogen), and leave the farm via milk, livestock, manure, and roughages. A positive balance indicates to which extent N and/or P are lost to the environment via gaseous emissions (N), leaching, run-off and accumulation in soil. A negative balance indicates that N and/or P are depleted from soil. ANCA was designed to calculate average nutrient flows on farm level (for the herd, manure, soil and crop components). ANCA was not designed to perform calculations of nutrient flows at the field level, as it uses averaged nutrient inputs and outputs across all fields, and it does not include field specific soil characteristics. Land management decisions, however, such as the level of N and P application, are typically taken at the field level given the specific crop and soil characteristics. Therefore the information that ANCA provides is likely not sufficient to support farmers' decisions on land management to minimize nutrient losses to the environment. This is particularly a problem when land management and soils vary between fields. For an accurate estimate of nutrient flows in a given farming system that can be used to optimize land management, the spatial scale of nutrient inputs and outputs (and thus the effect of land management and soil variation) could be essential. Our aim was to determine the effect of the spatial scale of nutrient inputs and outputs on modelled nutrient flows and nutrient use efficiencies
Forecasting timber, biomass, and tree carbon pools with the output of state and transition models
Xiaoping Zhou; Miles A. Hemstrom
2012-01-01
The Integrated Landscape Assessment Project (ILAP) uses spatial vegetation data and state and transition models (STM) to forecast future vegetation conditions and the interacting effects of natural disturbances and management activities. Results from ILAP will help land managers, planners, and policymakers evaluate management strategies that reduce fire risk, improve...
Spatially varying dispersion to model breakthrough curves.
Li, Guangquan
2011-01-01
Often the water flowing in a karst conduit is a combination of contaminated water entering at a sinkhole and cleaner water released from the limestone matrix. Transport processes in the conduit are controlled by advection, mixing (dilution and dispersion), and retention-release. In this article, a karst transport model considering advection, spatially varying dispersion, and dilution (from matrix seepage) is developed. Two approximate Green's functions are obtained using transformation of variables, respectively, for the initial-value problem and for the boundary-value problem. A numerical example illustrates that mixing associated with strong spatially varying conduit dispersion can cause strong skewness and long tailing in spring breakthrough curves. Comparison of the predicted breakthrough curve against that measured from a dye-tracing experiment between Ames Sink and Indian Spring, Northwest Florida, shows that the conduit dispersivity can be as large as 400 m. Such a large number is believed to imply strong solute interaction between the conduit and the matrix and/or multiple flow paths in a conduit network. It is concluded that Taylor dispersion is not dominant in transport in a karst conduit, and the complicated retention-release process between mobile- and immobile waters may be described by strong spatially varying conduit dispersion. Copyright © 2010 The Author(s). Journal compilation © 2010 National Ground Water Association.
Fathy, Alaa; Sabry, Yasser M.; Khalil, Diaa A.
2017-10-01
Multimode fibers (MMF) have many applications in illumination, spectroscopy, sensing and even in optical communication systems. In this work, we present a model for the MMF output field assuming the fiber end as a quasi-homogenous source. The fiber end is modeled by a group of partially coherent elementary sources, spatially shifted and uncorrelated with each other. The elementary source distribution is derived from the far field intensity measurement, while the weighting function of the sources is derived from the fiber end intensity measurement. The model is compared with practical measurements for fibers with different core/cladding diameters at different propagation distances and for different input excitations: laser, white light and LED. The obtained results show normalized root mean square error less than 8% in the intensity profile in most cases, even when the fiber end surface is not perfectly cleaved. Also, the comparison with the Gaussian-Schell model results shows a better agreement with the measurement. In addition, the complex degree of coherence, derived from the model results, is compared with the theoretical predictions of the modified Van Zernike equation showing very good agreement, which strongly supports the assumption that the large core MMF could be considered as a quasi-homogenous source.
Directory of Open Access Journals (Sweden)
Hans Schermeyer
2015-12-01
Full Text Available Electricity from renewable energy sources (RES-E is gaining more and more influence in traditional energy and electricity markets in Europe and around the world. When modeling RES-E feed-in on a high temporal and spatial resolution, energy systems analysts frequently use data generated by numerical weather models as input since there is no spatial inclusive and comprehensive measurement data available. However, the suitability of such model data depends on the research questions at hand and should be inspected individually. This paper focuses on new methodologies to carry out a performance evaluation of solar irradiation data provided by a numerical weather model when investigating photovoltaic feed-in and effects on the electricity grid. Suitable approaches of time series analysis are researched from literature and applied to both model and measurement data. The findings and limits of these approaches are illustrated and a new set of validation indicators is presented. These novel indicators complement the assessment by measuring relevant key figures in energy systems analysis: e.g., gradients in energy supply, maximum values and volatility. Thus, the results of this paper contribute to the scientific community of energy systems analysts and researchers who aim at modeling RES-E feed-in on a high temporal and spatial resolution using weather model data.
Prediction model and treatment of high-output ileostomy in colorectal cancer surgery.
Fujino, Shiki; Miyoshi, Norikatsu; Ohue, Masayuki; Takahashi, Yuske; Yasui, Masayoshi; Sugimura, Keijiro; Akita, Hirohumi; Takahashi, Hidenori; Kobayashi, Shogo; Yano, Masahiko; Sakon, Masato
2017-09-01
The aim of the present study was to examine the risk factors of high-output ileostomy (HOI), which is associated with electrolyte abnormalities and/or stoma complications, and to create a prediction model. The medical records of 68 patients who underwent colorectal cancer surgery with ileostomy between 2011 and 2016 were retrospectively investigated. All the patients underwent surgical resection for colorectal cancer at the Osaka Medical Center for Cancer and Cardiovascular Diseases (Osaka, Japan). A total of 7 patients with inadequate data on ileostomy output were excluded. Using a group of 50 patients who underwent surgery between 2011 and 2013, the risk of HOI was classified by a decision tree model using a partition platform. The HOI prediction model was validated in an additional group of 11 patients who underwent surgery between 2014 and 2016. Univariate analysis of clinical factors demonstrated that young age (P=0.003) and high white blood cell (WBC) count (Pmodel, three factors (gender, age and WBC on postoperative day 1) were generated for the prediction of HOI. The patients were classified into five groups, and HOI was observed in 0-88% of patients in each group. The area under the curve (AUC) was 0.838. The model was validated by an external dataset in an independent patient group, for which the AUC was 0.792. In conclusion, HOI patients were classified and an HOI prediction model was developed that may help clinicians in postoperative care.
Transport coefficient computation based on input/output reduced order models
Hurst, Joshua L.
The guiding purpose of this thesis is to address the optimal material design problem when the material description is a molecular dynamics model. The end goal is to obtain a simplified and fast model that captures the property of interest such that it can be used in controller design and optimization. The approach is to examine model reduction analysis and methods to capture a specific property of interest, in this case viscosity, or more generally complex modulus or complex viscosity. This property and other transport coefficients are defined by a input/output relationship and this motivates model reduction techniques that are tailored to preserve input/output behavior. In particular Singular Value Decomposition (SVD) based methods are investigated. First simulation methods are identified that are amenable to systems theory analysis. For viscosity, these models are of the Gosling and Lees-Edwards type. They are high order nonlinear Ordinary Differential Equations (ODEs) that employ Periodic Boundary Conditions. Properties can be calculated from the state trajectories of these ODEs. In this research local linear approximations are rigorously derived and special attention is given to potentials that are evaluated with Periodic Boundary Conditions (PBC). For the Gosling description LTI models are developed from state trajectories but are found to have limited success in capturing the system property, even though it is shown that full order LTI models can be well approximated by reduced order LTI models. For the Lees-Edwards SLLOD type model nonlinear ODEs will be approximated by a Linear Time Varying (LTV) model about some nominal trajectory and both balanced truncation and Proper Orthogonal Decomposition (POD) will be used to assess the plausibility of reduced order models to this system description. An immediate application of the derived LTV models is Quasilinearization or Waveform Relaxation. Quasilinearization is a Newton's method applied to the ODE operator
An Evolutionary Model of Spatial Competition
DEFF Research Database (Denmark)
Knudsen, Thorbjørn; Winter, Sidney G.
This paper sets forth an evolutionary model in which diverse businesses, with diverse offerings, compete in a stylized physical space. When a business firm attempts to expand its activity, so as to profit further from the capabilities it has developed, it necessarily does so in a "new location...... as well in the new environment as they did in the old; the firm may respond with effort to locate appropriate environments or by modification of its routines. Tradeoffs are presented between the complexity of a business model and its replication costs, as well as issues involving response....... Randomly generated firm policies are tested first by a local market environment, and then, if success leads the firm to grow spatially, in a gradually expanding environment. In the initial experiments reported here, we show that the model generates configurations that reflect features of the exogenous...
Two-dimensional modeling of x-ray output from switched foil implosions on Procyon
Bowers, R. L.; Nakafuji, G.; Greene, A. E.; McLenithan, K. D.; Peterson, D. L.; Roderick, N. F.
1996-09-01
A series of two-dimensional radiation magnetohydrodynamic calculations are presented of a Z-pinch implosion using a plasma flow switch. Results from a recent experiment using the high explosive driven generator Procyon, which delivered 16.5 MA to a plasma flow switch and switched about 15 MA into a static load, are used to study the implosion of a 29 mg load foil [J. H. Goforth et al., ``Review of the Procyon Explosive Pulsed Power System,'' in Ninth IEEE Pulsed Power Conference, June 1993, Albuquerque, edited by K. R. Prestwich and W. L. Baker (Institute of Electrical and Electronics Engineers, Piscataway, NJ, 1993), p. 36]. The interaction of the switch with the load plasma and the effects of background plasma on the total radiation output is examined. Models which assume ideal switching are also included. Also included are the effects of perturbations in the load plasma which may be associated with initial vaporization of the load foil. If the background plasma density in the switch region and in the load region does not affect the dynamics, the pinch is predicted to produce a total radiation output of about 4 MJ. Including perturbations of the load plasma associated with switching and assuming a background plasma density after switching in excess of 10-7 g/cm3 results in a total output from the pinch of about 0.6 MJ.
International Nuclear Information System (INIS)
Vidal-Codina, F.; Nguyen, N.C.; Giles, M.B.; Peraire, J.
2015-01-01
We present a model and variance reduction method for the fast and reliable computation of statistical outputs of stochastic elliptic partial differential equations. Our method consists of three main ingredients: (1) the hybridizable discontinuous Galerkin (HDG) discretization of elliptic partial differential equations (PDEs), which allows us to obtain high-order accurate solutions of the governing PDE; (2) the reduced basis method for a new HDG discretization of the underlying PDE to enable real-time solution of the parameterized PDE in the presence of stochastic parameters; and (3) a multilevel variance reduction method that exploits the statistical correlation among the different reduced basis approximations and the high-fidelity HDG discretization to accelerate the convergence of the Monte Carlo simulations. The multilevel variance reduction method provides efficient computation of the statistical outputs by shifting most of the computational burden from the high-fidelity HDG approximation to the reduced basis approximations. Furthermore, we develop a posteriori error estimates for our approximations of the statistical outputs. Based on these error estimates, we propose an algorithm for optimally choosing both the dimensions of the reduced basis approximations and the sizes of Monte Carlo samples to achieve a given error tolerance. We provide numerical examples to demonstrate the performance of the proposed method
Monetary Policy and Industrial Output in the BRICS Countries: A Markov-Switching Model
Directory of Open Access Journals (Sweden)
Kutu Adebayo Augustine
2017-12-01
Full Text Available This paper examines whether the five BRICS countries share similar business cycles and determines the probability of any of the countries moving from a contractionary regime to an expansionary regime. The study further examines the extent to which changes in monetary policy affect industrial output in expansions relative to contractions. Employing the Peersman and Smets (2001 Markov-Switching Model (MSM and monthly data from 1994.01–2013.12, the study reveals that the five BRICS countries have similar business cycles. The results further demonstrate that the BRICS countries’ business cycles are characterized by two distinct growth rate phases: a contractionary regime and an expansionary regime. It can also be observed that the area-wide monetary policy has significantly large effects on industrial output in recessions as well as in booms. It has also been established that there is a high probability of moving from state one (recession to state two (expansion and that on average, the probabilities of staying in state 2 (expansion are high for each of the five countries. It is, therefore, recommended that the BRICS countries should sustain uniform policy consistency (monetary policy, especially as they formulate and implement economic policies to stimulate industrial output.
Reactive Power Pricing Model Considering the Randomness of Wind Power Output
Dai, Zhong; Wu, Zhou
2018-01-01
With the increase of wind power capacity integrated into grid, the influence of the randomness of wind power output on the reactive power distribution of grid is gradually highlighted. Meanwhile, the power market reform puts forward higher requirements for reasonable pricing of reactive power service. Based on it, the article combined the optimal power flow model considering wind power randomness with integrated cost allocation method to price reactive power. Meanwhile, considering the advantages and disadvantages of the present cost allocation method and marginal cost pricing, an integrated cost allocation method based on optimal power flow tracing is proposed. The model realized the optimal power flow distribution of reactive power with the minimal integrated cost and wind power integration, under the premise of guaranteeing the balance of reactive power pricing. Finally, through the analysis of multi-scenario calculation examples and the stochastic simulation of wind power outputs, the article compared the results of the model pricing and the marginal cost pricing, which proved that the model is accurate and effective.
Panel data models extended to spatial error autocorrelation or a spatially lagged dependent variable
Elhorst, J. Paul
2001-01-01
This paper surveys panel data models extended to spatial error autocorrelation or a spatially lagged dependent variable. In particular, it focuses on the specification and estimation of four panel data models commonly used in applied research: the fixed effects model, the random effects model, the
Detection of no-model input-output pairs in closed-loop systems.
Potts, Alain Segundo; Alvarado, Christiam Segundo Morales; Garcia, Claudio
2017-11-01
The detection of no-model input-output (IO) pairs is important because it can speed up the multivariable system identification process, since all the pairs with null transfer functions are previously discarded and it can also improve the identified model quality, thus improving the performance of model based controllers. In the available literature, the methods focus just on the open-loop case, since in this case there is not the effect of the controller forcing the main diagonal in the transfer matrix to one and all the other terms to zero. In this paper, a modification of a previous method able to detect no-model IO pairs in open-loop systems is presented, but adapted to perform this duty in closed-loop systems. Tests are performed by using the traditional methods and the proposed one to show its effectiveness. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.
Spatial Stochastic Point Models for Reservoir Characterization
Energy Technology Data Exchange (ETDEWEB)
Syversveen, Anne Randi
1997-12-31
The main part of this thesis discusses stochastic modelling of geology in petroleum reservoirs. A marked point model is defined for objects against a background in a two-dimensional vertical cross section of the reservoir. The model handles conditioning on observations from more than one well for each object and contains interaction between objects, and the objects have the correct length distribution when penetrated by wells. The model is developed in a Bayesian setting. The model and the simulation algorithm are demonstrated by means of an example with simulated data. The thesis also deals with object recognition in image analysis, in a Bayesian framework, and with a special type of spatial Cox processes called log-Gaussian Cox processes. In these processes, the logarithm of the intensity function is a Gaussian process. The class of log-Gaussian Cox processes provides flexible models for clustering. The distribution of such a process is completely characterized by the intensity and the pair correlation function of the Cox process. 170 refs., 37 figs., 5 tabs.
Theoretical aspects of spatial-temporal modeling
Matsui, Tomoko
2015-01-01
This book provides a modern introductory tutorial on specialized theoretical aspects of spatial and temporal modeling. The areas covered involve a range of topics which reflect the diversity of this domain of research across a number of quantitative disciplines. For instance, the first chapter provides up-to-date coverage of particle association measures that underpin the theoretical properties of recently developed random set methods in space and time otherwise known as the class of probability hypothesis density framework (PHD filters). The second chapter gives an overview of recent advances in Monte Carlo methods for Bayesian filtering in high-dimensional spaces. In particular, the chapter explains how one may extend classical sequential Monte Carlo methods for filtering and static inference problems to high dimensions and big-data applications. The third chapter presents an overview of generalized families of processes that extend the class of Gaussian process models to heavy-tailed families known as alph...
Toward a more robust variance-based global sensitivity analysis of model outputs
Energy Technology Data Exchange (ETDEWEB)
Tong, C
2007-10-15
Global sensitivity analysis (GSA) measures the variation of a model output as a function of the variations of the model inputs given their ranges. In this paper we consider variance-based GSA methods that do not rely on certain assumptions about the model structure such as linearity or monotonicity. These variance-based methods decompose the output variance into terms of increasing dimensionality called 'sensitivity indices', first introduced by Sobol' [25]. Sobol' developed a method of estimating these sensitivity indices using Monte Carlo simulations. McKay [13] proposed an efficient method using replicated Latin hypercube sampling to compute the 'correlation ratios' or 'main effects', which have been shown to be equivalent to Sobol's first-order sensitivity indices. Practical issues with using these variance estimators are how to choose adequate sample sizes and how to assess the accuracy of the results. This paper proposes a modified McKay main effect method featuring an adaptive procedure for accuracy assessment and improvement. We also extend our adaptive technique to the computation of second-order sensitivity indices. Details of the proposed adaptive procedure as wells as numerical results are included in this paper.
Decision- rather than scenario-centred downscaling: Towards smarter use of climate model outputs
Wilby, Robert L.
2013-04-01
Climate model output has been used for hydrological impact assessments for at least 25 years. Scenario-led methods raise awareness about risks posed by climate variability and change to the security of supplies, performance of water infrastructure, and health of freshwater ecosystems. However, it is less clear how these analyses translate into actionable information for adaptation. One reason is that scenario-led methods typically yield very large uncertainty bounds in projected impacts at regional and river catchment scales. Consequently, there is growing interest in vulnerability-based frameworks and strategies for employing climate model output in decision-making contexts. This talk begins by summarising contrasting perspectives on climate models and principles for testing their utility for water sector applications. Using selected examples it is then shown how water resource systems may be adapted with varying levels of reliance on climate model information. These approaches include the conventional scenario-led risk assessment, scenario-neutral strategies, safety margins and sensitivity testing, and adaptive management of water systems. The strengths and weaknesses of each approach are outlined and linked to selected water management activities. These cases show that much progress can be made in managing water systems without dependence on climate models. Low-regret measures such as improved forecasting, better inter-agency co-operation, and contingency planning, yield benefits regardless of the climate outlook. Nonetheless, climate model scenarios are useful for evaluating adaptation portfolios, identifying system thresholds and fixing weak links, exploring the timing of investments, improving operating rules, or developing smarter licensing regimes. The most problematic application remains the climate change safety margin because of the very low confidence in extreme precipitation and river flows generated by climate models. In such cases, it is necessary to
Models and Inference for Multivariate Spatial Extremes
Vettori, Sabrina
2017-12-07
The development of flexible and interpretable statistical methods is necessary in order to provide appropriate risk assessment measures for extreme events and natural disasters. In this thesis, we address this challenge by contributing to the developing research field of Extreme-Value Theory. We initially study the performance of existing parametric and non-parametric estimators of extremal dependence for multivariate maxima. As the dimensionality increases, non-parametric estimators are more flexible than parametric methods but present some loss in efficiency that we quantify under various scenarios. We introduce a statistical tool which imposes the required shape constraints on non-parametric estimators in high dimensions, significantly improving their performance. Furthermore, by embedding the tree-based max-stable nested logistic distribution in the Bayesian framework, we develop a statistical algorithm that identifies the most likely tree structures representing the data\\'s extremal dependence using the reversible jump Monte Carlo Markov Chain method. A mixture of these trees is then used for uncertainty assessment in prediction through Bayesian model averaging. The computational complexity of full likelihood inference is significantly decreased by deriving a recursive formula for the nested logistic model likelihood. The algorithm performance is verified through simulation experiments which also compare different likelihood procedures. Finally, we extend the nested logistic representation to the spatial framework in order to jointly model multivariate variables collected across a spatial region. This situation emerges often in environmental applications but is not often considered in the current literature. Simulation experiments show that the new class of multivariate max-stable processes is able to detect both the cross and inner spatial dependence of a number of extreme variables at a relatively low computational cost, thanks to its Bayesian hierarchical
Multivariate Non-Symmetric Stochastic Models for Spatial Dependence Models
Haslauer, C. P.; Bárdossy, A.
2017-12-01
A copula based multivariate framework allows more flexibility to describe different kind of dependences than what is possible using models relying on the confining assumption of symmetric Gaussian models: different quantiles can be modelled with a different degree of dependence; it will be demonstrated how this can be expected given process understanding. maximum likelihood based multivariate quantitative parameter estimation yields stable and reliable results; not only improved results in cross-validation based measures of uncertainty are obtained but also a more realistic spatial structure of uncertainty compared to second order models of dependence; as much information as is available is included in the parameter estimation: incorporation of censored measurements (e.g., below detection limit, or ones that are above the sensitive range of the measurement device) yield to more realistic spatial models; the proportion of true zeros can be jointly estimated with and distinguished from censored measurements which allow estimates about the age of a contaminant in the system; secondary information (categorical and on the rational scale) has been used to improve the estimation of the primary variable; These copula based multivariate statistical techniques are demonstrated based on hydraulic conductivity observations at the Borden (Canada) site, the MADE site (USA), and a large regional groundwater quality data-set in south-west Germany. Fields of spatially distributed K were simulated with identical marginal simulation, identical second order spatial moments, yet substantially differing solute transport characteristics when numerical tracer tests were performed. A statistical methodology is shown that allows the delineation of a boundary layer separating homogenous parts of a spatial data-set. The effects of this boundary layer (macro structure) and the spatial dependence of K (micro structure) on solute transport behaviour is shown.
Modeling strategic investment decisions in spatial markets
International Nuclear Information System (INIS)
Lorenczik, Stefan; Malischek, Raimund
2014-01-01
Markets for natural resources and commodities are often oligopolistic. In these markets, production capacities are key for strategic interaction between the oligopolists. We analyze how different market structures influence oligopolistic capacity investments and thereby affect supply, prices and rents in spatial natural resource markets using mathematical programing models. The models comprise an investment period and a supply period in which players compete in quantities. We compare three models, one perfect competition and two Cournot models, in which the product is either traded through long-term contracts or on spot markets in the supply period. Tractability and practicality of the approach are demonstrated in an application to the international metallurgical coal market. Results may vary substantially between the different models. The metallurgical coal market has recently made progress in moving away from long-term contracts and more towards spot market-based trade. Based on our results, we conclude that this regime switch is likely to raise consumer rents but lower producer rents. The total welfare differs only negligibly.
Spatially explicit modelling of cholera epidemics
Finger, F.; Bertuzzo, E.; Mari, L.; Knox, A. C.; Gatto, M.; Rinaldo, A.
2013-12-01
Epidemiological models can provide crucial understanding about the dynamics of infectious diseases. Possible applications range from real-time forecasting and allocation of health care resources to testing alternative intervention mechanisms such as vaccines, antibiotics or the improvement of sanitary conditions. We apply a spatially explicit model to the cholera epidemic that struck Haiti in October 2010 and is still ongoing. The dynamics of susceptibles as well as symptomatic and asymptomatic infectives are modelled at the scale of local human communities. Dissemination of Vibrio cholerae through hydrological transport and human mobility along the road network is explicitly taken into account, as well as the effect of rainfall as a driver of increasing disease incidence. The model is calibrated using a dataset of reported cholera cases. We further model the long term impact of several types of interventions on the disease dynamics by varying parameters appropriately. Key epidemiological mechanisms and parameters which affect the efficiency of treatments such as antibiotics are identified. Our results lead to conclusions about the influence of different intervention strategies on the overall epidemiological dynamics.
Modeling strategic investment decisions in spatial markets
Energy Technology Data Exchange (ETDEWEB)
Lorenczik, Stefan; Malischek, Raimund [Koeln Univ. (Germany). Energiewirtschaftliches Inst.; Trueby, Johannes [International Energy Agency, 75 - Paris (France)
2014-04-15
Markets for natural resources and commodities are often oligopolistic. In these markets, production capacities are key for strategic interaction between the oligopolists. We analyze how different market structures influence oligopolistic capacity investments and thereby affect supply, prices and rents in spatial natural resource markets using mathematical programing models. The models comprise an investment period and a supply period in which players compete in quantities. We compare three models, one perfect competition and two Cournot models, in which the product is either traded through long-term contracts or on spot markets in the supply period. Tractability and practicality of the approach are demonstrated in an application to the international metallurgical coal market. Results may vary substantially between the different models. The metallurgical coal market has recently made progress in moving away from long-term contracts and more towards spot market-based trade. Based on our results, we conclude that this regime switch is likely to raise consumer rents but lower producer rents. The total welfare differs only negligibly.
Fuzzy portfolio model with fuzzy-input return rates and fuzzy-output proportions
Tsaur, Ruey-Chyn
2015-02-01
In the finance market, a short-term investment strategy is usually applied in portfolio selection in order to reduce investment risk; however, the economy is uncertain and the investment period is short. Further, an investor has incomplete information for selecting a portfolio with crisp proportions for each chosen security. In this paper we present a new method of constructing fuzzy portfolio model for the parameters of fuzzy-input return rates and fuzzy-output proportions, based on possibilistic mean-standard deviation models. Furthermore, we consider both excess or shortage of investment in different economic periods by using fuzzy constraint for the sum of the fuzzy proportions, and we also refer to risks of securities investment and vagueness of incomplete information during the period of depression economics for the portfolio selection. Finally, we present a numerical example of a portfolio selection problem to illustrate the proposed model and a sensitivity analysis is realised based on the results.
A Water-Withdrawal Input-Output Model of the Indian Economy.
Bogra, Shelly; Bakshi, Bhavik R; Mathur, Ritu
2016-02-02
Managing freshwater allocation for a highly populated and growing economy like India can benefit from knowledge about the effect of economic activities. This study transforms the 2003-2004 economic input-output (IO) table of India into a water withdrawal input-output model to quantify direct and indirect flows. This unique model is based on a comprehensive database compiled from diverse public sources, and estimates direct and indirect water withdrawal of all economic sectors. It distinguishes between green (rainfall), blue (surface and ground), and scarce groundwater. Results indicate that the total direct water withdrawal is nearly 3052 billion cubic meter (BCM) and 96% of this is used in agriculture sectors with the contribution of direct green water being about 1145 BCM, excluding forestry. Apart from 727 BCM direct blue water withdrawal for agricultural, other significant users include "Electricity" with 64 BCM, "Water supply" with 44 BCM and other industrial sectors with nearly 14 BCM. "Construction", "miscellaneous food products"; "Hotels and restaurants"; "Paper, paper products, and newsprint" are other significant indirect withdrawers. The net virtual water import is found to be insignificant compared to direct water used in agriculture nationally, while scarce ground water associated with crops is largely contributed by northern states.
International trade inoperability input-output model (IT-IIM): theory and application.
Jung, Jeesang; Santos, Joost R; Haimes, Yacov Y
2009-01-01
The inoperability input-output model (IIM) has been used for analyzing disruptions due to man-made or natural disasters that can adversely affect the operation of economic systems or critical infrastructures. Taking economic perturbation for each sector as inputs, the IIM provides the degree of economic production impacts on all industry sectors as the outputs for the model. The current version of the IIM does not provide a separate analysis for the international trade component of the inoperability. If an important port of entry (e.g., Port of Los Angeles) is disrupted, then international trade inoperability becomes a highly relevant subject for analysis. To complement the current IIM, this article develops the International Trade-IIM (IT-IIM). The IT-IIM investigates the resulting international trade inoperability for all industry sectors resulting from disruptions to a major port of entry. Similar to traditional IIM analysis, the inoperability metrics that the IT-IIM provides can be used to prioritize economic sectors based on the losses they could potentially incur. The IT-IIM is used to analyze two types of direct perturbations: (1) the reduced capacity of ports of entry, including harbors and airports (e.g., a shutdown of any port of entry); and (2) restrictions on commercial goods that foreign countries trade with the base nation (e.g., embargo).
Multiregional input-output model for the evaluation of Spanish water flows.
Cazcarro, Ignacio; Duarte, Rosa; Sánchez Chóliz, Julio
2013-01-01
We construct a multiregional input-output model for Spain, in order to evaluate the pressures on the water resources, virtual water flows, and water footprints of the regions, and the water impact of trade relationships within Spain and abroad. The study is framed with those interregional input-output models constructed to study water flows and impacts of regions in China, Australia, Mexico, or the UK. To build our database, we reconcile regional IO tables, national and regional accountancy of Spain, trade and water data. Results show an important imbalance between origin of water resources and final destination, with significant water pressures in the South, Mediterranean, and some central regions. The most populated and dynamic regions of Madrid and Barcelona are important drivers of water consumption in Spain. Main virtual water exporters are the South and Central agrarian regions: Andalusia, Castile-La Mancha, Castile-Leon, Aragon, and Extremadura, while the main virtual water importers are the industrialized regions of Madrid, Basque country, and the Mediterranean coast. The paper shows the different location of direct and indirect consumers of water in Spain and how the economic trade and consumption pattern of certain areas has significant impacts on the availability of water resources in other different and often drier regions.
A physically based analytical spatial air temperature and humidity model
Yang Yang; Theodore A. Endreny; David J. Nowak
2013-01-01
Spatial variation of urban surface air temperature and humidity influences human thermal comfort, the settling rate of atmospheric pollutants, and plant physiology and growth. Given the lack of observations, we developed a Physically based Analytical Spatial Air Temperature and Humidity (PASATH) model. The PASATH model calculates spatial solar radiation and heat...
Wavelet transform-vector quantization compression of supercomputer ocean model simulation output
Energy Technology Data Exchange (ETDEWEB)
Bradley, J N; Brislawn, C M
1992-11-12
We describe a new procedure for efficient compression of digital information for storage and transmission purposes. The algorithm involves a discrete wavelet transform subband decomposition of the data set, followed by vector quantization of the wavelet transform coefficients using application-specific vector quantizers. The new vector quantizer design procedure optimizes the assignment of both memory resources and vector dimensions to the transform subbands by minimizing an exponential rate-distortion functional subject to constraints on both overall bit-rate and encoder complexity. The wavelet-vector quantization method, which originates in digital image compression. is applicable to the compression of other multidimensional data sets possessing some degree of smoothness. In this paper we discuss the use of this technique for compressing the output of supercomputer simulations of global climate models. The data presented here comes from Semtner-Chervin global ocean models run at the National Center for Atmospheric Research and at the Los Alamos Advanced Computing Laboratory.
Hierarchical Bayesian spatial models for multispecies conservation planning and monitoring.
Carroll, Carlos; Johnson, Devin S; Dunk, Jeffrey R; Zielinski, William J
2010-12-01
Biologists who develop and apply habitat models are often familiar with the statistical challenges posed by their data's spatial structure but are unsure of whether the use of complex spatial models will increase the utility of model results in planning. We compared the relative performance of nonspatial and hierarchical Bayesian spatial models for three vertebrate and invertebrate taxa of conservation concern (Church's sideband snails [Monadenia churchi], red tree voles [Arborimus longicaudus], and Pacific fishers [Martes pennanti pacifica]) that provide examples of a range of distributional extents and dispersal abilities. We used presence-absence data derived from regional monitoring programs to develop models with both landscape and site-level environmental covariates. We used Markov chain Monte Carlo algorithms and a conditional autoregressive or intrinsic conditional autoregressive model framework to fit spatial models. The fit of Bayesian spatial models was between 35 and 55% better than the fit of nonspatial analogue models. Bayesian spatial models outperformed analogous models developed with maximum entropy (Maxent) methods. Although the best spatial and nonspatial models included similar environmental variables, spatial models provided estimates of residual spatial effects that suggested how ecological processes might structure distribution patterns. Spatial models built from presence-absence data improved fit most for localized endemic species with ranges constrained by poorly known biogeographic factors and for widely distributed species suspected to be strongly affected by unmeasured environmental variables or population processes. By treating spatial effects as a variable of interest rather than a nuisance, hierarchical Bayesian spatial models, especially when they are based on a common broad-scale spatial lattice (here the national Forest Inventory and Analysis grid of 24 km(2) hexagons), can increase the relevance of habitat models to multispecies
ElMallah, Mai K; Pagliardini, Silvia; Turner, Sara M; Cerreta, Anthony J; Falk, Darin J; Byrne, Barry J; Greer, John J; Fuller, David D
2015-09-01
Pompe disease results from a mutation in the acid α-glucosidase gene leading to lysosomal glycogen accumulation. Respiratory insufficiency is common, and the current U.S. Food and Drug Administration-approved treatment, enzyme replacement, has limited effectiveness. Ampakines are drugs that enhance α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptor responses and can increase respiratory motor drive. Recent work indicates that respiratory motor drive can be blunted in Pompe disease, and thus pharmacologic stimulation of breathing may be beneficial. Using a murine Pompe model with the most severe clinical genotype (the Gaa(-/-) mouse), our primary objective was to test the hypothesis that ampakines can stimulate respiratory motor output and increase ventilation. Our second objective was to confirm that neuropathology was present in Pompe mouse medullary respiratory control neurons. The impact of ampakine CX717 on breathing was determined via phrenic and hypoglossal nerve recordings in anesthetized mice and whole-body plethysmography in unanesthetized mice. The medulla was examined using standard histological methods coupled with immunochemical markers of respiratory control neurons. Ampakine CX717 robustly increased phrenic and hypoglossal inspiratory bursting and reduced respiratory cycle variability in anesthetized Pompe mice, and it increased inspiratory tidal volume in unanesthetized Pompe mice. CX717 did not significantly alter these variables in wild-type mice. Medullary respiratory neurons showed extensive histopathology in Pompe mice. Ampakines stimulate respiratory neuromotor output and ventilation in Pompe mice, and therefore they have potential as an adjunctive therapy in Pompe disease.
Reducing Spatial Data Complexity for Classification Models
International Nuclear Information System (INIS)
Ruta, Dymitr; Gabrys, Bogdan
2007-01-01
Intelligent data analytics gradually becomes a day-to-day reality of today's businesses. However, despite rapidly increasing storage and computational power current state-of-the-art predictive models still can not handle massive and noisy corporate data warehouses. What is more adaptive and real-time operational environment requires multiple models to be frequently retrained which further hinders their use. Various data reduction techniques ranging from data sampling up to density retention models attempt to address this challenge by capturing a summarised data structure, yet they either do not account for labelled data or degrade the classification performance of the model trained on the condensed dataset. Our response is a proposition of a new general framework for reducing the complexity of labelled data by means of controlled spatial redistribution of class densities in the input space. On the example of Parzen Labelled Data Compressor (PLDC) we demonstrate a simulatory data condensation process directly inspired by the electrostatic field interaction where the data are moved and merged following the attracting and repelling interactions with the other labelled data. The process is controlled by the class density function built on the original data that acts as a class-sensitive potential field ensuring preservation of the original class density distributions, yet allowing data to rearrange and merge joining together their soft class partitions. As a result we achieved a model that reduces the labelled datasets much further than any competitive approaches yet with the maximum retention of the original class densities and hence the classification performance. PLDC leaves the reduced dataset with the soft accumulative class weights allowing for efficient online updates and as shown in a series of experiments if coupled with Parzen Density Classifier (PDC) significantly outperforms competitive data condensation methods in terms of classification performance at the
Reducing Spatial Data Complexity for Classification Models
Ruta, Dymitr; Gabrys, Bogdan
2007-11-01
Intelligent data analytics gradually becomes a day-to-day reality of today's businesses. However, despite rapidly increasing storage and computational power current state-of-the-art predictive models still can not handle massive and noisy corporate data warehouses. What is more adaptive and real-time operational environment requires multiple models to be frequently retrained which further hinders their use. Various data reduction techniques ranging from data sampling up to density retention models attempt to address this challenge by capturing a summarised data structure, yet they either do not account for labelled data or degrade the classification performance of the model trained on the condensed dataset. Our response is a proposition of a new general framework for reducing the complexity of labelled data by means of controlled spatial redistribution of class densities in the input space. On the example of Parzen Labelled Data Compressor (PLDC) we demonstrate a simulatory data condensation process directly inspired by the electrostatic field interaction where the data are moved and merged following the attracting and repelling interactions with the other labelled data. The process is controlled by the class density function built on the original data that acts as a class-sensitive potential field ensuring preservation of the original class density distributions, yet allowing data to rearrange and merge joining together their soft class partitions. As a result we achieved a model that reduces the labelled datasets much further than any competitive approaches yet with the maximum retention of the original class densities and hence the classification performance. PLDC leaves the reduced dataset with the soft accumulative class weights allowing for efficient online updates and as shown in a series of experiments if coupled with Parzen Density Classifier (PDC) significantly outperforms competitive data condensation methods in terms of classification performance at the
Havens, Scott; Marks, Danny; Kormos, Patrick; Hedrick, Andrew
2017-12-01
In the Western US and many mountainous regions of the world, critical water resources and climate conditions are difficult to monitor because the observation network is generally very sparse. The critical resource from the mountain snowpack is water flowing into streams and reservoirs that will provide for irrigation, flood control, power generation, and ecosystem services. Water supply forecasting in a rapidly changing climate has become increasingly difficult because of non-stationary conditions. In response, operational water supply managers have begun to move from statistical techniques towards the use of physically based models. As we begin to transition physically based models from research to operational use, we must address the most difficult and time-consuming aspect of model initiation: the need for robust methods to develop and distribute the input forcing data. In this paper, we present a new open source framework, the Spatial Modeling for Resources Framework (SMRF), which automates and simplifies the common forcing data distribution methods. It is computationally efficient and can be implemented for both research and operational applications. We present an example of how SMRF is able to generate all of the forcing data required to a run physically based snow model at 50-100 m resolution over regions of 1000-7000 km2. The approach has been successfully applied in real time and historical applications for both the Boise River Basin in Idaho, USA and the Tuolumne River Basin in California, USA. These applications use meteorological station measurements and numerical weather prediction model outputs as input. SMRF has significantly streamlined the modeling workflow, decreased model set up time from weeks to days, and made near real-time application of a physically based snow model possible.
National Oceanic and Atmospheric Administration, Department of Commerce — The NWFSC OA team will model the effects of ocean acidification on regional marine species and ecosystems using food web models, life-cycle models, and bioenvelope...
Directory of Open Access Journals (Sweden)
Sutikno Sutikno
2010-08-01
Full Text Available One of the climate models used to predict the climatic conditions is Global Circulation Models (GCM. GCM is a computer-based model that consists of different equations. It uses numerical and deterministic equation which follows the physics rules. GCM is a main tool to predict climate and weather, also it uses as primary information source to review the climate change effect. Statistical Downscaling (SD technique is used to bridge the large-scale GCM with a small scale (the study area. GCM data is spatial and temporal data most likely to occur where the spatial correlation between different data on the grid in a single domain. Multicollinearity problems require the need for pre-processing of variable data X. Continuum Regression (CR and pre-processing with Principal Component Analysis (PCA methods is an alternative to SD modelling. CR is one method which was developed by Stone and Brooks (1990. This method is a generalization from Ordinary Least Square (OLS, Principal Component Regression (PCR and Partial Least Square method (PLS methods, used to overcome multicollinearity problems. Data processing for the station in Ambon, Pontianak, Losarang, Indramayu and Yuntinyuat show that the RMSEP values and R2 predict in the domain 8x8 and 12x12 by uses CR method produces results better than by PCR and PLS.
Sensitivity analysis of model output - a step towards robust safety indicators?
International Nuclear Information System (INIS)
Broed, R.; Pereira, A.; Moberg, L.
