Irrigation Requirement Estimation Using Vegetation Indices and Inverse Biophysical Modeling
Bounoua, Lahouari; Imhoff, Marc L.; Franks, Shannon
2010-01-01
We explore an inverse biophysical modeling process forced by satellite and climatological data to quantify irrigation requirements in semi-arid agricultural areas. We constrain the carbon and water cycles modeled under both equilibrium, balance between vegetation and climate, and non-equilibrium, water added through irrigation. We postulate that the degree to which irrigated dry lands vary from equilibrium climate conditions is related to the amount of irrigation. The amount of water required over and above precipitation is considered as an irrigation requirement. For July, results show that spray irrigation resulted in an additional amount of water of 1.3 mm per occurrence with a frequency of 24.6 hours. In contrast, the drip irrigation required only 0.6 mm every 45.6 hours or 46% of that simulated by the spray irrigation. The modeled estimates account for 87% of the total reported irrigation water use, when soil salinity is not important and 66% in saline lands.
Bounoua, L.; Imhoff, M.L.; Franks, S.
2008-01-01
Human demand for food influences the water cycle through diversion and extraction of fresh water needed to support agriculture. Future population growth and economic development alone will substantially increase water demand and much of it for agricultural uses. For many semi-arid lands, socio-economic shifts are likely to exacerbate changes in climate as a driver of future water supply and demand. For these areas in particular, where the balance between water supply and demand is fragile, variations in regional climate can have potentially predictable effect on agricultural production. Satellite data and biophysically-based models provide a powerful method to quantify the interactions between local climate, plant growth and water resource requirements. In irrigated agricultural lands, satellite observations indicate high vegetation density while the precipitation amount indicates otherwise. This inconsistency between the observed precipitation and the observed canopy leaf density triggers the possibility that the observed high leaf density is due to an alternate source of water, irrigation. We explore an inverse process approach using observations from the Moderate Resolution Imaging Spectroradiometer (MODIS), climatological data, and the NASA's Simple Biosphere model, SiB2, to quantitatively assess water demand in a semi-arid agricultural land by constraining the carbon and water cycles modeled under both equilibrium (balance between vegetation and prevailing local climate) and nonequilibrium (water added through irrigation) conditions. We postulate that the degree to which irrigated lands vary from equilibrium conditions is related to the amount of irrigation water used. We added water using two distribution methods: The first method adds water on top of the canopy and is a proxy for the traditional spray irrigation. The second method allows water to be applied directly into the soil layer and serves as proxy for drip irrigation. Our approach indicates that over
DEFF Research Database (Denmark)
Houborg, Rasmus Møller; Søgaard, Henrik; Bøgh, Eva
2007-01-01
for the inversion of a canopy reflectance model using Terra and Aqua MODIS multi-spectral, multi-temporal, and multi-angle reflectance observations to aid the determination of vegetation-specific physiological and structural canopy parameters. Land cover and site-specific inversion modeling was applied...
Biophysical models of radiobiological effects
International Nuclear Information System (INIS)
Obaturov, G.M.
1987-01-01
Radiobiological effect models at different organization levels, developed by the author, are presented. Classification and analysis of concepts and biophysical models at molecular, genetic and cellular levels, developed by Soviet and foreign authors in comparison to inherent models, are conducted from the viewpoint of system approach to radiobiological processes and of modelling principles. Models are compared with each other, limits of their applicability and drawbacks are determined. Evaluation of the model truthfulness is conducted according to a number of criteria, ways of further investigations and experimental examination of some models are proposed
Global energy modeling - A biophysical approach
Energy Technology Data Exchange (ETDEWEB)
Dale, Michael
2010-09-15
This paper contrasts the standard economic approach to energy modelling with energy models using a biophysical approach. Neither of these approaches includes changing energy-returns-on-investment (EROI) due to declining resource quality or the capital intensive nature of renewable energy sources. Both of these factors will become increasingly important in the future. An extension to the biophysical approach is outlined which encompasses a dynamic EROI function that explicitly incorporates technological learning. The model is used to explore several scenarios of long-term future energy supply especially concerning the global transition to renewable energy sources in the quest for a sustainable energy system.