2004-01-01
The protection of the environment from ionising radiation challenges the radioecological community with the issue of harmonising disparate safety indicators. These indicators should preferably cover the whole spectrum of model predictions on chemo-toxic and radiation impact of contaminants. In question is not only the protection of man and biota but also of abiotic systems. In many cases modelling will constitute the basis for an evaluation of potential impact. It is recognised that uncertainty and sensitivity analysis of model output will play an important role in the 'construction' of safety indicators that are robust, reliable and easy to explain to all groups of stakeholders including the general public. However, environmental models of transport of radionuclides have some extreme characteristics. They are, a) complex, b) non-linear, c) include a huge number of input parameters, d) input parameters are associated with large or very large uncertainties, e) parameters are often correlated to each other, f) uncertainties other than parameter-driven may be present in the modelling system, g) space variability and time-dependence of parameters are present, h) model predictions may cover geological time scales. Consequently, uncertainty and sensitivity analysis are non-trivial tasks, challenging the decision-maker when it comes to the interpretation of safety indicators or the application of regulatory criteria. In this work we use the IAEA model ISAM, to make a set of Monte Carlo calculations. The ISAM model includes several nuclides and decay chains, many compartments and variable parameters covering the range of nuclide migration pathways from the near field to the biosphere. The goal of our calculations is to make a global sensitivity analysis. After extracting the non-influential parameters, the M.C. calculations are repeated with those parameters frozen. Reducing the number of parameters to a few ones will simplify the interpretation of the results and the use
Design and Output Performance Model of Turbodrill Blade Used in a Slim Borehole
Directory of Open Access Journals (Sweden)
Yu Wang
2016-12-01
Full Text Available Small-diameter turbodrills have great potential for use in slim boreholes because of their lower cost and higher efficiency when used in geothermal energy and other underground resource applications. Multistage hydraulic components consisting of stators and rotors are key aspects of turbodrills. This study aimed to develop a suitable blade that can be used under high temperature in granite formations. First, prediction models for single- and multi-stage blades were established based on Bernoulli’s Equation. The design requirement of the blade for high-temperature geothermal drilling in granite was proposed. A Φ89 blade was developed based on the dimensionless parameter method and Bezier curve; the parameters of the blade, including its radial size, symotric parameters, and blade profiles, were input into ANASYS and CFX to establish a calculation model of the single-stage blade. The optimization of the blade structure of the small-diameter turbodrill enabled a multistage turbodrill model to be established and the turbodrill’s overall output performance to be predicted. The results demonstrate that the design can meet the turbodrill’s performance requirements and that the multistage model can effectively improve the accuracy of the prediction.
Consequences of spatial autocorrelation for niche-based models
DEFF Research Database (Denmark)
Segurado, P.; Araújo, Miguel B.; Kunin, W. E.
2006-01-01
1. Spatial autocorrelation is an important source of bias in most spatial analyses. We explored the bias introduced by spatial autocorrelation on the explanatory and predictive power of species' distribution models, and make recommendations for dealing with the problem. 2. Analyses were based o...
Spatial Econometric data analysis: moving beyond traditional models
Florax, R.J.G.M.; Vlist, van der A.J.
2003-01-01
This article appraises recent advances in the spatial econometric literature. It serves as the introduction too collection of new papers on spatial econometric data analysis brought together in this special issue, dealing specifically with new extensions to the spatial econometric modeling
The spatial limitations of current neutral models of biodiversity.
Directory of Open Access Journals (Sweden)
Rampal S Etienne
Full Text Available The unified neutral theory of biodiversity and biogeography is increasingly accepted as an informative null model of community composition and dynamics. It has successfully produced macro-ecological patterns such as species-area relationships and species abundance distributions. However, the models employed make many unrealistic auxiliary assumptions. For example, the popular spatially implicit version assumes a local plot exchanging migrants with a large panmictic regional source pool. This simple structure allows rigorous testing of its fit to data. In contrast, spatially explicit models assume that offspring disperse only limited distances from their parents, but one cannot as yet test the significance of their fit to data. Here we compare the spatially explicit and the spatially implicit model, fitting the most-used implicit model (with two levels, local and regional to data simulated by the most-used spatially explicit model (where offspring are distributed about their parent on a grid according to either a radially symmetric Gaussian or a 'fat-tailed' distribution. Based on these fits, we express spatially implicit parameters in terms of spatially explicit parameters. This suggests how we may obtain estimates of spatially explicit parameters from spatially implicit ones. The relationship between these parameters, however, makes no intuitive sense. Furthermore, the spatially implicit model usually fits observed species-abundance distributions better than those calculated from the spatially explicit model's simulated data. Current spatially explicit neutral models therefore have limited descriptive power. However, our results suggest that a fatter tail of the dispersal kernel seems to improve the fit, suggesting that dispersal kernels with even fatter tails should be studied in future. We conclude that more advanced spatially explicit models and tools to analyze them need to be developed.
CM-DataONE: A Framework for collaborative analysis of climate model output
Xu, Hao; Bai, Yuqi; Li, Sha; Dong, Wenhao; Huang, Wenyu; Xu, Shiming; Lin, Yanluan; Wang, Bin
2015-04-01
CM-DataONE is a distributed collaborative analysis framework for climate model data which aims to break through the data access barriers of increasing file size and to accelerate research process. As data size involved in project such as the fifth Coupled Model Intercomparison Project (CMIP5) has reached petabytes, conventional methods for analysis and diagnosis of model outputs have been rather time-consuming and redundant. CM-DataONE is developed for data publishers and researchers from relevant areas. It can enable easy access to distributed data and provide extensible analysis functions based on tools such as NCAR Command Language, NetCDF Operators (NCO) and Climate Data Operators (CDO). CM-DataONE can be easily installed, configured, and maintained. The main web application has two separate parts which communicate with each other through APIs based on HTTP protocol. The analytic server is designed to be installed in each data node while a data portal can be configured anywhere and connect to a nearest node. Functions such as data query, analytic task submission, status monitoring, visualization and product downloading are provided to end users by data portal. Data conform to CMIP5 Model Output Format in each peer node can be scanned by the server and mapped to a global information database. A scheduler included in the server is responsible for task decomposition, distribution and consolidation. Analysis functions are always executed where data locate. Analysis function package included in the server has provided commonly used functions such as EOF analysis, trend analysis and time series. Functions are coupled with data by XML descriptions and can be easily extended. Various types of results can be obtained by users for further studies. This framework has significantly decreased the amount of data to be transmitted and improved efficiency in model intercomparison jobs by supporting online analysis and multi-node collaboration. To end users, data query is
Model for Atmospheric Propagation of Spatially Combined Laser Beams
2016-09-01
NAVAL POSTGRADUATE SCHOOL MONTEREY, CALIFORNIA THESIS MODEL FOR ATMOSPHERIC PROPAGATION OF SPATIALLY COMBINED LASER BEAMS by Kum Leong Lee September...MODEL FOR ATMOSPHERIC PROPAGATION OF SPATIALLY COMBINED LASER BEAMS 5. FUNDING NUMBERS 6. AUTHOR(S) Kum Leong Lee 7. PERFORMING ORGANIZATION NAME(S) AND...BLANK ii Approved for public release. Distribution is unlimited. MODEL FOR ATMOSPHERIC PROPAGATION OF SPATIALLY COMBINED LASER BEAMS Kum Leong Lee
Lakra, Suchita; Mandal, Sanjoy
2017-06-01
A quadruple micro-optical ring resonator (QMORR) with multiple output bus waveguides is mathematically modeled and analyzed by making use of the delay-line signal processing approach in Z-domain and Mason's gain formula. The performances of QMORR with two output bus waveguides with vertical coupling are analyzed. This proposed structure is capable of providing wider free spectral response from both the output buses with appreciable cross talk. Thus, this configuration could provide increased capacity to insert a large number of communication channels. The simulated frequency response characteristic and its dispersion and group delay characteristics are graphically presented using the MATLAB environment.
A new chance-constrained DEA model with birandom input and output data
Tavana, M.; Shiraz, R. K.; Hatami-Marbini, A.
2013-01-01
The purpose of conventional Data Envelopment Analysis (DEA) is to evaluate the performance of a set of firms or Decision-Making Units using deterministic input and output data. However, the input and output data in the real-life performance evaluation problems are often stochastic. The stochastic input and output data in DEA can be represented with random variables. Several methods have been proposed to deal with the random input and output data in DEA. In this paper, we propose a new chance-...
Alligné, S.; Nicolet, C.; Béguin, A.; Landry, C.; Gomes, J.; Avellan, F.
2017-04-01
The prediction of pressure and output power fluctuations amplitudes on Francis turbine prototype is a challenge for hydro-equipment industry since it is subjected to guarantees to ensure smooth and reliable operation of the hydro units. The European FP7 research project Hyperbole aims to setup a methodology to transpose the pressure fluctuations induced by the cavitation vortex rope from the reduced scale model to the prototype generating units. A Francis turbine unit of 444MW with a specific speed value of ν = 0.29, is considered as case study. A SIMSEN model of the power station including electrical system, controllers, rotating train and hydraulic system with transposed draft tube excitation sources is setup. Based on this model, a frequency analysis of the hydroelectric system is performed for all technologies to analyse potential interactions between hydraulic excitation sources and electrical components. Three technologies have been compared: the classical fixed speed configuration with Synchronous Machine (SM) and the two variable speed technologies which are Doubly Fed Induction Machine (DFIM) and Full Size Frequency Converter (FSFC).
A new solar power output prediction based on hybrid forecast engine and decomposition model.
Zhang, Weijiang; Dang, Hongshe; Simoes, Rolando
2018-06-12
Regarding to the growing trend of photovoltaic (PV) energy as a clean energy source in electrical networks and its uncertain nature, PV energy prediction has been proposed by researchers in recent decades. This problem is directly effects on operation in power network while, due to high volatility of this signal, an accurate prediction model is demanded. A new prediction model based on Hilbert Huang transform (HHT) and integration of improved empirical mode decomposition (IEMD) with feature selection and forecast engine is presented in this paper. The proposed approach is divided into three main sections. In the first section, the signal is decomposed by the proposed IEMD as an accurate decomposition tool. To increase the accuracy of the proposed method, a new interpolation method has been used instead of cubic spline curve (CSC) fitting in EMD. Then the obtained output is entered into the new feature selection procedure to choose the best candidate inputs. Finally, the signal is predicted by a hybrid forecast engine composed of support vector regression (SVR) based on an intelligent algorithm. The effectiveness of the proposed approach has been verified over a number of real-world engineering test cases in comparison with other well-known models. The obtained results prove the validity of the proposed method. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.
A Spatial Model of the Mere Exposure Effect.
Fink, Edward L.; And Others
1989-01-01
Uses a spatial model to examine the relationship between stimulus exposure, cognition, and affect. Notes that this model accounts for cognitive changes that a stimulus may acquire as a result of exposure. Concludes that the spatial model is useful for evaluating the mere exposure effect and that affective change does not require cognitive change.…
Does the DHET research output subsidy model penalise high-citation publication? A case study
Directory of Open Access Journals (Sweden)
Yolande X. Harley
2016-05-01
Full Text Available South African universities are awarded annual subsidy from the Department of Higher Education and Training (DHET based on their research publication output. Journal article subsidy is based on the number of research publications in DHET-approved journals as well as the proportional contribution of authors from the university. Co-authorship with other institutions reduces the subsidy received by a university, which may be a disincentive to collaboration. Inter-institutional collaboration may affect the scientific impact of resulting publications, as indicated by the number of citations received. We analysed 812 journal articles published in 2011 by authors from the University of Cape Town’s Faculty of Health Sciences to determine if there was a significant relationship between subsidy units received and (1 citation count and (2 field-weighted citation impact. We found that subsidy units had a significant inverse relationship with both citation count (r= -0.247; CI = -0.311 – -0.182; p"less than"0.0001 and field-weighted citation impact (r= -0.192; CI= -0.258 – -0.125; p"less than"0.0001. These findings suggest that the annual subsidy awarded to universities for research output may inadvertently penalise high-citation publication. Revision of the funding model to address this possibility would better align DHET funding allocation with the strategic plans of the South African Department of Science and Technology, the National Research Foundation and the South African Medical Research Council, and may better support publication of greater impact research.
Impacts of manure application on SWAT model outputs in the Xiangxi River watershed
Liu, Ruimin; Wang, Qingrui; Xu, Fei; Men, Cong; Guo, Lijia
2017-12-01
SWAT (Soil and Water Assessment Tool) model has been widely used to simulate agricultural non-point source (ANPS) pollution; however, the impacts of livestock manure application on SWAT model outputs have not been well studied. The objective of this study was to investigate the environmental effects of livestock manure application based on the SWAT model in the Xiangxi River watershed, which is one of the largest tributaries of the Three Gorges Reservoir in China. Three newly-built manure databases (NB) were created and applied to different subbasins based on the actual livestock manure discharging amount. The calibration and validation values of SWAT model outputs obtained from the NB manure application and the original mixed (OM) manure were compared. The study results are as follows: (1) The livestock industry of Xingshan County developed quickly between 2005 and 2015. The downstream of the Xiangxi River (Huangliang, Shuiyuesi and Xiakou) had the largest livestock amount, and largely accounted for manure, total nitrogen (TN) and total phosphorus (TP) production (>50%). (2) The NB manure application resulted in less phosphorus pollution (1686.35 kg for ORGP and 31.70 kg for MINP) than the OM manure application. Compared with the upstream, the downstream was influenced more by the manure application. (3) The SWAT results obtained from the NB manure had a better calibration and validation values than those from the OM manure. For ORGP, R2 and NSE values were 0.77 and 0.65 for the NB manure calibration; and the same values for the OM manure were 0.72 and 0.61, respectively. For MINP, R2 values were 0.65 and 0.62 for the NB manure and the OM manure, and the NSE values were 0.60 and 0.58, respectively. The results indicated that the built-in fertilizer database in SWAT has its limitation because it is set up for the simulation in the USA. Thus, when livestock manure is considered in a SWAT simulation, a newly built fertilizer database needs to be set up to represent
International Nuclear Information System (INIS)
Suolanen, V.; Ilvonen, M.
1998-10-01
Computer model DETRA applies a dynamic compartment modelling approach. The compartment structure of each considered application can be tailored individually. This flexible modelling method makes it possible that the transfer of radionuclides can be considered in various cases: aquatic environment and related food chains, terrestrial environment, food chains in general and food stuffs, body burden analyses of humans, etc. In the former study on this subject, modernization of the user interface of DETRA code was carried out. This new interface works in Windows environment and the usability of the code has been improved. The objective of this study has been to further develop and diversify the user interface so that also probabilistic uncertainty analyses can be performed by DETRA. The most common probability distributions are available: uniform, truncated Gaussian and triangular. The corresponding logarithmic distributions are also available. All input data related to a considered case can be varied, although this option is seldomly needed. The calculated output values can be selected as monitored values at certain simulation time points defined by the user. The results of a sensitivity run are immediately available after simulation as graphical presentations. These outcomes are distributions generated for varied parameters, density functions of monitored parameters and complementary cumulative density functions (CCDF). An application considered in connection with this work was the estimation of contamination of milk caused by radioactive deposition of Cs (10 kBq(Cs-137)/m 2 ). The multi-sequence calculation model applied consisted of a pasture modelling part and a dormant season modelling part. These two sequences were linked periodically simulating the realistic practice of care taking of domestic animals in Finland. The most important parameters were varied in this exercise. The performed diversifying of the user interface of DETRA code seems to provide an easily
What spatial scales are believable for climate model projections of sea surface temperature?
Kwiatkowski, Lester; Halloran, Paul R.; Mumby, Peter J.; Stephenson, David B.
2014-09-01
Earth system models (ESMs) provide high resolution simulations of variables such as sea surface temperature (SST) that are often used in off-line biological impact models. Coral reef modellers have used such model outputs extensively to project both regional and global changes to coral growth and bleaching frequency. We assess model skill at capturing sub-regional climatologies and patterns of historical warming. This study uses an established wavelet-based spatial comparison technique to assess the skill of the coupled model intercomparison project phase 5 models to capture spatial SST patterns in coral regions. We show that models typically have medium to high skill at capturing climatological spatial patterns of SSTs within key coral regions, with model skill typically improving at larger spatial scales (≥4°). However models have much lower skill at modelling historical warming patters and are shown to often perform no better than chance at regional scales (e.g. Southeast Asian) and worse than chance at finer scales (coral bleaching frequency and other marine processes linked to SST warming.
Spatial data modelling and maximum entropy theory
Czech Academy of Sciences Publication Activity Database
Klimešová, Dana; Ocelíková, E.
2005-01-01
Roč. 51, č. 2 (2005), s. 80-83 ISSN 0139-570X Institutional research plan: CEZ:AV0Z10750506 Keywords : spatial data classification * distribution function * error distribution Subject RIV: BD - Theory of Information
Philbin, R.
2015-05-22
This study validates the near-surface temperature and precipitation output from decadal runs of eight atmospheric ocean general circulation models (AOGCMs) against observational proxy data from the National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis temperatures and Global Precipitation Climatology Project (GPCP) precipitation data. We model the joint distribution of these two fields with a parsimonious bivariate Matérn spatial covariance model, accounting for the two fields\\' spatial cross-correlation as well as their own smoothnesses. We fit output from each AOGCM (30-year seasonal averages from 1981 to 2010) to a statistical model on each of 21 land regions. Both variance and smoothness values agree for both fields over all latitude bands except southern mid-latitudes. Our results imply that temperature fields have smaller smoothness coefficients than precipitation fields, while both have decreasing smoothness coefficients with increasing latitude. Models predict fields with smaller smoothness coefficients than observational proxy data for the tropics. The estimated spatial cross-correlations of these two fields, however, are quite different for most GCMs in mid-latitudes. Model correlation estimates agree well with those for observational proxy data for Australia, at high northern latitudes across North America, Europe and Asia, as well as across the Sahara, India, and Southeast Asia, but elsewhere, little consistent agreement exists.
Philbin, R.; Jun, M.
2015-01-01
This study validates the near-surface temperature and precipitation output from decadal runs of eight atmospheric ocean general circulation models (AOGCMs) against observational proxy data from the National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis temperatures and Global Precipitation Climatology Project (GPCP) precipitation data. We model the joint distribution of these two fields with a parsimonious bivariate Matérn spatial covariance model, accounting for the two fields' spatial cross-correlation as well as their own smoothnesses. We fit output from each AOGCM (30-year seasonal averages from 1981 to 2010) to a statistical model on each of 21 land regions. Both variance and smoothness values agree for both fields over all latitude bands except southern mid-latitudes. Our results imply that temperature fields have smaller smoothness coefficients than precipitation fields, while both have decreasing smoothness coefficients with increasing latitude. Models predict fields with smaller smoothness coefficients than observational proxy data for the tropics. The estimated spatial cross-correlations of these two fields, however, are quite different for most GCMs in mid-latitudes. Model correlation estimates agree well with those for observational proxy data for Australia, at high northern latitudes across North America, Europe and Asia, as well as across the Sahara, India, and Southeast Asia, but elsewhere, little consistent agreement exists.
Modeling imbalanced economic recovery following a natural disaster using input-output analysis.
Li, Jun; Crawford-Brown, Douglas; Syddall, Mark; Guan, Dabo
2013-10-01
Input-output analysis is frequently used in studies of large-scale weather-related (e.g., Hurricanes and flooding) disruption of a regional economy. The economy after a sudden catastrophe shows a multitude of imbalances with respect to demand and production and may take months or years to recover. However, there is no consensus about how the economy recovers. This article presents a theoretical route map for imbalanced economic recovery called dynamic inequalities. Subsequently, it is applied to a hypothetical postdisaster economic scenario of flooding in London around the year 2020 to assess the influence of future shocks to a regional economy and suggest adaptation measures. Economic projections are produced by a macro econometric model and used as baseline conditions. The results suggest that London's economy would recover over approximately 70 months by applying a proportional rationing scheme under the assumption of initial 50% labor loss (with full recovery in six months), 40% initial loss to service sectors, and 10-30% initial loss to other sectors. The results also suggest that imbalance will be the norm during the postdisaster period of economic recovery even though balance may occur temporarily. Model sensitivity analysis suggests that a proportional rationing scheme may be an effective strategy to apply during postdisaster economic reconstruction, and that policies in transportation recovery and in health care are essential for effective postdisaster economic recovery. © 2013 Society for Risk Analysis.
Linear summation of outputs in a balanced network model of motor cortex.
Capaday, Charles; van Vreeswijk, Carl
2015-01-01
Given the non-linearities of the neural circuitry's elements, we would expect cortical circuits to respond non-linearly when activated. Surprisingly, when two points in the motor cortex are activated simultaneously, the EMG responses are the linear sum of the responses evoked by each of the points activated separately. Additionally, the corticospinal transfer function is close to linear, implying that the synaptic interactions in motor cortex must be effectively linear. To account for this, here we develop a model of motor cortex composed of multiple interconnected points, each comprised of reciprocally connected excitatory and inhibitory neurons. We show how non-linearities in neuronal transfer functions are eschewed by strong synaptic interactions within each point. Consequently, the simultaneous activation of multiple points results in a linear summation of their respective outputs. We also consider the effects of reduction of inhibition at a cortical point when one or more surrounding points are active. The network response in this condition is linear over an approximately two- to three-fold decrease of inhibitory feedback strength. This result supports the idea that focal disinhibition allows linear coupling of motor cortical points to generate movement related muscle activation patterns; albeit with a limitation on gain control. The model also explains why neural activity does not spread as far out as the axonal connectivity allows, whilst also explaining why distant cortical points can be, nonetheless, functionally coupled by focal disinhibition. Finally, we discuss the advantages that linear interactions at the cortical level afford to motor command synthesis.
The efficiency of the agricultural sector in Poland in the light output-input model1
Directory of Open Access Journals (Sweden)
Czyżewski Andrzej
2015-05-01
Full Text Available The study turns attention to the use of the input-output model (account of interbranch flows in macroeconomic assessments of the effectiveness of the agricultural sector. In the introductory part the essence of the account of interbranch flows has been specified, pointing to its historical origin and place in the economic theory, and the morphological structure of the individual parts (quarters of the model has been presented. Then the study discusses the application of the account of interbranch flows in macroeconomic assessments of the effectiveness of the agricultural sector, defining and characterizing a number of indicators which allow to conclude on the effectiveness of the agricultural sector on the basis of the account of interbranch flows. The last, empirical part of the study assesses the effectiveness of the agricultural sector in Poland on the basis of interbranch flows statistics for the years 2000 and 2005. The analyses allowed to demonstrate increased efficiency of the agricultural sector in Poland after Poland joined the EU, and also to say that the account of interbranch flows is an important tool enabling comprehensive assessment of the effectiveness of the agricultural sector in the macro-scale, through the prism of the effect - disbursement, which accounts for its exceptional suitability in this kind of analyses.
Gridded Calibration of Ensemble Wind Vector Forecasts Using Ensemble Model Output Statistics
Lazarus, S. M.; Holman, B. P.; Splitt, M. E.
2017-12-01
A computationally efficient method is developed that performs gridded post processing of ensemble wind vector forecasts. An expansive set of idealized WRF model simulations are generated to provide physically consistent high resolution winds over a coastal domain characterized by an intricate land / water mask. Ensemble model output statistics (EMOS) is used to calibrate the ensemble wind vector forecasts at observation locations. The local EMOS predictive parameters (mean and variance) are then spread throughout the grid utilizing flow-dependent statistical relationships extracted from the downscaled WRF winds. Using data withdrawal and 28 east central Florida stations, the method is applied to one year of 24 h wind forecasts from the Global Ensemble Forecast System (GEFS). Compared to the raw GEFS, the approach improves both the deterministic and probabilistic forecast skill. Analysis of multivariate rank histograms indicate the post processed forecasts are calibrated. Two downscaling case studies are presented, a quiescent easterly flow event and a frontal passage. Strengths and weaknesses of the approach are presented and discussed.
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Olav Slupphaug
2001-01-01
Full Text Available We present a mathematical programming approach to robust control of nonlinear systems with uncertain, possibly time-varying, parameters. The uncertain system is given by different local affine parameter dependent models in different parts of the state space. It is shown how this representation can be obtained from a nonlinear uncertain system by solving a set of continuous linear semi-infinite programming problems, and how each of these problems can be solved as a (finite series of ordinary linear programs. Additionally, the system representation includes control- and state constraints. The controller design method is derived from Lyapunov stability arguments and utilizes an affine parameter dependent quadratic Lyapunov function. The controller has a piecewise affine output feedback structure, and the design amounts to finding a feasible solution to a set of linear matrix inequalities combined with one spectral radius constraint on the product of two positive definite matrices. A local solution approach to this nonconvex feasibility problem is proposed. Complexity of the design method and some special cases such as state- feedback are discussed. Finally, an application of the results is given by proposing an on-line computationally feasible algorithm for constrained nonlinear state- feedback model predictive control with robust stability.
Modelling Implicit Communication in Multi-Agent Systems with Hybrid Input/Output Automata
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Marta Capiluppi
2012-10-01
Full Text Available We propose an extension of Hybrid I/O Automata (HIOAs to model agent systems and their implicit communication through perturbation of the environment, like localization of objects or radio signals diffusion and detection. To this end we decided to specialize some variables of the HIOAs whose values are functions both of time and space. We call them world variables. Basically they are treated similarly to the other variables of HIOAs, but they have the function of representing the interaction of each automaton with the surrounding environment, hence they can be output, input or internal variables. Since these special variables have the role of simulating implicit communication, their dynamics are specified both in time and space, because they model the perturbations induced by the agent to the environment, and the perturbations of the environment as perceived by the agent. Parallel composition of world variables is slightly different from parallel composition of the other variables, since their signals are summed. The theory is illustrated through a simple example of agents systems.
Finding the Root Causes of Statistical Inconsistency in Community Earth System Model Output
Milroy, D.; Hammerling, D.; Baker, A. H.
2017-12-01
Baker et al (2015) developed the Community Earth System Model Ensemble Consistency Test (CESM-ECT) to provide a metric for software quality assurance by determining statistical consistency between an ensemble of CESM outputs and new test runs. The test has proved useful for detecting statistical difference caused by compiler bugs and errors in physical modules. However, detection is only the necessary first step in finding the causes of statistical difference. The CESM is a vastly complex model comprised of millions of lines of code which is developed and maintained by a large community of software engineers and scientists. Any root cause analysis is correspondingly challenging. We propose a new capability for CESM-ECT: identifying the sections of code that cause statistical distinguishability. The first step is to discover CESM variables that cause CESM-ECT to classify new runs as statistically distinct, which we achieve via Randomized Logistic Regression. Next we use a tool developed to identify CESM components that define or compute the variables found in the first step. Finally, we employ the application Kernel GENerator (KGEN) created in Kim et al (2016) to detect fine-grained floating point differences. We demonstrate an example of the procedure and advance a plan to automate this process in our future work.
Energy efficiency of selected OECD countries: A slacks based model with undesirable outputs
International Nuclear Information System (INIS)
Apergis, Nicholas; Aye, Goodness C.; Barros, Carlos Pestana; Gupta, Rangan; Wanke, Peter
2015-01-01
This paper presents an efficiency assessment of selected OECD countries using a Slacks Based Model with undesirable or bad outputs (SBM-Undesirable). In this research, SBM-Undesirable is used first in a two-stage approach to assess the relative efficiency of OECD countries using the most frequent indicators adopted by the literature on energy efficiency. Besides, in the second stage, GLMM–MCMC methods are combined with SBM-Undesirable results as part of an attempt to produce a model for energy performance with effective predictive ability. The results reveal different impacts of contextual variables, such as economic blocks and capital–labor ratio, on energy efficiency levels. - Highlights: • We analyze the energy efficiency of selected OECD countries. • SBM-Undesirable and MCMC–GLMM are combined for this purpose. • Find that efficiency levels are high but declining over time. • Analysis with contextual variables shows varying efficiency levels across groups. • Capital-intensive countries are more energy efficient than labor-intensive countries.
Mechanisms Regulating the Cardiac Output Response to Cyanide Infusion, a Model of Hypoxia
Liang, Chang-seng; Huckabee, William E.
1973-01-01
When tissue metabolic changes like those of hypoxia were induced by intra-aortic infusion of cyanide in dogs, cardiac output began to increase after 3 to 5 min, reached a peak (220% of the control value) at 15 min, and returned to control in 40 min. This pattern of cardiac output rise was not altered by vagotomy with or without atropine pretreatment. However, this cardiac output response could be differentiated into three phases by pretreating the animals with agents that block specific activities of the sympatho-adrenal system. First, ganglionic blockade produced by mecamylamine or sympathetic nerve blockade by bretylium abolished the middle phase of the cardiac output seen in the untreated animal, but early and late phases still could be discerned. Second, beta-adrenergic receptor blockade produced by propranolol shortened the total duration of the cardiac output rise by abolishing the late phase. Third, when given together, propranolol and mecamylamine (or bretylium) prevented most of the cardiac output rise that follows the early phase. When cyanide was given to splenectomized dogs, the duration of the cardiac output response was not shortened, but the response became biphasic, resembling that seen after chemical sympathectomy. A similar biphasic response of the cardiac output also resulted from splenic denervation; sham operation or nephrectomy had no effect on the monophasic pattern of the normal response. Splenic venous blood obtained from cyanide-treated dogs, when infused intraportally, caused an increase in cardiac output in recipient dogs; similar infusion of arterial blood had no effects. These results suggest that the cardiac output response to cyanide infusion consists of three components: an early phase, related neither to the autonomic nervous system nor to circulating catecholamines; a middle phase, caused by a nonadrenergic humoral substance released from the spleen by sympathetic stimulation; and a late phase, dependent upon adrenergic receptors
Moulds, S.; Djordjevic, S.; Savic, D.
2017-12-01
The Global Change Assessment Model (GCAM), an integrated assessment model, provides insight into the interactions and feedbacks between physical and human systems. The land system component of GCAM, which simulates land use activities and the production of major crops, produces output at the subregional level which must be spatially downscaled in order to use with gridded impact assessment models. However, existing downscaling routines typically consider cropland as a homogeneous class and do not provide information about land use intensity or specific management practices such as irrigation and multiple cropping. This paper presents a spatial allocation procedure to downscale crop production data from GCAM to a spatial grid, producing a time series of maps which show the spatial distribution of specific crops (e.g. rice, wheat, maize) at four input levels (subsistence, low input rainfed, high input rainfed and high input irrigated). The model algorithm is constrained by available cropland at each time point and therefore implicitly balances extensification and intensification processes in order to meet global food demand. It utilises a stochastic approach such that an increase in production of a particular crop is more likely to occur in grid cells with a high biophysical suitability and neighbourhood influence, while a fall in production will occur more often in cells with lower suitability. User-supplied rules define the order in which specific crops are downscaled as well as allowable transitions. A regional case study demonstrates the ability of the model to reproduce historical trends in India by comparing the model output with district-level agricultural inventory data. Lastly, the model is used to predict the spatial distribution of crops globally under various GCAM scenarios.
Getis, Arthur
1997-01-01
In recent years, spatial analysis has become an increasingly active field, as evidenced by the establishment of educational and research programs at many universities. Its popularity is due mainly to new technologies and the development of spatial data infrastructures. This book illustrates some recent developments in spatial analysis, behavioural modelling, and computational intelligence. World renown spatial analysts explain and demonstrate their new and insightful models and methods. The applications are in areas of societal interest such as the spread of infectious diseases, migration behaviour, and retail and agricultural location strategies. In addition, there is emphasis on the uses of new technologoies for the analysis of spatial data through the application of neural network concepts.
Directory of Open Access Journals (Sweden)
David W Redding
for spatial autocorrelation in an SDM context and, by taking account of random effects, produce outputs that can better elucidate the role of covariates in predicting species occurrence. Given that it is often unclear what the drivers are behind data clumping in an empirical occurrence dataset, or indeed how geographically restricted these data are, spatially-explicit Bayesian SDMs may be the better choice when modelling the spatial distribution of target species.
Redding, David W; Lucas, Tim C D; Blackburn, Tim M; Jones, Kate E
2017-01-01
autocorrelation in an SDM context and, by taking account of random effects, produce outputs that can better elucidate the role of covariates in predicting species occurrence. Given that it is often unclear what the drivers are behind data clumping in an empirical occurrence dataset, or indeed how geographically restricted these data are, spatially-explicit Bayesian SDMs may be the better choice when modelling the spatial distribution of target species.
A computational model for how cells choose temporal or spatial sensing during chemotaxis.
Tan, Rui Zhen; Chiam, Keng-Hwee
2018-03-01
Cell size is thought to play an important role in choosing between temporal and spatial sensing in chemotaxis. Large cells are thought to use spatial sensing due to large chemical difference at its ends whereas small cells are incapable of spatial sensing due to rapid homogenization of proteins within the cell. However, small cells have been found to polarize and large cells like sperm cells undergo temporal sensing. Thus, it remains an open question what exactly governs spatial versus temporal sensing. Here, we identify the factors that determines sensing choices through mathematical modeling of chemotactic circuits. Comprehensive computational search of three-node signaling circuits has identified the negative integral feedback (NFB) and incoherent feedforward (IFF) circuits as capable of adaptation, an important property for chemotaxis. Cells are modeled as one-dimensional circular system consisting of diffusible activator, inactivator and output proteins, traveling across a chemical gradient. From our simulations, we find that sensing outcomes are similar for NFB or IFF circuits. Rather than cell size, the relevant parameters are the 1) ratio of cell speed to the product of cell diameter and rate of signaling, 2) diffusivity of the output protein and 3) ratio of the diffusivities of the activator to inactivator protein. Spatial sensing is favored when all three parameters are low. This corresponds to a cell moving slower than the time it takes for signaling to propagate across the cell diameter, has an output protein that is polarizable and has a local-excitation global-inhibition system to amplify the chemical gradient. Temporal sensing is favored otherwise. We also find that temporal sensing is more robust to noise. By performing extensive literature search, we find that our prediction agrees with observation in a wide range of species and cell types ranging from E. coli to human Fibroblast cells and propose that our result is universally applicable.
Directory of Open Access Journals (Sweden)
S.N.M.P. Simamora
2014-10-01
Full Text Available Efficiency condition occurs when the value of the used outputs compared to the resource total that has been used almost close to the value 1 (absolute environment. An instrument to achieve efficiency if the power output level has decreased significantly in the life of the instrument used, if it compared to the previous condition, when the instrument is not equipped with additional systems (or proposed model improvement. Even more effective if the inputs model that are used in unison to achieve a homogeneous output. On this research has been designed and implemented the automatic control system for models of single input-dual-output, wherein the sampling instruments used are lamp and fan. Source voltage used is AC (alternate-current and tested using quantitative research methods and instrumentation (with measuring instruments are observed. The results obtained demonstrate the efficiency of the instrument experienced a significant current model of single-input-dual-output applied separately instrument trials such as lamp and fan when it compared to the condition or state before. And the result show that the design has been built, can also run well.
Multiregional input-output model for China's farm land and water use.
Guo, Shan; Shen, Geoffrey Qiping
2015-01-06
Land and water are the two main drivers of agricultural production. Pressure on farm land and water resources is increasing in China due to rising food demand. Domestic trade affects China's regional farm land and water use by distributing resources associated with the production of goods and services. This study constructs a multiregional input-output model to simultaneously analyze China's farm land and water uses embodied in consumption and interregional trade. Results show a great similarity for both China's farm land and water endowments. Shandong, Henan, Guangdong, and Yunnan are the most important drivers of farm land and water consumption in China, even though they have relatively few land and water resource endowments. Significant net transfers of embodied farm land and water flows are identified from the central and western areas to the eastern area via interregional trade. Heilongjiang is the largest farm land and water supplier, in contrast to Shanghai as the largest receiver. The results help policy makers to comprehensively understand embodied farm land and water flows in a complex economy network. Improving resource utilization efficiency and reshaping the embodied resource trade nexus should be addressed by considering the transfer of regional responsibilities.
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May Tan May
2018-01-01
Full Text Available In recent years, there has been an increase in crude palm oil (CPO demand, resulting in palm oil mills (POMs seizing the opportunity to increase CPO production to make more profits. A series of equipment are designed to operate in their optimum capacities in the current existing POMs. Some equipment may be limited by their maximum design capacities when there is a need to increase CPO production, resulting in process bottlenecks. In this research, a framework is developed to provide stepwise procedures on identifying bottlenecks and retrofitting a POM process to cater for the increase in production capacity. This framework adapts an algebraic approach known as Inoperability Input-Output Modelling (IIM. To illustrate the application of the framework, an industrial POM case study was solved using LINGO software in this work, by maximising its production capacity. Benefit-to-Cost Ratio (BCR analysis was also performed to assess the economic feasibility. As results, the Screw Press was identified as the bottleneck. The retrofitting recommendation was to purchase an additional Screw Press to cater for the new throughput with BCR of 54.57. It was found the POM to be able to achieve the maximum targeted production capacity of 8,139.65 kg/hr of CPO without any bottlenecks.
A Hierarchical multi-input and output Bi-GRU Model for Sentiment Analysis on Customer Reviews
Zhang, Liujie; Zhou, Yanquan; Duan, Xiuyu; Chen, Ruiqi
2018-03-01
Multi-label sentiment classification on customer reviews is a practical challenging task in Natural Language Processing. In this paper, we propose a hierarchical multi-input and output model based bi-directional recurrent neural network, which both considers the semantic and lexical information of emotional expression. Our model applies two independent Bi-GRU layer to generate part of speech and sentence representation. Then the lexical information is considered via attention over output of softmax activation on part of speech representation. In addition, we combine probability of auxiliary labels as feature with hidden layer to capturing crucial correlation between output labels. The experimental result shows that our model is computationally efficient and achieves breakthrough improvements on customer reviews dataset.
Modeling spatial processes with unknown extremal dependence class
Huser, Raphaë l G.; Wadsworth, Jennifer L.
2017-01-01
Many environmental processes exhibit weakening spatial dependence as events become more extreme. Well-known limiting models, such as max-stable or generalized Pareto processes, cannot capture this, which can lead to a preference for models
Modeling and notation of DEA with strong and weak disposable outputs.
Kuntz, Ludwig; Sülz, Sandra
2011-12-01
Recent articles published in Health Care Management Science have described DEA applications under the assumption of strong and weak disposable outputs. As we confidently assume that these papers include some methodical deficiencies, we aim to illustrate a revised approach.
Johnson, M. V. V.; Behrman, K. D.; Atwood, J. D.; White, M. J.; Norfleet, M. L.
2017-12-01
takes time for monitoring efforts to measure meaningful changes over time. Careful interpretation of model outputs is imperative for appropriate application of current scientific knowledge to inform decision making, especially when models are used to set spatial and temporal goals around conservation practice adoption and water quality.
Modeling the spatial reach of the LFP
DEFF Research Database (Denmark)
Lindén, Henrik; Tetzlaff, Tom; Potjans, Tobias C
2011-01-01
The local field potential (LFP) reflects activity of many neurons in the vicinity of the recording electrode and is therefore useful for studying local network dynamics. Much of the nature of the LFP is, however, still unknown. There are, for instance, contradicting reports on the spatial extent ...
Spatial modeling of potential woody biomass flow
Woodam Chung; Nathaniel Anderson
2012-01-01
The flow of woody biomass to end users is determined by economic factors, especially the amount available across a landscape and delivery costs of bioenergy facilities. The objective of this study develop methodology to quantify landscape-level stocks and potential biomass flows using the currently available spatial database road network analysis tool. We applied this...
Modeling fixation locations using spatial point processes.
Barthelmé, Simon; Trukenbrod, Hans; Engbert, Ralf; Wichmann, Felix
2013-10-01
Whenever eye movements are measured, a central part of the analysis has to do with where subjects fixate and why they fixated where they fixated. To a first approximation, a set of fixations can be viewed as a set of points in space; this implies that fixations are spatial data and that the analysis of fixation locations can be beneficially thought of as a spatial statistics problem. We argue that thinking of fixation locations as arising from point processes is a very fruitful framework for eye-movement data, helping turn qualitative questions into quantitative ones. We provide a tutorial introduction to some of the main ideas of the field of spatial statistics, focusing especially on spatial Poisson processes. We show how point processes help relate image properties to fixation locations. In particular we show how point processes naturally express the idea that image features' predictability for fixations may vary from one image to another. We review other methods of analysis used in the literature, show how they relate to point process theory, and argue that thinking in terms of point processes substantially extends the range of analyses that can be performed and clarify their interpretation.