Peddle, D. R.; Hall, F.
2009-12-01
The BIOPHYS algorithm provides innovative and flexible methods for the inversion of canopy reflectance models (CRM) to derive essential biophysical structural information (BSI) for quantifying vegetation state and disturbance, and for input to ecosystem, climate and carbon models. Based on spectral, angular, temporal and scene geometry inputs that can be provided or automatically derived, the BIOPHYS Multiple-Forward Mode (MFM) approach generates look-up tables (LUTs) that comprise reflectance data, structural inputs over specified or computed ranges, and the associated CRM output from forward mode runs. Image pixel and model LUT spectral values are then matched. The corresponding BSI retrieved from the LUT matches is output as the BSI results. BIOPHYS-MFM has been extensively used with agencies in Canada and the USA over the past decade (Peddle et al 2000-09; Soenen et al 2005-09; Gamon et al 2004; Cihlar et al 2003), such as CCRS, CFS, AICWR, NASA LEDAPS, BOREAS and MODIS Science Teams, and for the North American Carbon Program. The algorithm generates BSI products such as land cover, biomass, stand volume, stem density, height, crown closure, leaf area index (LAI) and branch area, crown dimension, productivity, topographic correction, structural change from harvest, forest fires and mountain pine beetle damage, and water / hydrology applications. BIOPHYS-MFM has been applied in different locations in Canada (six provinces from Newfoundland to British Columbia) and USA (NASA COVER, MODIS and LEDAPS sites) using 7 different CRM models and a variety of imagery (e.g. MODIS, Landsat, SPOT, IKONOS, airborne MSV, MMR, casi, Probe-1, AISA). In this paper we summarise the BIOPHYS-MFM algorithm and results from Terra-MODIS imagery from MODIS validation sites at Kananaskis Alberta in the Canadian Rocky Mountains, and from the Boreal Ecosystem Atmosphere Study (BOREAS) in Saskatchewan Canada. At the montane Rocky Mountain site, BIOPHYS-MFM density estimates were within
Glaser, Roland
1999-01-01
The message of this book is that biophysics is the science of physical principles underlying the "phenomenon life" on all levels of organization. Rather than teaching "physics for biologists" or "physical methods applied to biology", it regards its subject as a defined discipline with its own network of ideas and approaches. The book starts by explaining molecular structures of biological systems, various kinds of atomic, molecular and ionic interactions, movements, energy transfer, self organization of supramolecular structures and dynamic properties of biological membranes. It then goes on to introduce the biological organism as a non-equilibrium system, before treating thermodynamic concepts of osmotic and electrolyte equilibria as well as currents and potential profiles. It continues with topics of environmental biophysics and such medical aspects as the influence of electromagnetic fields or radiation on living systems and the biophysics of hearing and noice protection. The book concludes with a discussi...
Biophysical models of radiobiological effects
International Nuclear Information System (INIS)
Obaturov, G.M.
1984-01-01
Models of radiation effect on biological structures and objects are presented. Physical and molecular models based on target theory and DNA or chromosome injuries, respectively, and reparation ''saturation'' theory, are considered
Biophysical models in radiation oncology
International Nuclear Information System (INIS)
Cohen, L.