Directory of Open Access Journals (Sweden)
Asma Foughali
2015-07-01
Full Text Available This work aims to evaluate the performance of a hydrological balance model in a watershed located in northern Tunisia (wadi Sejnane, 378 km2 in present climate conditions using input variables provided by four regional climate models. A modified version (MBBH of the lumped and single layer surface model BBH (Bucket with Bottom Hole model, in which pedo-transfer parameters estimated using watershed physiographic characteristics are introduced is adopted to simulate the water balance components. Only two parameters representing respectively the water retention capacity of the soil and the vegetation resistance to evapotranspiration are calibrated using rainfall-runoff data. The evaluation criterions for the MBBH model calibration are: relative bias, mean square error and the ratio of mean actual evapotranspiration to mean potential evapotranspiration. Daily air temperature, rainfall and runoff observations are available from 1960 to 1984. The period 1960–1971 is selected for calibration while the period 1972–1984 is chosen for validation. Air temperature and precipitation series are provided by four regional climate models (DMI, ARP, SMH and ICT from the European program ENSEMBLES, forced by two global climate models (GCM: ECHAM and ARPEGE. The regional climate model outputs (precipitation and air temperature are compared to the observations in terms of statistical distribution. The analysis was performed at the seasonal scale for precipitation. We found out that RCM precipitation must be corrected before being introduced as MBBH inputs. Thus, a non-parametric quantile-quantile bias correction method together with a dry day correction is employed. Finally, simulated runoff generated using corrected precipitation from the regional climate model SMH is found the most acceptable by comparison with runoff simulated using observed precipitation data, to reproduce the temporal variability of mean monthly runoff. The SMH model is the most accurate to
Wind climate estimation using WRF model output: method and model sensitivities over the sea
DEFF Research Database (Denmark)
Hahmann, Andrea N.; Vincent, Claire Louise; Peña, Alfredo
2015-01-01
setup parameters. The results of the year-long sensitivity simulations show that the long-term mean wind speed simulated by the WRF model offshore in the region studied is quite insensitive to the global reanalysis, the number of vertical levels, and the horizontal resolution of the sea surface...... temperature used as lower boundary conditions. Also, the strength and form (grid vs spectral) of the nudging is quite irrelevant for the mean wind speed at 100 m. Large sensitivity is found to the choice of boundary layer parametrization, and to the length of the period that is discarded as spin-up to produce...... a wind climatology. It is found that the spin-up period for the boundary layer winds is likely larger than 12 h over land and could affect the wind climatology for points offshore for quite a distance downstream from the coast....
Modeling DPOAE input/output function compression: comparisons with hearing thresholds.
Bhagat, Shaum P
2014-09-01
Basilar membrane input/output (I/O) functions in mammalian animal models are characterized by linear and compressed segments when measured near the location corresponding to the characteristic frequency. A method of studying basilar membrane compression indirectly in humans involves measuring distortion-product otoacoustic emission (DPOAE) I/O functions. Previous research has linked compression estimates from behavioral growth-of-masking functions to hearing thresholds. The aim of this study was to compare compression estimates from DPOAE I/O functions and hearing thresholds at 1 and 2 kHz. A prospective correlational research design was performed. The relationship between DPOAE I/O function compression estimates and hearing thresholds was evaluated with Pearson product-moment correlations. Normal-hearing adults (n = 16) aged 22-42 yr were recruited. DPOAE I/O functions (L₂ = 45-70 dB SPL) and two-interval forced-choice hearing thresholds were measured in normal-hearing adults. A three-segment linear regression model applied to DPOAE I/O functions supplied estimates of compression thresholds, defined as breakpoints between linear and compressed segments and the slopes of the compressed segments. Pearson product-moment correlations between DPOAE compression estimates and hearing thresholds were evaluated. A high correlation between DPOAE compression thresholds and hearing thresholds was observed at 2 kHz, but not at 1 kHz. Compression slopes also correlated highly with hearing thresholds only at 2 kHz. The derivation of cochlear compression estimates from DPOAE I/O functions provides a means to characterize basilar membrane mechanics in humans and elucidates the role of compression in tone detection in the 1-2 kHz frequency range. American Academy of Audiology.
DEFF Research Database (Denmark)
Ranaboldo, Matteo; Giebel, Gregor; Codina, Bernat
2013-01-01
A combination of physical and statistical treatments to post‐process numerical weather predictions (NWP) outputs is needed for successful short‐term wind power forecasts. One of the most promising and effective approaches for statistical treatment is the Model Output Statistics (MOS) technique....... The proposed MOS performed well in both wind farms, and its forecasts compare positively with an actual operative model in use at Risø DTU and other MOS types, showing minimum BIAS and improving NWP power forecast of around 15% in terms of root mean square error. Further improvements could be obtained...
Chonggang Xu; Hong S. He; Yuanman Hu; Yu Chang; Xiuzhen Li; Rencang Bu
2005-01-01
Geostatistical stochastic simulation is always combined with Monte Carlo method to quantify the uncertainty in spatial model simulations. However, due to the relatively long running time of spatially explicit forest models as a result of their complexity, it is always infeasible to generate hundreds or thousands of Monte Carlo simulations. Thus, it is of great...
. Redundancy and blocking in the spatial domain: A connectionist model
Directory of Open Access Journals (Sweden)
I. P. L. Mc Laren
2002-01-01
Full Text Available How can the observations of spatial blocking (Rodrigo, Chamizo, McLaren & Mackintosh, 1997 and cue redundancy (OKeefe and Conway, 1978 be reconciled within the framework provided by an error-correcting, connectionist account of spatial navigation? I show that an implementation of McLarens (1995 better beta model can serve this purpose, and examine some of the implications for spatial learning and memory.
Linking sediment fingerprinting and modeling outputs for a Spanish Pyrenean river catchment.
Palazón, Leticia; Latorre, Borja; Gaspar, Leticia; Blake, Williams H.; Smith, Hugh G.; Navas, Ana
2015-04-01
Indirect techniques to study fine sediment redistribution in river catchments could provide unique and diverse information, which, when combined become a powerful tool to address catchment management problems. Such combinations could solve limitations of individual techniques and provide different lines of information to address a particular problem. The Barasona reservoir has suffered from siltation since its construction, with the loss of over one third of its storage volume in around 30 study years (period 1972-1996). Information on sediment production from tributary catchments for the reservoir is required to develop management plans for maintaining reservoir sustainability. Large spatial variability in sediment delivery was found in previous studies in the Barasona catchment and the major sediment sources identified included badlands developed in the middle part of the catchment and the agricultural fields in its lower part. From the diverse range of indirect techniques, fingerprinting sediment sources and computer models could be linked to obtain a more holistic view of the processes related to sediment redistribution in the Barasona river catchment (1509 km2, Central Spanish Pyrenees), which comprises agricultural and forest land uses. In the present study, the results from a fingerprinting procedure and the SWAT model were compared and combined to improve the knowledge of land use sediment source contributions to the reservoir. Samples from the study catchment were used to define soil parameters for the model and for fingerprinting the land use sources. The fingerprinting approach provided information about relative contributions from land use sources to the superficial sediment samples taken from the reservoir infill. The calibration and validation of the model provided valuable information, for example on the timescale of sediment production from the different land uses within the catchment. Linking results from both techniques enabled us to achieve a
Huang, Honglan; Mao, Hanying; Mao, Hanling; Zheng, Weixue; Huang, Zhenfeng; Li, Xinxin; Wang, Xianghong
2017-12-01
Cumulative fatigue damage detection for used parts plays a key role in the process of remanufacturing engineering and is related to the service safety of the remanufactured parts. In light of the nonlinear properties of used parts caused by cumulative fatigue damage, the based nonlinear output frequency response functions detection approach offers a breakthrough to solve this key problem. First, a modified PSO-adaptive lasso algorithm is introduced to improve the accuracy of the NARMAX model under impulse hammer excitation, and then, an effective new algorithm is derived to estimate the nonlinear output frequency response functions under rectangular pulse excitation, and a based nonlinear output frequency response functions index is introduced to detect the cumulative fatigue damage in used parts. Then, a novel damage detection approach that integrates the NARMAX model and the rectangular pulse is proposed for nonlinear output frequency response functions identification and cumulative fatigue damage detection of used parts. Finally, experimental studies of fatigued plate specimens and used connecting rod parts are conducted to verify the validity of the novel approach. The obtained results reveal that the new approach can detect cumulative fatigue damages of used parts effectively and efficiently and that the various values of the based nonlinear output frequency response functions index can be used to detect the different fatigue damages or working time. Since the proposed new approach can extract nonlinear properties of systems by only a single excitation of the inspected system, it shows great promise for use in remanufacturing engineering applications.
Kassianov, E.; Pekour, M. S.; Flynn, C. J.; Berg, L. K.; Beranek, J.; Zelenyuk, A.; Zhao, C.; Leung, L. R.; Ma, P. L.; Riihimaki, L.; Fast, J. D.; Barnard, J.; Hallar, G. G.; McCubbin, I.; Eloranta, E. W.; McComiskey, A. C.; Rasch, P. J.
2017-12-01
Understanding the effects of dust on the regional and global climate requires detailed information on particle size distributions and their changes with distance from the source. Awareness is now growing about the tendency of the dust coarse mode with moderate ( 3.5 µm) volume median diameter (VMD) to be rather insensitive to complex removal processes associated with long-range transport of dust from the main sources. Our study, with a focus on the transpacific transport of dust, demonstrates that the impact of coarse mode aerosol (VMD 3µm) is well defined at the high-elevation mountain-top Storm Peak Laboratory (SPL, about 3.2 km MSL) and nearby Atmospheric Radiation Measurement (ARM) Climate Research Facility Mobile Facility (AMF) during March 2011. Significant amounts of coarse mode aerosol are also found at the nearest Aerosol Robotic Network (AERONET) site. Outputs from the high-resolution Weather Research and Forecasting (WRF) Model coupled with chemistry (WRF-Chem) show that the major dust event is likely associated with transpacific transport of Asian and African plumes. Satellite data, including the Moderate Resolution Imaging Spectroradiometer (MODIS) and Multiangle Imaging SpectroRadiometer (MISR) aerosol optical depth (AOD) and plume height from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) lidar data provide the observational support of the WRF-Chem simulations. Our study complements previous findings by indicating that the quasi-static nature of the coarse mode appears to be a reasonable approximation for Asian and African dust despite expected frequent orographic precipitation over mountainous regions in the western United States.
Spatial Uncertainty Model for Visual Features Using a Kinect™ Sensor
Directory of Open Access Journals (Sweden)
Jae-Han Park
2012-06-01
Full Text Available This study proposes a mathematical uncertainty model for the spatial measurement of visual features using Kinect™ sensors. This model can provide qualitative and quantitative analysis for the utilization of Kinect™ sensors as 3D perception sensors. In order to achieve this objective, we derived the propagation relationship of the uncertainties between the disparity image space and the real Cartesian space with the mapping function between the two spaces. Using this propagation relationship, we obtained the mathematical model for the covariance matrix of the measurement error, which represents the uncertainty for spatial position of visual features from Kinect™ sensors. In order to derive the quantitative model of spatial uncertainty for visual features, we estimated the covariance matrix in the disparity image space using collected visual feature data. Further, we computed the spatial uncertainty information by applying the covariance matrix in the disparity image space and the calibrated sensor parameters to the proposed mathematical model. This spatial uncertainty model was verified by comparing the uncertainty ellipsoids for spatial covariance matrices and the distribution of scattered matching visual features. We expect that this spatial uncertainty model and its analyses will be useful in various Kinect™ sensor applications.
Spatial uncertainty model for visual features using a Kinect™ sensor.
Park, Jae-Han; Shin, Yong-Deuk; Bae, Ji-Hun; Baeg, Moon-Hong
2012-01-01
This study proposes a mathematical uncertainty model for the spatial measurement of visual features using Kinect™ sensors. This model can provide qualitative and quantitative analysis for the utilization of Kinect™ sensors as 3D perception sensors. In order to achieve this objective, we derived the propagation relationship of the uncertainties between the disparity image space and the real Cartesian space with the mapping function between the two spaces. Using this propagation relationship, we obtained the mathematical model for the covariance matrix of the measurement error, which represents the uncertainty for spatial position of visual features from Kinect™ sensors. In order to derive the quantitative model of spatial uncertainty for visual features, we estimated the covariance matrix in the disparity image space using collected visual feature data. Further, we computed the spatial uncertainty information by applying the covariance matrix in the disparity image space and the calibrated sensor parameters to the proposed mathematical model. This spatial uncertainty model was verified by comparing the uncertainty ellipsoids for spatial covariance matrices and the distribution of scattered matching visual features. We expect that this spatial uncertainty model and its analyses will be useful in various Kinect™ sensor applications.
Investigating Spatial Interdependence in E-Bike Choice Using Spatially Autoregressive Model
Directory of Open Access Journals (Sweden)
Chengcheng Xu
2017-08-01
Full Text Available Increased attention has been given to promoting e-bike usage in recent years. However, the research gap still exists in understanding the effects of spatial interdependence on e-bike choice. This study investigated how spatial interdependence affected the e-bike choice. The Moran’s I statistic test showed that spatial interdependence exists in e-bike choice at aggregated level. Bayesian spatial autoregressive logistic analyses were then used to investigate the spatial interdependence at individual level. Separate models were developed for commuting and non-commuting trips. The factors affecting e-bike choice are different between commuting and non-commuting trips. Spatial interdependence exists at both origin and destination sides of commuting and non-commuting trips. Travellers are more likely to choose e-bikes if their neighbours at the trip origin and destination also travel by e-bikes. And the magnitude of this spatial interdependence is different across various traffic analysis zones. The results suggest that, without considering spatial interdependence, the traditional methods may have biased estimation results and make systematic forecasting errors.
Extinction threshold of a population in spatial and stochastic model
Soroka, Yevheniia; Rublyov, Bogdan
2016-01-01
In this study, spatial stochastic and logistic model (SSLM) describing dynamics of a population of a certain species was analysed. The behaviour of the extinction threshold as a function of model parameters was studied. More specifically, we studied how the critical values for the model parameters that separate the cases of extinction and persistence depend on the spatial scales of the competition and dispersal kernels. We compared the simulations and analytical results to examine if and how ...
Spatial Modeling of Deforestation in FMU of Poigar, North Sulawesi
Ahmad, Afandi; Saleh, Muhammad Buce; Rusolono, Teddy
2016-01-01
Forest is a part of the ecosystem that provides environmental services. Deforestation may decrease forest function in an ecosystem. This study aims to build a spatial model of deforestation in a forest management unit (FMU) of Poigar. Deforestation analysis carried out by analyze the change of forest cover into non-forest cover with post classification comparison technique. Driving forces of deforestation carried out by spatial modeling using binary logistic regression models (LRM). Result of...
A model relating Eulerian spatial and temporal velocity correlations
Cholemari, Murali R.; Arakeri, Jaywant H.
2006-03-01
In this paper we propose a model to relate Eulerian spatial and temporal velocity autocorrelations in homogeneous, isotropic and stationary turbulence. We model the decorrelation as the eddies of various scales becoming decorrelated. This enables us to connect the spatial and temporal separations required for a certain decorrelation through the ‘eddy scale’. Given either the spatial or the temporal velocity correlation, we obtain the ‘eddy scale’ and the rate at which the decorrelation proceeds. This leads to a spatial separation from the temporal correlation and a temporal separation from the spatial correlation, at any given value of the correlation relating the two correlations. We test the model using experimental data from a stationary axisymmetric turbulent flow with homogeneity along the axis.
IMPACT OF TRADE OPENNESS ON OUTPUT GROWTH: CO INTEGRATION AND ERROR CORRECTION MODEL APPROACH
Directory of Open Access Journals (Sweden)
Asma Arif
2012-01-01
Full Text Available This study analyzed the long run relationship between trade openness and output growth for Pakistan using annual time series data for 1972-2010. This study follows the Engle and Granger co integration analysis and error correction approach to analyze the long run relationship between the two variables. The Error Correction Term (ECT for output growth and trade openness is significant at 5% level of significance and indicates a positive long run relation between the variables. This study has also analyzed the causality between trade openness and output growth by using granger causality test. The results of granger causality show that there is a bi-directional significant relationship between trade openness and economic growth.
Quantifying measurement uncertainty and spatial variability in the context of model evaluation
Choukulkar, A.; Brewer, A.; Pichugina, Y. L.; Bonin, T.; Banta, R. M.; Sandberg, S.; Weickmann, A. M.; Djalalova, I.; McCaffrey, K.; Bianco, L.; Wilczak, J. M.; Newman, J. F.; Draxl, C.; Lundquist, J. K.; Wharton, S.; Olson, J.; Kenyon, J.; Marquis, M.
2017-12-01
In an effort to improve wind forecasts for the wind energy sector, the Department of Energy and the NOAA funded the second Wind Forecast Improvement Project (WFIP2). As part of the WFIP2 field campaign, a large suite of in-situ and remote sensing instrumentation was deployed to the Columbia River Gorge in Oregon and Washington from October 2015 - March 2017. The array of instrumentation deployed included 915-MHz wind profiling radars, sodars, wind- profiling lidars, and scanning lidars. The role of these instruments was to provide wind measurements at high spatial and temporal resolution for model evaluation and improvement of model physics. To properly determine model errors, the uncertainties in instrument-model comparisons need to be quantified accurately. These uncertainties arise from several factors such as measurement uncertainty, spatial variability, and interpolation of model output to instrument locations, to name a few. In this presentation, we will introduce a formalism to quantify measurement uncertainty and spatial variability. The accuracy of this formalism will be tested using existing datasets such as the eXperimental Planetary boundary layer Instrumentation Assessment (XPIA) campaign. Finally, the uncertainties in wind measurement and the spatial variability estimates from the WFIP2 field campaign will be discussed to understand the challenges involved in model evaluation.
Stochastic Spatial Models in Ecology: A Statistical Physics Approach
Pigolotti, Simone; Cencini, Massimo; Molina, Daniel; Muñoz, Miguel A.
2017-11-01
Ecosystems display a complex spatial organization. Ecologists have long tried to characterize them by looking at how different measures of biodiversity change across spatial scales. Ecological neutral theory has provided simple predictions accounting for general empirical patterns in communities of competing species. However, while neutral theory in well-mixed ecosystems is mathematically well understood, spatial models still present several open problems, limiting the quantitative understanding of spatial biodiversity. In this review, we discuss the state of the art in spatial neutral theory. We emphasize the connection between spatial ecological models and the physics of non-equilibrium phase transitions and how concepts developed in statistical physics translate in population dynamics, and vice versa. We focus on non-trivial scaling laws arising at the critical dimension D = 2 of spatial neutral models, and their relevance for biological populations inhabiting two-dimensional environments. We conclude by discussing models incorporating non-neutral effects in the form of spatial and temporal disorder, and analyze how their predictions deviate from those of purely neutral theories.
Output variability caused by random seeds in a multi-agent transport simulation model
DEFF Research Database (Denmark)
Paulsen, Mads; Rasmussen, Thomas Kjær; Nielsen, Otto Anker
2018-01-01
Dynamic transport simulators are intended to support decision makers in transport-related issues, and as such it is valuable that the random variability of their outputs is as small as possible. In this study we analyse the output variability caused by random seeds of a multi-agent transport...... simulator (MATSim) when applied to a case study of Santiago de Chile. Results based on 100 different random seeds shows that the relative accuracies of estimated link loads tend to increase with link load, but that relative errors of up to 10 % do occur even for links with large volumes. Although...
Updates to the Demographic and Spatial Allocation Models to ...
EPA announced the availability of the draft report, Updates to the Demographic and Spatial Allocation Models to Produce Integrated Climate and Land Use Scenarios (ICLUS) for a 30-day public comment period. The ICLUS version 2 (v2) modeling tool furthered land change modeling by providing nationwide housing development scenarios up to 2100. ICLUS V2 includes updated population and land use data sets and addressing limitations identified in ICLUS v1 in both the migration and spatial allocation models. The companion user guide describes the development of ICLUS v2 and the updates that were made to the original data sets and the demographic and spatial allocation models. [2017 UPDATE] Get the latest version of ICLUS and stay up-to-date by signing up to the ICLUS mailing list. The GIS tool enables users to run SERGoM with the population projections developed for the ICLUS project and allows users to modify the spatial allocation housing density across the landscape.
Measurement needs guided by synthetic radar scans in high-resolution model output
Varble, A.; Nesbitt, S. W.; Borque, P.
2017-12-01
Microphysical and dynamical process interactions within deep convective clouds are not well understood, partly because measurement strategies often focus on statistics of cloud state rather than cloud processes. While processes cannot be directly measured, they can be inferred with sufficiently frequent and detailed scanning radar measurements focused on the life cycleof individual cloud regions. This is a primary goal of the 2018-19 DOE ARM Cloud, Aerosol, and Complex Terrain Interactions (CACTI) and NSF Remote sensing of Electrification, Lightning, And Mesoscale/microscale Processes with Adaptive Ground Observations (RELAMPAGO) field campaigns in central Argentina, where orographic deep convective initiation is frequent with some high-impact systems growing into the tallest and largest in the world. An array of fixed and mobile scanning multi-wavelength dual-polarization radars will be coupled with surface observations, sounding systems, multi-wavelength vertical profilers, and aircraft in situ measurements to characterize convective cloud life cycles and their relationship with environmental conditions. While detailed cloud processes are an observational target, the radar scan patterns that are most ideal for observing them are unclear. They depend on the locations and scales of key microphysical and dynamical processes operating within the cloud. High-resolution simulations of clouds, while imperfect, can provide information on these locations and scales that guide radar measurement needs. Radar locations are set in the model domain based on planned experiment locations, and simulatedorographic deep convective initiation and upscale growth are sampled using a number of different scans involving RHIs or PPIs with predefined elevation and azimuthal angles that approximately conform with radar range and beam width specifications. Each full scan pattern is applied to output atsingle model time steps with time step intervals that depend on the length of time
An API for Integrating Spatial Context Models with Spatial Reasoning Algorithms
DEFF Research Database (Denmark)
Kjærgaard, Mikkel Baun
2006-01-01
The integration of context-aware applications with spatial context models is often done using a common query language. However, algorithms that estimate and reason about spatial context information can benefit from a tighter integration. An object-oriented API makes such integration possible...... and can help reduce the complexity of algorithms making them easier to maintain and develop. This paper propose an object-oriented API for context models of the physical environment and extensions to a location modeling approach called geometric space trees for it to provide adequate support for location...... modeling. The utility of the API is evaluated in several real-world cases from an indoor location system, and spans several types of spatial reasoning algorithms....
Spatial modeling of households' knowledge about arsenic pollution in Bangladesh.
Sarker, M Mizanur Rahman
2012-04-01
Arsenic in drinking water is an important public health issue in Bangladesh, which is affected by households' knowledge about arsenic threats from their drinking water. In this study, spatial statistical models were used to investigate the determinants and spatial dependence of households' knowledge about arsenic risk. The binary join matrix/binary contiguity matrix and inverse distance spatial weight matrix techniques are used to capture spatial dependence in the data. This analysis extends the spatial model by allowing spatial dependence to vary across divisions and regions. A positive spatial correlation was found in households' knowledge across neighboring districts at district, divisional and regional levels, but the strength of this spatial correlation varies considerably by spatial weight. Literacy rate, daily wage rate of agricultural labor, arsenic status, and percentage of red mark tube well usage in districts were found to contribute positively and significantly to households' knowledge. These findings have policy implications both at regional and national levels in mitigating the present arsenic crisis and to ensure arsenic-free water in Bangladesh. Copyright © 2012 Elsevier Ltd. All rights reserved.
The input and output management of solid waste using DEA models: A case study at Jengka, Pahang
Mohamed, Siti Rosiah; Ghazali, Nur Fadzrina Mohd; Mohd, Ainun Hafizah
2017-08-01
Data Envelopment Analysis (DEA) as a tool for obtaining performance indices has been used extensively in several of organizations sector. The ways to improve the efficiency of Decision Making Units (DMUs) is impractical because some of inputs and outputs are uncontrollable and in certain situation its produce weak efficiency which often reflect the impact for operating environment. Based on the data from Alam Flora Sdn. Bhd Jengka, the researcher wants to determine the efficiency of solid waste management (SWM) in town Jengka Pahang using CCRI and CCRO model of DEA and duality formulation with vector average input and output. Three input variables (length collection in meter, frequency time per week in hour and number of garbage truck) and 2 outputs variables (frequency collection and the total solid waste collection in kilogram) are analyzed. As a conclusion, it shows only three roads from 23 roads are efficient that achieve efficiency score 1. Meanwhile, 20 other roads are in an inefficient management.
Bayesian disease mapping: hierarchical modeling in spatial epidemiology
National Research Council Canada - National Science Library
Lawson, Andrew
2013-01-01
Since the publication of the first edition, many new Bayesian tools and methods have been developed for space-time data analysis, the predictive modeling of health outcomes, and other spatial biostatistical areas...
Bayesian disease mapping: hierarchical modeling in spatial epidemiology
National Research Council Canada - National Science Library
Lawson, Andrew
2013-01-01
.... Exploring these new developments, Bayesian Disease Mapping: Hierarchical Modeling in Spatial Epidemiology, Second Edition provides an up-to-date, cohesive account of the full range of Bayesian disease mapping methods and applications...
International Nuclear Information System (INIS)
Su, Yan; Chan, Lai-Cheong; Shu, Lianjie; Tsui, Kwok-Leung
2012-01-01
Highlights: ► We develop online prediction models for solar photovoltaic system performance. ► The proposed prediction models are simple but with reasonable accuracy. ► The maximum monthly average minutely efficiency varies 10.81–12.63%. ► The average efficiency tends to be slightly higher in winter months. - Abstract: This paper develops new real time prediction models for output power and energy efficiency of solar photovoltaic (PV) systems. These models were validated using measured data of a grid-connected solar PV system in Macau. Both time frames based on yearly average and monthly average are considered. It is shown that the prediction model for the yearly/monthly average of the minutely output power fits the measured data very well with high value of R 2 . The online prediction model for system efficiency is based on the ratio of the predicted output power to the predicted solar irradiance. This ratio model is shown to be able to fit the intermediate phase (9 am to 4 pm) very well but not accurate for the growth and decay phases where the system efficiency is near zero. However, it can still serve as a useful purpose for practitioners as most PV systems work in the most efficient manner over this period. It is shown that the maximum monthly average minutely efficiency varies over a small range of 10.81% to 12.63% in different months with slightly higher efficiency in winter months.
Meshkat, Nicolette; Anderson, Chris; Distefano, Joseph J
2011-09-01
When examining the structural identifiability properties of dynamic system models, some parameters can take on an infinite number of values and yet yield identical input-output data. These parameters and the model are then said to be unidentifiable. Finding identifiable combinations of parameters with which to reparameterize the model provides a means for quantitatively analyzing the model and computing solutions in terms of the combinations. In this paper, we revisit and explore the properties of an algorithm for finding identifiable parameter combinations using Gröbner Bases and prove useful theoretical properties of these parameter combinations. We prove a set of M algebraically independent identifiable parameter combinations can be found using this algorithm and that there exists a unique rational reparameterization of the input-output equations over these parameter combinations. We also demonstrate application of the procedure to a nonlinear biomodel. Copyright © 2011 Elsevier Inc. All rights reserved.
Testing spatial heterogeneity with stock assessment models
DEFF Research Database (Denmark)
Jardim, Ernesto; Eero, Margit; Silva, Alexandra
2018-01-01
sub-populations and applied to two case studies, North Sea cod (Gadus morua) and Northeast Atlantic sardine (Sardina pilchardus). Considering that the biological components of a population can be partitioned into discrete spatial units, we extended this idea into a property of additivity of sub......, the better the diffusion process will be detected. On the other hand it showed that weak to moderate diffusion processes are not easy to identify and large differences between sub-populations productivities may be confounded with weak diffusion processes. The application to North Sea cod and Atlantic sardine...... exemplified how much insight can be gained. In both cases the results obtained were sufficiently robust to support the regional analysis....
Bekti, Rokhana Dwi; Nurhadiyanti, Gita; Irwansyah, Edy
2014-10-01
The diarrhea case pattern information, especially for toddler, is very important. It is used to show the distribution of diarrhea in every region, relationship among that locations, and regional economic characteristic or environmental behavior. So, this research uses spatial pattern to perform them. This method includes: Moran's I, Spatial Autoregressive Models (SAR), and Local Indicator of Spatial Autocorrelation (LISA). It uses sample from 23 sub districts of Bekasi Regency, West Java, Indonesia. Diarrhea case, regional economic, and environmental behavior of households have a spatial relationship among sub district. SAR shows that the percentage of Regional Gross Domestic Product is significantly effect on diarrhea at α = 10%. Therefore illiteracy and health center facilities are significant at α = 5%. With LISA test, sub districts in southern Bekasi have high dependencies with Cikarang Selatan, Serang Baru, and Setu. This research also builds development application that is based on java and R to support data analysis.
On Angular Sampling Methods for 3-D Spatial Channel Models
DEFF Research Database (Denmark)
Fan, Wei; Jämsä, Tommi; Nielsen, Jesper Ødum
2015-01-01
This paper discusses generating three dimensional (3D) spatial channel models with emphasis on the angular sampling methods. Three angular sampling methods, i.e. modified uniform power sampling, modified uniform angular sampling, and random pairing methods are proposed and investigated in detail....... The random pairing method, which uses only twenty sinusoids in the ray-based model for generating the channels, presents good results if the spatial channel cluster is with a small elevation angle spread. For spatial clusters with large elevation angle spreads, however, the random pairing method would fail...... and the other two methods should be considered....
How does spatial study design influence density estimates from spatial capture-recapture models?
Directory of Open Access Journals (Sweden)
Rahel Sollmann
Full Text Available When estimating population density from data collected on non-invasive detector arrays, recently developed spatial capture-recapture (SCR models present an advance over non-spatial models by accounting for individual movement. While these models should be more robust to changes in trapping designs, they have not been well tested. Here we investigate how the spatial arrangement and size of the trapping array influence parameter estimates for SCR models. We analysed black bear data collected with 123 hair snares with an SCR model accounting for differences in detection and movement between sexes and across the trapping occasions. To see how the size of the trap array and trap dispersion influence parameter estimates, we repeated analysis for data from subsets of traps: 50% chosen at random, 50% in the centre of the array and 20% in the South of the array. Additionally, we simulated and analysed data under a suite of trap designs and home range sizes. In the black bear study, we found that results were similar across trap arrays, except when only 20% of the array was used. Black bear density was approximately 10 individuals per 100 km(2. Our simulation study showed that SCR models performed well as long as the extent of the trap array was similar to or larger than the extent of individual movement during the study period, and movement was at least half the distance between traps. SCR models performed well across a range of spatial trap setups and animal movements. Contrary to non-spatial capture-recapture models, they do not require the trapping grid to cover an area several times the average home range of the studied species. This renders SCR models more appropriate for the study of wide-ranging mammals and more flexible to design studies targeting multiple species.
Spatial Epidemic Modelling in Social Networks
Simoes, Joana Margarida
2005-06-01
The spread of infectious diseases is highly influenced by the structure of the underlying social network. The target of this study is not the network of acquaintances, but the social mobility network: the daily movement of people between locations, in regions. It was already shown that this kind of network exhibits small world characteristics. The model developed is agent based (ABM) and comprehends a movement model and a infection model. In the movement model, some assumptions are made about its structure and the daily movement is decomposed into four types: neighborhood, intra region, inter region and random. The model is Geographical Information Systems (GIS) based, and uses real data to define its geometry. Because it is a vector model, some optimization techniques were used to increase its efficiency.
Directory of Open Access Journals (Sweden)
Ping Xu
2018-01-01
Full Text Available Public transport plays an important role in the environment. This study established a Spatial Computable General Equilibrium (SCGE model to examine the economic and environmental effects of public transport subsidy policies. The model includes firms, consumers, and traffic modules in one framework. Statistical data from Beijing were used in calibration to obtain benchmark equilibrium. Based on the equilibrium, simulations compared citywide social welfare, jobs-housing spatial population distribution, and environmental outputs under four subsidy policies: fare subsidy, cash grants, road expansion, and public transport speedup. Based on the results regarding the effects of public transport policies, conclusions can be drawn about which policies will have greater overall social influence and should therefore be used.
Directory of Open Access Journals (Sweden)
Guangyi Zhang
2016-09-01
Full Text Available In this paper, a utility piezoelectric energy harvester with low frequency and high-output voltage is presented. Firstly, the harvester’s three theoretical models are presented, namely the static model, the quasi static model and the dynamic vibration model. By analyzing the influence of the mass ratio of the mass block to the beam on output characteristics of the harvester, we compare the quasi static model and the dynamic vibration model and then define their applicable ranges. Secondly, simulation and experiments are done to verify the models, using the harvester with PZT-5H piezoelectric material, which are proved to be consistent with each other. The experimental results show that the output open-circuit voltage and the output power can reach up to 86.36V and 27.5mW respectively. The experiments are conducted when this harvester system is excited by the first modal frequency (58.90Hz with the acceleration 10m/s2. In this low frequency vibration case, it is easy to capture the energy in the daily environment. In addition, LTC 3588-1 chip (Linear Technology Corporation is used as the medium energy circuit to transfer charges from the PZT-5H electrode to the 0.22F 5V super capacitor and ML621 rechargeable button battery. For this super-capacitor, it takes about 100min for the capacitor voltage to rise from 0V to 3.6V. For this button battery, it takes about 200min to increase the battery voltage from 2.5V to 3.48V.
Spatial modelling with R-INLA: A review
Bakka, Haakon; Rue, Haavard; Fuglstad, Geir-Arne; Riebler, Andrea; Bolin, David; Krainski, Elias; Simpson, Daniel; Lindgren, Finn
2018-01-01
Coming up with Bayesian models for spatial data is easy, but performing inference with them can be challenging. Writing fast inference code for a complex spatial model with realistically-sized datasets from scratch is time-consuming, and if changes are made to the model, there is little guarantee that the code performs well. The key advantages of R-INLA are the ease with which complex models can be created and modified, without the need to write complex code, and the speed at which inference can be done even for spatial problems with hundreds of thousands of observations. R-INLA handles latent Gaussian models, where fixed effects, structured and unstructured Gaussian random effects are combined linearly in a linear predictor, and the elements of the linear predictor are observed through one or more likelihoods. The structured random effects can be both standard areal model such as the Besag and the BYM models, and geostatistical models from a subset of the Mat\\'ern Gaussian random fields. In this review, we discuss the large success of spatial modelling with R-INLA and the types of spatial models that can be fitted, we give an overview of recent developments for areal models, and we give an overview of the stochastic partial differential equation (SPDE) approach and some of the ways it can be extended beyond the assumptions of isotropy and separability. In particular, we describe how slight changes to the SPDE approach leads to straight-forward approaches for non-stationary spatial models and non-separable space-time models.
Spatial modelling with R-INLA: A review
Bakka, Haakon
2018-02-18
Coming up with Bayesian models for spatial data is easy, but performing inference with them can be challenging. Writing fast inference code for a complex spatial model with realistically-sized datasets from scratch is time-consuming, and if changes are made to the model, there is little guarantee that the code performs well. The key advantages of R-INLA are the ease with which complex models can be created and modified, without the need to write complex code, and the speed at which inference can be done even for spatial problems with hundreds of thousands of observations. R-INLA handles latent Gaussian models, where fixed effects, structured and unstructured Gaussian random effects are combined linearly in a linear predictor, and the elements of the linear predictor are observed through one or more likelihoods. The structured random effects can be both standard areal model such as the Besag and the BYM models, and geostatistical models from a subset of the Mat\\\\\\'ern Gaussian random fields. In this review, we discuss the large success of spatial modelling with R-INLA and the types of spatial models that can be fitted, we give an overview of recent developments for areal models, and we give an overview of the stochastic partial differential equation (SPDE) approach and some of the ways it can be extended beyond the assumptions of isotropy and separability. In particular, we describe how slight changes to the SPDE approach leads to straight-forward approaches for non-stationary spatial models and non-separable space-time models.
International Nuclear Information System (INIS)
Liu, Xiuli; Moreno, Blanca; García, Ana Salomé
2016-01-01
A combined forecast of Grey forecasting method and neural network back propagation model, which is called Grey Neural Network and Input-Output Combined Forecasting Model (GNF-IO model), is proposed. A real case of energy consumption forecast is used to validate the effectiveness of the proposed model. The GNF-IO model predicts coal, crude oil, natural gas, renewable and nuclear primary energy consumption volumes by Spain's 36 sub-sectors from 2010 to 2015 according to three different GDP growth scenarios (optimistic, baseline and pessimistic). Model test shows that the proposed model has higher simulation and forecasting accuracy on energy consumption than Grey models separately and other combination methods. The forecasts indicate that the primary energies as coal, crude oil and natural gas will represent on average the 83.6% percent of the total of primary energy consumption, raising concerns about security of supply and energy cost and adding risk for some industrial production processes. Thus, Spanish industry must speed up its transition to an energy-efficiency economy, achieving a cost reduction and increase in the level of self-supply. - Highlights: • Forecasting System Using Grey Models combined with Input-Output Models is proposed. • Primary energy consumption in Spain is used to validate the model. • The grey-based combined model has good forecasting performance. • Natural gas will represent the majority of the total of primary energy consumption. • Concerns about security of supply, energy cost and industry competitiveness are raised.
Does the DHET research output subsidy model penalise high-citation publication? A case study
Yolande X. Harley; Esmari Huysamen; Carlette Hlungwani; Tania S. Douglas
2016-01-01
South African universities are awarded annual subsidy from the Department of Higher Education and Training (DHET) based on their research publication output. Journal article subsidy is based on the number of research publications in DHET-approved journals as well as the proportional contribution of authors from the university. Co-authorship with other institutions reduces the subsidy received by a university, which may be a disincentive to collaboration. Inter-institutional collaboration may ...
Modeling structural change in spatial system dynamics: A Daisyworld example.
Neuwirth, C; Peck, A; Simonović, S P
2015-03-01
System dynamics (SD) is an effective approach for helping reveal the temporal behavior of complex systems. Although there have been recent developments in expanding SD to include systems' spatial dependencies, most applications have been restricted to the simulation of diffusion processes; this is especially true for models on structural change (e.g. LULC modeling). To address this shortcoming, a Python program is proposed to tightly couple SD software to a Geographic Information System (GIS). The approach provides the required capacities for handling bidirectional and synchronized interactions of operations between SD and GIS. In order to illustrate the concept and the techniques proposed for simulating structural changes, a fictitious environment called Daisyworld has been recreated in a spatial system dynamics (SSD) environment. The comparison of spatial and non-spatial simulations emphasizes the importance of considering spatio-temporal feedbacks. Finally, practical applications of structural change models in agriculture and disaster management are proposed.
A Structural Equation Approach to Models with Spatial Dependence
Oud, Johan H. L.; Folmer, Henk
We introduce the class of structural equation models (SEMs) and corresponding estimation procedures into a spatial dependence framework. SEM allows both latent and observed variables within one and the same (causal) model. Compared with models with observed variables only, this feature makes it
A structural equation approach to models with spatial dependence
Oud, J.H.L.; Folmer, H.
2008-01-01
We introduce the class of structural equation models (SEMs) and corresponding estimation procedures into a spatial dependence framework. SEM allows both latent and observed variables within one and the same (causal) model. Compared with models with observed variables only, this feature makes it
A Structural Equation Approach to Models with Spatial Dependence
Oud, J.H.L.; Folmer, H.
2008-01-01
We introduce the class of structural equation models (SEMs) and corresponding estimation procedures into a spatial dependence framework. SEM allows both latent and observed variables within one and the same (causal) model. Compared with models with observed variables only, this feature makes it
Modelling the spatial distribution of Fasciola hepatica in dairy cattle in Europe
Directory of Open Access Journals (Sweden)
Els Ducheyne
2015-03-01
Full Text Available A harmonized sampling approach in combination with spatial modelling is required to update current knowledge of fasciolosis in dairy cattle in Europe. Within the scope of the EU project GLOWORM, samples from 3,359 randomly selected farms in 849 municipalities in Belgium, Germany, Ireland, Poland and Sweden were collected and their infection status assessed using an indirect bulk tank milk (BTM enzyme-linked immunosorbent assay (ELISA. Dairy farms were considered exposed when the optical density ratio (ODR exceeded the 0.3 cut-off. Two ensemble-modelling techniques, Random Forests (RF and Boosted Regression Trees (BRT, were used to obtain the spatial distribution of the probability of exposure to Fasciola hepatica using remotely sensed environmental variables (1-km spatial resolution and interpolated values from meteorological stations as predictors. The median ODRs amounted to 0.31, 0.12, 0.54, 0.25 and 0.44 for Belgium, Germany, Ireland, Poland and southern Sweden, respectively. Using the 0.3 threshold, 571 municipalities were categorized as positive and 429 as negative. RF was seen as capable of predicting the spatial distribution of exposure with an area under the receiver operation characteristic (ROC curve (AUC of 0.83 (0.96 for BRT. Both models identified rainfall and temperature as the most important factors for probability of exposure. Areas of high and low exposure were identified by both models, with BRT better at discriminating between low-probability and high-probability exposure; this model may therefore be more useful in practise. Given a harmonized sampling strategy, it should be possible to generate robust spatial models for fasciolosis in dairy cattle in Europe to be used as input for temporal models and for the detection of deviations in baseline probability. Further research is required for model output in areas outside the eco-climatic range investigated.