1984-01-01
The paper examines and describes dose-time relationships in clinical radiation oncology. Realistic models and parameters for specific tissues, organs, and tumor types are discussed in order to solve difficult problems which arise in radiation oncology. The computer programs presented were written to: derive parameters from experimental and clinical data; plot normal- and tumor-cell survival curves; generate iso-effect tables of tumor-curative doses; identify alternative, equally effective procedures for fraction numbers and treatment times; determine whether a proposed course of treatment is safe and adequate, and what adjustments are needed should results suggest that the procedure is unsafe or inadequate; combine the physical isodose distribution with computed cellular surviving fractions for the tumor and all normal tissues traversed by the beam, estimating the risks of recurrence or complications at various points in the irradiated volume, and adjusting the treatment plan and fractionation scheme to minimize these risks
Biophysical models of larval dispersal in the Benguela Current ...
African Journals Online (AJOL)
We synthesise and update results from the suite of biophysical, larval-dispersal models developed in the Benguela Current ecosystem. Biophysical models of larval dispersal use outputs of physical hydrodynamic models as inputs to individual-based models in which biological processes acting during the larval life are ...
Biophysically realistic minimal model of dopamine neuron
Oprisan, Sorinel
2008-03-01
We proposed and studied a new biophysically relevant computational model of dopaminergic neurons. Midbrain dopamine neurons are involved in motivation and the control of movement, and have been implicated in various pathologies such as Parkinson's disease, schizophrenia, and drug abuse. The model we developed is a single-compartment Hodgkin-Huxley (HH)-type parallel conductance membrane model. The model captures the essential mechanisms underlying the slow oscillatory potentials and plateau potential oscillations. The main currents involved are: 1) a voltage-dependent fast calcium current, 2) a small conductance potassium current that is modulated by the cytosolic concentration of calcium, and 3) a slow voltage-activated potassium current. We developed multidimensional bifurcation diagrams and extracted the effective domains of sustained oscillations. The model includes a calcium balance due to the fundamental importance of calcium influx as proved by simultaneous electrophysiological and calcium imaging procedure. Although there are significant evidences to suggest a partially electrogenic calcium pump, all previous models considered only elecrtogenic pumps. We investigated the effect of the electrogenic calcium pump on the bifurcation diagram of the model and compared our findings against the experimental results.
MODELLING BIOPHYSICAL PARAMETERS OF MAIZE USING LANDSAT 8 TIME SERIES
Directory of Open Access Journals (Sweden)
T. Dahms
2016-06-01
Full Text Available Open and free access to multi-frequent high-resolution data (e.g. Sentinel – 2 will fortify agricultural applications based on satellite data. The temporal and spatial resolution of these remote sensing datasets directly affects the applicability of remote sensing methods, for instance a robust retrieving of biophysical parameters over the entire growing season with very high geometric resolution. In this study we use machine learning methods to predict biophysical parameters, namely the fraction of absorbed photosynthetic radiation (FPAR, the leaf area index (LAI and the chlorophyll content, from high resolution remote sensing. 30 Landsat 8 OLI scenes were available in our study region in Mecklenburg-Western Pomerania, Germany. In-situ data were weekly to bi-weekly collected on 18 maize plots throughout the summer season 2015. The study aims at an optimized prediction of biophysical parameters and the identification of the best explaining spectral bands and vegetation indices. For this purpose, we used the entire in-situ dataset from 24.03.2015 to 15.10.2015. Random forest and conditional inference forests were used because of their explicit strong exploratory and predictive character. Variable importance measures allowed for analysing the relation between the biophysical parameters with respect to the spectral response, and the performance of the two approaches over the plant stock evolvement. Classical random forest regression outreached the performance of conditional inference forests, in particular when modelling the biophysical parameters over the entire growing period. For example, modelling biophysical parameters of maize for the entire vegetation period using random forests yielded: FPAR: R² = 0.85; RMSE = 0.11; LAI: R² = 0.64; RMSE = 0.9 and chlorophyll content (SPAD: R² = 0.80; RMSE=4.9. Our results demonstrate the great potential in using machine-learning methods for the interpretation of long-term multi-frequent remote sensing