Can spatial statistical river temperature models be transferred between catchments?
Jackson, Faye L.; Fryer, Robert J.; Hannah, David M.; Malcolm, Iain A.
2017-09-01
There has been increasing use of spatial statistical models to understand and predict river temperature (Tw) from landscape covariates. However, it is not financially or logistically feasible to monitor all rivers and the transferability of such models has not been explored. This paper uses Tw data from four river catchments collected in August 2015 to assess how well spatial regression models predict the maximum 7-day rolling mean of daily maximum Tw (Twmax) within and between catchments. Models were fitted for each catchment separately using (1) landscape covariates only (LS models) and (2) landscape covariates and an air temperature (Ta) metric (LS_Ta models). All the LS models included upstream catchment area and three included a river network smoother (RNS) that accounted for unexplained spatial structure. The LS models transferred reasonably to other catchments, at least when predicting relative levels of Twmax. However, the predictions were biased when mean Twmax differed between catchments. The RNS was needed to characterise and predict finer-scale spatially correlated variation. Because the RNS was unique to each catchment and thus non-transferable, predictions were better within catchments than between catchments. A single model fitted to all catchments found no interactions between the landscape covariates and catchment, suggesting that the landscape relationships were transferable. The LS_Ta models transferred less well, with particularly poor performance when the relationship with the Ta metric was physically implausible or required extrapolation outside the range of the data. A single model fitted to all catchments found catchment-specific relationships between Twmax and the Ta metric, indicating that the Ta metric was not transferable. These findings improve our understanding of the transferability of spatial statistical river temperature models and provide a foundation for developing new approaches for predicting Tw at unmonitored locations across
Spatial Modeling Tools for Cell Biology
National Research Council Canada - National Science Library
Przekwas, Andrzej; Friend, Tom; Teixeira, Rodrigo; Chen, Z. J; Wilkerson, Patrick
2006-01-01
.... Scientific potentials and military relevance of computational biology and bioinformatics have inspired DARPA/IPTO's visionary BioSPICE project to develop computational framework and modeling tools for cell biology...
International Nuclear Information System (INIS)
Evans, Philip M.; Mosleh-Shirazi, M. Amin; Harris, Emma J.; Seco, Joao
2006-01-01
A new model of the light output from single-crystal scintillators in megavoltage energy x-ray beams has been developed, based on the concept of a Lambertian light guide model (LLG). This was evaluated in comparison with a Monte Carlo (MC) model of optical photon transport, previously developed and reported in the literature, which was used as a gold standard. The LLG model was developed to enable optimization of scintillator detector design. In both models the dose deposition and light propagation were decoupled, the scintillators were cuboids, split into a series of cells as a function of depth, with Lambertian side and entrance faces, and a specular exit face. The signal in a sensor placed 1 and 1000 mm beyond the exit face was calculated. Cesium iodide (CSI) crystals of 1.5 and 3 mm square cross section and 1, 5, and 10 mm depth were modeled. Both models were also used to determine detector signal and optical gain factor as a function of CsI scintillator thickness, from 2 to 10 mm. Results showed a variation in light output with position of dose deposition of a factor of up to approximately 5, for long, thin scintillators (such as 10x1.5x1.5 mm 3 ). For short, fat scintillators (such as 1x3x3 mm 3 ) the light output was more uniform with depth. MC and LLG generally agreed to within 5%. Results for a sensor distance of 1 mm showed an increase in light output the closer the light originates to the exit face, while a distance of 1000 mm showed a decrease in light output the closer the light originates to the exit face. For a sensor distance of 1 mm, the ratio of signal for a 10 mm scintillator to that for a 2 mm scintillator was 1.98, whereas for the 1000 mm distance the ratio was 3.00. The ratio of quantum efficiency (QE) between 10 and 2 mm thicknesses was 4.62. We conclude that these models may be used for detector optimization, with the light guide model suitable for parametric study
Bayesian spatial transformation models with applications in neuroimaging data.
Miranda, Michelle F; Zhu, Hongtu; Ibrahim, Joseph G
2013-12-01
The aim of this article is to develop a class of spatial transformation models (STM) to spatially model the varying association between imaging measures in a three-dimensional (3D) volume (or 2D surface) and a set of covariates. The proposed STM include a varying Box-Cox transformation model for dealing with the issue of non-Gaussian distributed imaging data and a Gaussian Markov random field model for incorporating spatial smoothness of the imaging data. Posterior computation proceeds via an efficient Markov chain Monte Carlo algorithm. Simulations and real data analysis demonstrate that the STM significantly outperforms the voxel-wise linear model with Gaussian noise in recovering meaningful geometric patterns. Our STM is able to reveal important brain regions with morphological changes in children with attention deficit hyperactivity disorder. © 2013, The International Biometric Society.
Crash rates analysis in China using a spatial panel model
Directory of Open Access Journals (Sweden)
Wonmongo Lacina Soro
2017-10-01
Full Text Available The consideration of spatial externalities in traffic safety analysis is of paramount importance for the success of road safety policies. Yet, the quasi-totality of spatial dependence studies on crash rates is performed within the framework of single-equation spatial cross-sectional studies. The present study extends the spatial cross-sectional scheme to a spatial fixed-effects panel model estimated using the maximum likelihood method. The spatial units are the 31 administrative regions of mainland China over the period 2004–2013. The presence of neighborhood effects is evidenced through the Moran's I statistic. Consistent with previous studies, the analysis reveals that omitting the spatial effects in traffic safety analysis is likely to bias the estimation results. The spatial and error lags are all positive and statistically significant suggesting similarities of crash rates pattern in neighboring regions. Some other explanatory variables, such as freight traffic, the length of paved roads and the populations of age 65 and above are related to higher rates while the opposite trend is observed for the Gross Regional Product, the urban unemployment rate and passenger traffic.
Linear multivariate evaluation models for spatial perception of soundscape.
Deng, Zhiyong; Kang, Jian; Wang, Daiwei; Liu, Aili; Kang, Joe Zhengyu
2015-11-01
Soundscape is a sound environment that emphasizes the awareness of auditory perception and social or cultural understandings. The case of spatial perception is significant to soundscape. However, previous studies on the auditory spatial perception of the soundscape environment have been limited. Based on 21 native binaural-recorded soundscape samples and a set of auditory experiments for subjective spatial perception (SSP), a study of the analysis among semantic parameters, the inter-aural-cross-correlation coefficient (IACC), A-weighted-equal sound-pressure-level (L(eq)), dynamic (D), and SSP is introduced to verify the independent effect of each parameter and to re-determine some of their possible relationships. The results show that the more noisiness the audience perceived, the worse spatial awareness they received, while the closer and more directional the sound source image variations, dynamics, and numbers of sound sources in the soundscape are, the better the spatial awareness would be. Thus, the sensations of roughness, sound intensity, transient dynamic, and the values of Leq and IACC have a suitable range for better spatial perception. A better spatial awareness seems to promote the preference slightly for the audience. Finally, setting SSPs as functions of the semantic parameters and Leq-D-IACC, two linear multivariate evaluation models of subjective spatial perception are proposed.
SPATIAL MODELLING FOR DESCRIBING SPATIAL VARIABILITY OF SOIL PHYSICAL PROPERTIES IN EASTERN CROATIA
Directory of Open Access Journals (Sweden)
Igor Bogunović
2016-06-01
Full Text Available The objectives of this study were to characterize the field-scale spatial variability and test several interpolation methods to identify the best spatial predictor of penetration resistance (PR, bulk density (BD and gravimetric water content (GWC in the silty loam soil in Eastern Croatia. The measurements were made on a 25 x 25-m grid which created 40 individual grid cells. Soil properties were measured at the center of the grid cell deep 0-10 cm and 10-20 cm. Results demonstrated that PR and GWC displayed strong spatial dependence at 0-10 cm BD, while there was moderate and weak spatial dependence of PR, BD and GWC at depth of 10-20 cm. Semi-variogram analysis suggests that future sampling intervals for investigated parameters can be increased to 35 m in order to reduce research costs. Additionally, interpolation models recorded similar root mean square values with high predictive accuracy. Results suggest that investigated properties do not have uniform interpolation method implying the need for spatial modelling in the evaluation of these soil properties in Eastern Croatia.
Spatial Rule-Based Modeling: A Method and Its Application to the Human Mitotic Kinetochore
Directory of Open Access Journals (Sweden)
Jan Huwald
2013-07-01
Full Text Available A common problem in the analysis of biological systems is the combinatorial explosion that emerges from the complexity of multi-protein assemblies. Conventional formalisms, like differential equations, Boolean networks and Bayesian networks, are unsuitable for dealing with the combinatorial explosion, because they are designed for a restricted state space with fixed dimensionality. To overcome this problem, the rule-based modeling language, BioNetGen, and the spatial extension, SRSim, have been developed. Here, we describe how to apply rule-based modeling to integrate experimental data from different sources into a single spatial simulation model and how to analyze the output of that model. The starting point for this approach can be a combination of molecular interaction data, reaction network data, proximities, binding and diffusion kinetics and molecular geometries at different levels of detail. We describe the technique and then use it to construct a model of the human mitotic inner and outer kinetochore, including the spindle assembly checkpoint signaling pathway. This allows us to demonstrate the utility of the procedure, show how a novel perspective for understanding such complex systems becomes accessible and elaborate on challenges that arise in the formulation, simulation and analysis of spatial rule-based models.
Empirical spatial econometric modelling of small scale neighbourhood
Gerkman, Linda
2012-07-01
The aim of the paper is to model small scale neighbourhood in a house price model by implementing the newest methodology in spatial econometrics. A common problem when modelling house prices is that in practice it is seldom possible to obtain all the desired variables. Especially variables capturing the small scale neighbourhood conditions are hard to find. If there are important explanatory variables missing from the model, the omitted variables are spatially autocorrelated and they are correlated with the explanatory variables included in the model, it can be shown that a spatial Durbin model is motivated. In the empirical application on new house price data from Helsinki in Finland, we find the motivation for a spatial Durbin model, we estimate the model and interpret the estimates for the summary measures of impacts. By the analysis we show that the model structure makes it possible to model and find small scale neighbourhood effects, when we know that they exist, but we are lacking proper variables to measure them.
Spatial memory tasks in rodents: what do they model?
Morellini, Fabio
2013-10-01
The analysis of spatial learning and memory in rodents is commonly used to investigate the mechanisms underlying certain forms of human cognition and to model their dysfunction in neuropsychiatric and neurodegenerative diseases. Proper interpretation of rodent behavior in terms of spatial memory and as a model of human cognitive functions is only possible if various navigation strategies and factors controlling the performance of the animal in a spatial task are taken into consideration. The aim of this review is to describe the experimental approaches that are being used for the study of spatial memory in rats and mice and the way that they can be interpreted in terms of general memory functions. After an introduction to the classification of memory into various categories and respective underlying neuroanatomical substrates, I explain the concept of spatial memory and its measurement in rats and mice by analysis of their navigation strategies. Subsequently, I describe the most common paradigms for spatial memory assessment with specific focus on methodological issues relevant for the correct interpretation of the results in terms of cognitive function. Finally, I present recent advances in the use of spatial memory tasks to investigate episodic-like memory in mice.
Modelling the effects of spatial variability on radionuclide migration
International Nuclear Information System (INIS)
1998-01-01
The NEA workshop reflect the present status in national waste management program, specifically in spatial variability and performance assessment of geologic disposal sites for deed repository system the four sessions were: Spatial Variability: Its Definition and Significance to Performance Assessment and Site Characterisation; Experience with the Modelling of Radionuclide Migration in the Presence of Spatial Variability in Various Geological Environments; New Areas for Investigation: Two Personal Views; What is Wanted and What is Feasible: Views and Future Plans in Selected Waste Management Organisations. The 26 papers presented on the four oral sessions and on the poster session have been abstracted and indexed individually for the INIS database. (R.P.)
Spatial distance in a technology gap model
Verspagen, B.; Caniëls, M.C.J.
1999-01-01
This paper analyses the effect of locally bounded knowledge spillovers on regional differences in growth. A model will be developed that allows spillovers to take place across regions. Certain conditions determine the amount of spillovers a region receives. By use of simulations (with randomised
Properties of spatial Cox process models
DEFF Research Database (Denmark)
Møller, Jesper
Probabilistic properties of Cox processes of relevance for statistical modelling and inference are studied. Particularly, we study the most important classes of Cox processes, including log Gaussian Cox processes, shot noise Cox processes, and permanent Cox processes. We consider moment properties...... and point process operations such as thinning, displacements, and superpositioning. We also discuss how to simulate specific Cox processes....
Statistical model of natural stimuli predicts edge-like pooling of spatial frequency channels in V2
Directory of Open Access Journals (Sweden)
Gutmann Michael
2005-02-01
Full Text Available Abstract Background It has been shown that the classical receptive fields of simple and complex cells in the primary visual cortex emerge from the statistical properties of natural images by forcing the cell responses to be maximally sparse or independent. We investigate how to learn features beyond the primary visual cortex from the statistical properties of modelled complex-cell outputs. In previous work, we showed that a new model, non-negative sparse coding, led to the emergence of features which code for contours of a given spatial frequency band. Results We applied ordinary independent component analysis to modelled outputs of complex cells that span different frequency bands. The analysis led to the emergence of features which pool spatially coherent across-frequency activity in the modelled primary visual cortex. Thus, the statistically optimal way of processing complex-cell outputs abandons separate frequency channels, while preserving and even enhancing orientation tuning and spatial localization. As a technical aside, we found that the non-negativity constraint is not necessary: ordinary independent component analysis produces essentially the same results as our previous work. Conclusion We propose that the pooling that emerges allows the features to code for realistic low-level image features related to step edges. Further, the results prove the viability of statistical modelling of natural images as a framework that produces quantitative predictions of visual processing.
A random spatial network model based on elementary postulates
Karlinger, Michael R.; Troutman, Brent M.
1989-01-01
A model for generating random spatial networks that is based on elementary postulates comparable to those of the random topology model is proposed. In contrast to the random topology model, this model ascribes a unique spatial specification to generated drainage networks, a distinguishing property of some network growth models. The simplicity of the postulates creates an opportunity for potential analytic investigations of the probabilistic structure of the drainage networks, while the spatial specification enables analyses of spatially dependent network properties. In the random topology model all drainage networks, conditioned on magnitude (number of first-order streams), are equally likely, whereas in this model all spanning trees of a grid, conditioned on area and drainage density, are equally likely. As a result, link lengths in the generated networks are not independent, as usually assumed in the random topology model. For a preliminary model evaluation, scale-dependent network characteristics, such as geometric diameter and link length properties, and topologic characteristics, such as bifurcation ratio, are computed for sets of drainage networks generated on square and rectangular grids. Statistics of the bifurcation and length ratios fall within the range of values reported for natural drainage networks, but geometric diameters tend to be relatively longer than those for natural networks.
Spatial-temporal modeling of malware propagation in networks.
Chen, Zesheng; Ji, Chuanyi
2005-09-01
Network security is an important task of network management. One threat to network security is malware (malicious software) propagation. One type of malware is called topological scanning that spreads based on topology information. The focus of this work is on modeling the spread of topological malwares, which is important for understanding their potential damages, and for developing countermeasures to protect the network infrastructure. Our model is motivated by probabilistic graphs, which have been widely investigated in machine learning. We first use a graphical representation to abstract the propagation of malwares that employ different scanning methods. We then use a spatial-temporal random process to describe the statistical dependence of malware propagation in arbitrary topologies. As the spatial dependence is particularly difficult to characterize, the problem becomes how to use simple (i.e., biased) models to approximate the spatially dependent process. In particular, we propose the independent model and the Markov model as simple approximations. We conduct both theoretical analysis and extensive simulations on large networks using both real measurements and synthesized topologies to test the performance of the proposed models. Our results show that the independent model can capture temporal dependence and detailed topology information and, thus, outperforms the previous models, whereas the Markov model incorporates a certain spatial dependence and, thus, achieves a greater accuracy in characterizing both transient and equilibrium behaviors of malware propagation.
Spatial analysis and modelling based on activities
CSIR Research Space (South Africa)
Conradie, Dirk CU
2010-01-01
Full Text Available (deliberative attitudes) (Pokahr, 2005). The BDI model does not cover emotional and other ‘higher’ human attitudes. KRONOS is a generic Computational Building Simulation (CBS) tool that was developed over the past three years to work on advanced... featured, stable, mature and platform independent with an easy to use C/C++ Application Program Interface (API). It has advanced joint types and integrated collision detection with friction. ODE is particularly useful for simulating vehicles, objects...
Xu, Yiming; Smith, Scot E; Grunwald, Sabine; Abd-Elrahman, Amr; Wani, Suhas P; Nair, Vimala D
2017-09-11
Digital soil mapping (DSM) is gaining momentum as a technique to help smallholder farmers secure soil security and food security in developing regions. However, communications of the digital soil mapping information between diverse audiences become problematic due to the inconsistent scale of DSM information. Spatial downscaling can make use of accessible soil information at relatively coarse spatial resolution to provide valuable soil information at relatively fine spatial resolution. The objective of this research was to disaggregate the coarse spatial resolution soil exchangeable potassium (K ex ) and soil total nitrogen (TN) base map into fine spatial resolution soil downscaled map using weighted generalized additive models (GAMs) in two smallholder villages in South India. By incorporating fine spatial resolution spectral indices in the downscaling process, the soil downscaled maps not only conserve the spatial information of coarse spatial resolution soil maps but also depict the spatial details of soil properties at fine spatial resolution. The results of this study demonstrated difference between the fine spatial resolution downscaled maps and fine spatial resolution base maps is smaller than the difference between coarse spatial resolution base maps and fine spatial resolution base maps. The appropriate and economical strategy to promote the DSM technique in smallholder farms is to develop the relatively coarse spatial resolution soil prediction maps or utilize available coarse spatial resolution soil maps at the regional scale and to disaggregate these maps to the fine spatial resolution downscaled soil maps at farm scale.
Uncertainty in a spatial evacuation model
Mohd Ibrahim, Azhar; Venkat, Ibrahim; Wilde, Philippe De
2017-08-01
Pedestrian movements in crowd motion can be perceived in terms of agents who basically exhibit patient or impatient behavior. We model crowd motion subject to exit congestion under uncertainty conditions in a continuous space and compare the proposed model via simulations with the classical social force model. During a typical emergency evacuation scenario, agents might not be able to perceive with certainty the strategies of opponents (other agents) owing to the dynamic changes entailed by the neighborhood of opponents. In such uncertain scenarios, agents will try to update their strategy based on their own rules or their intrinsic behavior. We study risk seeking, risk averse and risk neutral behaviors of such agents via certain game theory notions. We found that risk averse agents tend to achieve faster evacuation time whenever the time delay in conflicts appears to be longer. The results of our simulations also comply with previous work and conform to the fact that evacuation time of agents becomes shorter once mutual cooperation among agents is achieved. Although the impatient strategy appears to be the rational strategy that might lead to faster evacuation times, our study scientifically shows that the more the agents are impatient, the slower is the egress time.
Stability of a spatial model of social interactions
International Nuclear Information System (INIS)
Bragard, Jean; Mossay, Pascal
2016-01-01
We study a spatial model of social interactions. Though the properties of the spatial equilibrium have been largely discussed in the existing literature, the stability of equilibrium remains an unaddressed issue. Our aim is to fill up this gap by introducing dynamics in the model and by determining the stability of equilibrium. First we derive a variational equation useful for the stability analysis. This allows to study the corresponding eigenvalue problem. While odd modes are shown to be always stable, there is a single even mode of which stability depends on the model parameters. Finally various numerical simulations illustrate our theoretical results.
Modeling individuals’ cognitive and affective responses in spatial learning behavior
Han, Q.; Arentze, T.A.; Timmermans, H.J.P.; Janssens, D.; Wets, G.; Lo, H.P.; Leung, Stephen C.H.; Tan, Susanna M.L.
2008-01-01
Activity-based analysis has slowly shifted gear from analysis of daily activity patterns to analysis and modeling of dynamic activity-travel patterns. In this paper, we describe a dynamic model that is concerned with simulating cognitive and affective responses in spatial learning behavior for a
Modelling firm heterogeneity with spatial 'trends'
Energy Technology Data Exchange (ETDEWEB)
Sarmiento, C. [North Dakota State University, Fargo, ND (United States). Dept. of Agricultural Business & Applied Economics
2004-04-15
The hypothesis underlying this article is that firm heterogeneity can be captured by spatial characteristics of the firm (similar to the inclusion of a time trend in time series models). The hypothesis is examined in the context of modelling electric generation by coal powered plants in the presence of firm heterogeneity.
Spatially adaptive mixture modeling for analysis of FMRI time series.
Vincent, Thomas; Risser, Laurent; Ciuciu, Philippe
2010-04-01
Within-subject analysis in fMRI essentially addresses two problems, the detection of brain regions eliciting evoked activity and the estimation of the underlying dynamics. In Makni et aL, 2005 and Makni et aL, 2008, a detection-estimation framework has been proposed to tackle these problems jointly, since they are connected to one another. In the Bayesian formalism, detection is achieved by modeling activating and nonactivating voxels through independent mixture models (IMM) within each region while hemodynamic response estimation is performed at a regional scale in a nonparametric way. Instead of IMMs, in this paper we take advantage of spatial mixture models (SMM) for their nonlinear spatial regularizing properties. The proposed method is unsupervised and spatially adaptive in the sense that the amount of spatial correlation is automatically tuned from the data and this setting automatically varies across brain regions. In addition, the level of regularization is specific to each experimental condition since both the signal-to-noise ratio and the activation pattern may vary across stimulus types in a given brain region. These aspects require the precise estimation of multiple partition functions of underlying Ising fields. This is addressed efficiently using first path sampling for a small subset of fields and then using a recently developed fast extrapolation technique for the large remaining set. Simulation results emphasize that detection relying on supervised SMM outperforms its IMM counterpart and that unsupervised spatial mixture models achieve similar results without any hand-tuning of the correlation parameter. On real datasets, the gain is illustrated in a localizer fMRI experiment: brain activations appear more spatially resolved using SMM in comparison with classical general linear model (GLM)-based approaches, while estimating a specific parcel-based HRF shape. Our approach therefore validates the treatment of unsmoothed fMRI data without fixed GLM
A Statistical Toolbox For Mining And Modeling Spatial Data
Directory of Open Access Journals (Sweden)
D’Aubigny Gérard
2016-12-01
Full Text Available Most data mining projects in spatial economics start with an evaluation of a set of attribute variables on a sample of spatial entities, looking for the existence and strength of spatial autocorrelation, based on the Moran’s and the Geary’s coefficients, the adequacy of which is rarely challenged, despite the fact that when reporting on their properties, many users seem likely to make mistakes and to foster confusion. My paper begins by a critical appraisal of the classical definition and rational of these indices. I argue that while intuitively founded, they are plagued by an inconsistency in their conception. Then, I propose a principled small change leading to corrected spatial autocorrelation coefficients, which strongly simplifies their relationship, and opens the way to an augmented toolbox of statistical methods of dimension reduction and data visualization, also useful for modeling purposes. A second section presents a formal framework, adapted from recent work in statistical learning, which gives theoretical support to our definition of corrected spatial autocorrelation coefficients. More specifically, the multivariate data mining methods presented here, are easily implementable on the existing (free software, yield methods useful to exploit the proposed corrections in spatial data analysis practice, and, from a mathematical point of view, whose asymptotic behavior, already studied in a series of papers by Belkin & Niyogi, suggests that they own qualities of robustness and a limited sensitivity to the Modifiable Areal Unit Problem (MAUP, valuable in exploratory spatial data analysis.
A spatial model of mosquito host-seeking behavior.
Directory of Open Access Journals (Sweden)
Bree Cummins
Full Text Available Mosquito host-seeking behavior and heterogeneity in host distribution are important factors in predicting the transmission dynamics of mosquito-borne infections such as dengue fever, malaria, chikungunya, and West Nile virus. We develop and analyze a new mathematical model to describe the effect of spatial heterogeneity on the contact rate between mosquito vectors and hosts. The model includes odor plumes generated by spatially distributed hosts, wind velocity, and mosquito behavior based on both the prevailing wind and the odor plume. On a spatial scale of meters and a time scale of minutes, we compare the effectiveness of different plume-finding and plume-tracking strategies that mosquitoes could use to locate a host. The results show that two different models of chemotaxis are capable of producing comparable results given appropriate parameter choices and that host finding is optimized by a strategy of flying across the wind until the odor plume is intercepted. We also assess the impact of changing the level of host aggregation on mosquito host-finding success near the end of the host-seeking flight. When clusters of hosts are more tightly associated on smaller patches, the odor plume is narrower and the biting rate per host is decreased. For two host groups of unequal number but equal spatial density, the biting rate per host is lower in the group with more individuals, indicative of an attack abatement effect of host aggregation. We discuss how this approach could assist parameter choices in compartmental models that do not explicitly model the spatial arrangement of individuals and how the model could address larger spatial scales and other probability models for mosquito behavior, such as Lévy distributions.
International Nuclear Information System (INIS)
Pantic, Lana S.; Pavlović, Tomislav M.; Milosavljević, Dragana D.; Radonjic, Ivana S.; Radovic, Miodrag K.; Sazhko, Galina
2016-01-01
Five different models for calculating solar module temperature, output power and efficiency for sunny days with different solar radiation intensities and ambient temperatures are assessed in this paper. Thereafter, modeled values are compared to the experimentally obtained values for the horizontal solar module in Nis, Serbia. The criterion for determining the best model was based on the statistical analysis and the agreement between the calculated and the experimental values. The calculated values of solar module temperature are in good agreement with the experimentally obtained ones, with some variations over and under the measured values. The best agreement between calculated and experimentally obtained values was for summer months with high solar radiation intensity. The nonlinear model for calculating the output power is much better than the linear model and at the same time predicts better the total electrical energy generated by the solar module during the day. The nonlinear model for calculating the solar module efficiency predicts the efficiency higher than the STC (Standard Test Conditions) value of solar module efficiency for all conditions, while the linear model predicts the solar module efficiency very well. This paper provides a simple and efficient guideline to estimate relevant parameters of a monocrystalline silicon solar module under the moderate-continental climate conditions. - Highlights: • Linear model for solar module temperature gives accurate predictions for August. • The nonlinear model better predicts the solar module power than the linear model. • For calculating solar module power for Nis we propose the nonlinear model. • For calculating solar model efficiency for Nis we propose adoption of linear model. • The adopted models can be used for calculations throughout the year.
A study of spatial resolution in pollution exposure modelling
Directory of Open Access Journals (Sweden)
Gustafsson Susanna
2007-06-01
Full Text Available Abstract Background This study is part of several ongoing projects concerning epidemiological research into the effects on health of exposure to air pollutants in the region of Scania, southern Sweden. The aim is to investigate the optimal spatial resolution, with respect to temporal resolution, for a pollutant database of NOx-values which will be used mainly for epidemiological studies with durations of days, weeks or longer periods. The fact that a pollutant database has a fixed spatial resolution makes the choice critical for the future use of the database. Results The results from the study showed that the accuracy between the modelled concentrations of the reference grid with high spatial resolution (100 m, denoted the fine grid, and the coarser grids (200, 400, 800 and 1600 meters improved with increasing spatial resolution. When the pollutant values were aggregated in time (from hours to days and weeks the disagreement between the fine grid and the coarser grids were significantly reduced. The results also illustrate a considerable difference in optimal spatial resolution depending on the characteristic of the study area (rural or urban areas. To estimate the accuracy of the modelled values comparison were made with measured NOx values. The mean difference between the modelled and the measured value were 0.6 μg/m3 and the standard deviation 5.9 μg/m3 for the daily difference. Conclusion The choice of spatial resolution should not considerably deteriorate the accuracy of the modelled NOx values. Considering the comparison between modelled and measured values we estimate that an error due to coarse resolution greater than 1 μg/m3 is inadvisable if a time resolution of one day is used. Based on the study of different spatial resolutions we conclude that for urban areas a spatial resolution of 200–400 m is suitable; and for rural areas the spatial resolution could be coarser (about 1600 m. This implies that we should develop a pollutant
A physically based analytical spatial air temperature and humidity model
Yang, Yang; Endreny, Theodore A.; Nowak, David J.
2013-09-01
Spatial variation of urban surface air temperature and humidity influences human thermal comfort, the settling rate of atmospheric pollutants, and plant physiology and growth. Given the lack of observations, we developed a Physically based Analytical Spatial Air Temperature and Humidity (PASATH) model. The PASATH model calculates spatial solar radiation and heat storage based on semiempirical functions and generates spatially distributed estimates based on inputs of topography, land cover, and the weather data measured at a reference site. The model assumes that for all grids under the same mesoscale climate, grid air temperature and humidity are modified by local variation in absorbed solar radiation and the partitioning of sensible and latent heat. The model uses a reference grid site for time series meteorological data and the air temperature and humidity of any other grid can be obtained by solving the heat flux network equations. PASATH was coupled with the USDA iTree-Hydro water balance model to obtain evapotranspiration terms and run from 20 to 29 August 2010 at a 360 m by 360 m grid scale and hourly time step across a 285 km2 watershed including the urban area of Syracuse, NY. PASATH predictions were tested at nine urban weather stations representing variability in urban topography and land cover. The PASATH model predictive efficiency R2 ranged from 0.81 to 0.99 for air temperature and 0.77 to 0.97 for dew point temperature. PASATH is expected to have broad applications on environmental and ecological models.
Modelling Spatial Compositional Data: Reconstructions of past land cover and uncertainties
DEFF Research Database (Denmark)
Pirzamanbein, Behnaz; Lindström, Johan; Poska, Anneli
2018-01-01
In this paper, we construct a hierarchical model for spatial compositional data, which is used to reconstruct past land-cover compositions (in terms of coniferous forest, broadleaved forest, and unforested/open land) for five time periods during the past $6\\,000$ years over Europe. The model...... to a fast MCMC algorithm. Reconstructions are obtained by combining pollen-based estimates of vegetation cover at a limited number of locations with scenarios of past deforestation and output from a dynamic vegetation model. To evaluate uncertainties in the predictions a novel way of constructing joint...... confidence regions for the entire composition at each prediction location is proposed. The hierarchical model's ability to reconstruct past land cover is evaluated through cross validation for all time periods, and by comparing reconstructions for the recent past to a present day European forest map...
Toward micro-scale spatial modeling of gentrification
O'Sullivan, David
A simple preliminary model of gentrification is presented. The model is based on an irregular cellular automaton architecture drawing on the concept of proximal space, which is well suited to the spatial externalities present in housing markets at the local scale. The rent gap hypothesis on which the model's cell transition rules are based is discussed. The model's transition rules are described in detail. Practical difficulties in configuring and initializing the model are described and its typical behavior reported. Prospects for further development of the model are discussed. The current model structure, while inadequate, is well suited to further elaboration and the incorporation of other interesting and relevant effects.
Modeling Urban Spatial Growth in Mountainous Regions of Western China
Directory of Open Access Journals (Sweden)
Guoping Huang
2017-08-01
Full Text Available The scale and speed of urbanization in the mountainous regions of western China have received little attention from researchers. These cities are facing rapid population growth and severe environmental degradation. This study analyzed historical urban growth trends in this mountainous region to better understand the interaction between the spatial growth pattern and the mountainous topography. Three major factors—slope, accessibility, and land use type—were studied in light of their relationships with urban spatial growth. With the analysis of historical data as the basis, a conceptual urban spatial growth model was devised. In this model, slope, accessibility, and land use type together create resistance to urban growth, while accessibility controls the sequence of urban development. The model was tested and evaluated using historical data. It serves as a potential tool for planners to envision and assess future urban growth scenarios and their potential environmental impacts to make informed decisions.
Directory of Open Access Journals (Sweden)
K. Li
2015-08-01
Full Text Available At the present, in trend of shifting the old 2D-output oriented survey to a new 3D-output oriented survey based on BIM technology, the corresponding working methods and workflow for data capture, process, representation, etc. have to be changed.Based on case study of two buildings in the Summer Palace of Beijing, and Jiayuguan Pass at the west end of the Great Wall (both World Heritage sites, this paper puts forward a “structure-and-type method” by means of typological method used in archaeology, Revit family system, and the tectonic logic of building to realize a good coordination between understanding of historic buildings and BIM modelling.
Xiao, WenBo; Nazario, Gina; Wu, HuaMing; Zhang, HuaMing; Cheng, Feng
2017-01-01
In this article, we introduced an artificial neural network (ANN) based computational model to predict the output power of three types of photovoltaic cells, mono-crystalline (mono-), multi-crystalline (multi-), and amorphous (amor-) crystalline. The prediction results are very close to the experimental data, and were also influenced by numbers of hidden neurons. The order of the solar generation power output influenced by the external conditions from smallest to biggest is: multi-, mono-, and amor- crystalline silicon cells. In addition, the dependences of power prediction on the number of hidden neurons were studied. For multi- and amorphous crystalline cell, three or four hidden layer units resulted in the high correlation coefficient and low MSEs. For mono-crystalline cell, the best results were achieved at the hidden layer unit of 8.
Directory of Open Access Journals (Sweden)
WenBo Xiao
Full Text Available In this article, we introduced an artificial neural network (ANN based computational model to predict the output power of three types of photovoltaic cells, mono-crystalline (mono-, multi-crystalline (multi-, and amorphous (amor- crystalline. The prediction results are very close to the experimental data, and were also influenced by numbers of hidden neurons. The order of the solar generation power output influenced by the external conditions from smallest to biggest is: multi-, mono-, and amor- crystalline silicon cells. In addition, the dependences of power prediction on the number of hidden neurons were studied. For multi- and amorphous crystalline cell, three or four hidden layer units resulted in the high correlation coefficient and low MSEs. For mono-crystalline cell, the best results were achieved at the hidden layer unit of 8.
Hydrological model uncertainty due to spatial evapotranspiration estimation methods
Yu, Xuan; Lamačová, Anna; Duffy, Christopher; Krám, Pavel; Hruška, Jakub
2016-05-01
Evapotranspiration (ET) continues to be a difficult process to estimate in seasonal and long-term water balances in catchment models. Approaches to estimate ET typically use vegetation parameters (e.g., leaf area index [LAI], interception capacity) obtained from field observation, remote sensing data, national or global land cover products, and/or simulated by ecosystem models. In this study we attempt to quantify the uncertainty that spatial evapotranspiration estimation introduces into hydrological simulations when the age of the forest is not precisely known. The Penn State Integrated Hydrologic Model (PIHM) was implemented for the Lysina headwater catchment, located 50°03‧N, 12°40‧E in the western part of the Czech Republic. The spatial forest patterns were digitized from forest age maps made available by the Czech Forest Administration. Two ET methods were implemented in the catchment model: the Biome-BGC forest growth sub-model (1-way coupled to PIHM) and with the fixed-seasonal LAI method. From these two approaches simulation scenarios were developed. We combined the estimated spatial forest age maps and two ET estimation methods to drive PIHM. A set of spatial hydrologic regime and streamflow regime indices were calculated from the modeling results for each method. Intercomparison of the hydrological responses to the spatial vegetation patterns suggested considerable variation in soil moisture and recharge and a small uncertainty in the groundwater table elevation and streamflow. The hydrologic modeling with ET estimated by Biome-BGC generated less uncertainty due to the plant physiology-based method. The implication of this research is that overall hydrologic variability induced by uncertain management practices was reduced by implementing vegetation models in the catchment models.
Harteveld, E.; Waterhout, B.; Broekhans, B.; Zonneveld, W.A.M.
2015-01-01
Spatial planning practices are constantly evolving to be more effective in a dynamic context. In the face of the latest developments, the practice of collaborative spatial planning through the formation of regional collaborations has emerged as the contemporary solution. The practice of working with a multitude of public actors that cooperate to formulate spatial strategies for issues that transcend their own planning capacity is relatively new and the ideal structure, organization and scope ...
Spatial risk modelling for water shortage and nitrate pollution in the lower Jordan valley
International Nuclear Information System (INIS)
Loibl, W.; Orthofer, R.
2002-02-01
This report summarizes the results of the spatial risk modeling activities (work package WP-4.4, 'GIS Risk Modeling') of the INCO-DC project 'Developing Sustainable Water Management in the Jordan Valley'. The project was funded by European Commission's INCO-DC research program. The main objective of the project was to develop the scientific basis for an integral management plan of water resources and their use in the Lower Jordan Valley. The outputs of the project were expected to allow a better understanding of the water management situation, and to provide a sound basis for a better future water management - not only separately in the three countries, but in the overall valley region. The risk modeling was done by the ARCS Seibersdorf research (ARCS), based on information and data provided by the regional partners from Israel (Hebrew University, Jerusalem, HUJ), Palestine (Applied Research Institute, Jerusalem, Bethlehem, ARIJ) and Jordan (EnviroConsult Office, Amman, ECO). The land use classification has been established through a cooperation between ARCS and the Yale University Center for Earth Observation (YUCEO). As a result of the work, the spatial patterns of agricultural and domestic water demand in the Lower Jordan Valley were established, and the spatial dimension of driving forces for water usage and water supply was analyzed. Furthermore, a conceptual model for nitrate leakage (established by HUJ) was translated into a GIS system, and the risks for nitrate pollution of groundwater were quantified. (author)
Spatial capture-recapture models for search-encounter data
Royle, J. Andrew; Kery, Marc; Guelat, Jerome
2011-01-01
1. Spatial capture–recapture models make use of auxiliary data on capture location to provide density estimates for animal populations. Previously, models have been developed primarily for fixed trap arrays which define the observable locations of individuals by a set of discrete points. 2. Here, we develop a class of models for 'search-encounter' data, i.e. for detections of recognizable individuals in continuous space, not restricted to trap locations. In our hierarchical model, detection probability is related to the average distance between individual location and the survey path. The locations are allowed to change over time owing to movements of individuals, and individual locations are related formally by a model describing individual activity or home range centre which is itself regarded as a latent variable in the model. We provide a Bayesian analysis of the model in WinBUGS, and develop a custom MCMC algorithm in the R language. 3. The model is applied to simulated data and to territory mapping data for the Willow Tit from the Swiss Breeding Bird Survey MHB. While the observed density was 15 territories per nominal 1 km2 plot of unknown effective sample area, the model produced a density estimate of 21∙12 territories per square km (95% posterior interval: 17–26). 4. Spatial capture–recapture models are relevant to virtually all animal population studies that seek to estimate population size or density, yet existing models have been proposed mainly for conventional sampling using arrays of traps. Our model for search-encounter data, where the spatial pattern of searching can be arbitrary and may change over occasions, greatly expands the scope and utility of spatial capture–recapture models.
International Nuclear Information System (INIS)
Zhu Qin; Peng Xizhe; Wu Kaiya
2012-01-01
Based on the input–output model and the comparable price input–output tables, the current paper investigates the indirect carbon emissions from residential consumption in China in 1992–2005, and examines the impacts on the emissions using the structural decomposition method. The results demonstrate that the rise of the residential consumption level played a dominant role in the growth of residential indirect emissions. The persistent decline of the carbon emission intensity of industrial sectors presented a significant negative effect on the emissions. The change in the intermediate demand of industrial sectors resulted in an overall positive effect, except in the initial years. The increase in population prompted the indirect emissions to a certain extent; however, population size is no longer the main reason for the growth of the emissions. The change in the consumption structure showed a weak positive effect, demonstrating the importance for China to control and slow down the increase in the emissions while in the process of optimizing the residential consumption structure. The results imply that the means for restructuring the economy and improving efficiency, rather than for lowering the consumption scale, should be adopted by China to achieve the targets of energy conservation and emission reduction. - Highlights: ► We build the input–output model of indirect carbon emissions from residential consumption. ► We calculate the indirect emissions using the comparable price input–output tables. ► We examine the impacts on the indirect emissions using the structural decomposition method. ► The change in the consumption structure showed a weak positive effect on the emissions. ► China's population size is no longer the main reason for the growth of the emissions.