Modelling Biophysical Parameters of Maize Using Landsat 8 Time Series
Dahms, Thorsten; Seissiger, Sylvia; Conrad, Christopher; Borg, Erik
2016-06-01
Open and free access to multi-frequent high-resolution data (e.g. Sentinel - 2) will fortify agricultural applications based on satellite data. The temporal and spatial resolution of these remote sensing datasets directly affects the applicability of remote sensing methods, for instance a robust retrieving of biophysical parameters over the entire growing season with very high geometric resolution. In this study we use machine learning methods to predict biophysical parameters, namely the fraction of absorbed photosynthetic radiation (FPAR), the leaf area index (LAI) and the chlorophyll content, from high resolution remote sensing. 30 Landsat 8 OLI scenes were available in our study region in Mecklenburg-Western Pomerania, Germany. In-situ data were weekly to bi-weekly collected on 18 maize plots throughout the summer season 2015. The study aims at an optimized prediction of biophysical parameters and the identification of the best explaining spectral bands and vegetation indices. For this purpose, we used the entire in-situ dataset from 24.03.2015 to 15.10.2015. Random forest and conditional inference forests were used because of their explicit strong exploratory and predictive character. Variable importance measures allowed for analysing the relation between the biophysical parameters with respect to the spectral response, and the performance of the two approaches over the plant stock evolvement. Classical random forest regression outreached the performance of conditional inference forests, in particular when modelling the biophysical parameters over the entire growing period. For example, modelling biophysical parameters of maize for the entire vegetation period using random forests yielded: FPAR: R² = 0.85; RMSE = 0.11; LAI: R² = 0.64; RMSE = 0.9 and chlorophyll content (SPAD): R² = 0.80; RMSE=4.9. Our results demonstrate the great potential in using machine-learning methods for the interpretation of long-term multi-frequent remote sensing datasets to model
Modelling benthic biophysical drivers of ecosystem structure and biogeochemical response
Stephens, Nicholas; Bruggeman, Jorn; Lessin, Gennadi; Allen, Icarus
2016-04-01
The fate of carbon deposited at the sea floor is ultimately decided by biophysical drivers that control the efficiency of remineralisation and timescale of carbon burial in sediments. Specifically, these drivers include bioturbation through ingestion and movement, burrow-flushing and sediment reworking, which enhance vertical particulate transport and solute diffusion. Unfortunately, these processes are rarely satisfactorily resolved in models. To address this, a benthic model that explicitly describes the vertical position of biology (e.g., habitats) and biogeochemical processes is presented that includes biological functionality and biogeochemical response capturing changes in ecosystem structure, benthic-pelagic fluxes and biodiversity on inter-annual timescales. This is demonstrated by the model's ability to reproduce temporal variability in benthic infauna, vertical pore water nutrients and pelagic-benthic solute fluxes compared to in-situ data. A key advance is the replacement of bulk parameterisation of bioturbation by explicit description of the bio-physical processes responsible. This permits direct comparison with observations and determination of key parameters in experiments. Crucially, the model resolves the two-way interaction between sediment biogeochemistry and ecology, allowing exploration of the benthic response to changing environmental conditions, the importance of infaunal functional traits in shaping benthic ecological structure and the feedback the resulting bio-physical processes exert on pore water nutrient profiles. The model is actively being used to understand shelf sea carbon cycling, the response of the benthos to climatic change, food provision and other societal benefits.
Inverse and Predictive Modeling
Energy Technology Data Exchange (ETDEWEB)
Syracuse, Ellen Marie [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
2017-09-27
The LANL Seismo-Acoustic team has a strong capability in developing data-driven models that accurately predict a variety of observations. These models range from the simple – one-dimensional models that are constrained by a single dataset and can be used for quick and efficient predictions – to the complex – multidimensional models that are constrained by several types of data and result in more accurate predictions. Team members typically build models of geophysical characteristics of Earth and source distributions at scales of 1 to 1000s of km, the techniques used are applicable for other types of physical characteristics at an even greater range of scales. The following cases provide a snapshot of some of the modeling work done by the Seismo- Acoustic team at LANL.