Analysing earthquake slip models with the spatial prediction comparison test
Zhang, L.; Mai, Paul Martin; Thingbaijam, Kiran Kumar; Razafindrakoto, H. N. T.; Genton, Marc G.
2014-01-01
Earthquake rupture models inferred from inversions of geophysical and/or geodetic data exhibit remarkable variability due to uncertainties in modelling assumptions, the use of different inversion algorithms, or variations in data selection and data processing. A robust statistical comparison of different rupture models obtained for a single earthquake is needed to quantify the intra-event variability, both for benchmark exercises and for real earthquakes. The same approach may be useful to characterize (dis-)similarities in events that are typically grouped into a common class of events (e.g. moderate-size crustal strike-slip earthquakes or tsunamigenic large subduction earthquakes). For this purpose, we examine the performance of the spatial prediction comparison test (SPCT), a statistical test developed to compare spatial (random) fields by means of a chosen loss function that describes an error relation between a 2-D field (‘model’) and a reference model. We implement and calibrate the SPCT approach for a suite of synthetic 2-D slip distributions, generated as spatial random fields with various characteristics, and then apply the method to results of a benchmark inversion exercise with known solution. We find the SPCT to be sensitive to different spatial correlations lengths, and different heterogeneity levels of the slip distributions. The SPCT approach proves to be a simple and effective tool for ranking the slip models with respect to a reference model.
Analysing earthquake slip models with the spatial prediction comparison test
Zhang, L.
2014-11-10
Earthquake rupture models inferred from inversions of geophysical and/or geodetic data exhibit remarkable variability due to uncertainties in modelling assumptions, the use of different inversion algorithms, or variations in data selection and data processing. A robust statistical comparison of different rupture models obtained for a single earthquake is needed to quantify the intra-event variability, both for benchmark exercises and for real earthquakes. The same approach may be useful to characterize (dis-)similarities in events that are typically grouped into a common class of events (e.g. moderate-size crustal strike-slip earthquakes or tsunamigenic large subduction earthquakes). For this purpose, we examine the performance of the spatial prediction comparison test (SPCT), a statistical test developed to compare spatial (random) fields by means of a chosen loss function that describes an error relation between a 2-D field (‘model’) and a reference model. We implement and calibrate the SPCT approach for a suite of synthetic 2-D slip distributions, generated as spatial random fields with various characteristics, and then apply the method to results of a benchmark inversion exercise with known solution. We find the SPCT to be sensitive to different spatial correlations lengths, and different heterogeneity levels of the slip distributions. The SPCT approach proves to be a simple and effective tool for ranking the slip models with respect to a reference model.
Gaussian Process Regression Model in Spatial Logistic Regression
Sofro, A.; Oktaviarina, A.
2018-01-01
Spatial analysis has developed very quickly in the last decade. One of the favorite approaches is based on the neighbourhood of the region. Unfortunately, there are some limitations such as difficulty in prediction. Therefore, we offer Gaussian process regression (GPR) to accommodate the issue. In this paper, we will focus on spatial modeling with GPR for binomial data with logit link function. The performance of the model will be investigated. We will discuss the inference of how to estimate the parameters and hyper-parameters and to predict as well. Furthermore, simulation studies will be explained in the last section.
Spatial modeling of agricultural land use change at global scale
Meiyappan, P.; Dalton, M.; O'Neill, B. C.; Jain, A. K.
2014-11-01
Long-term modeling of agricultural land use is central in global scale assessments of climate change, food security, biodiversity, and climate adaptation and mitigation policies. We present a global-scale dynamic land use allocation model and show that it can reproduce the broad spatial features of the past 100 years of evolution of cropland and pastureland patterns. The modeling approach integrates economic theory, observed land use history, and data on both socioeconomic and biophysical determinants of land use change, and estimates relationships using long-term historical data, thereby making it suitable for long-term projections. The underlying economic motivation is maximization of expected profits by hypothesized landowners within each grid cell. The model predicts fractional land use for cropland and pastureland within each grid cell based on socioeconomic and biophysical driving factors that change with time. The model explicitly incorporates the following key features: (1) land use competition, (2) spatial heterogeneity in the nature of driving factors across geographic regions, (3) spatial heterogeneity in the relative importance of driving factors and previous land use patterns in determining land use allocation, and (4) spatial and temporal autocorrelation in land use patterns. We show that land use allocation approaches based solely on previous land use history (but disregarding the impact of driving factors), or those accounting for both land use history and driving factors by mechanistically fitting models for the spatial processes of land use change do not reproduce well long-term historical land use patterns. With an example application to the terrestrial carbon cycle, we show that such inaccuracies in land use allocation can translate into significant implications for global environmental assessments. The modeling approach and its evaluation provide an example that can be useful to the land use, Integrated Assessment, and the Earth system modeling
Verhoef, Anne; Cook, Peter; Black, Emily; Macdonald, David; Sorensen, James
2017-04-01
This research addresses the terrestrial water balance for West Africa. Emphasis is on the prediction of groundwater recharge and how this may change in the future, which has relevance to the management of surface and groundwater resources. The study was conducted as part of the BRAVE research project, "Building understanding of climate variability into planning of groundwater supplies from low storage aquifers in Africa - Second Phase", funded under the NERC/DFID/ESRC Programme, Unlocking the Potential of Groundwater for the Poor (UPGro). We used model output data of water balance components (precipitation, surface and subsurface run-off, evapotranspiration and soil moisture content) from ERA-Interim/ERA-LAND reanalysis, CMIP5, and high resolution model runs with HadGEM3 (UPSCALE; Mizielinski et al., 2014), for current and future time-periods. Water balance components varied widely between the different models; variation was particularly large for sub-surface runoff (defined as drainage from the bottom-most soil layer of each model). In-situ data for groundwater recharge obtained from the peer-reviewed literature were compared with the model outputs. Separate off-line model sensitivity studies with key land surface models were performed to gain understanding of the reasons behind the model differences. These analyses were centered on vegetation, and soil hydraulic parameters. The modelled current and future recharge time series that had the greatest degree of confidence were used to examine the spatiotemporal variability in groundwater storage. Finally, the implications for water supply planning were assessed. Mizielinski, M.S. et al., 2014. High-resolution global climate modelling: the UPSCALE project, a large-simulation campaign. Geoscientific Model Development, 7(4), pp.1629-1640.
A spatial emergy model for Alachua County, Florida
Lambert, James David
A spatial model of the distribution of energy flows and storages in Alachua County, Florida, was created and used to analyze spatial patterns of energy transformation hierarchy in relation to spatial patterns of human settlement. Emergy, the available energy of one kind previously required directly or indirectly to make a product or service, was used as a measure of the quality of the different forms of energy flows and storages. Emergy provides a common unit of measure for comparing the productive contributions of natural processes with those of economic and social processes---it is an alternative to using money for measuring value. A geographic information system was used to create a spatial model and make maps that show the distribution and magnitude of different types of energy and emergy flows and storages occurring in one-hectare land units. Energy transformities were used to convert individual energy flows and storages into emergy units. Maps of transformities were created that reveal a clear spatial pattern of energy transformation hierarchy. The maps display patterns of widely-dispersed areas with lower transformity energy flows and storages, and smaller, centrally-located areas with higher transformities. Energy signature graphs and spatial unit transformities were used to characterize and compare the types and amounts of energy being consumed and stored according to land use classification, planning unit, and neighborhood categories. Emergy ratio maps and spatial unit ratios were created by dividing the values for specific emergy flows or storages by the values for other emergy flows or storages. Spatial context analysis was used to analyze the spatial distribution patterns of mean and maximum values for emergy flows and storages. The modeling method developed for this study is general and applicable to all types of landscapes and could be applied at any scale. An advantage of this general approach is that the results of other studies using this method
Multivariate Receptor Models for Spatially Correlated Multipollutant Data
Jun, Mikyoung
2013-08-01
The goal of multivariate receptor modeling is to estimate the profiles of major pollution sources and quantify their impacts based on ambient measurements of pollutants. Traditionally, multivariate receptor modeling has been applied to multiple air pollutant data measured at a single monitoring site or measurements of a single pollutant collected at multiple monitoring sites. Despite the growing availability of multipollutant data collected from multiple monitoring sites, there has not yet been any attempt to incorporate spatial dependence that may exist in such data into multivariate receptor modeling. We propose a spatial statistics extension of multivariate receptor models that enables us to incorporate spatial dependence into estimation of source composition profiles and contributions given the prespecified number of sources and the model identification conditions. The proposed method yields more precise estimates of source profiles by accounting for spatial dependence in the estimation. More importantly, it enables predictions of source contributions at unmonitored sites as well as when there are missing values at monitoring sites. The method is illustrated with simulated data and real multipollutant data collected from eight monitoring sites in Harris County, Texas. Supplementary materials for this article, including data and R code for implementing the methods, are available online on the journal web site. © 2013 Copyright Taylor and Francis Group, LLC.
Harteveld, E.; Waterhout, B.; Broekhans, B.; Zonneveld, W.A.M.
2015-01-01
Spatial planning practices are constantly evolving to be more effective in a dynamic context. In the face of the latest developments, the practice of collaborative spatial planning through the formation of regional collaborations has emerged as the contemporary solution. The practice of working with
A Non-Gaussian Spatial Generalized Linear Latent Variable Model
Irincheeva, Irina
2012-08-03
We consider a spatial generalized linear latent variable model with and without normality distributional assumption on the latent variables. When the latent variables are assumed to be multivariate normal, we apply a Laplace approximation. To relax the assumption of marginal normality in favor of a mixture of normals, we construct a multivariate density with Gaussian spatial dependence and given multivariate margins. We use the pairwise likelihood to estimate the corresponding spatial generalized linear latent variable model. The properties of the resulting estimators are explored by simulations. In the analysis of an air pollution data set the proposed methodology uncovers weather conditions to be a more important source of variability than air pollution in explaining all the causes of non-accidental mortality excluding accidents. © 2012 International Biometric Society.
A Non-Gaussian Spatial Generalized Linear Latent Variable Model
Irincheeva, Irina; Cantoni, Eva; Genton, Marc G.
2012-01-01
We consider a spatial generalized linear latent variable model with and without normality distributional assumption on the latent variables. When the latent variables are assumed to be multivariate normal, we apply a Laplace approximation. To relax the assumption of marginal normality in favor of a mixture of normals, we construct a multivariate density with Gaussian spatial dependence and given multivariate margins. We use the pairwise likelihood to estimate the corresponding spatial generalized linear latent variable model. The properties of the resulting estimators are explored by simulations. In the analysis of an air pollution data set the proposed methodology uncovers weather conditions to be a more important source of variability than air pollution in explaining all the causes of non-accidental mortality excluding accidents. © 2012 International Biometric Society.
Boluwade, Alaba; Madramootoo, Chandra
2013-01-01
Spatial accuracy of hydrologic modeling inputs influences the output from hydrologic models. A pertinent question is to know the optimal level of soil sampling or how many soil samples are needed for model input, in order to improve model predictions. In this study, measured soil properties were clustered into five different configurations as inputs to the Soil and Water Assessment Tool (SWAT) simulation of the Castor River watershed (11-km 2 area) in southern Quebec, Canada. SWAT is a process-based model that predicts the impacts of climate and land use management on water yield, sediment, and nutrient fluxes. SWAT requires geographical information system inputs such as the digital elevation model as well as soil and land use maps. Mean values of soil properties are used in soil polygons (soil series); thus, the spatial variability of these properties is neglected. The primary objective of this study was to quantify the impacts of spatial variability of soil properties on the prediction of runoff, sediment, and total phosphorus using SWAT. The spatial clustering of the measured soil properties was undertaken using the regionalized with dynamically constrained agglomerative clustering and partitioning method. Measured soil data were clustered into 5, 10, 15, 20, and 24 heterogeneous regions. Soil data from the Castor watershed which have been used in previous studies was also set up and termed "Reference". Overall, there was no significant difference in runoff simulation across the five configurations including the reference. This may be attributable to SWAT's use of the soil conservation service curve number method in flow simulation. Therefore having high spatial resolution inputs for soil data may not necessarily improve predictions when they are used in hydrologic modeling.
Directory of Open Access Journals (Sweden)
Mengistu Ketema
2016-06-01
Full Text Available In Ethiopia, the national agricultural research system has been generating and disseminating different agricultural technologies since its establishment in 1966. Although these technologies are meant to increase agricultural productivity, they have to be evaluated for their impact on production and for the benefit that the farmers get out of them. Hence, the main objectives of this study were to examine the impact of technological innovations on wheat production and to decompose the total change in wheat output resulting from the introduction of new technologies into its constituent parts. Cobb-Douglas production function was employed to estimate the regression coefficients under old variety growers, new variety growers, and pooled data cases. Output decomposition model was applied to decompose the total change in output into its constituent parts. The econometric results of this study indicated that, out of 55% of the observed productivity difference between old and new variety grown plots, technological change and change in associated input levels contributed about 24% and 31%, respectively. Of the 31% increment attributed to input use levels, an increased use of herbicides and fertilizers caused the biggest jump in the productivity of improved wheat varieties (15.5% and 11% respectively. The major implications included the need to exploit the full potential of new varieties using recommended input levels, strengthening the research system, fostering coordinated efforts among various actors in agricultural development, and strengthening the technology instrument in rural development and poverty reduction strategies of the country.
Zhu, Kaiqun; Song, Yan; Zhang, Sunjie; Zhong, Zhaozhun
2017-07-01
In this paper, a non-fragile observer-based output feedback control problem for the polytopic uncertain system under distributed model predictive control (MPC) approach is discussed. By decomposing the global system into some subsystems, the computation complexity is reduced, so it follows that the online designing time can be saved.Moreover, an observer-based output feedback control algorithm is proposed in the framework of distributed MPC to deal with the difficulties in obtaining the states measurements. In this way, the presented observer-based output-feedback MPC strategy is more flexible and applicable in practice than the traditional state-feedback one. What is more, the non-fragility of the controller has been taken into consideration in favour of increasing the robustness of the polytopic uncertain system. After that, a sufficient stability criterion is presented by using Lyapunov-like functional approach, meanwhile, the corresponding control law and the upper bound of the quadratic cost function are derived by solving an optimisation subject to convex constraints. Finally, some simulation examples are employed to show the effectiveness of the method.
Practical likelihood analysis for spatial generalized linear mixed models
DEFF Research Database (Denmark)
Bonat, W. H.; Ribeiro, Paulo Justiniano
2016-01-01
We investigate an algorithm for maximum likelihood estimation of spatial generalized linear mixed models based on the Laplace approximation. We compare our algorithm with a set of alternative approaches for two datasets from the literature. The Rhizoctonia root rot and the Rongelap are......, respectively, examples of binomial and count datasets modeled by spatial generalized linear mixed models. Our results show that the Laplace approximation provides similar estimates to Markov Chain Monte Carlo likelihood, Monte Carlo expectation maximization, and modified Laplace approximation. Some advantages...... of Laplace approximation include the computation of the maximized log-likelihood value, which can be used for model selection and tests, and the possibility to obtain realistic confidence intervals for model parameters based on profile likelihoods. The Laplace approximation also avoids the tuning...
International Nuclear Information System (INIS)
Lo, Y. E.; Yi, B. Y.; Ahn, S. D.; Kim, J. H.; Lee, S. W.; Choi, E. K.
2002-01-01
A practical calculation algorithm which calculates the relative output factor (ROF) for electron irregular shaped-field has been developed and evaluated the accuracy and the effectiveness of the algorithm by comparing the measurements and the calculation results for irregular fields used in clinic. The algorithm assumes that the electron dose can be express as sum of the primary source component and the scattered component from the shielding block. The primary source is assumed to have Gaussian distribution, while the scattered component follows the inverse square law. Depth and angular dependency of the primary and the scattered are ignored for maximizing the practicability by reducing the number of parameters for the algorithm. Electron dose can be calculated with three parameters such as, the effective source distance, the variance of primary source, and the scattering power of the block. The coefficients are obtained from the square shaped-block measurements and these are confirmed from the rectangular or irregular shaped-fields. The results showed less than 1.5% difference between the calculation and measurements. The algorithm is proved to be practical, since one can acquire the full parameters with minimum measurements and generates accurate results within the clinically acceptable range
International Nuclear Information System (INIS)
Drechsler, Martin; Ohl, Cornelia; Meyerhoff, Juergen; Eichhorn, Marcus; Monsees, Jan
2011-01-01
Although wind power is currently the most efficient source of renewable energy, the installation of wind turbines (WT) in landscapes often leads to conflicts in the affected communities. We propose that such conflicts can be mitigated by a welfare-optimal spatial allocation of WT in the landscape so that a given energy target is reached at minimum social costs. The energy target is motivated by the fact that wind power production is associated with relatively low CO 2 emissions. Social costs comprise energy production costs as well as external costs caused by harmful impacts on humans and biodiversity. We present a modeling approach that combines spatially explicit ecological-economic modeling and choice experiments to determine the welfare-optimal spatial allocation of WT in West Saxony, Germany. The welfare-optimal sites balance production and external costs. Results indicate that in the welfare-optimal allocation the external costs represent about 14% of the total costs (production costs plus external costs). Optimizing wind power production without consideration of the external costs would lead to a very different allocation of WT that would marginally reduce the production costs but strongly increase the external costs and thus lead to substantial welfare losses. - Highlights: → We combine modeling and economic valuation to optimally allocate wind turbines. → Welfare-optimal allocation balances energy production costs and external costs. → External costs (impacts on the environment) can be substantial. → Ignoring external costs leads to suboptimal allocations and welfare losses.
Towards a 3d Spatial Urban Energy Modelling Approach
Bahu, J.-M.; Koch, A.; Kremers, E.; Murshed, S. M.
2013-09-01
Today's needs to reduce the environmental impact of energy use impose dramatic changes for energy infrastructure and existing demand patterns (e.g. buildings) corresponding to their specific context. In addition, future energy systems are expected to integrate a considerable share of fluctuating power sources and equally a high share of distributed generation of electricity. Energy system models capable of describing such future systems and allowing the simulation of the impact of these developments thus require a spatial representation in order to reflect the local context and the boundary conditions. This paper describes two recent research approaches developed at EIFER in the fields of (a) geo-localised simulation of heat energy demand in cities based on 3D morphological data and (b) spatially explicit Agent-Based Models (ABM) for the simulation of smart grids. 3D city models were used to assess solar potential and heat energy demand of residential buildings which enable cities to target the building refurbishment potentials. Distributed energy systems require innovative modelling techniques where individual components are represented and can interact. With this approach, several smart grid demonstrators were simulated, where heterogeneous models are spatially represented. Coupling 3D geodata with energy system ABMs holds different advantages for both approaches. On one hand, energy system models can be enhanced with high resolution data from 3D city models and their semantic relations. Furthermore, they allow for spatial analysis and visualisation of the results, with emphasis on spatially and structurally correlations among the different layers (e.g. infrastructure, buildings, administrative zones) to provide an integrated approach. On the other hand, 3D models can benefit from more detailed system description of energy infrastructure, representing dynamic phenomena and high resolution models for energy use at component level. The proposed modelling strategies
International Nuclear Information System (INIS)
Carvalho, Ariovaldo Lopes de; Antunes, Carlos Henggeler; Freire, Fausto; Henriques, Carla Oliveira
2015-01-01
A multi-objective linear programming (MOLP) model based on a hybrid Input–Output (IO) framework is presented. This model aims at assessing the trade-offs between economic, energy, environmental (E3) and social objectives in the Brazilian economic system. This combination of multi-objective models with Input–Output Analysis (IOA) plays a supplementary role in understanding the interactions between the economic and energy systems, and the corresponding impacts on the environment, offering a consistent framework for assessing the effects of distinct policies on these systems. Firstly, the System of National Accounts (SNA) is reorganized to include the National Energy Balance, creating a hybrid IO framework that is extended to assess Greenhouse Gas (GHG) emissions and the employment level. The objective functions considered are the maximization of GDP (gross domestic product) and employment levels, as well as the minimization of energy consumption and GHG emissions. An interactive method enabling a progressive and selective search of non-dominated solutions with distinct characteristics and underlying trade-offs is utilized. Illustrative results indicate that the maximization of GDP and the employment levels lead to an increase of both energy consumption and GHG emissions, while the minimization of either GHG emissions or energy consumption cause negative impacts on GDP and employment. - Highlights: • A hybrid Input–Output multi-objective model is applied to the Brazilian economy. • Objective functions are GDP, employment level, energy consumption and GHG emissions. • Interactive search process identifies trade-offs between the competing objectives. • Positive correlations between GDP growth and employment. • Positive correlations between energy consumption and GHG emissions
Atlantis Modeled Output Data for the Coral Reef Ecosystems of Guam
National Oceanic and Atmospheric Administration, Department of Commerce — A proof-of-concept Guam Atlantis Coral Reef Ecosystem Model has been developed and an added coral module to the Atlantis framework has been validated. The model is...
Energy Technology Data Exchange (ETDEWEB)
Reichert, B.K.; Bengtsson, L. [Max-Planck-Institut fuer Meteorologie, Hamburg (Germany); Aakesson, O. [Sveriges Meteorologiska och Hydrologiska Inst., Norrkoeping (Sweden)
1998-08-01
Recent proxy data obtained from ice core measurements, dendrochronology and valley glaciers provide important information on the evolution of the regional or local climate. General circulation models integrated over a long period of time could help to understand the (external and internal) forcing mechanisms of natural climate variability. For a systematic interpretation of in situ paleo proxy records, a combined method of dynamical and statistical modeling is proposed. Local 'paleo records' can be simulated from GCM output by first undertaking a model-consistent statistical downscaling and then using a process-based forward modeling approach to obtain the behavior of valley glaciers and the growth of trees under specific conditions. The simulated records can be compared to actual proxy records in order to investigate whether e.g. the response of glaciers to climatic change can be reproduced by models and to what extent climate variability obtained from proxy records (with the main focus on the last millennium) can be represented. For statistical downscaling to local weather conditions, a multiple linear forward regression model is used. Daily sets of observed weather station data and various large-scale predictors at 7 pressure levels obtained from ECMWF reanalyses are used for development of the model. Daily data give the closest and most robust relationships due to the strong dependence on individual synoptic-scale patterns. For some local variables, the performance of the model can be further increased by developing seasonal specific statistical relationships. The model is validated using both independent and restricted predictor data sets. The model is applied to a long integration of a mixed layer GCM experiment simulating pre-industrial climate variability. The dynamical-statistical local GCM output within a region around Nigardsbreen glacier, Norway is compared to nearby observed station data for the period 1868-1993. Patterns of observed
Modelling the Spatial Isotope Variability of Precipitation in Syria
Energy Technology Data Exchange (ETDEWEB)
Kattan, Z.; Kattaa, B. [Department of Geology, Atomic Energy Commission of Syria (AECS), Damascus (Syrian Arab Republic)
2013-07-15
Attempts were made to model the spatial variability of environmental isotope ({sup 18}O, {sup 2}H and {sup 3}H) compositions of precipitation in syria. Rainfall samples periodically collected on a monthly basis from 16 different stations were used for processing and demonstrating the spatial distributions of these isotopes, together with those of deuterium excess (d) values. Mathematically, the modelling process was based on applying simple polynomial models that take into consideration the effects of major geographic factors (Lon.E., Lat.N., and altitude). The modelling results of spatial distribution of stable isotopes ({sup 18}O and {sup 2}H) were generally good, as shown from the high correlation coefficients (R{sup 2} = 0.7-0.8), calculated between the observed and predicted values. In the case of deuterium excess and tritium distributions, the results were most likely approximates (R{sup 2} = 0.5-0.6). Improving the simulation of spatial isotope variability probably requires the incorporation of other local meteorological factors, such as relative air humidity, precipitation amount and vapour pressure, which are supposed to play an important role in such an arid country. (author)
Design of spatial experiments: Model fitting and prediction
Energy Technology Data Exchange (ETDEWEB)
Fedorov, V.V.
1996-03-01
The main objective of the paper is to describe and develop model oriented methods and algorithms for the design of spatial experiments. Unlike many other publications in this area, the approach proposed here is essentially based on the ideas of convex design theory.
Classifying and comparing spatial models of fire dynamics
Geoffrey J. Cary; Robert E. Keane; Mike D. Flannigan
2007-01-01
Wildland fire is a significant disturbance in many ecosystems worldwide and the interaction of fire with climate and vegetation over long time spans has major effects on vegetation dynamics, ecosystem carbon budgets, and patterns of biodiversity. Landscape-Fire-Succession Models (LFSMs) that simulate the linked processes of fire and vegetation development in a spatial...
Thinking Egyptian: Active Models for Understanding Spatial Representation.
Schiferl, Ellen
This paper highlights how introductory textbooks on Egyptian art inhibit understanding by reinforcing student preconceptions, and demonstrates another approach to discussing space with a classroom exercise and software. The alternative approach, an active model for spatial representation, introduced here was developed by adapting classroom…
Spatially Informed Plant PRA Models for Security Assessment
International Nuclear Information System (INIS)
Wheeler, Timothy A.; Thomas, Willard; Thornsbury, Eric
2006-01-01
Traditional risk models can be adapted to evaluate plant response for situations where plant systems and structures are intentionally damaged, such as from sabotage or terrorism. This paper describes a process by which traditional risk models can be spatially informed to analyze the effects of compound and widespread harsh environments through the use of 'damage footprints'. A 'damage footprint' is a spatial map of regions of the plant (zones) where equipment could be physically destroyed or disabled as a direct consequence of an intentional act. The use of 'damage footprints' requires that the basic events from the traditional probabilistic risk assessment (PRA) be spatially transformed so that the failure of individual components can be linked to the destruction of or damage to specific spatial zones within the plant. Given the nature of intentional acts, extensive modifications must be made to the risk models to account for the special nature of the 'initiating events' associated with deliberate adversary actions. Intentional acts might produce harsh environments that in turn could subject components and structures to one or more insults, such as structural, fire, flood, and/or vibration and shock damage. Furthermore, the potential for widespread damage from some of these insults requires an approach that addresses the impacts of these potentially severe insults even when they occur in locations distant from the actual physical location of a component or structure modeled in the traditional PRA. (authors)
Rockfall hazard analysis using LiDAR and spatial modeling
Lan, Hengxing; Martin, C. Derek; Zhou, Chenghu; Lim, Chang Ho
2010-05-01
Rockfalls have been significant geohazards along the Canadian Class 1 Railways (CN Rail and CP Rail) since their construction in the late 1800s. These rockfalls cause damage to infrastructure, interruption of business, and environmental impacts, and their occurrence varies both spatially and temporally. The proactive management of these rockfall hazards requires enabling technologies. This paper discusses a hazard assessment strategy for rockfalls along a section of a Canadian railway using LiDAR and spatial modeling. LiDAR provides accurate topographical information of the source area of rockfalls and along their paths. Spatial modeling was conducted using Rockfall Analyst, a three dimensional extension to GIS, to determine the characteristics of the rockfalls in terms of travel distance, velocity and energy. Historical rockfall records were used to calibrate the physical characteristics of the rockfall processes. The results based on a high-resolution digital elevation model from a LiDAR dataset were compared with those based on a coarse digital elevation model. A comprehensive methodology for rockfall hazard assessment is proposed which takes into account the characteristics of source areas, the physical processes of rockfalls and the spatial attribution of their frequency and energy.
Individual based model of slug population and spatial dynamics
Choi, Y.H.; Bohan, D.A.; Potting, R.P.J.; Semenov, M.A.; Glen, D.M.
2006-01-01
The slug, Deroceras reticulatum, is one of the most important pests of agricultural and horticultural crops in UK and Europe. In this paper, a spatially explicit individual based model (IbM) is developed to study the dynamics of a population of D. reticulatum. The IbM establishes a virtual field
Directory of Open Access Journals (Sweden)
Muayad Al-Qaisy
2015-02-01
Full Text Available In this article, multi-input multi-output (MIMO linear model predictive controller (LMPC based on state space model and nonlinear model predictive controller based on neural network (NNMPC are applied on a continuous stirred tank reactor (CSTR. The idea is to have a good control system that will be able to give optimal performance, reject high load disturbance, and track set point change. In order to study the performance of the two model predictive controllers, MIMO Proportional-Integral-Derivative controller (PID strategy is used as benchmark. The LMPC, NNMPC, and PID strategies are used for controlling the residual concentration (CA and reactor temperature (T. NNMPC control shows a superior performance over the LMPC and PID controllers by presenting a smaller overshoot and shorter settling time.
Embodied water analysis for Hebei Province, China by input-output modelling
Liu, Siyuan; Han, Mengyao; Wu, Xudong; Wu, Xiaofang; Li, Zhi; Xia, Xiaohua; Ji, Xi
2018-03-01
With the accelerating coordinated development of the Beijing-Tianjin-Hebei region, regional economic integration is recognized as a national strategy. As water scarcity places Hebei Province in a dilemma, it is of critical importance for Hebei Province to balance water resources as well as make full use of its unique advantages in the transition to sustainable development. To our knowledge, related embodied water accounting analysis has been conducted for Beijing and Tianjin, while similar works with the focus on Hebei are not found. In this paper, using the most complete and recent statistics available for Hebei Province, the embodied water use in Hebei Province is analyzed in detail. Based on input-output analysis, it presents a complete set of systems accounting framework for water resources. In addition, a database of embodied water intensity is proposed which is applicable to both intermediate inputs and final demand. The result suggests that the total amount of embodied water in final demand is 10.62 billion m3, of which the water embodied in urban household consumption accounts for more than half. As a net embodied water importer, the water embodied in the commodity trade in Hebei Province is 17.20 billion m3. The outcome of this work implies that it is particularly urgent to adjust industrial structure and trade policies for water conservation, to upgrade technology and to improve water utilization. As a result, to relieve water shortages in Hebei Province, it is of crucial importance to regulate the balance of water use within the province, thus balancing water distribution in the various industrial sectors.
Differences in spatial understanding between physical and virtual models
Directory of Open Access Journals (Sweden)
Lei Sun
2014-03-01
Full Text Available In the digital age, physical models are still used as major tools in architectural and urban design processes. The reason why designers still use physical models remains unclear. In addition, physical and 3D virtual models have yet to be differentiated. The answers to these questions are too complex to account for in all aspects. Thus, this study only focuses on the differences in spatial understanding between physical and virtual models. In particular, it emphasizes on the perception of scale. For our experiment, respondents were shown a physical model and a virtual model consecutively. A questionnaire was then used to ask the respondents to evaluate these models objectively and to establish which model was more accurate in conveying object size. Compared with the virtual model, the physical model tended to enable quicker and more accurate comparisons of building heights.
Spatial variability and parametric uncertainty in performance assessment models
International Nuclear Information System (INIS)
Pensado, Osvaldo; Mancillas, James; Painter, Scott; Tomishima, Yasuo
2011-01-01
The problem of defining an appropriate treatment of distribution functions (which could represent spatial variability or parametric uncertainty) is examined based on a generic performance assessment model for a high-level waste repository. The generic model incorporated source term models available in GoldSim ® , the TDRW code for contaminant transport in sparse fracture networks with a complex fracture-matrix interaction process, and a biosphere dose model known as BDOSE TM . Using the GoldSim framework, several Monte Carlo sampling approaches and transport conceptualizations were evaluated to explore the effect of various treatments of spatial variability and parametric uncertainty on dose estimates. Results from a model employing a representative source and ensemble-averaged pathway properties were compared to results from a model allowing for stochastic variation of transport properties along streamline segments (i.e., explicit representation of spatial variability within a Monte Carlo realization). We concluded that the sampling approach and the definition of an ensemble representative do influence consequence estimates. In the examples analyzed in this paper, approaches considering limited variability of a transport resistance parameter along a streamline increased the frequency of fast pathways resulting in relatively high dose estimates, while those allowing for broad variability along streamlines increased the frequency of 'bottlenecks' reducing dose estimates. On this basis, simplified approaches with limited consideration of variability may suffice for intended uses of the performance assessment model, such as evaluation of site safety. (author)
Spatial Development Modeling Methodology Application Possibilities in Vilnius
Directory of Open Access Journals (Sweden)
Lina Panavaitė
2017-05-01
Full Text Available In order to control the continued development of high-rise buildings and their irreversible visual impact on the overall silhouette of the city, the great cities of the world introduced new methodological principles to city’s spatial development models. These methodologies and spatial planning guidelines are focused not only on the controlled development of high-rise buildings, but on the spatial modelling of the whole city by defining main development criteria and estimating possible consequences. Vilnius city is no exception, however the re-establishment of independence of Lithuania caused uncontrolled urbanization process, so most of the city development regulations emerged as a consequence of unmanaged processes of investors’ expectations legalization. The importance of consistent urban fabric as well as conservation and representation of city’s most important objects gained attention only when an actual threat of overshadowing them with new architecture along with unmanaged urbanization in the city center or urban sprawl at suburbia, caused by land-use projects, had emerged. Current Vilnius’ spatial planning documents clearly define urban structure and key development principles, however the definitions are relatively abstract, causing uniform building coverage requirements for territories with distinct qualities and simplifying planar designs which do not meet quality standards. The overall quality of urban architecture is not regulated. The article deals with current spatial modeling methods, their individual parts, principles, the criteria for quality assessment and their applicability in Vilnius. The text contains an outline of possible building coverage regulations and impact assessment criteria for new development. The article contains a compendium of requirements for high-quality spatial planning and building design.
A spatial haplotype copying model with applications to genotype imputation.
Yang, Wen-Yun; Hormozdiari, Farhad; Eskin, Eleazar; Pasaniuc, Bogdan
2015-05-01
Ever since its introduction, the haplotype copy model has proven to be one of the most successful approaches for modeling genetic variation in human populations, with applications ranging from ancestry inference to genotype phasing and imputation. Motivated by coalescent theory, this approach assumes that any chromosome (haplotype) can be modeled as a mosaic of segments copied from a set of chromosomes sampled from the same population. At the core of the model is the assumption that any chromosome from the sample is equally likely to contribute a priori to the copying process. Motivated by recent works that model genetic variation in a geographic continuum, we propose a new spatial-aware haplotype copy model that jointly models geography and the haplotype copying process. We extend hidden Markov models of haplotype diversity such that at any given location, haplotypes that are closest in the genetic-geographic continuum map are a priori more likely to contribute to the copying process than distant ones. Through simulations starting from the 1000 Genomes data, we show that our model achieves superior accuracy in genotype imputation over the standard spatial-unaware haplotype copy model. In addition, we show the utility of our model in selecting a small personalized reference panel for imputation that leads to both improved accuracy as well as to a lower computational runtime than the standard approach. Finally, we show our proposed model can be used to localize individuals on the genetic-geographical map on the basis of their genotype data.
Directory of Open Access Journals (Sweden)
Marzieh Mokarrama
2018-04-01
Full Text Available The purpose of the present study is preparing a landform classification by using digital elevation model (DEM which has a high spatial resolution. To reach the mentioned aim, a sub-pixel spatial attraction model was used as a novel method for preparing DEM with a high spatial resolution in the north of Darab, Fars province, Iran. The sub-pixel attraction models convert the pixel into sub-pixels based on the neighboring pixels fraction values, which can only be attracted by a central pixel. Based on this approach, a mere maximum of eight neighboring pixels can be selected for calculating of the attraction value. In the mentioned model, other pixels are supposed to be far from the central pixel to receive any attraction. In the present study by using a sub-pixel attraction model, the spatial resolution of a DEM was increased. The design of the algorithm is accomplished by using a DEM with a spatial resolution of 30 m (the Advanced Space borne Thermal Emission and Reflection Radiometer; (ASTER and a 90 m (the Shuttle Radar Topography Mission; (SRTM. In the attraction model, scale factors of (S = 2, S = 3, and S = 4 with two neighboring methods of touching (T = 1 and quadrant (T = 2 are applied to the DEMs by using MATLAB software. The algorithm is evaluated by taking the best advantages of 487 sample points, which are measured by surveyors. The spatial attraction model with scale factor of (S = 2 gives better results compared to those scale factors which are greater than 2. Besides, the touching neighborhood method is turned to be more accurate than the quadrant method. In fact, dividing each pixel into more than two sub-pixels decreases the accuracy of the resulted DEM. On the other hand, in these cases DEM, is itself in charge of increasing the value of root-mean-square error (RMSE and shows that attraction models could not be used for S which is greater than 2. Thus considering results, the proposed model is highly capable of
Modern methodology and applications in spatial-temporal modeling
Matsui, Tomoko
2015-01-01
This book provides a modern introductory tutorial on specialized methodological and applied aspects of spatial and temporal modeling. The areas covered involve a range of topics which reflect the diversity of this domain of research across a number of quantitative disciplines. For instance, the first chapter deals with non-parametric Bayesian inference via a recently developed framework known as kernel mean embedding which has had a significant influence in machine learning disciplines. The second chapter takes up non-parametric statistical methods for spatial field reconstruction and exceedance probability estimation based on Gaussian process-based models in the context of wireless sensor network data. The third chapter presents signal-processing methods applied to acoustic mood analysis based on music signal analysis. The fourth chapter covers models that are applicable to time series modeling in the domain of speech and language processing. This includes aspects of factor analysis, independent component an...
Directory of Open Access Journals (Sweden)
Xubin Ping
2015-01-01
Full Text Available For the quasi-linear parameter varying (quasi-LPV system with bounded disturbance, a synthesis approach of dynamic output feedback robust model predictive control (OFRMPC is investigated. The estimation error set is represented by a zonotope and refreshed by the zonotopic set-membership estimation method. By properly refreshing the estimation error set online, the bounds of true state at the next sampling time can be obtained. Furthermore, the feasibility of the main optimization problem at the next sampling time can be determined at the current time. A numerical example is given to illustrate the effectiveness of the approach.
Stochastic geometry, spatial statistics and random fields models and algorithms
2015-01-01
Providing a graduate level introduction to various aspects of stochastic geometry, spatial statistics and random fields, this volume places a special emphasis on fundamental classes of models and algorithms as well as on their applications, for example in materials science, biology and genetics. This book has a strong focus on simulations and includes extensive codes in Matlab and R, which are widely used in the mathematical community. It can be regarded as a continuation of the recent volume 2068 of Lecture Notes in Mathematics, where other issues of stochastic geometry, spatial statistics and random fields were considered, with a focus on asymptotic methods.
Analog model for analysis of spatial instability of neutron flux
International Nuclear Information System (INIS)
Radanovic, Lj.
1964-12-01
The objective of this task was to develop a model for analysing spatial instability of the neutron flux and defining the optimum number and position of regulating rods. The developed model enables calculation of higher harmonics to be taken into account for each type of reactor, to define zones for regulation rods, position and number of points for detecting reactor state, and number and position of the regulating rods
Integrating remote sensing and spatially explicit epidemiological modeling
Finger, Flavio; Knox, Allyn; Bertuzzo, Enrico; Mari, Lorenzo; Bompangue, Didier; Gatto, Marino; Rinaldo, Andrea
2015-04-01
Spatially explicit epidemiological models are a crucial tool for the prediction of epidemiological patterns in time and space as well as for the allocation of health care resources. In addition they can provide valuable information about epidemiological processes and allow for the identification of environmental drivers of the disease spread. Most epidemiological models rely on environmental data as inputs. They can either be measured in the field by the means of conventional instruments or using remote sensing techniques to measure suitable proxies of the variables of interest. The later benefit from several advantages over conventional methods, including data availability, which can be an issue especially in developing, and spatial as well as temporal resolution of the data, which is particularly crucial for spatially explicit models. Here we present the case study of a spatially explicit, semi-mechanistic model applied to recurring cholera outbreaks in the Lake Kivu area (Democratic Republic of the Congo). The model describes the cholera incidence in eight health zones on the shore of the lake. Remotely sensed datasets of chlorophyll a concentration in the lake, precipitation and indices of global climate anomalies are used as environmental drivers. Human mobility and its effect on the disease spread is also taken into account. Several model configurations are tested on a data set of reported cases. The best models, accounting for different environmental drivers, and selected using the Akaike information criterion, are formally compared via cross validation. The best performing model accounts for seasonality, El Niño Southern Oscillation, precipitation and human mobility.
User's Guide To CHEAP0 II-Economic Analysis of Stand Prognosis Model Outputs
Joseph E. Horn; E. Lee Medema; Ervin G. Schuster
1986-01-01
CHEAP0 II provides supplemental economic analysis capability for users of version 5.1 of the Stand Prognosis Model, including recent regeneration and insect outbreak extensions. Although patterned after the old CHEAP0 model, CHEAP0 II has more features and analytic capabilities, especially for analysis of existing and uneven-aged stands....
Comparison of Soil Moisture in Switzerland Using In-Situ Measurements and Model Output
Mittelbach, H.; Orth, R.; Seneviratne, S. I.
2011-01-01
Soil moisture is an essential contributor to land surface- atmosphere interactions. In this study we evaluate the two Land surface models CLM3.5 and SIB3 regarding their performance in simulating soil moisture and its anomalies for the one year period 01.09.2009 to 31.08.2010. Four grassland sites from the SwissSMEX/- Veg project were used as reference soil moisture data. In general, both models represent the soil moisture anomalies and their distribution better than the absolute soil moisture. Furthermore, both models show a seasonal dependence of the correlation and root mean square error. In contrast to the SIB3 model, the CLM3.5 model shows stronger seasonal variation of the root mean square error and a larger interquantile range for soil moisture anomalies.
International Nuclear Information System (INIS)
Ukidwe, Nandan U.; Bakshi, Bhavik R.
2007-01-01
This paper develops a thermodynamic input-output (TIO) model of the 1997 United States economy that accounts for the flow of cumulative exergy in the 488-sector benchmark economic input-output model in two different ways. Industrial cumulative exergy consumption (ICEC) captures the exergy of all natural resources consumed directly and indirectly by each economic sector, while ecological cumulative exergy consumption (ECEC) also accounts for the exergy consumed in ecological systems for producing each natural resource. Information about exergy consumed in nature is obtained from the thermodynamics of biogeochemical cycles. As used in this work, ECEC is analogous to the concept of emergy, but does not rely on any of its controversial claims. The TIO model can also account for emissions from each sector and their impact and the role of labor. The use of consistent exergetic units permits the combination of various streams to define aggregate metrics that may provide insight into aspects related to the impact of economic sectors on the environment. Accounting for the contribution of natural capital by ECEC has been claimed to permit better representation of the quality of ecosystem goods and services than ICEC. The results of this work are expected to permit evaluation of these claims. If validated, this work is expected to lay the foundation for thermodynamic life cycle assessment, particularly of emerging technologies and with limited information
Spatial Temporal Modelling of Particulate Matter for Health Effects Studies
Hamm, N. A. S.
2016-10-01
Epidemiological studies of the health effects of air pollution require estimation of individual exposure. It is not possible to obtain measurements at all relevant locations so it is necessary to predict at these space-time locations, either on the basis of dispersion from emission sources or by interpolating observations. This study used data obtained from a low-cost sensor network of 32 air quality monitoring stations in the Dutch city of Eindhoven, which make up the ILM (innovative air (quality) measurement system). These stations currently provide PM10 and PM2.5 (particulate matter less than 10 and 2.5 m in diameter), aggregated to hourly means. The data provide an unprecedented level of spatial and temporal detail for a city of this size. Despite these benefits the time series of measurements is characterized by missing values and noisy values. In this paper a space-time analysis is presented that is based on a dynamic model for the temporal component and a Gaussian process geostatistical for the spatial component. Spatial-temporal variability was dominated by the temporal component, although the spatial variability was also substantial. The model delivered accurate predictions for both isolated missing values and 24-hour periods of missing values (RMSE = 1.4 μg m-3 and 1.8 μg m-3 respectively). Outliers could be detected by comparison to the 95% prediction interval. The model shows promise for predicting missing values, outlier detection and for mapping to support health impact studies.
Spatial modelling and ecology of Echinococcus multilocularis transmission in China.
Danson, F Mark; Giraudoux, Patrick; Craig, Philip S
2006-01-01
Recent research in central China has suggested that the most likely transmission mechanism for Echinococcus multilocularis to humans is via domestic dogs which are allowed to roam freely and hunt (infected) small mammals within areas close to villages or in areas of tented pasture. This assertion has led to the hypothesis that there is a landscape control on transmission risk since the proximity of suitable habitat for susceptible small mammals appears to be the key. We have tested this hypothesis in a number of endemic areas in China, notably south Gansu Province and the Tibetan region of western Sichuan Province. The fundamental landscape control is its effect at a regional scale on small mammal species assemblages (susceptible species are not ubiquitous) and, at a local scale, the spatial distributions of small mammal populations. To date the research has examined relationships between landscape composition and patterns of human infection, landscape and small mammal distributions and recently the relationships between landscape and dog infection rates. The key tool to characterize landscape is satellite remote sensing and these data are used as inputs to drive spatial models of transmission risk. This paper reviews the progress that has been made so far in spatial modeling of the ecology of E. multilocularis with particular reference to China, outlines current research issues, and describes a framework for building a spatial-temporal model of transmission ecology.
International Nuclear Information System (INIS)
Song, Junnian; Yang, Wei; Li, Zhaoling; Higano, Yoshiro; Wang, Xian’en
2016-01-01
Highlights: • A waste-to-energy system is constructed incorporating various urban wastes and technologies. • Waste-to-energy industries are formed and introduced into current socioeconomic system. • A novel input–output simulation model is developed and applied to a metropolis. • Complete energy, economic and environmental potentials of urban wastes are discovered. - Abstract: Tremendous amounts of wastes are generated in urban areas due to accelerating industrialization and urbanization. The current unreasonable waste disposal patterns and potential energy value of urban wastes necessitates the promotion of waste-to-energy implementation. This study is intent on discovering the complete energy, economic and environmental potentials of urban wastes taking municipal solid wastes, waste oil, organic wastewater and livestock manure into consideration. A waste-to-energy system is constructed incorporating these wastes and five waste-to-energy technologies. A novel input–output simulation model is developed and applied to a metropolis to introduce the waste-to-energy system into the current socioeconomic system and form five waste-to-energy industries. The trends in waste generation and energy recovery potential, economic benefits and greenhouse gas mitigation contribution for the study area are estimated and explored from 2011 to 2025. By 2025, biodiesel production and power generation could amount to 72.11 thousand t and 1.59 billion kW h respectively. Due to the highest energy recovery and the most subsidies, the organic wastewater biogas industry has the highest output and net profit, followed by the waste incineration power generation industry. In total 17.97 million t (carbon dioxide-equivalent) accumulative greenhouse gas emission could be mitigated. The organic wastewater biogas industry and waste incineration power generation industry are more advantageous for the study area in terms of better energy, economic and environmental performances. The
Food web model output - Trophic impacts of bald eagles in the Puget Sound food web
National Oceanic and Atmospheric Administration, Department of Commerce — This project is developing models to examine the ecological roles of bald eagles in the Puget Sound region. It is primarily being done by NMFS FTEs, in collaboration...
Bioenergetics model output - Trophic impacts of bald eagles in the Puget Sound food web
National Oceanic and Atmospheric Administration, Department of Commerce — This project is developing models to examine the ecological roles of bald eagles in the Puget Sound region. It is primarily being done by NMFS FTEs, in collaboration...
The Output Cost of Gender Discrimination: A Model-Based Macroeconomic Estimate
Cavalcanti, Tiago V. de V.; Tavares, José
2008-01-01
Gender-based discrimination is a pervasive and costly phenomenon. To a greater or lesser extent, all economies present a gender wage gap, associated with lower female labour force participation rates and higher fertility. This paper presents a growth model where saving, fertility and labour market participation are endogenously determined, and there is wage discrimination. The model is calibrated to mimic the performance of the U.S. economy, including the gender wage gap and relative female l...
Indoor 3D Route Modeling Based On Estate Spatial Data
Zhang, H.; Wen, Y.; Jiang, J.; Huang, W.
2014-04-01
Indoor three-dimensional route model is essential for space intelligence navigation and emergency evacuation. This paper is motivated by the need of constructing indoor route model automatically and as far as possible. By comparing existing building data sources, this paper firstly explained the reason why the estate spatial management data is chosen as the data source. Then, an applicable method of construction three-dimensional route model in a building is introduced by establishing the mapping relationship between geographic entities and their topological expression. This data model is a weighted graph consist of "node" and "path" to express the spatial relationship and topological structure of a building components. The whole process of modelling internal space of a building is addressed by two key steps: (1) each single floor route model is constructed, including path extraction of corridor using Delaunay triangulation algorithm with constrained edge, fusion of room nodes into the path; (2) the single floor route model is connected with stairs and elevators and the multi-floor route model is eventually generated. In order to validate the method in this paper, a shopping mall called "Longjiang New City Plaza" in Nanjing is chosen as a case of study. And the whole building space is constructed according to the modelling method above. By integrating of existing path finding algorithm, the usability of this modelling method is verified, which shows the indoor three-dimensional route modelling method based on estate spatial data in this paper can support indoor route planning and evacuation route design very well.
Computing Models of M-type Host Stars and their Panchromatic Spectral Output
Linsky, Jeffrey; Tilipman, Dennis; France, Kevin
2018-06-01
We have begun a program of computing state-of-the-art model atmospheres from the photospheres to the coronae of M stars that are the host stars of known exoplanets. For each model we are computing the emergent radiation at all wavelengths that are critical for assessingphotochemistry and mass-loss from exoplanet atmospheres. In particular, we are computing the stellar extreme ultraviolet radiation that drives hydrodynamic mass loss from exoplanet atmospheres and is essential for determing whether an exoplanet is habitable. The model atmospheres are computed with the SSRPM radiative transfer/statistical equilibrium code developed by Dr. Juan Fontenla. The code solves for the non-LTE statistical equilibrium populations of 18,538 levels of 52 atomic and ion species and computes the radiation from all species (435,986 spectral lines) and about 20,000,000 spectral lines of 20 diatomic species.The first model computed in this program was for the modestly active M1.5 V star GJ 832 by Fontenla et al. (ApJ 830, 152 (2016)). We will report on a preliminary model for the more active M5 V star GJ 876 and compare this model and its emergent spectrum with GJ 832. In the future, we will compute and intercompare semi-empirical models and spectra for all of the stars observed with the HST MUSCLES Treasury Survey, the Mega-MUSCLES Treasury Survey, and additional stars including Proxima Cen and Trappist-1.This multiyear theory program is supported by a grant from the Space Telescope Science Institute.
Single Canonical Model of Reflexive Memory and Spatial Attention
Patel, Saumil S.; Red, Stuart; Lin, Eric; Sereno, Anne B.
2015-01-01
Many neurons in the dorsal and ventral visual stream have the property that after a brief visual stimulus presentation in their receptive field, the spiking activity in these neurons persists above their baseline levels for several seconds. This maintained activity is not always correlated with the monkey’s task and its origin is unknown. We have previously proposed a simple neural network model, based on shape selective neurons in monkey lateral intraparietal cortex, which predicts the valence and time course of reflexive (bottom-up) spatial attention. In the same simple model, we demonstrate here that passive maintained activity or short-term memory of specific visual events can result without need for an external or top-down modulatory signal. Mutual inhibition and neuronal adaptation play distinct roles in reflexive attention and memory. This modest 4-cell model provides the first simple and unified physiologically plausible mechanism of reflexive spatial attention and passive short-term memory processes. PMID:26493949
Chaotic and stable perturbed maps: 2-cycles and spatial models
Braverman, E.; Haroutunian, J.
2010-06-01
As the growth rate parameter increases in the Ricker, logistic and some other maps, the models exhibit an irreversible period doubling route to chaos. If a constant positive perturbation is introduced, then the Ricker model (but not the classical logistic map) experiences period doubling reversals; the break of chaos finally gives birth to a stable two-cycle. We outline the maps which demonstrate a similar behavior and also study relevant discrete spatial models where the value in each cell at the next step is defined only by the values at the cell and its nearest neighbors. The stable 2-cycle in a scalar map does not necessarily imply 2-cyclic-type behavior in each cell for the spatial generalization of the map.
Single Canonical Model of Reflexive Memory and Spatial Attention.
Patel, Saumil S; Red, Stuart; Lin, Eric; Sereno, Anne B
2015-10-23
Many neurons in the dorsal and ventral visual stream have the property that after a brief visual stimulus presentation in their receptive field, the spiking activity in these neurons persists above their baseline levels for several seconds. This maintained activity is not always correlated with the monkey's task and its origin is unknown. We have previously proposed a simple neural network model, based on shape selective neurons in monkey lateral intraparietal cortex, which predicts the valence and time course of reflexive (bottom-up) spatial attention. In the same simple model, we demonstrate here that passive maintained activity or short-term memory of specific visual events can result without need for an external or top-down modulatory signal. Mutual inhibition and neuronal adaptation play distinct roles in reflexive attention and memory. This modest 4-cell model provides the first simple and unified physiologically plausible mechanism of reflexive spatial attention and passive short-term memory processes.
Neuromorphic model of magnocellular and parvocellular visual paths: spatial resolution
International Nuclear Information System (INIS)
Aguirre, Rolando C; Felice, Carmelo J; Colombo, Elisa M
2007-01-01
Physiological studies of the human retina show the existence of at least two visual information processing channels, the magnocellular and the parvocellular ones. Both have different spatial, temporal and chromatic features. This paper focuses on the different spatial resolution of these two channels. We propose a neuromorphic model, so that they match the retina's physiology. Considering the Deutsch and Deutsch model (1992), we propose two configurations (one for each visual channel) of the connection between the retina's different cell layers. The responses of the proposed model have similar behaviour to those of the visual cells: each channel has an optimum response corresponding to a given stimulus size which decreases for larger or smaller stimuli. This size is bigger for the magno path than for the parvo path and, in the end, both channels produce a magnifying of the borders of a stimulus
Comparison of squashing and self-consistent input-output models of quantum feedback
Peřinová, V.; Lukš, A.; Křepelka, J.
2018-03-01
The paper (Yanagisawa and Hope, 2010) opens with two ways of analysis of a measurement-based quantum feedback. The scheme of the feedback includes, along with the homodyne detector, a modulator and a beamsplitter, which does not enable one to extract the nonclassical field. In the present scheme, the beamsplitter is replaced by the quantum noise evader, which makes it possible to extract the nonclassical field. We re-approach the comparison of two models related to the same scheme. The first one admits that in the feedback loop between the photon annihilation and creation operators, unusual commutation relations hold. As a consequence, in the feedback loop, squashing of the light occurs. In the second one, the description arrives at the feedback loop via unitary transformations. But it is obvious that the unitary transformation which describes the modulator changes even the annihilation operator of the mode which passes by the modulator which is not natural. The first model could be called "squashing model" and the second one could be named "self-consistent model". Although the predictions of the two models differ only a little and both the ways of analysis have their advantages, they have also their drawbacks and further investigation is possible.
Governmentally amplified output volatility
Funashima, Yoshito
2016-11-01
Predominant government behavior is decomposed by frequency into several periodic components: updating cycles of infrastructure, Kuznets cycles, fiscal policy over business cycles, and election cycles. Little is known, however, about the theoretical impact of such cyclical behavior in public finance on output fluctuations. Based on a standard neoclassical growth model, this study intends to examine the frequency at which public investment cycles are relevant to output fluctuations. We find an inverted U-shaped relationship between output volatility and length of cycle in public investment. This implies that periodic behavior in public investment at a certain frequency range can cause aggravated output resonance. Moreover, we present an empirical analysis to test the theoretical implication, using the U.S. data in the period from 1968 to 2015. The empirical results suggest that such resonance phenomena change from low to high frequency.
Berg, Matthew; Hartley, Brian; Richters, Oliver
2015-01-01
By synthesizing stock-flow consistent models, input-output models, and aspects of ecological macroeconomics, a method is developed to simultaneously model monetary flows through the financial system, flows of produced goods and services through the real economy, and flows of physical materials through the natural environment. This paper highlights the linkages between the physical environment and the economic system by emphasizing the role of the energy industry. A conceptual model is developed in general form with an arbitrary number of sectors, while emphasizing connections with the agent-based, econophysics, and complexity economics literature. First, we use the model to challenge claims that 0% interest rates are a necessary condition for a stationary economy and conduct a stability analysis within the parameter space of interest rates and consumption parameters of an economy in stock-flow equilibrium. Second, we analyze the role of energy price shocks in contributing to recessions, incorporating several propagation and amplification mechanisms. Third, implied heat emissions from energy conversion and the effect of anthropogenic heat flux on climate change are considered in light of a minimal single-layer atmosphere climate model, although the model is only implicitly, not explicitly, linked to the economic model.
Model Predictive Current Control for High-Power Grid-Connected Converters with Output LCL Filter
DEFF Research Database (Denmark)
Delpino, Hernan Anres Miranda; Teodorescu, Remus; Rodriguez, Pedro
2009-01-01
A model predictive control strategy for a highpower, grid connected 3-level neutral clamped point converter is presented. Power losses constraints set a limit on commutation losses so reduced switching frequency is required, thus producing low frequency current harmonics. To reduce these harmonics...
Persis, Claudio De; Jayawardhana, Bayu
2012-01-01
The role of internal model principle is investigated in this paper in the context of collective synchronization and formation control problems. In the collective synchronization problem for nonlinear systems, we propose distributed control laws for passive systems which synchronize to the solution
Modeling spin magnetization transport in a spatially varying magnetic field
International Nuclear Information System (INIS)
Picone, Rico A.R.; Garbini, Joseph L.; Sidles, John A.
2015-01-01
We present a framework for modeling the transport of any number of globally conserved quantities in any spatial configuration and apply it to obtain a model of magnetization transport for spin-systems that is valid in new regimes (including high-polarization). The framework allows an entropy function to define a model that explicitly respects the laws of thermodynamics. Three facets of the model are explored. First, it is expressed as nonlinear partial differential equations that are valid for the new regime of high dipole-energy and polarization. Second, the nonlinear model is explored in the limit of low dipole-energy (semi-linear), from which is derived a physical parameter characterizing separative magnetization transport (SMT). It is shown that the necessary and sufficient condition for SMT to occur is that the parameter is spatially inhomogeneous. Third, the high spin-temperature (linear) limit is shown to be equivalent to the model of nuclear spin transport of Genack and Redfield (1975) [1]. Differences among the three forms of the model are illustrated by numerical solution with parameters corresponding to a magnetic resonance force microscopy (MRFM) experiment (Degen et al., 2009 [2]; Kuehn et al., 2008 [3]; Sidles et al., 2003 [4]; Dougherty et al., 2000 [5]). A family of analytic, steady-state solutions to the nonlinear equation is derived and shown to be the spin-temperature analog of the Langevin paramagnetic equation and Curie's law. Finally, we analyze the separative quality of magnetization transport, and a steady-state solution for the magnetization is shown to be compatible with Fenske's separative mass transport equation (Fenske, 1932 [6]). - Highlights: • A framework for modeling the transport of conserved magnetic and thermodynamic quantities in any spatial configuration. • A thermodynamically grounded model of spin magnetization transport valid in new regimes, including high-polarization. • Analysis of the separative quality of
Modeling spin magnetization transport in a spatially varying magnetic field
Energy Technology Data Exchange (ETDEWEB)
Picone, Rico A.R., E-mail: rpicone@stmartin.edu [Department of Mechanical Engineering, University of Washington, Seattle (United States); Garbini, Joseph L. [Department of Mechanical Engineering, University of Washington, Seattle (United States); Sidles, John A. [Department of Orthopædics, University of Washington, Seattle (United States)
2015-01-15
We present a framework for modeling the transport of any number of globally conserved quantities in any spatial configuration and apply it to obtain a model of magnetization transport for spin-systems that is valid in new regimes (including high-polarization). The framework allows an entropy function to define a model that explicitly respects the laws of thermodynamics. Three facets of the model are explored. First, it is expressed as nonlinear partial differential equations that are valid for the new regime of high dipole-energy and polarization. Second, the nonlinear model is explored in the limit of low dipole-energy (semi-linear), from which is derived a physical parameter characterizing separative magnetization transport (SMT). It is shown that the necessary and sufficient condition for SMT to occur is that the parameter is spatially inhomogeneous. Third, the high spin-temperature (linear) limit is shown to be equivalent to the model of nuclear spin transport of Genack and Redfield (1975) [1]. Differences among the three forms of the model are illustrated by numerical solution with parameters corresponding to a magnetic resonance force microscopy (MRFM) experiment (Degen et al., 2009 [2]; Kuehn et al., 2008 [3]; Sidles et al., 2003 [4]; Dougherty et al., 2000 [5]). A family of analytic, steady-state solutions to the nonlinear equation is derived and shown to be the spin-temperature analog of the Langevin paramagnetic equation and Curie's law. Finally, we analyze the separative quality of magnetization transport, and a steady-state solution for the magnetization is shown to be compatible with Fenske's separative mass transport equation (Fenske, 1932 [6]). - Highlights: • A framework for modeling the transport of conserved magnetic and thermodynamic quantities in any spatial configuration. • A thermodynamically grounded model of spin magnetization transport valid in new regimes, including high-polarization. • Analysis of the separative quality of
Koch, Julian; Cüneyd Demirel, Mehmet; Stisen, Simon
2018-05-01
The process of model evaluation is not only an integral part of model development and calibration but also of paramount importance when communicating modelling results to the scientific community and stakeholders. The modelling community has a large and well-tested toolbox of metrics to evaluate temporal model performance. In contrast, spatial performance evaluation does not correspond to the grand availability of spatial observations readily available and to the sophisticate model codes simulating the spatial variability of complex hydrological processes. This study makes a contribution towards advancing spatial-pattern-oriented model calibration by rigorously testing a multiple-component performance metric. The promoted SPAtial EFficiency (SPAEF) metric reflects three equally weighted components: correlation, coefficient of variation and histogram overlap. This multiple-component approach is found to be advantageous in order to achieve the complex task of comparing spatial patterns. SPAEF, its three components individually and two alternative spatial performance metrics, i.e. connectivity analysis and fractions skill score, are applied in a spatial-pattern-oriented model calibration of a catchment model in Denmark. Results suggest the importance of multiple-component metrics because stand-alone metrics tend to fail to provide holistic pattern information. The three SPAEF components are found to be independent, which allows them to complement each other in a meaningful way. In order to optimally exploit spatial observations made available by remote sensing platforms, this study suggests applying bias insensitive metrics which further allow for a comparison of variables which are related but may differ in unit. This study applies SPAEF in the hydrological context using the mesoscale Hydrologic Model (mHM; version 5.8), but we see great potential across disciplines related to spatially distributed earth system modelling.
Modeling molecular mixing in a spatially inhomogeneous turbulent flow
Meyer, Daniel W.; Deb, Rajdeep
2012-02-01
Simulations of spatially inhomogeneous turbulent mixing in decaying grid turbulence with a joint velocity-concentration probability density function (PDF) method were conducted. The inert mixing scenario involves three streams with different compositions. The mixing model of Meyer ["A new particle interaction mixing model for turbulent dispersion and turbulent reactive flows," Phys. Fluids 22(3), 035103 (2010)], the interaction by exchange with the mean (IEM) model and its velocity-conditional variant, i.e., the IECM model, were applied. For reference, the direct numerical simulation data provided by Sawford and de Bruyn Kops ["Direct numerical simulation and lagrangian modeling of joint scalar statistics in ternary mixing," Phys. Fluids 20(9), 095106 (2008)] was used. It was found that velocity conditioning is essential to obtain accurate concentration PDF predictions. Moreover, the model of Meyer provides significantly better results compared to the IECM model at comparable computational expense.
A general modeling framework for describing spatially structured population dynamics
Sample, Christine; Fryxell, John; Bieri, Joanna; Federico, Paula; Earl, Julia; Wiederholt, Ruscena; Mattsson, Brady; Flockhart, Tyler; Nicol, Sam; Diffendorfer, James E.; Thogmartin, Wayne E.; Erickson, Richard A.; Norris, D. Ryan
2017-01-01
Variation in movement across time and space fundamentally shapes the abundance and distribution of populations. Although a variety of approaches model structured population dynamics, they are limited to specific types of spatially structured populations and lack a unifying framework. Here, we propose a unified network-based framework sufficiently novel in its flexibility to capture a wide variety of spatiotemporal processes including metapopulations and a range of migratory patterns. It can accommodate different kinds of age structures, forms of population growth, dispersal, nomadism and migration, and alternative life-history strategies. Our objective was to link three general elements common to all spatially structured populations (space, time and movement) under a single mathematical framework. To do this, we adopt a network modeling approach. The spatial structure of a population is represented by a weighted and directed network. Each node and each edge has a set of attributes which vary through time. The dynamics of our network-based population is modeled with discrete time steps. Using both theoretical and real-world examples, we show how common elements recur across species with disparate movement strategies and how they can be combined under a unified mathematical framework. We illustrate how metapopulations, various migratory patterns, and nomadism can be represented with this modeling approach. We also apply our network-based framework to four organisms spanning a wide range of life histories, movement patterns, and carrying capacities. General computer code to implement our framework is provided, which can be applied to almost any spatially structured population. This framework contributes to our theoretical understanding of population dynamics and has practical management applications, including understanding the impact of perturbations on population size, distribution, and movement patterns. By working within a common framework, there is less chance
Spatial distribution of emissions to air - the SPREAD model
Energy Technology Data Exchange (ETDEWEB)
Plejdrup, M S; Gyldenkaerne, S
2011-04-15
The National Environmental Research Institute (NERI), Aarhus University, completes the annual national emission inventories for greenhouse gases and air pollutants according to Denmark's obligations under international conventions, e.g. the climate convention, UNFCCC and the convention on long-range transboundary air pollution, CLRTAP. NERI has developed a model to distribute emissions from the national emission inventories on a 1x1 km grid covering the Danish land and sea territory. The new spatial high resolution distribution model for emissions to air (SPREAD) has been developed according to the requirements for reporting of gridded emissions to CLRTAP. Spatial emission data is e.g. used as input for air quality modelling, which again serves as input for assessment and evaluation of health effects. For these purposes distributions with higher spatial resolution have been requested. Previously, a distribution on the 17x17 km EMEP grid has been set up and used in research projects combined with detailed distributions for a few sectors or sub-sectors e.g. a distribution for emissions from road traffic on 1x1 km resolution. SPREAD is developed to generate improved spatial emission data for e.g. air quality modelling in exposure studies. SPREAD includes emission distributions for each sector in the Danish inventory system; stationary combustion, mobile sources, fugitive emissions from fuels, industrial processes, solvents and other product use, agriculture and waste. This model enables generation of distributions for single sectors and for a number of sub-sectors and single sources as well. This report documents the methodologies in this first version of SPREAD and presents selected results. Further, a number of potential improvements for later versions of SPREAD are addressed and discussed. (Author)
Sparse modeling of spatial environmental variables associated with asthma.
Chang, Timothy S; Gangnon, Ronald E; David Page, C; Buckingham, William R; Tandias, Aman; Cowan, Kelly J; Tomasallo, Carrie D; Arndt, Brian G; Hanrahan, Lawrence P; Guilbert, Theresa W
2015-02-01
Geographically distributed environmental factors influence the burden of diseases such as asthma. Our objective was to identify sparse environmental variables associated with asthma diagnosis gathered from a large electronic health record (EHR) dataset while controlling for spatial variation. An EHR dataset from the University of Wisconsin's Family Medicine, Internal Medicine and Pediatrics Departments was obtained for 199,220 patients aged 5-50years over a three-year period. Each patient's home address was geocoded to one of 3456 geographic census block groups. Over one thousand block group variables were obtained from a commercial database. We developed a Sparse Spatial Environmental Analysis (SASEA). Using this method, the environmental variables were first dimensionally reduced with sparse principal component analysis. Logistic thin plate regression spline modeling was then used to identify block group variables associated with asthma from sparse principal components. The addresses of patients from the EHR dataset were distributed throughout the majority of Wisconsin's geography. Logistic thin plate regression spline modeling captured spatial variation of asthma. Four sparse principal components identified via model selection consisted of food at home, dog ownership, household size, and disposable income variables. In rural areas, dog ownership and renter occupied housing units from significant sparse principal components were associated with asthma. Our main contribution is the incorporation of sparsity in spatial modeling. SASEA sequentially added sparse principal components to Logistic thin plate regression spline modeling. This method allowed association of geographically distributed environmental factors with asthma using EHR and environmental datasets. SASEA can be applied to other diseases with environmental risk factors. Copyright © 2014 Elsevier Inc. All rights reserved.
Spatial distribution of emissions to air - the SPREAD model
Energy Technology Data Exchange (ETDEWEB)
Plejdrup, M.S.; Gyldenkaerne, S.
2011-04-15
The National Environmental Research Institute (NERI), Aarhus University, completes the annual national emission inventories for greenhouse gases and air pollutants according to Denmark's obligations under international conventions, e.g. the climate convention, UNFCCC and the convention on long-range transboundary air pollution, CLRTAP. NERI has developed a model to distribute emissions from the national emission inventories on a 1x1 km grid covering the Danish land and sea territory. The new spatial high resolution distribution model for emissions to air (SPREAD) has been developed according to the requirements for reporting of gridded emissions to CLRTAP. Spatial emission data is e.g. used as input for air quality modelling, which again serves as input for assessment and evaluation of health effects. For these purposes distributions with higher spatial resolution have been requested. Previously, a distribution on the 17x17 km EMEP grid has been set up and used in research projects combined with detailed distributions for a few sectors or sub-sectors e.g. a distribution for emissions from road traffic on 1x1 km resolution. SPREAD is developed to generate improved spatial emission data for e.g. air quality modelling in exposure studies. SPREAD includes emission distributions for each sector in the Danish inventory system; stationary combustion, mobile sources, fugitive emissions from fuels, industrial processes, solvents and other product use, agriculture and waste. This model enables generation of distributions for single sectors and for a number of sub-sectors and single sources as well. This report documents the methodologies in this first version of SPREAD and presents selected results. Further, a number of potential improvements for later versions of SPREAD are addressed and discussed. (Author)
Tapered composite likelihood for spatial max-stable models
Sang, Huiyan
2014-05-01
Spatial extreme value analysis is useful to environmental studies, in which extreme value phenomena are of interest and meaningful spatial patterns can be discerned. Max-stable process models are able to describe such phenomena. This class of models is asymptotically justified to characterize the spatial dependence among extremes. However, likelihood inference is challenging for such models because their corresponding joint likelihood is unavailable and only bivariate or trivariate distributions are known. In this paper, we propose a tapered composite likelihood approach by utilizing lower dimensional marginal likelihoods for inference on parameters of various max-stable process models. We consider a weighting strategy based on a "taper range" to exclude distant pairs or triples. The "optimal taper range" is selected to maximize various measures of the Godambe information associated with the tapered composite likelihood function. This method substantially reduces the computational cost and improves the efficiency over equally weighted composite likelihood estimators. We illustrate its utility with simulation experiments and an analysis of rainfall data in Switzerland.
Database modeling to integrate macrobenthos data in Spatial Data Infrastructure
Directory of Open Access Journals (Sweden)
José Alberto Quintanilha
2012-08-01
Full Text Available Coastal zones are complex areas that include marine and terrestrial environments. Besides its huge environmental wealth, they also attracts humans because provides food, recreation, business, and transportation, among others. Some difficulties to manage these areas are related with their complexity, diversity of interests and the absence of standardization to collect and share data to scientific community, public agencies, among others. The idea to organize, standardize and share this information based on Web Atlas is essential to support planning and decision making issues. The construction of a spatial database integrating the environmental business, to be used on Spatial Data Infrastructure (SDI is illustrated by a bioindicator that indicates the quality of the sediments. The models show the phases required to build Macrobenthos spatial database based on Santos Metropolitan Region as a reference. It is concluded that, when working with environmental data the structuring of knowledge in a conceptual model is essential for their subsequent integration into the SDI. During the modeling process it can be noticed that methodological issues related to the collection process may obstruct or prejudice the integration of data from different studies of the same area. The development of a database model, as presented in this study, can be used as a reference for further research with similar goals.
Tapered composite likelihood for spatial max-stable models
Sang, Huiyan; Genton, Marc G.
2014-01-01
Spatial extreme value analysis is useful to environmental studies, in which extreme value phenomena are of interest and meaningful spatial patterns can be discerned. Max-stable process models are able to describe such phenomena. This class of models is asymptotically justified to characterize the spatial dependence among extremes. However, likelihood inference is challenging for such models because their corresponding joint likelihood is unavailable and only bivariate or trivariate distributions are known. In this paper, we propose a tapered composite likelihood approach by utilizing lower dimensional marginal likelihoods for inference on parameters of various max-stable process models. We consider a weighting strategy based on a "taper range" to exclude distant pairs or triples. The "optimal taper range" is selected to maximize various measures of the Godambe information associated with the tapered composite likelihood function. This method substantially reduces the computational cost and improves the efficiency over equally weighted composite likelihood estimators. We illustrate its utility with simulation experiments and an analysis of rainfall data in Switzerland.
Output from Statistical Predictive Models as Input to eLearning Dashboards
Directory of Open Access Journals (Sweden)
Marlene A. Smith
2015-06-01
Full Text Available We describe how statistical predictive models might play an expanded role in educational analytics by giving students automated, real-time information about what their current performance means for eventual success in eLearning environments. We discuss how an online messaging system might tailor information to individual students using predictive analytics. The proposed system would be data-driven and quantitative; e.g., a message might furnish the probability that a student will successfully complete the certificate requirements of a massive open online course. Repeated messages would prod underperforming students and alert instructors to those in need of intervention. Administrators responsible for accreditation or outcomes assessment would have ready documentation of learning outcomes and actions taken to address unsatisfactory student performance. The article’s brief introduction to statistical predictive models sets the stage for a description of the messaging system. Resources and methods needed to develop and implement the system are discussed.
Spatial Modelling of Sediment Transport over the Upper Citarum Catchment
Directory of Open Access Journals (Sweden)
Poerbandono
2006-05-01
Full Text Available This paper discusses set up of a spatial model applied in Geographic Information System (GIS environment for predicting annual erosion rate and sediment yield of a watershed. The study area is situated in the Upper Citarum Catchment of West Java. Annual sediment yield is considered as product of erosion rate and sediment delivery ratio to be modelled under similar modeling tool. Sediment delivery ratio is estimated on the basis of sediment resident time. The modeling concept is based on the calculation of water flow velocity through sub-catchment surface, which is controlled by topography, rainfall, soil characteristics and various types of land use. Relating velocity to known distance across digital elevation model, sediment resident time can be estimated. Data from relevance authorities are used. Bearing in mind limited knowledge of some governing factors due to lack of observation, the result has shown the potential of GIS for spatially modeling regional sediment transport. Validation of model result is carried out by evaluating measured and computed total sediment yield at the main outlet. Computed total sediment yields for 1994 and 2001 are found to be 1.96×106 and 2.10×106tons/year. They deviate roughly 54 and 8% with respect to those measured in the field. Model response due to land use change observed in 2001 and 1994 is also recognised. Under presumably constant rainfall depth, an increase of overall average annual erosion rate of 11% resulted in an increase of overall average sediment yield of 7%.
Supplementary Material for: Factor Copula Models for Replicated Spatial Data
Krupskii, Pavel; Huser, Raphaë l; Genton, Marc G.
2016-01-01
We propose a new copula model that can be used with replicated spatial data. Unlike the multivariate normal copula, the proposed copula is based on the assumption that a common factor exists and affects the joint dependence of all measurements of the process. Moreover, the proposed copula can model tail dependence and tail asymmetry. The model is parameterized in terms of a covariance function that may be chosen from the many models proposed in the literature, such as the Matérn model. For some choice of common factors, the joint copula density is given in closed form and therefore likelihood estimation is very fast. In the general case, one-dimensional numerical integration is needed to calculate the likelihood, but estimation is still reasonably fast even with large data sets. We use simulation studies to show the wide range of dependence structures that can be generated by the proposed model with different choices of common factors. We apply the proposed model to spatial temperature data and compare its performance with some popular geostatistics models.
A spatial structural derivative model for ultraslow diffusion
Directory of Open Access Journals (Sweden)
Xu Wei
2017-01-01
Full Text Available This study investigates the ultraslow diffusion by a spatial structural derivative, in which the exponential function ex is selected as the structural function to construct the local structural derivative diffusion equation model. The analytical solution of the diffusion equation is a form of Biexponential distribution. Its corresponding mean squared displacement is numerically calculated, and increases more slowly than the logarithmic function of time. The local structural derivative diffusion equation with the structural function ex in space is an alternative physical and mathematical modeling model to characterize a kind of ultraslow diffusion.
Hamdy, M; Hamdan, I
2015-07-01
In this paper, a robust H∞ fuzzy output feedback controller is designed for a class of affine nonlinear systems with disturbance via Takagi-Sugeno (T-S) fuzzy bilinear model. The parallel distributed compensation (PDC) technique is utilized to design a fuzzy controller. The stability conditions of the overall closed loop T-S fuzzy bilinear model are formulated in terms of Lyapunov function via linear matrix inequality (LMI). The control law is robustified by H∞ sense to attenuate external disturbance. Moreover, the desired controller gains can be obtained by solving a set of LMI. A continuous stirred tank reactor (CSTR), which is a benchmark problem in nonlinear process control, is discussed in detail to verify the effectiveness of the proposed approach with a comparative study. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.
Spatial modeling of HIV and HSV-2 among women in Kenya with spatially varying coefficients
Directory of Open Access Journals (Sweden)
Elphas Okango
2016-04-01
Full Text Available Abstract Background Disease mapping has become popular in the field of statistics as a method to explain the spatial distribution of disease outcomes and as a tool to help design targeted intervention strategies. Most of these models however have been implemented with assumptions that may be limiting or altogether lead to less meaningful results and hence interpretations. Some of these assumptions include the linearity, stationarity and normality assumptions. Studies have shown that the linearity assumption is not necessarily true for all covariates. Age for example has been found to have a non-linear relationship with HIV and HSV-2 prevalence. Other studies have made stationarity assumption in that one stimulus e.g. education, provokes the same response in all the regions under study and this is also quite restrictive. Responses to stimuli may vary from region to region due to aspects like culture, preferences and attitudes. Methods We perform a spatial modeling of HIV and HSV-2 among women in Kenya, while relaxing these assumptions i.e. the linearity assumption by allowing the covariate age to have a non-linear effect on HIV and HSV-2 prevalence using the random walk model of order 2 and the stationarity assumption by allowing the rest of the covariates to vary spatially using the conditional autoregressive model. The women data used in this study were derived from the 2007 Kenya AIDS indicator survey where women aged 15–49 years were surveyed. A full Bayesian approach was used and the models were implemented in R-INLA software. Results Age was found to have a non-linear relationship with both HIV and HSV-2 prevalence, and the spatially varying coefficient model provided a significantly better fit for HSV-2. Age-at first sex also had a greater effect on HSV-2 prevalence in the Coastal and some parts of North Eastern regions suggesting either early marriages or child prostitution. The effect of education on HIV prevalence among women was more
The sensitivity of ecosystem service models to choices of input data and spatial resolution
Bagstad, Kenneth J.; Cohen, Erika; Ancona, Zachary H.; McNulty, Steven; Sun, Ge
2018-01-01
Although ecosystem service (ES) modeling has progressed rapidly in the last 10–15 years, comparative studies on data and model selection effects have become more common only recently. Such studies have drawn mixed conclusions about whether different data and model choices yield divergent results. In this study, we compared the results of different models to address these questions at national, provincial, and subwatershed scales in Rwanda. We compared results for carbon, water, and sediment as modeled using InVEST and WaSSI using (1) land cover data at 30 and 300 m resolution and (2) three different input land cover datasets. WaSSI and simpler InVEST models (carbon storage and annual water yield) were relatively insensitive to the choice of spatial resolution, but more complex InVEST models (seasonal water yield and sediment regulation) produced large differences when applied at differing resolution. Six out of nine ES metrics (InVEST annual and seasonal water yield and WaSSI) gave similar predictions for at least two different input land cover datasets. Despite differences in mean values when using different data sources and resolution, we found significant and highly correlated results when using Spearman's rank correlation, indicating consistent spatial patterns of high and low values. Our results confirm and extend conclusions of past studies, showing that in certain cases (e.g., simpler models and national-scale analyses), results can be robust to data and modeling choices. For more complex models, those with different output metrics, and subnational to site-based analyses in heterogeneous environments, data and model choices may strongly influence study findings.
Radhakrishnan, A.; Balaji, V.; Schweitzer, R.; Nikonov, S.; O'Brien, K.; Vahlenkamp, H.; Burger, E. F.
2016-12-01
There are distinct phases in the development cycle of an Earth system model. During the model development phase, scientists make changes to code and parameters and require rapid access to results for evaluation. During the production phase, scientists may make an ensemble of runs with different settings, and produce large quantities of output, that must be further analyzed and quality controlled for scientific papers and submission to international projects such as the Climate Model Intercomparison Project (CMIP). During this phase, provenance is a key concern:being able to track back from outputs to inputs. We will discuss one of the paths taken at GFDL in delivering tools across this lifecycle, offering on-demand analysis of data by integrating the use of GFDL's in-house FRE-Curator, Unidata's THREDDS and NOAA PMEL's Live Access Servers (LAS).Experience over this lifecycle suggests that a major difficulty in developing analysis capabilities is only partially the scientific content, but often devoted to answering the questions "where is the data?" and "how do I get to it?". "FRE-Curator" is the name of a database-centric paradigm used at NOAA GFDL to ingest information about the model runs into an RDBMS (Curator database). The components of FRE-Curator are integrated into Flexible Runtime Environment workflow and can be invoked during climate model simulation. The front end to FRE-Curator, known as the Model Development Database Interface (MDBI) provides an in-house web-based access to GFDL experiments: metadata, analysis output and more. In order to provide on-demand visualization, MDBI uses Live Access Servers which is a highly configurable web server designed to provide flexible access to geo-referenced scientific data, that makes use of OPeNDAP. Model output saved in GFDL's tape archive, the size of the database and experiments, continuous model development initiatives with more dynamic configurations add complexity and challenges in providing an on
Models of chromatin spatial organisation in the cell nucleus
Nicodemi, Mario
2014-03-01
In the cell nucleus chromosomes have a complex architecture serving vital functional purposes. Recent experiments have started unveiling the interaction map of DNA sites genome-wide, revealing different levels of organisation at different scales. The principles, though, which orchestrate such a complex 3D structure remain still mysterious. I will overview the scenario emerging from some classical polymer physics models of the general aspect of chromatin spatial organisation. The available experimental data, which can be rationalised in a single framework, support a picture where chromatin is a complex mixture of differently folded regions, self-organised across spatial scales according to basic physical mechanisms. I will also discuss applications to specific DNA loci, e.g. the HoxB locus, where models informed with biological details, and tested against targeted experiments, can help identifying the determinants of folding.
Mapping and spatial-temporal modeling of Bromus tectorum invasion in central Utah
Jin, Zhenyu
Cheatgrass, or Downy Brome, is an exotic winter annual weed native to the Mediterranean region. Since its introduction to the U.S., it has become a significant weed and aggressive invader of sagebrush, pinion-juniper, and other shrub communities, where it can completely out-compete native grasses and shrubs. In this research, remotely sensed data combined with field collected data are used to investigate the distribution of the cheatgrass in Central Utah, to characterize the trend of the NDVI time-series of cheatgrass, and to construct a spatially explicit population-based model to simulate the spatial-temporal dynamics of the cheatgrass. This research proposes a method for mapping the canopy closure of invasive species using remotely sensed data acquired at different dates. Different invasive species have their own distinguished phenologies and the satellite images in different dates could be used to capture the phenology. The results of cheatgrass abundance prediction have a good fit with the field data for both linear regression and regression tree models, although the regression tree model has better performance than the linear regression model. To characterize the trend of NDVI time-series of cheatgrass, a novel smoothing algorithm named RMMEH is presented in this research to overcome some drawbacks of many other algorithms. By comparing the performance of RMMEH in smoothing a 16-day composite of the MODIS NDVI time-series with that of two other methods, which are the 4253EH, twice and the MVI, we have found that RMMEH not only keeps the original valid NDVI points, but also effectively removes the spurious spikes. The reconstructed NDVI time-series of different land covers are of higher quality and have smoother temporal trend. To simulate the spatial-temporal dynamics of cheatgrass, a spatially explicit population-based model is built applying remotely sensed data. The comparison between the model output and the ground truth of cheatgrass closure demonstrates
Linking Fine-Scale Observations and Model Output with Imagery at Multiple Scales
Sadler, J.; Walthall, C. L.
2014-12-01
The development and implementation of a system for seasonal worldwide agricultural yield estimates is underway with the international Group on Earth Observations GeoGLAM project. GeoGLAM includes a research component to continually improve and validate its algorithms. There is a history of field measurement campaigns going back decades to draw upon for ways of linking surface measurements and model results with satellite observations. Ground-based, in-situ measurements collected by interdisciplinary teams include yields, model inputs and factors affecting scene radiation. Data that is comparable across space and time with careful attention to calibration is essential for the development and validation of agricultural applications of remote sensing. Data management to ensure stewardship, availability and accessibility of the data are best accomplished when considered an integral part of the research. The expense and logistical challenges of field measurement campaigns can be cost-prohibitive and because of short funding cycles for research, access to consistent, stable study sites can be lost. The use of a dedicated staff for baseline data needed by multiple investigators, and conducting measurement campaigns using existing measurement networks such as the USDA Long Term Agroecosystem Research network can fulfill these needs and ensure long-term access to study sites.
Directory of Open Access Journals (Sweden)
Jianhuan Huang
2014-01-01
Full Text Available There are two typical subprocesses in bank production—deposit generation and loan generation. Aiming to open the black box of input-output production of banks and provide comprehensive and accurate assessment on the efficiency of each stage, this paper proposes a two-stage network model with bad outputs and supper efficiency (US-NSBM. Empirical comparisons show that the US-NSBM may be promising and practical for taking the nonperforming loans into account and being able to rank all samples. Applying it to measure the efficiency of Chinese commercial banks from 2008 to 2012, this paper explores the characteristics of overall and divisional efficiency, as well as the determinants of them. Some interesting results are discovered. The polarization of efficiency occurs in the bank level and deposit generation, yet does not in the loan generation. Five hypotheses work as expected in the bank level, but not all of them are supported in the stage level. Our results extend and complement some earlier empirical publications in the bank level.
Directory of Open Access Journals (Sweden)
Hongzhi Hu
2015-01-01
Full Text Available This paper deals with the modeling of fault for analog circuits. A two-dimensional (2D fault model is first proposed based on collaborative analysis of supply current and output voltage. This model is a family of circle loci on the complex plane, and it simplifies greatly the algorithms for test point selection and potential fault simulations, which are primary difficulties in fault diagnosis of analog circuits. Furthermore, in order to reduce the difficulty of fault location, an improved fault model in three-dimensional (3D complex space is proposed, which achieves a far better fault detection ratio (FDR against measurement error and parametric tolerance. To address the problem of fault masking in both 2D and 3D fault models, this paper proposes an effective design for testability (DFT method. By adding redundant bypassing-components in the circuit under test (CUT, this method achieves excellent fault isolation ratio (FIR in ambiguity group isolation. The efficacy of the proposed model and testing method is validated through experimental results provided in this paper.
Modeling Spatial Dependence of Rainfall Extremes Across Multiple Durations
Le, Phuong Dong; Leonard, Michael; Westra, Seth
2018-03-01
Determining the probability of a flood event in a catchment given that another flood has occurred in a nearby catchment is useful in the design of infrastructure such as road networks that have multiple river crossings. These conditional flood probabilities can be estimated by calculating conditional probabilities of extreme rainfall and then transforming rainfall to runoff through a hydrologic model. Each catchment's hydrological response times are unlikely to be the same, so in order to estimate these conditional probabilities one must consider the dependence of extreme rainfall both across space and across critical storm durations. To represent these types of dependence, this study proposes a new approach for combining extreme rainfall across different durations within a spatial extreme value model using max-stable process theory. This is achieved in a stepwise manner. The first step defines a set of common parameters for the marginal distributions across multiple durations. The parameters are then spatially interpolated to develop a spatial field. Storm-level dependence is represented through the max-stable process for rainfall extremes across different durations. The dependence model shows a reasonable fit between the observed pairwise extremal coefficients and the theoretical pairwise extremal coefficient function across all durations. The study demonstrates how the approach can be applied to develop conditional maps of the return period and return level across different durations.
Spatial Models of Prebiotic Evolution: Soup Before Pizza?
Scheuring, István; Czárán, Tamás; Szabó, Péter; Károlyi, György; Toroczkai, Zoltán
2003-10-01
The problem of information integration and resistance to the invasion of parasitic mutants in prebiotic replicator systems is a notorious issue of research on the origin of life. Almost all theoretical studies published so far have demonstrated that some kind of spatial structure is indispensable for the persistence and/or the parasite resistance of any feasible replicator system. Based on a detailed critical survey of spatial models on prebiotic information integration, we suggest a possible scenario for replicator system evolution leading to the emergence of the first protocells capable of independent life. We show that even the spatial versions of the hypercycle model are vulnerable to selfish parasites in heterogeneous habitats. Contrary, the metabolic system remains persistent and coexistent with its parasites both on heterogeneous surfaces and in chaotically mixing flowing media. Persistent metabolic parasites can be converted to metabolic cooperators, or they can gradually obtain replicase activity. Our simulations show that, once replicase activity emerged, a gradual and simultaneous evolutionary improvement of replicase functionality (speed and fidelity) and template efficiency is possible only on a surface that constrains the mobility of macromolecule replicators. Based on the results of the models reviewed, we suggest that open chaotic flows (`soup') and surface dynamics (`pizza') both played key roles in the sequence of evolutionary events ultimately concluding in the appearance of the first living cell on Earth.
Cloud archiving and data mining of High-Resolution Rapid Refresh forecast model output
Blaylock, Brian K.; Horel, John D.; Liston, Samuel T.
2017-12-01
Weather-related research often requires synthesizing vast amounts of data that need archival solutions that are both economical and viable during and past the lifetime of the project. Public cloud computing services (e.g., from Amazon, Microsoft, or Google) or private clouds managed by research institutions are providing object data storage systems potentially appropriate for long-term archives of such large geophysical data sets. We illustrate the use of a private cloud object store developed by the Center for High Performance Computing (CHPC) at the University of Utah. Since early 2015, we have been archiving thousands of two-dimensional gridded fields (each one containing over 1.9 million values over the contiguous United States) from the High-Resolution Rapid Refresh (HRRR) data assimilation and forecast modeling system. The archive is being used for retrospective analyses of meteorological conditions during high-impact weather events, assessing the accuracy of the HRRR forecasts, and providing initial and boundary conditions for research simulations. The archive is accessible interactively and through automated download procedures for researchers at other institutions that can be tailored by the user to extract individual two-dimensional grids from within the highly compressed files. Characteristics of the CHPC object storage system are summarized relative to network file system storage or tape storage solutions. The CHPC storage system is proving to be a scalable, reliable, extensible, affordable, and usable archive solution for our research.
Thekdi, Shital A; Santos, Joost R
2016-05-01
Disruptive events such as natural disasters, loss or reduction of resources, work stoppages, and emergent conditions have potential to propagate economic losses across trade networks. In particular, disruptions to the operation of container port activity can be detrimental for international trade and commerce. Risk assessment should anticipate the impact of port operation disruptions with consideration of how priorities change due to uncertain scenarios and guide investments that are effective and feasible for implementation. Priorities for protective measures and continuity of operations planning must consider the economic impact of such disruptions across a variety of scenarios. This article introduces new performance metrics to characterize resiliency in interdependency modeling and also integrates scenario-based methods to measure economic sensitivity to sudden-onset disruptions. The methods will be demonstrated on a U.S. port responsible for handling $36.1 billion of cargo annually. The methods will be useful to port management, private industry supply chain planning, and transportation infrastructure management. © 2015 Society for Risk Analysis.
Modeling spatial invasion of Ebola in West Africa.
D'Silva, Jeremy P; Eisenberg, Marisa C
2017-09-07
The 2014-2016 Ebola Virus Disease (EVD) epidemic in West Africa was the largest ever recorded, representing a fundamental shift in Ebola epidemiology with unprecedented spatiotemporal complexity. To understand the spatiotemporal dynamics of EVD in West Africa, we developed spatial transmission models using a gravity-model framework at both the national and district-level scales, which we used to compare effectiveness of local interventions (e.g. local quarantine) and long-range interventions (e.g. border-closures). The country-level gravity model captures the epidemic data, including multiple waves of initial epidemic growth observed in Guinea. We found that local-transmission reductions were most effective in Liberia, while long-range transmission was dominant in Sierra Leone. Both models illustrated that interventions in one region result in an amplified protective effect on other regions by preventing spatial transmission. In the district-level model, interventions in the strongest of these amplifying regions reduced total cases in all three countries by over 20%, in spite of the region itself generating only ∼0.1% of total cases. This model structure and associated intervention analysis provide information that can be used by public health policymakers to assist planning and response efforts for future epidemics. Copyright © 2017 Elsevier Ltd. All rights reserved.
A modal approach to modeling spatially distributed vibration energy dissipation.
Energy Technology Data Exchange (ETDEWEB)
Segalman, Daniel Joseph
2010-08-01
The nonlinear behavior of mechanical joints is a confounding element in modeling the dynamic response of structures. Though there has been some progress in recent years in modeling individual joints, modeling the full structure with myriad frictional interfaces has remained an obstinate challenge. A strategy is suggested for structural dynamics modeling that can account for the combined effect of interface friction distributed spatially about the structure. This approach accommodates the following observations: (1) At small to modest amplitudes, the nonlinearity of jointed structures is manifest primarily in the energy dissipation - visible as vibration damping; (2) Correspondingly, measured vibration modes do not change significantly with amplitude; and (3) Significant coupling among the modes does not appear to result at modest amplitudes. The mathematical approach presented here postulates the preservation of linear modes and invests all the nonlinearity in the evolution of the modal coordinates. The constitutive form selected is one that works well in modeling spatially discrete joints. When compared against a mathematical truth model, the distributed dissipation approximation performs well.
Using gridded multimedia model to simulate spatial fate of Benzo[α]pyrene on regional scale.
Liu, Shijie; Lu, Yonglong; Wang, Tieyu; Xie, Shuangwei; Jones, Kevin C; Sweetman, Andrew J
2014-02-01
Predicting the environmental multimedia fate is an essential step in the process of assessing the human exposure and health impacts of chemicals released into the environment. Multimedia fate models have been widely applied to calculate the fate and distribution of chemicals in the environment, which can serve as input to a human exposure model. In this study, a grid based multimedia fugacity model at regional scale was developed together with a case study modeling the fate and transfer of Benzo[α]pyrene (BaP) in Bohai coastal region, China. Based on the estimated emission and in-site survey in 2008, the BaP concentrations in air, vegetation, soil, fresh water, fresh water sediment and coastal water as well as the transfer fluxes were derived under the steady-state assumption. The model results were validated through comparison between the measured and modeled concentrations of BaP. The model results indicated that the predicted concentrations of BaP in air, fresh water, soil and sediment generally agreed with field observations. Model predictions suggest that soil was the dominant sink of BaP in terrestrial systems. Flow from air to soil, vegetation and costal water were three major pathways of BaP inter-media transport processes. Most of the BaP entering the sea was transferred by air flow, which was also the crucial driving force in the spatial distribution processes of BaP. The Yellow River, Liaohe River and Daliao River played an important role in the spatial transformation processes of BaP. Compared with advection outflow, degradation was more important in removal processes of BaP. Sensitivities of the model estimates to input parameters were tested. The result showed that emission rates, compartment dimensions, transport velocity and degradation rates of BaP were the most influential parameters for the model output. Monte Carlo simulation was carried out to determine parameter uncertainty, from which the coefficients of variation for the estimated Ba
Spatial interpolation schemes of daily precipitation for hydrologic modeling
Hwang, Y.; Clark, M.R.; Rajagopalan, B.; Leavesley, G.
2012-01-01
Distributed hydrologic models typically require spatial estimates of precipitation interpolated from sparsely located observational points to the specific grid points. We compare and contrast the performance of regression-based statistical methods for the spatial estimation of precipitation in two hydrologically different basins and confirmed that widely used regression-based estimation schemes fail to describe the realistic spatial variability of daily precipitation field. The methods assessed are: (1) inverse distance weighted average; (2) multiple linear regression (MLR); (3) climatological MLR; and (4) locally weighted polynomial regression (LWP). In order to improve the performance of the interpolations, the authors propose a two-step regression technique for effective daily precipitation estimation. In this simple two-step estimation process, precipitation occurrence is first generated via a logistic regression model before estimate the amount of precipitation separately on wet days. This process generated the precipitation occurrence, amount, and spatial correlation effectively. A distributed hydrologic model (PRMS) was used for the impact analysis in daily time step simulation. Multiple simulations suggested noticeable differences between the input alternatives generated by three different interpolation schemes. Differences are shown in overall simulation error against the observations, degree of explained variability, and seasonal volumes. Simulated streamflows also showed different characteristics in mean, maximum, minimum, and peak flows. Given the same parameter optimization technique, LWP input showed least streamflow error in Alapaha basin and CMLR input showed least error (still very close to LWP) in Animas basin. All of the two-step interpolation inputs resulted in lower streamflow error compared to the directly interpolated inputs. ?? 2011 Springer-Verlag.
Energy Technology Data Exchange (ETDEWEB)
Zhou, Sha [Tsinghua Univ., Beijing (China); Yu, Bofu [Griffith Univ., Nathan Queensland (Australia); Schwalm, Christopher R. [Woods Hole Research Center, Falmouth, MA (United States); Northern Arizona Univ., Flagstaff, AZ (United States); Ciais, Philippe [Lab. des Sciences du Climat et de l' Environnement, Gif-sur-Yvette (France); Zhang, Yao [Univ. of Oklahoma, Norman, OK (United States); Fisher, Joshua B. [California Institute of Technology, Pasadena, CA (United States); Michalak, Anna M. [Carnegie Institution for Science, Stanford, CA (United States); Wang, Weile [California State Uni., Monterey Bay, Seasid, CA (United States); Poulter, Benjamin [Montana State Univ., Bozeman, MT (United States); Huntzinger, Deborah N. [Northern Arizona Univ., Flagstaff, AZ (United States); Niu, Shuli [Institute of Geographic Sciences and Natural Resources Research, Beijing (China); Chinese Academy of Sciences (CAS), Beijing (China); Mao, Jiafu [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Jain, Atul [Univ. of Illinois at Urbana-Champaign, Urbana, IL (United States); Ricciuto, Daniel M. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Shi, Xiaoying [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Ito, Akihiko [Tohoku Univ., Sendai (Japan); Wei, Yaxing [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Huang, Yuefei [Tsinghua Univ., Beijing (China); Qinghai Univ., Xining (China); Wang, Guangqian [Tsinghua Univ., Beijing (China)
2017-10-18
Here, water use efficiency (WUE), defined as the ratio of gross primary productivity and evapotranspiration at the ecosystem scale, is a critical variable linking the carbon and water cycles. Incorporating a dependency on vapor pressure deficit, apparent underlying WUE (uWUE) provides a better indicator of how terrestrial ecosystems respond to environmental changes than other WUE formulations. Here we used 20th century simulations from four terrestrial biosphere models to develop a novel variance decomposition method. With this method, we attributed variations in apparent uWUE to both the trend and interannual variation of environmental drivers. The secular increase in atmospheric CO_{2} explained a clear majority of total variation (66 ± 32%: mean ± one standard deviation), followed by positive trends in nitrogen deposition and climate, as well as a negative trend in land use change. In contrast, interannual variation was mostly driven by interannual climate variability. To analyze the mechanism of the CO_{2} effect, we partitioned the apparent uWUE into the transpiration ratio (transpiration over evapotranspiration) and potential uWUE. The relative increase in potential uWUE parallels that of CO_{2}, but this direct CO_{2} effect was offset by 20 ± 4% by changes in ecosystem structure, that is, leaf area index for different vegetation types. However, the decrease in transpiration due to stomatal closure with rising CO_{2} was reduced by 84% by an increase in leaf area index, resulting in small changes in the transpiration ratio. CO_{2} concentration thus plays a dominant role in driving apparent uWUE variations over time, but its role differs for the two constituent components: potential uWUE and transpiration.
Zhou, Sha; Yu, Bofu; Schwalm, Christopher R.; Ciais, Philippe; Zhang, Yao; Fisher, Joshua B.; Michalak, Anna M.; Wang, Weile; Poulter, Benjamin; Huntzinger, Deborah N.; Niu, Shuli; Mao, Jiafu; Jain, Atul; Ricciuto, Daniel M.; Shi, Xiaoying; Ito, Akihiko; Wei, Yaxing; Huang, Yuefei; Wang, Guangqian
2017-11-01
Water use efficiency (WUE), defined as the ratio of gross primary productivity and evapotranspiration at the ecosystem scale, is a critical variable linking the carbon and water cycles. Incorporating a dependency on vapor pressure deficit, apparent underlying WUE (uWUE) provides a better indicator of how terrestrial ecosystems respond to environmental changes than other WUE formulations. Here we used 20th century simulations from four terrestrial biosphere models to develop a novel variance decomposition method. With this method, we attributed variations in apparent uWUE to both the trend and interannual variation of environmental drivers. The secular increase in atmospheric CO2 explained a clear majority of total variation (66 ± 32%: mean ± one standard deviation), followed by positive trends in nitrogen deposition and climate, as well as a negative trend in land use change. In contrast, interannual variation was mostly driven by interannual climate variability. To analyze the mechanism of the CO2 effect, we partitioned the apparent uWUE into the transpiration ratio (transpiration over evapotranspiration) and potential uWUE. The relative increase in potential uWUE parallels that of CO2, but this direct CO2 effect was offset by 20 ± 4% by changes in ecosystem structure, that is, leaf area index for different vegetation types. However, the decrease in transpiration due to stomatal closure with rising CO2 was reduced by 84% by an increase in leaf area index, resulting in small changes in the transpiration ratio. CO2 concentration thus plays a dominant role in driving apparent uWUE variations over time, but its role differs for the two constituent components: potential uWUE and transpiration.
International Nuclear Information System (INIS)
Joseph, A.T.; Grenney, W.J.; Stevens, D.K.
1994-01-01
The VIP model, which simulates the concentration profiles of the hazardous compounds in the soil, water, and the air phases, assumes a fixed oily phase. The purpose of this study was to measure oil migration in soil systems and to determine its effect on the VIP model output. Experiments were conducted to demonstrate the mobility of an oil through the unsaturated zone of the soil. The studies were conducted in laboratory scale glass columns. A light petroleum oil and two types of soil were used. The experiments demonstrated that oil migrates down significantly through the soil columns. The extent of migration depended on the volume of oil applied and the type of soil. However, the applied oil was completely immobilized in the columns. The model was modified to incorporate oil migration. The modified model can be expected to produce more realistic contaminant concentration profiles during land treatment of oily wastes when compared to that produced by the present version of the VIP model. (Author)
Directory of Open Access Journals (Sweden)
Sofia Costanzini
2018-01-01
Full Text Available This work originates from an epidemiological study aimed to assess the correlation between population exposure to pesticides used in agriculture and adverse health effects. In support of the population exposure evaluation two models implemented by the authors were applied: a GIS-based proximity model and the CAREA atmospheric dispersion model. In this work, the results of the two models are presented and compared. Despite the proximity analysis is widely used for these kinds of studies, it was investigated how meteorology could affect the exposure assessment. Both models were applied to pesticides emitted by 1519 agricultural fields and considering 2584 receptors distributed over an area of 8430 km2. CAREA output shows a considerable enhancement in the percentage of exposed receptors, from the 4% of the proximity model to the 54% of the CAREA model. Moreover, the spatial analysis of the results on a specific test site showed that the effects of meteorology considered by CAREA led to an anisotropic exposure distribution that differs considerably from the symmetric distribution resulting by the proximity model. In addition, the results of a field campaign for the definition and planning of ground measurement of concentration for the validation of CAREA are presented. The preliminary results showed how, during treatments, pesticide concentrations distant from the fields are significantly higher than background values.
Propagation dynamics for a spatially periodic integrodifference competition model
Wu, Ruiwen; Zhao, Xiao-Qiang
2018-05-01
In this paper, we study the propagation dynamics for a class of integrodifference competition models in a periodic habitat. An interesting feature of such a system is that multiple spreading speeds can be observed, which biologically means different species may have different spreading speeds. We show that the model system admits a single spreading speed, and it coincides with the minimal wave speed of the spatially periodic traveling waves. A set of sufficient conditions for linear determinacy of the spreading speed is also given.
How to get rid of W: a latent variables approach to modelling spatially lagged variables
Folmer, H.; Oud, J.
2008-01-01
In this paper we propose a structural equation model (SEM) with latent variables to model spatial dependence. Rather than using the spatial weights matrix W, we propose to use latent variables to represent spatial dependence and spillover effects, of which the observed spatially lagged variables are
How to get rid of W : a latent variables approach to modelling spatially lagged variables
Folmer, Henk; Oud, Johan
2008-01-01
In this paper we propose a structural equation model (SEM) with latent variables to model spatial dependence. Rather than using the spatial weights matrix W, we propose to use latent variables to represent spatial dependence and spillover effects, of which the observed spatially lagged variables are
Hamzalouh, L.; Ismail, M. T.; Rahman, R. A.
2017-09-01
In this paper, spatial panel models were used and the method for selecting the best model amongst the spatial fixed effects model and the spatial random effects model to estimate the fitting model by using the robust Hausman test for analysis of the exports pattern of the Common Market for Eastern and Southern African (COMESA) countries. And examine the effects of the interactions of the economic statistic of explanatory variables on the exports of the COMESA. Results indicated that the spatial Durbin model with fixed effects specification should be tested and considered in most cases of this study. After that, the direct and indirect effects among COMESA regions were assessed, and the role of indirect spatial effects in estimating exports was empirically demonstrated. Regarding originality and research value, and to the best of the authors’ knowledge, this is the first attempt to examine exports between COMESA and its member countries through spatial panel models using XSMLE, which is a new command for spatial analysis using STATA.
Jia, Jun; Li, Bo; Sun, Zuo-Li; Yu, Fen; Wang, Xuan; Wang, Xiao-Min
2010-04-01
The role of electro-acupuncture (EA) stimulation on motor symptoms in Parkinson's disease (PD) has not been well studied. In a rat hemiparkinsonian model induced by unilateral transection of the medial forebrain bundle (MFB), EA stimulation improved motor impairment in a frequency-dependent manner. Whereas EA stimulation at a low frequency (2 Hz) had no effect, EA stimulation at a high frequency (100 Hz) significantly improved motor coordination. However, neither low nor high EA stimulation could significantly enhance dopamine levels in the striatum. EA stimulation at 100 Hz normalized the MFB lesion-induced increase in midbrain GABA content, but it had no effect on GABA content in the globus pallidus. These results suggest that high-frequency EA stimulation improves motor impairment in MFB-lesioned rats by increasing GABAergic inhibition in the output structure of the basal ganglia.
Spatial modeling for groundwater arsenic levels in North Carolina.
Kim, Dohyeong; Miranda, Marie Lynn; Tootoo, Joshua; Bradley, Phil; Gelfand, Alan E
2011-06-01
To examine environmental and geologic determinants of arsenic in groundwater, detailed geologic data were integrated with well water arsenic concentration data and well construction data for 471 private wells in Orange County, NC, via a geographic information system. For the statistical analysis, the geologic units were simplified into four generalized categories based on rock type and interpreted mode of deposition/emplacement. The geologic transitions from rocks of a primary pyroclastic origin to rocks of volcaniclastic sedimentary origin were designated as polylines. The data were fitted to a left-censored regression model to identify key determinants of arsenic levels in groundwater. A Bayesian spatial random effects model was then developed to capture any spatial patterns in groundwater arsenic residuals into model estimation. Statistical model results indicate (1) wells close to a transition zone or fault are more likely to contain detectible arsenic; (2) welded tuffs and hydrothermal quartz bodies are associated with relatively higher groundwater arsenic concentrations and even higher for those proximal to a pluton; and (3) wells of greater depth are more likely to contain elevated arsenic. This modeling effort informs policy intervention by creating three-dimensional maps of predicted arsenic levels in groundwater for any location and depth in the area.
Modeling spatial processes with unknown extremal dependence class
Huser, Raphaël G.
2017-03-17
Many environmental processes exhibit weakening spatial dependence as events become more extreme. Well-known limiting models, such as max-stable or generalized Pareto processes, cannot capture this, which can lead to a preference for models that exhibit a property known as asymptotic independence. However, weakening dependence does not automatically imply asymptotic independence, and whether the process is truly asymptotically (in)dependent is usually far from clear. The distinction is key as it can have a large impact upon extrapolation, i.e., the estimated probabilities of events more extreme than those observed. In this work, we present a single spatial model that is able to capture both dependence classes in a parsimonious manner, and with a smooth transition between the two cases. The model covers a wide range of possibilities from asymptotic independence through to complete dependence, and permits weakening dependence of extremes even under asymptotic dependence. Censored likelihood-based inference for the implied copula is feasible in moderate dimensions due to closed-form margins. The model is applied to oceanographic datasets with ambiguous true limiting dependence structure.
Spatial Modeling for Groundwater Arsenic Levels in North Carolina
Kim, Dohyeong; Miranda, Marie Lynn; Tootoo, Joshua; Bradley, Phil; Gelfand, Alan E.
2013-01-01
To examine environmental and geologic determinants of arsenic in groundwater, detailed geologic data were integrated with well water arsenic concentration data and well construction data for 471 private wells in Orange County, NC, via a geographic information system. For the statistical analysis, the geologic units were simplified into four generalized categories based on rock type and interpreted mode of deposition/emplacement. The geologic transitions from rocks of a primary pyroclastic origin to rocks of volcaniclastic sedimentary origin were designated as polylines. The data were fitted to a left-censored regression model to identify key determinants of arsenic levels in groundwater. A Bayesian spatial random effects model was then developed to capture any spatial patterns in groundwater arsenic residuals into model estimation. Statistical model results indicate (1) wells close to a transition zone or fault are more likely to contain detectible arsenic; (2) welded tuffs and hydrothermal quartz bodies are associated with relatively higher groundwater arsenic concentrations and even higher for those proximal to a pluton; and (3) wells of greater depth are more likely to contain elevated arsenic. This modeling effort informs policy intervention by creating three-dimensional maps of predicted arsenic levels in groundwater for any location and depth in the area. PMID:21528844
Spatially balanced topological interaction grants optimal cohesion in flocking models.
Camperi, Marcelo; Cavagna, Andrea; Giardina, Irene; Parisi, Giorgio; Silvestri, Edmondo
2012-12-06
Models of self-propelled particles (SPPs) are an indispensable tool to investigate collective animal behaviour. Originally, SPP models were proposed with metric interactions, where each individual coordinates with neighbours within a fixed metric radius. However, recent experiments on bird flocks indicate that interactions are topological: each individual interacts with a fixed number of neighbours, irrespective of their distance. It has been argued that topological interactions are more robust than metric ones against external perturbations, a significant evolutionary advantage for systems under constant predatory pressure. Here, we test this hypothesis by comparing the stability of metric versus topological SPP models in three dimensions. We show that topological models are more stable than metric ones. We also show that a significantly better stability is achieved when neighbours are selected according to a spatially balanced topological rule, namely when interacting neighbours are evenly distributed in angle around the focal individual. Finally, we find that the minimal number of interacting neighbours needed to achieve fully stable cohesion in a spatially balanced model is compatible with the value observed in field experiments on starling flocks.
Spatial modeling for groundwater arsenic levels in North Carolina
Kim, D.; Miranda, M.L.; Tootoo, J.; Bradley, P.; Gelfand, A.E.
2011-01-01
To examine environmental and geologic determinants of arsenic in groundwater, detailed geologic data were integrated with well water arsenic concentration data and well construction data for 471 private wells in Orange County, NC, via a geographic information system. For the statistical analysis, the geologic units were simplified into four generalized categories based on rock type and interpreted mode of deposition/emplacement. The geologic transitions from rocks of a primary pyroclastic origin to rocks of volcaniclastic sedimentary origin were designated as polylines. The data were fitted to a left-censored regression model to identify key determinants of arsenic levels in groundwater. A Bayesian spatial random effects model was then developed to capture any spatial patterns in groundwater arsenic residuals into model estimation. Statistical model results indicate (1) wells close to a transition zone or fault are more likely to contain detectible arsenic; (2) welded tuffs and hydrothermal quartz bodies are associated with relatively higher groundwater arsenic concentrations and even higher for those proximal to a pluton; and (3) wells of greater depth are more likely to contain elevated arsenic. This modeling effort informs policy intervention by creating three-dimensional maps of predicted arsenic levels in groundwater for any location and depth in the area. ?? 2011 American Chemical Society.
Arsad, Roslah; Nasir Abdullah, Mohammad; Alias, Suriana; Isa, Zaidi
2017-09-01
Stock evaluation has always been an interesting problem for investors. In this paper, a comparison regarding the efficiency stocks of listed companies in Bursa Malaysia were made through the application of estimation method of Data Envelopment Analysis (DEA). One of the interesting research subjects in DEA is the selection of appropriate input and output parameter. In this study, DEA was used to measure efficiency of stocks of listed companies in Bursa Malaysia in terms of the financial ratio to evaluate performance of stocks. Based on previous studies and Fuzzy Delphi Method (FDM), the most important financial ratio was selected. The results indicated that return on equity, return on assets, net profit margin, operating profit margin, earnings per share, price to earnings and debt to equity were the most important ratios. Using expert information, all the parameter were clarified as inputs and outputs. The main objectives were to identify most critical financial ratio, clarify them based on expert information and compute the relative efficiency scores of stocks as well as rank them in the construction industry and material completely. The methods of analysis using Alirezaee and Afsharian’s model were employed in this study, where the originality of Charnes, Cooper and Rhodes (CCR) with the assumption of Constant Return to Scale (CSR) still holds. This method of ranking relative efficiency of decision making units (DMUs) was value-added by the Balance Index. The interested data was made for year 2015 and the population of the research includes accepted companies in stock markets in the construction industry and material (63 companies). According to the ranking, the proposed model can rank completely for 63 companies using selected financial ratio.
International Nuclear Information System (INIS)
Wu, Kuei-Yen; Wu, Jung-Hua; Huang, Yun-Hsun; Fu, Szu-Chi; Chen, Chia-Yon
2016-01-01
Most existing literature focuses on the direct rebound effect on the demand side for consumers. This study analyses direct and indirect rebound effects in Taiwan's industry from the perspective of producers. However, most studies on the producers' viewpoint may overlook inter-industry linkages. This study applies a supply-driven input-output model to quantify the magnitude of rebound effects by explicitly considering inter-industry linkages. Empirical results showed that total rebound effects for most Taiwan's sectors were less than 10% in 2011. A comparison among the sectors yields that sectors with lower energy efficiency had higher direct rebound effects, while sectors with higher forward linkages generated higher indirect rebound effects. Taking the Mining sector (S3) as an example, which is an upstream supplier and has high forward linkages; it showed high indirect rebound effects that are derived from the accumulation of additional energy consumption by its downstream producers. The findings also showed that in almost all sectors, indirect rebound effects were higher than direct rebound effects. In other words, if indirect rebound effects are neglected, the total rebound effects will be underestimated. Hence, the energy-saving potential may be overestimated. - Highlights: • This study quantifies rebound effects by a supply-driven input-output model. • For most Taiwan's sectors, total rebound magnitudes were less than 10% in 2011. • Direct rebound effects and energy efficiency were inverse correlation. • Indirect rebound effects and industrial forward linkages were positive correlation. • Indirect rebound effects were generally higher than direct rebound effects.
Spatial Durbin model analysis macroeconomic loss due to natural disasters
Kusrini, D. E.; Mukhtasor
2015-03-01
Magnitude of the damage and losses caused by natural disasters is huge for Indonesia, therefore this study aimed to analyze the effects of natural disasters for macroeconomic losses that occurred in 115 cities/districts across Java during 2012. Based on the results of previous studies it is suspected that it contains effects of spatial dependencies in this case, so that the completion of this case is performed using a regression approach to the area, namely Analysis of Spatial Durbin Model (SDM). The obtained significant predictor variable is population, and predictor variable with a significant weighting is the number of occurrences of disasters, i.e., disasters in the region which have an impact on other neighboring regions. Moran's I index value using the weighted Queen Contiguity also showed significant results, meaning that the incidence of disasters in the region will decrease the value of GDP in other.
Spatial and spatio-temporal bayesian models with R - INLA
Blangiardo, Marta
2015-01-01
Dedication iiiPreface ix1 Introduction 11.1 Why spatial and spatio-temporal statistics? 11.2 Why do we use Bayesian methods for modelling spatial and spatio-temporal structures? 21.3 Why INLA? 31.4 Datasets 32 Introduction to 212.1 The language 212.2 objects 222.3 Data and session management 342.4 Packages 352.5 Programming in 362.6 Basic statistical analysis with 393 Introduction to Bayesian Methods 533.1 Bayesian Philosophy 533.2 Basic Probability Elements 573.3 Bayes Theorem 623.4 Prior and Posterior Distributions 643.5 Working with the Posterior Distribution 663.6 Choosing the Prior Distr
Sherwood, John; Clabeaux, Raeanne; Carbajales-Dale, Michael
2017-10-01
We developed a physically-based environmental account of US food production systems and integrated these data into the environmental-input-output life cycle assessment (EIO-LCA) model. The extended model was used to characterize the food, energy, and water (FEW) intensities of every US economic sector. The model was then applied to every Bureau of Economic Analysis metropolitan statistical area (MSA) to determine their FEW usages. The extended EIO-LCA model can determine the water resource use (kGal), energy resource use (TJ), and food resource use in units of mass (kg) or energy content (kcal) of any economic activity within the United States. We analyzed every economic sector to determine its FEW intensities per dollar of economic output. This data was applied to each of the 382 MSAs to determine their total and per dollar of GDP FEW usages by allocating MSA economic production to the corresponding FEW intensities of US economic sectors. Additionally, a longitudinal study was performed for the Los Angeles-Long Beach-Anaheim, CA, metropolitan statistical area to examine trends from this singular MSA and compare it to the overall results. Results show a strong correlation between GDP and energy use, and between food and water use across MSAs. There is also a correlation between GDP and greenhouse gas emissions. The longitudinal study indicates that these correlations can shift alongside a shifting industrial composition. Comparing MSAs on a per GDP basis reveals that central and southern California tend to be more resource intensive than many other parts of the country, while much of Florida has abnormally low resource requirements. Results of this study enable a more complete understanding of food, energy, and water as key ingredients to a functioning economy. With the addition of the food data to the EIO-LCA framework, researchers will be able to better study the food-energy-water nexus and gain insight into how these three vital resources are interconnected
Whittington, Jesse; Sawaya, Michael A
2015-01-01
Capture-recapture studies are frequently used to monitor the status and trends of wildlife populations. Detection histories from individual animals are used to estimate probability of detection and abundance or density. The accuracy of abundance and density estimates depends on the ability to model factors affecting detection probability. Non-spatial capture-recapture models have recently evolved into spatial capture-recapture models that directly include the effect of distances between an animal's home range centre and trap locations on detection probability. Most studies comparing non-spatial and spatial capture-recapture biases focussed on single year models and no studies have compared the accuracy of demographic parameter estimates from open population models. We applied open population non-spatial and spatial capture-recapture models to three years of grizzly bear DNA-based data from Banff National Park and simulated data sets. The two models produced similar estimates of grizzly bear apparent survival, per capita recruitment, and population growth rates but the spatial capture-recapture models had better fit. Simulations showed that spatial capture-recapture models produced more accurate parameter estimates with better credible interval coverage than non-spatial capture-recapture models. Non-spatial capture-recapture models produced negatively biased estimates of apparent survival and positively biased estimates of per capita recruitment. The spatial capture-recapture grizzly bear population growth rates and 95% highest posterior density averaged across the three years were 0.925 (0.786-1.071) for females, 0.844 (0.703-0.975) for males, and 0.882 (0.779-0.981) for females and males combined. The non-spatial capture-recapture population growth rates were 0.894 (0.758-1.024) for females, 0.825 (0.700-0.948) for males, and 0.863 (0.771-0.957) for both sexes. The combination of low densities, low reproductive rates, and predominantly negative population growth
Directory of Open Access Journals (Sweden)
Jesse Whittington
Full Text Available Capture-recapture studies are frequently used to monitor the status and trends of wildlife populations. Detection histories from individual animals are used to estimate probability of detection and abundance or density. The accuracy of abundance and density estimates depends on the ability to model factors affecting detection probability. Non-spatial capture-recapture models have recently evolved into spatial capture-recapture models that directly include the effect of distances between an animal's home range centre and trap locations on detection probability. Most studies comparing non-spatial and spatial capture-recapture biases focussed on single year models and no studies have compared the accuracy of demographic parameter estimates from open population models. We applied open population non-spatial and spatial capture-recapture models to three years of grizzly bear DNA-based data from Banff National Park and simulated data sets. The two models produced similar estimates of grizzly bear apparent survival, per capita recruitment, and population growth rates but the spatial capture-recapture models had better fit. Simulations showed that spatial capture-recapture models produced more accurate parameter estimates with better credible interval coverage than non-spatial capture-recapture models. Non-spatial capture-recapture models produced negatively biased estimates of apparent survival and positively biased estimates of per capita recruitment. The spatial capture-recapture grizzly bear population growth rates and 95% highest posterior density averaged across the three years were 0.925 (0.786-1.071 for females, 0.844 (0.703-0.975 for males, and 0.882 (0.779-0.981 for females and males combined. The non-spatial capture-recapture population growth rates were 0.894 (0.758-1.024 for females, 0.825 (0.700-0.948 for males, and 0.863 (0.771-0.957 for both sexes. The combination of low densities, low reproductive rates, and predominantly negative
Alexander, Patrick M.; Tedesco, Marco; Schlegel, Nicole-Jeanne; Luthcke, Scott B.; Fettweis, Xavier; Larour, Eric
2016-06-01
Improving the ability of regional climate models (RCMs) and ice sheet models (ISMs) to simulate spatiotemporal variations in the mass of the Greenland Ice Sheet (GrIS) is crucial for prediction of future sea level rise. While several studies have examined recent trends in GrIS mass loss, studies focusing on mass variations at sub-annual and sub-basin-wide scales are still lacking. At these scales, processes responsible for mass change are less well understood and modeled, and could potentially play an important role in future GrIS mass change. Here, we examine spatiotemporal variations in mass over the GrIS derived from the Gravity Recovery and Climate Experiment (GRACE) satellites for the January 2003-December 2012 period using a "mascon" approach, with a nominal spatial resolution of 100 km, and a temporal resolution of 10 days. We compare GRACE-estimated mass variations against those simulated by the Modèle Atmosphérique Régionale (MAR) RCM and the Ice Sheet System Model (ISSM). In order to properly compare spatial and temporal variations in GrIS mass from GRACE with model outputs, we find it necessary to spatially and temporally filter model results to reproduce leakage of mass inherent in the GRACE solution. Both modeled and satellite-derived results point to a decline (of -178.9 ± 4.4 and -239.4 ± 7.7 Gt yr-1 respectively) in GrIS mass over the period examined, but the models appear to underestimate the rate of mass loss, especially in areas below 2000 m in elevation, where the majority of recent GrIS mass loss is occurring. On an ice-sheet-wide scale, the timing of the modeled seasonal cycle of cumulative mass (driven by summer mass loss) agrees with the GRACE-derived seasonal cycle, within limits of uncertainty from the GRACE solution. However, on sub-ice-sheet-wide scales, some areas exhibit significant differences in the timing of peaks in the annual cycle of mass change. At these scales, model biases, or processes not accounted for by models related
International Nuclear Information System (INIS)
Tan, Raymond R.; Aviso, Kathleen B.; Barilea, Ivan U.; Culaba, Alvin B.; Cruz, Jose B.
2012-01-01
Interest in bioenergy in recent years has been stimulated by both energy security and climate change concerns. Fuels derived from agricultural crops offer the promise of reducing energy dependence for countries that have traditionally been dependent on imported energy. Nevertheless, it is evident that the potential for biomass production is heavily dependent on the availability of land and water resources. Furthermore, capacity expansion through land conversion is now known to incur a significant carbon debt that may offset any benefits in greenhouse gas reductions arising from the biofuel life cycle. Because of such constraints, there is increasing use of non-local biomass through regional trading. The main challenge in the analysis of such arrangements is that individual geographic regions have their own respective goals. This work presents a multi-region, fuzzy input–output optimization model that reflects production and consumption of bioenergy under land, water and carbon footprint constraints. To offset any local production deficits or surpluses, the model allows for trade to occur among different regions within a defined system; furthermore, importation of additional biofuel from external sources is also allowed. Two illustrative case studies are given to demonstrate the key features of the model.
Modeling spin magnetization transport in a spatially varying magnetic field
Picone, Rico A. R.; Garbini, Joseph L.; Sidles, John A.
2015-01-01
We present a framework for modeling the transport of any number of globally conserved quantities in any spatial configuration and apply it to obtain a model of magnetization transport for spin-systems that is valid in new regimes (including high-polarization). The framework allows an entropy function to define a model that explicitly respects the laws of thermodynamics. Three facets of the model are explored. First, it is expressed as nonlinear partial differential equations that are valid for the new regime of high dipole-energy and polarization. Second, the nonlinear model is explored in the limit of low dipole-energy (semi-linear), from which is derived a physical parameter characterizing separative magnetization transport (SMT). It is shown that the necessary and sufficient condition for SMT to occur is that the parameter is spatially inhomogeneous. Third, the high spin-temperature (linear) limit is shown to be equivalent to the model of nuclear spin transport of Genack and Redfield (1975) [1]. Differences among the three forms of the model are illustrated by numerical solution with parameters corresponding to a magnetic resonance force microscopy (MRFM) experiment (Degen et al., 2009 [2]; Kuehn et al., 2008 [3]; Sidles et al., 2003 [4]; Dougherty et al., 2000 [5]). A family of analytic, steady-state solutions to the nonlinear equation is derived and shown to be the spin-temperature analog of the Langevin paramagnetic equation and Curie's law. Finally, we analyze the separative quality of magnetization transport, and a steady-state solution for the magnetization is shown to be compatible with Fenske's separative mass transport equation (Fenske, 1932 [6]).
Directory of Open Access Journals (Sweden)
Ramona Iulia Țarțavulea (Dieaconescu
2016-08-01
Full Text Available The process of globalization has stimulated the demand for logistics services at a level of speed and increased efficiency, which involves using of techniques, tools, technologies and modern models in supply chain management. The aim of this research paper is to present a model that can be used in order to achieve an optimized supply chain, associated with minimum transportation costs. The utilization of spatial modeling for determining the optimal locations for logistics centers in a predefined economic area is proposd in this paper. The principal methods used to design the model are mathematic optimization and linear programming. The output data of the model are the precise placement of one up to ten logistics centers, in terms of minimum operational costs for delivery from the optimum locations to consumer points. The results of the research indicate that by using the proposed model, an efficient supply chain that is consistent with optimization of transport can be designed, in order to streamline the delivery process and thus reduce operational costs
International Nuclear Information System (INIS)
Church, H.W.; Zak, B.D.; Behl, Y.K.
1995-06-01
The report describes a preliminary evaluation of models for transforming regional climate model output from a regional to a local scale for the Yucca Mountain area. Evaluation and analysis of both empirical and numerical modeling are discussed which is aimed at providing site-specific, climate-based information for use by interfacing activities. Two semiempirical approaches are recommended for further analysis
Toward Accessing Spatial Structure from Building Information Models
Schultz, C.; Bhatt, M.
2011-08-01
Data about building designs and layouts is becoming increasingly more readily available. In the near future, service personal (such as maintenance staff or emergency rescue workers) arriving at a building site will have immediate real-time access to enormous amounts of data relating to structural properties, utilities, materials, temperature, and so on. The critical problem for users is the taxing and error prone task of interpreting such a large body of facts in order to extract salient information. This is necessary for comprehending a situation and deciding on a plan of action, and is a particularly serious issue in time-critical and safety-critical activities such as firefighting. Current unifying building models such as the Industry Foundation Classes (IFC), while being comprehensive, do not directly provide data structures that focus on spatial reasoning and spatial modalities that are required for high-level analytical tasks. The aim of the research presented in this paper is to provide computational tools for higher level querying and reasoning that shift the cognitive burden of dealing with enormous amounts of data away from the user. The user can then spend more energy and time in planning and decision making in order to accomplish the tasks at hand. We present an overview of our framework that provides users with an enhanced model of "built-up space". In order to test our approach using realistic design data (in terms of both scale and the nature of the building models) we describe how our system interfaces with IFC, and we conduct timing experiments to determine the practicality of our approach. We discuss general computational approaches for deriving higher-level spatial modalities by focusing on the example of route graphs. Finally, we present a firefighting scenario with alternative route graphs to motivate the application of our framework.
Energy Technology Data Exchange (ETDEWEB)
Ferguson, S; Ahmad, S; Chen, Y; Ferreira, C; Islam, M; Lau, A; Jin, H [University of Oklahoma Health Sciences Center, Oklahoma City, OK (United States); Keeling, V [Carti, Inc., Little Rock, AR (United States)
2016-06-15
Purpose: To commission and investigate the accuracy of an output (cGy/MU) prediction model for a compact passively scattered proton therapy system. Methods: A previously published output prediction model (Sahoo et al, Med Phys, 35, 5088–5097, 2008) was commissioned for our Mevion S250 proton therapy system. This model is a correction-based model that multiplies correction factors (d/MUwnc=ROFxSOBPF xRSFxSOBPOCFxOCRxFSFxISF). These factors accounted for changes in output due to options (12 large, 5 deep, and 7 small), modulation width M, range R, off-center, off-axis, field-size, and off-isocenter. In this study, the model was modified to ROFxSOBPFxRSFxOCRxFSFxISF-OCFxGACF by merging SOBPOCF and ISF for simplicity and introducing a gantry angle correction factor (GACF). To commission the model, outputs over 1,000 data points were taken at the time of the system commissioning. The output was predicted by interpolation (1D for SOBPF, FSF, and GACF; 2D for RSF and OCR) with inverse-square calculation (ISF-OCR). The outputs of 273 combinations of R and M covering total 24 options were measured to test the model. To minimize fluence perturbation, scattered dose from range compensator and patient was not considered. The percent differences between the predicted (P) and measured (M) outputs were calculated to test the prediction accuracy ([P-M]/Mx100%). Results: GACF was required because of up to 3.5% output variation dependence on the gantry angle. A 2D interpolation was required for OCR because the dose distribution was not radially symmetric especially for the deep options. The average percent differences were −0.03±0.98% (mean±SD) and the differences of all the measurements fell within ±3%. Conclusion: It is concluded that the model can be clinically used for the compact passively scattered proton therapy system. However, great care should be taken when the field-size is less than 5×5 cm{sup 2} where a direct output measurement is required due to substantial
Directory of Open Access Journals (Sweden)
J. Schottler
2017-08-01
Full Text Available The effect of vertical wind shear on the total power output of two aligned model wind turbines as a function of yaw misalignment of the upstream turbine is studied experimentally. It is shown that asymmetries of the power output of the downstream turbine and the combined power of both with respect to the upstream turbine's yaw misalignment angle can be linked to the vertical wind shear of the inflow.
A new spatial multiple discrete-continuous modeling approach to land use change analysis.
2013-09-01
This report formulates a multiple discrete-continuous probit (MDCP) land-use model within a : spatially explicit economic structural framework for land-use change decisions. The spatial : MDCP model is capable of predicting both the type and intensit...
Stochastic population oscillations in spatial predator-prey models
International Nuclear Information System (INIS)
Taeuber, Uwe C
2011-01-01
It is well-established that including spatial structure and stochastic noise in models for predator-prey interactions invalidates the classical deterministic Lotka-Volterra picture of neutral population cycles. In contrast, stochastic models yield long-lived, but ultimately decaying erratic population oscillations, which can be understood through a resonant amplification mechanism for density fluctuations. In Monte Carlo simulations of spatial stochastic predator-prey systems, one observes striking complex spatio-temporal structures. These spreading activity fronts induce persistent correlations between predators and prey. In the presence of local particle density restrictions (finite prey carrying capacity), there exists an extinction threshold for the predator population. The accompanying continuous non-equilibrium phase transition is governed by the directed-percolation universality class. We employ field-theoretic methods based on the Doi-Peliti representation of the master equation for stochastic particle interaction models to (i) map the ensuing action in the vicinity of the absorbing state phase transition to Reggeon field theory, and (ii) to quantitatively address fluctuation-induced renormalizations of the population oscillation frequency, damping, and diffusion coefficients in the species coexistence phase.
Spatial Modeling of Geometallurgical Properties: Techniques and a Case Study
Energy Technology Data Exchange (ETDEWEB)
Deutsch, Jared L., E-mail: jdeutsch@ualberta.ca [University of Alberta, School of Mining and Petroleum Engineering, Department of Civil and Environmental Engineering (Canada); Palmer, Kevin [Teck Resources Limited (Canada); Deutsch, Clayton V.; Szymanski, Jozef [University of Alberta, School of Mining and Petroleum Engineering, Department of Civil and Environmental Engineering (Canada); Etsell, Thomas H. [University of Alberta, Department of Chemical and Materials Engineering (Canada)
2016-06-15
High-resolution spatial numerical models of metallurgical properties constrained by geological controls and more extensively by measured grade and geomechanical properties constitute an important part of geometallurgy. Geostatistical and other numerical techniques are adapted and developed to construct these high-resolution models accounting for all available data. Important issues that must be addressed include unequal sampling of the metallurgical properties versus grade assays, measurements at different scale, and complex nonlinear averaging of many metallurgical parameters. This paper establishes techniques to address each of these issues with the required implementation details and also demonstrates geometallurgical mineral deposit characterization for a copper–molybdenum deposit in South America. High-resolution models of grades and comminution indices are constructed, checked, and are rigorously validated. The workflow demonstrated in this case study is applicable to many other deposit types.
Spatial generalised linear mixed models based on distances.
Melo, Oscar O; Mateu, Jorge; Melo, Carlos E
2016-10-01
Risk models derived from environmental data have been widely shown to be effective in delineating geographical areas of risk because they are intuitively easy to understand. We present a new method based on distances, which allows the modelling of continuous and non-continuous random variables through distance-based spatial generalised linear mixed models. The parameters are estimated using Markov chain Monte Carlo maximum likelihood, which is a feasible and a useful technique. The proposed method depends on a detrending step built from continuous or categorical explanatory variables, or a mixture among them, by using an appropriate Euclidean distance. The method is illustrated through the analysis of the variation in the prevalence of Loa loa among a sample of village residents in Cameroon, where the explanatory variables included elevation, together with maximum normalised-difference vegetation index and the standard deviation of normalised-difference vegetation index calculated from repeated satellite scans over time. © The Author(s) 2013.
Li, L.; Yang, C.
2017-12-01
Climate extremes often manifest as rare events in terms of surface air temperature and precipitation with an annual reoccurrence period. In order to represent the manifold characteristics of climate extremes for monitoring and analysis, the Expert Team on Climate Change Detection and Indices (ETCCDI) had worked out a set of 27 core indices based on daily temperature and precipitation data, describing extreme weather and climate events on an annual basis. The CLIMDEX project (http://www.climdex.org) had produced public domain datasets of such indices for data from a variety of sources, including output from global climate models (GCM) participating in the Coupled Model Intercomparison Project Phase 5 (CMIP5). Among the 27 ETCCDI indices, there are six percentile-based temperature extremes indices that may fall into two groups: exceedance rates (ER) (TN10p, TN90p, TX10p and TX90p) and durations (CSDI and WSDI). Percentiles must be estimated prior to the calculation of the indices, and could more or less be biased by the adopted algorithm. Such biases will in turn be propagated to the final results of indices. The CLIMDEX used an empirical quantile estimator combined with a bootstrap resampling procedure to reduce the inhomogeneity in the annual series of the ER indices. However, there are still some problems remained in the CLIMDEX datasets, namely the overestimated climate variability due to unaccounted autocorrelation in the daily temperature data, seasonally varying biases and inconsistency between algorithms applied to the ER indices and to the duration indices. We now present new results of the six indices through a semiparametric quantile regression approach for the CMIP5 model output. By using the base-period data as a whole and taking seasonality and autocorrelation into account, this approach successfully addressed the aforementioned issues and came out with consistent results. The new datasets cover the historical and three projected (RCP2.6, RCP4.5 and RCP
Xie, Xing Long; Xian Xue, Wei
2017-12-01
The aim of this study is to qualitatively and quantitatively explore an energy engineering model termed quaternity-dominating pattern emerging in North China’s countryside. This study finds methane produced in this model serves household activities such as cooking, inducing reduction of coal or biomass spending, which otherwise would provoke air pollution, water loss and land erosion, and ultimately leading to ecological environment betterment. Additionally, this project generates byproducts, biogas liquids and residuals, which can, as a category of fertilizer, can promote straightening of fertility preservation capacity and improvement in the chemical and physical quality of land as well as increasing crop output and quality. This study also finds this engineering could encourage social stability via efficiently allocating bucolic surplus labor during winter and successful running this engineering project would trigger an increase of scientific and technological qualifications for rural citizens. Moreover, cost-profit analysis indicates this pattern can allow one rural home to obtain access to a hygienic energy resource of biogas in the yearly volume of 375m3, generate annual net earnings of US3458.82 and make investment return in about 2.73 years. Especially for poverty-stricken areas, this energy engineering project enjoys high values and great significance, which can lift these impoverished areas from poverty both in economics and energy. The paper concludes with pointing out practical proposals on launching and operating this energy engineering project.
International Nuclear Information System (INIS)
Chang Yuan; Ries, Robert J.; Wang Yaowu
2010-01-01
A complete understanding of the resource consumption, embodied energy, and environmental emissions of civil projects in China is difficult due to the lack of comprehensive national statistics. To quantitatively assess the energy and environmental impacts of civil construction at a macro-level, this study developed a 24 sector environmental input-output life-cycle assessment model (I-O LCA) based on 2002 Chinese national economic and environmental data. The model generates an economy-wide inventory of energy use and environmental emissions. Estimates based on the level of economic activity related to planned future civil works in 2015 are made. Results indicate that the embodied energy of construction projects accounts for nearly one-sixth of the total economy's energy consumption in 2007, and may account for approximately one-fifth of the total energy use by 2015. This energy consumption is dominated by coal and oil consumptions. Energy-related emissions are the main polluters of the country's atmosphere and environment. If the industry's energy use and manufacturing techniques remain the same as in 2002, challenges to the goals for total energy consumption in China will appear in the next decade. Thus, effective implementation of efficient energy technologies and regulations are indispensable for achieving China's energy and environmental quality goals.
An alternative to the standard spatial econometric approaches in hedonic house price models
DEFF Research Database (Denmark)
von Graevenitz, Kathrine; Panduro, Toke Emil
2015-01-01
Omitted, misspecified, or mismeasured spatially varying characteristics are a cause for concern in hedonic house price models. Spatial econometrics or spatial fixed effects have become popular ways of addressing these concerns. We discuss the limitations of standard spatial approaches to hedonic...
A Biophysical Neural Model To Describe Spatial Visual Attention
International Nuclear Information System (INIS)
Hugues, Etienne; Jose, Jorge V.
2008-01-01
Visual scenes have enormous spatial and temporal information that are transduced into neural spike trains. Psychophysical experiments indicate that only a small portion of a spatial image is consciously accessible. Electrophysiological experiments in behaving monkeys have revealed a number of modulations of the neural activity in special visual area known as V4, when the animal is paying attention directly towards a particular stimulus location. The nature of the attentional input to V4, however, remains unknown as well as to the mechanisms responsible for these modulations. We use a biophysical neural network model of V4 to address these issues. We first constrain our model to reproduce the experimental results obtained for different external stimulus configurations and without paying attention. To reproduce the known neuronal response variability, we found that the neurons should receive about equal, or balanced, levels of excitatory and inhibitory inputs and whose levels are high as they are in in vivo conditions. Next we consider attentional inputs that can induce and reproduce the observed spiking modulations. We also elucidate the role played by the neural network to generate these modulations
Spatial and functional modeling of carnivore and insectivore molariform teeth.
Evans, Alistair R; Sanson, Gordon D
2006-06-01
The interaction between the two main competing geometric determinants of teeth (the geometry of function and the geometry of occlusion) were investigated through the construction of three-dimensional spatial models of several mammalian tooth forms (carnassial, insectivore premolar, zalambdodont, dilambdodont, and tribosphenic). These models aim to emulate the shape and function of mammalian teeth. The geometric principles of occlusion relating to single- and double-crested teeth are reviewed. Function was considered using engineering principles that relate tooth shape to function. Substantial similarity between the models and mammalian teeth were achieved. Differences between the two indicate the influence of tooth strength, geometric relations between upper and lower teeth (including the presence of the protocone), and wear on tooth morphology. The concept of "autocclusion" is expanded to include any morphological features that ensure proper alignment of cusps on the same tooth and other teeth in the tooth row. It is concluded that the tooth forms examined are auto-aligning, and do not require additional morphological guides for correct alignment. The model of therian molars constructed by Crompton and Sita-Lumsden ([1970] Nature 227:197-199) is reconstructed in 3D space to show that their hypothesis of crest geometry is erroneous, and that their model is a special case of a more general class of models. (c) 2004 Wiley-Liss, Inc.
Simulation of the Role of Government in Spatial Agent-Based Model
Directory of Open Access Journals (Sweden)
Viktor Ivanovich Suslov
2016-09-01
Full Text Available The paper describes the further development of an agent-based multiregional input-output model of the Russian economy. We consider the idea of incorporating the government into the model and analyze the results of experimental calculations for the conditional example of spatial economy. New agents are included into the model such as the federal and regional governments, pension fund and also the state enterprises producing public goods at the federal and regional levels. The government sets four types of taxes (personal and business income taxes, VAT and payroll taxes, ensures the provision of public goods and provides social, investment and interbudgetary transfers to households, firms and budgets. Social transfers consist of social assistance and unemployment benefits. The utility functions of households are expanded by the terms associated with national and regional public goods. The budget policy is designed in accordance with the maximization of isoelastic function of social welfare that formalizes the choice between the different concepts of social justice. The Gini index is used for the monitoring the inequality of income distribution. The results of experimental calculations present the convergence of the new version of the model to the state of quasi-equilibrium. The special attention is paid an optimal level of the taxation maximizing the social welfare function. Four variants of the optimal tax rates are defined: for three major taxes at a fixed proportion of rates and for each of the tax separately at zero rates of two other taxes. The further directions of modelling are identified, they allow to investigate the spatial development of the Russian economy taking into account the decision-making by private agents in responding to government policies.
O'Kelly, Morton E.; Niedzielski, Michael A.; Gleeson, Justin
2012-10-01
Core and peripheral contrasts in journey-to-work trip length can be interpreted as imputing the relative value of origin and destination accessibility (yielding theoretical proxies for rent and wages). Because the main variables are shown to be critically dependent on spatial structure, they may be interpreted as showing the shadow prices due to comparative location. There is also a unifying connection between these results and the existing literature on many dimensions: rent gradients, accessibility, and emissivity. In an empirical example, the advantages of a panoramic view of national commuting statistics are shown, using an Irish data set. Variations in the rates of participation in trip making by location, occupation, and gender are examined. Places that emit more trips than would be expected from their relative location are identified. Further, examining ways in which such emissivity is sensitive to a change in trip length highlights the regions where trips could possibly be adjusted to produce a shorter average trip length or which might be especially sensitive to reduction in employment. A careful reinterpretation of one of the key outputs from a calibrated spatial interaction model is shown to be consistent with the declining rent gradient expected from Alonso's theory of land use.
Resilience in rural social-ecological systems : a spatially explicit agent-based modelling approach
Schouten, M.A.H.
2013-01-01
Rural areas are increasingly changed by drivers on large spatial scales such as economic globalization and climate change. These international drivers may bring forth abrupt disturbances, such as high output price peaks and falls, water floods and droughts, and massive outbreaks of animal
Olatinwo, Rabiu O.; Prabha, Thara V.; Paz, Joel O.; Hoogenboom, Gerrit
2012-03-01
Early leaf spot of peanut ( Arachis hypogaea L.), a disease caused by Cercospora arachidicola S. Hori, is responsible for an annual crop loss of several million dollars in the southeastern United States alone. The development of early leaf spot on peanut and subsequent spread of the spores of C. arachidicola relies on favorable weather conditions. Accurate spatio-temporal weather information is crucial for monitoring the progression of favorable conditions and determining the potential threat of the disease. Therefore, the development of a prediction model for mitigating the risk of early leaf spot in peanut production is important. The specific objective of this study was to demonstrate the application of the high-resolution Weather Research and Forecasting (WRF) model for management of early leaf spot in peanut. We coupled high-resolution weather output of the WRF, i.e. relative humidity and temperature, with the Oklahoma peanut leaf spot advisory model in predicting favorable conditions for early leaf spot infection over Georgia in 2007. Results showed a more favorable infection condition in the southeastern coastline of Georgia where the infection threshold were met sooner compared to the southwestern and central part of Georgia where the disease risk was lower. A newly introduced infection threat index indicates that the leaf spot threat threshold was met sooner at Alma, GA, compared to Tifton and Cordele, GA. The short-term prediction of weather parameters and their use in the management of peanut diseases is a viable and promising technique, which could help growers make accurate management decisions, and lower disease impact through optimum timing of fungicide applications.
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Patrick d'Aquino
2013-12-01
Full Text Available Over the centuries, local communities have shaped atypical rules to deal with the uncertainty of their environment. They have developed complex prototypes for flexible overlapping institutions and arrangements to adapt their rules and uses to their uncertain environment. Today, this indigenous way of flexibly institutionalizing access rules could provide blueprints for dealing with uncertainty issues resulting from global change as well as designing practical guidelines for implementing resilient management. However, transforming indigenous skills for developing institutional flexibility into operational management rules that are appropriate in the current environmental and socioeconomic context is a huge challenge. However, communities could easily succeed in this reframing because the structuring principles of institutional flexibility are embedded in their mind frame. In this perspective, a participatory modeling process was applied in Senegal to explore, first, how to design a methodological platform to enable local people to shape different forms of environmental management and policies they consider appropriate in the new context of environmental uncertainty by drawing on their own attitudes to environmental management. Second, to increase the value of such "self-designed" outputs in improving knowledge about, and improving, the practical management of uncertainty, especially in drylands.
Parameterizing the Spatial Markov Model From Breakthrough Curve Data Alone
Sherman, Thomas; Fakhari, Abbas; Miller, Savannah; Singha, Kamini; Bolster, Diogo
2017-12-01
The spatial Markov model (SMM) is an upscaled Lagrangian model that effectively captures anomalous transport across a diverse range of hydrologic systems. The distinct feature of the SMM relative to other random walk models is that successive steps are correlated. To date, with some notable exceptions, the model has primarily been applied to data from high-resolution numerical simulations and correlation effects have been measured from simulated particle trajectories. In real systems such knowledge is practically unattainable and the best one might hope for is breakthrough curves (BTCs) at successive downstream locations. We introduce a novel methodology to quantify velocity correlation from BTC data alone. By discretizing two measured BTCs into a set of arrival times and developing an inverse model, we estimate velocity correlation, thereby enabling parameterization of the SMM in studies where detailed Lagrangian velocity statistics are unavailable. The proposed methodology is applied to two synthetic numerical problems, where we measure all details and thus test the veracity of the approach by comparison of estimated parameters with known simulated values. Our results suggest that our estimated transition probabilities agree with simulated values and using the SMM with this estimated parameterization accurately predicts BTCs downstream. Our methodology naturally allows for estimates of uncertainty by calculating lower and upper bounds of velocity correlation, enabling prediction of a range of BTCs. The measured BTCs fall within the range of predicted BTCs. This novel method to parameterize the SMM from BTC data alone is quite parsimonious, thereby widening the SMM's practical applicability.
Vines, Aleksander; Hamre, Torill; Lygre, Kjetil
2014-05-01
The GreenSeas project (Development of global plankton data base and model system for eco-climate early warning) aims to advance the knowledge and predictive capacities of how marine ecosystems will respond to global change. A main task has been to set up a data delivery and monitoring core service following the open and free data access policy implemented in the Global Monitoring for the Environment and Security (GMES) programme. The aim is to ensure open and free access to historical plankton data, new data (EO products and in situ measurements), model data (including estimates of simulation error) and biological, environmental and climatic indicators to a range of stakeholders, such as scientists, policy makers and environmental managers. To this end, we have developed a geo-spatial database of both historical and new in situ physical, biological and chemical parameters for the Southern Ocean, Atlantic, Nordic Seas and the Arctic, and organized related satellite-derived quantities and model forecasts in a joint geo-spatial repository. For easy access to these data, we have implemented a web-based GIS (Geographical Information Systems) where observed, derived and forcasted parameters can be searched, displayed, compared and exported. Model forecasts can also be uploaded dynamically to the system, to allow modelers to quickly compare their results with available in situ and satellite observations. We have implemented the web-based GIS(Geographical Information Systems) system based on free and open source technologies: Thredds Data Server, ncWMS, GeoServer, OpenLayers, PostGIS, Liferay, Apache Tomcat, PRTree, NetCDF-Java, json-simple, Geotoolkit, Highcharts, GeoExt, MapFish, FileSaver, jQuery, jstree and qUnit. We also wanted to used open standards to communicate between the different services and we use WMS, WFS, netCDF, GML, OPeNDAP, JSON, and SLD. The main advantage we got from using FOSS was that we did not have to invent the wheel all over again, but could use
Energy Technology Data Exchange (ETDEWEB)
Lall, Pradeep; Wei, Junchao; Davis, J Lynn
2014-06-24
Abstract— Solid-state lighting (SSL) luminaires containing light emitting diodes (LEDs) have the potential of seeing excessive temperatures when being transported across country or being stored in non-climate controlled warehouses. They are also being used in outdoor applications in desert environments that see little or no humidity but will experience extremely high temperatures during the day. This makes it important to increase our understanding of what effects high temperature exposure for a prolonged period of time will have on the usability and survivability of these devices. Traditional light sources “burn out” at end-of-life. For an incandescent bulb, the lamp life is defined by B50 life. However, the LEDs have no filament to “burn”. The LEDs continually degrade and the light output decreases eventually below useful levels causing failure. Presently, the TM-21 test standard is used to predict the L70 life of LEDs from LM-80 test data. Several failure mechanisms may be active in a LED at a single time causing lumen depreciation. The underlying TM-21 Model may not capture the failure physics in presence of multiple failure mechanisms. Correlation of lumen maintenance with underlying physics of degradation at system-level is needed. In this paper, Kalman Filter (KF) and Extended Kalman Filters (EKF) have been used to develop a 70-percent Lumen Maintenance Life Prediction Model for LEDs used in SSL luminaires. Ten-thousand hour LM-80 test data for various LEDs have been used for model development. System state at each future time has been computed based on the state space at preceding time step, system dynamics matrix, control vector, control matrix, measurement matrix, measured vector, process noise and measurement noise. The future state of the lumen depreciation has been estimated based on a second order Kalman Filter model and a Bayesian Framework. Life prediction of L70 life for the LEDs used in SSL luminaires from KF and EKF based models have
Infection dynamics on spatial small-world network models
Iotti, Bryan; Antonioni, Alberto; Bullock, Seth; Darabos, Christian; Tomassini, Marco; Giacobini, Mario
2017-11-01
The study of complex networks, and in particular of social networks, has mostly concentrated on relational networks, abstracting the distance between nodes. Spatial networks are, however, extremely relevant in our daily lives, and a large body of research exists to show that the distances between nodes greatly influence the cost and probability of establishing and maintaining a link. A random geometric graph (RGG) is the main type of synthetic network model used to mimic the statistical properties and behavior of many social networks. We propose a model, called REDS, that extends energy-constrained RGGs to account for the synergic effect of sharing the cost of a link with our neighbors, as is observed in real relational networks. We apply both the standard Watts-Strogatz rewiring procedure and another method that conserves the degree distribution of the network. The second technique was developed to eliminate unwanted forms of spatial correlation between the degree of nodes that are affected by rewiring, limiting the effect on other properties such as clustering and assortativity. We analyze both the statistical properties of these two network types and their epidemiological behavior when used as a substrate for a standard susceptible-infected-susceptible compartmental model. We consider and discuss the differences in properties and behavior between RGGs and REDS as rewiring increases and as infection parameters are changed. We report considerable differences both between the network types and, in the case of REDS, between the two rewiring schemes. We conclude that REDS represent, with the application of these rewiring mechanisms, extremely useful and interesting tools in the study of social and epidemiological phenomena in synthetic complex networks.
Spatial Distribution of Hydrologic Ecosystem Service Estimates: Comparing Two Models
Dennedy-Frank, P. J.; Ghile, Y.; Gorelick, S.; Logsdon, R. A.; Chaubey, I.; Ziv, G.
2014-12-01
We compare estimates of the spatial distribution of water quantity provided (annual water yield) from two ecohydrologic models: the widely-used Soil and Water Assessment Tool (SWAT) and the much simpler water models from the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) toolbox. These two models differ significantly in terms of complexity, timescale of operation, effort, and data required for calibration, and so are often used in different management contexts. We compare two study sites in the US: the Wildcat Creek Watershed (2083 km2) in Indiana, a largely agricultural watershed in a cold aseasonal climate, and the Upper Upatoi Creek Watershed (876 km2) in Georgia, a mostly forested watershed in a temperate aseasonal climate. We evaluate (1) quantitative estimates of water yield to explore how well each model represents this process, and (2) ranked estimates of water yield to indicate how useful the models are for management purposes where other social and financial factors may play significant roles. The SWAT and InVEST models provide very similar estimates of the water yield of individual subbasins in the Wildcat Creek Watershed (Pearson r = 0.92, slope = 0.89), and a similar ranking of the relative water yield of those subbasins (Spearman r = 0.86). However, the two models provide relatively different estimates of the water yield of individual subbasins in the Upper Upatoi Watershed (Pearson r = 0.25, slope = 0.14), and very different ranking of the relative water yield of those subbasins (Spearman r = -0.10). The Upper Upatoi watershed has a significant baseflow contribution due to its sandy, well-drained soils. InVEST's simple seasonality terms, which assume no change in storage over the time of the model run, may not accurately estimate water yield processes when baseflow provides such a strong contribution. Our results suggest that InVEST users take care in situations where storage changes are significant.
Is a matrix exponential specification suitable for the modeling of spatial correlation structures?
Strauß, Magdalena E; Mezzetti, Maura; Leorato, Samantha
2017-05-01
This paper investigates the adequacy of the matrix exponential spatial specifications (MESS) as an alternative to the widely used spatial autoregressive models (SAR). To provide as complete a picture as possible, we extend the analysis to all the main spatial models governed by matrix exponentials comparing them with their spatial autoregressive counterparts. We propose a new implementation of Bayesian parameter estimation for the MESS model with vague prior distributions, which is shown to be precise and computationally efficient. Our implementations also account for spatially lagged regressors. We further allow for location-specific heterogeneity, which we model by including spatial splines. We conclude by comparing the performances of the different model specifications in applications to a real data set and by running simulations. Both the applications and the simulations suggest that the spatial splines are a flexible and efficient way to account for spatial heterogeneities governed by unknown mechanisms.
Warren, Joshua; Fuentes, Montserrat; Herring, Amy; Langlois, Peter
2012-12-01
Exposure to high levels of air pollution during the pregnancy is associated with increased probability of preterm birth (PTB), a major cause of infant morbidity and mortality. New statistical methodology is required to specifically determine when a particular pollutant impacts the PTB outcome, to determine the role of different pollutants, and to characterize the spatial variability in these results. We develop a new Bayesian spatial model for PTB which identifies susceptible windows throughout the pregnancy jointly for multiple pollutants (PM(2.5) , ozone) while allowing these windows to vary continuously across space and time. We geo-code vital record birth data from Texas (2002-2004) and link them with standard pollution monitoring data and a newly introduced EPA product of calibrated air pollution model output. We apply the fully spatial model to a region of 13 counties in eastern Texas consisting of highly urban as well as rural areas. Our results indicate significant signal in the first two trimesters of pregnancy with different pollutants leading to different critical windows. Introducing the spatial aspect uncovers critical windows previously unidentified when space is ignored. A proper inference procedure is introduced to correctly analyze these windows. © 2012, The International Biometric Society.
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M. F. McCabe
2005-01-01
Full Text Available Characterising the development of evapotranspiration through time is a difficult task, particularly when utilising remote sensing data, because retrieved information is often spatially dense, but temporally sparse. Techniques to expand these essentially instantaneous measures are not only limited, they are restricted by the general paucity of information describing the spatial distribution and temporal evolution of evaporative patterns. In a novel approach, temporal changes in land surface temperatures, derived from NOAA-AVHRR imagery and a generalised split-window algorithm, are used as a calibration variable in a simple land surface scheme (TOPUP and combined within the Generalised Likelihood Uncertainty Estimation (GLUE methodology to provide estimates of areal evapotranspiration at the pixel scale. Such an approach offers an innovative means of transcending the patch or landscape scale of SVAT type models, to spatially distributed estimates of model output. The resulting spatial and temporal patterns of land surface fluxes and surface resistance are used to more fully understand the hydro-ecological trends observed across a study catchment in eastern Australia. The modelling approach is assessed by comparing predicted cumulative evapotranspiration values with surface fluxes determined from Bowen ratio systems and using auxiliary information such as in-situ soil moisture measurements and depth to groundwater to corroborate observed responses.
Spatial Modeling of Risk in Natural Resource Management
Directory of Open Access Journals (Sweden)
Peter Jones
2002-01-01
Full Text Available Making decisions in natural resource management involves an understanding of the risk and uncertainty of the outcomes, such as crop failure or cattle starvation, and of the normal spread of the expected production. Hedging against poor outcomes often means lack of investment and slow adoption of new methods. At the household level, production instability can have serious effects on income and food security. At the national level, it can have social and economic impacts that may affect all sectors of society. Crop models such as CERES-Maize are excellent tools for assessing weather-related production variability. WATBAL is a water balance model that can provide robust estimates of the potential growing days for a pasture. These models require large quantities of daily weather data that are rarely available. MarkSim is an application for generating synthetic daily weather files by estimating the third-order Markov model parameters from interpolated climate surfaces. The models can then be run for each distinct point on the map. This paper examines the growth of maize and pasture in dryland agriculture in southern Africa. Weather simulators produce independent estimates for each point on the map; however, we know that a spatial coherence of weather exists. We investigated a method of incorporating spatial coherence into MarkSim and show that it increases the variance of production. This means that all of the farmers in a coherent area share poor yields, with important consequences for food security, markets, transport, and shared grazing lands. The long-term aspects of risk are associated with global climate change. We used the results of a Global Circulation Model to extrapolate to the year 2055. We found that low maize yields would become more likely in the marginal areas, whereas they may actually increase in some areas. The same trend was found with pasture growth. We outline areas where further work is required before these tools and methods
The lack of spatial soil erosion data has been a major constraint on the refinement and application of physically based erosion models. Spatially distributed models can only be thoroughly validated with distributed erosion data. The fallout cesium-137 has been widely used to generate spatial soil re...
Using the gravity model to estimate the spatial spread of vector-borne diseases
Barrios, J.M.; Verstraeten, W.W.; Maes, P.; Aerts, J.; Farifteh, J.; Coppin, P.
2012-01-01
The gravity models are commonly used spatial interaction models. They have been widely applied in a large set of domains dealing with interactions amongst spatial entities. The spread of vector-borne diseases is also related to the intensity of interaction between spatial entities, namely, the
Lessons learned for spatial modelling of ecosystem services in support of ecosystem accounting
Schroter, M.; Remme, R.P.; Sumarga, E.; Barton, D.N.; Hein, L.G.
2015-01-01
Assessment of ecosystem services through spatial modelling plays a key role in ecosystem accounting. Spatial models for ecosystem services try to capture spatial heterogeneity with high accuracy. This endeavour, however, faces several practical constraints. In this article we analyse the trade-offs