A biophysical model for transcription factories
International Nuclear Information System (INIS)
Canals-Hamann, Ana Z; Neves, Ricardo Pires das; Reittie, Joyce E; Iñiguez, Carlos; Soneji, Shamit; Enver, Tariq; Buckle, Veronica J; Iborra, Francisco J
2013-01-01
Transcription factories are nuclear domains where gene transcription takes place although the molecular basis for their formation and maintenance are unknown. In this study, we explored how the properties of chromatin as a polymer may contribute to the structure of transcription factories. We found that transcriptional active chromatin contains modifications like histone H4 acetylated at Lysine 16 (H4K16ac). Single fibre analysis showed that this modification spans the entire body of the gene. Furthermore, H4K16ac genes cluster in regions up to 500 Kb alternating active and inactive chromatin. The introduction of H4K16ac in chromatin induces stiffness in the chromatin fibre. The result of this change in flexibility is that chromatin could behave like a multi-block copolymer with repetitions of stiff-flexible (active-inactive chromatin) components. Copolymers with such structure self-organize through spontaneous phase separation into microdomains. Consistent with such model H4K16ac chromatin form foci that associates with nascent transcripts. We propose that transcription factories are the result of the spontaneous concentration of H4K16ac chromatin that are in proximity, mainly in cis
Hydrophobic ampersand hydrophilic: Theoretical models of solvation for molecular biophysics
International Nuclear Information System (INIS)
Pratt, L.R.; Tawa, G.J.; Hummer, G.; Garcia, A.E.; Corcelli, S.A.
1996-01-01
Molecular statistical thermodynamic models of hydration for chemistry and biophysics have advanced abruptly in recent years. With liquid water as solvent, salvation phenomena are classified as either hydrophobic or hydrophilic effects. Recent progress in treatment of hydrophilic effects have been motivated by continuum dielectric models interpreted as a modelistic implementation of second order perturbation theory. New results testing that perturbation theory of hydrophilic effects are presented and discussed. Recent progress in treatment of hydrophobic effects has been achieved by applying information theory to discover models of packing effects in dense liquids. The simplest models to which those ideas lead are presented and discussed
Short-Term Memory and Its Biophysical Model
Wang, Wei; Zhang, Kai; Tang, Xiao-wei
1996-12-01
The capacity of short-term memory has been studied using an integrate-and-fire neuronal network model. It is found that the storage of events depend on the manner of the correlation between the events, and the capacity is dominated by the value of after-depolarization potential. There is a monotonic increasing relationship between the value of after-depolarization potential and the memory numbers. The biophysics relevance of the network model is discussed and different kinds of the information processes are studied too.
Electrophysiological Data and the Biophysical Modelling of Local Cortical Circuits
Directory of Open Access Journals (Sweden)
Dimitris Pinotsis
2014-03-01
Full Text Available This paper shows how recordings of gamma oscillations – under different experimental conditions or from different subjects – can be combined with a class of population models called neural fields and dynamic causal modeling (DCM to distinguish among alternative hypotheses regarding cortical structure and function. This approach exploits inter-subject variability and trial-specific effects associated with modulations in the peak frequency of gamma oscillations. It draws on the computational power of Bayesian model inversion, when applied to neural field models of cortical dynamics. Bayesian model comparison allows one to adjudicate among different mechanistic hypotheses about cortical excitability, synaptic kinetics and the cardinal topographic features of local cortical circuits. It also provides optimal parameter estimates that quantify neuromodulation and the spatial dispersion of axonal connections or summation of receptive fields in the visual cortex. This paper provides an overview of a family of neural field models that have been recently implemented using the DCM toolbox of the academic freeware Statistical Parametric Mapping (SPM. The SPM software is a popular platform for analyzing neuroimaging data, used by several neuroscience communities worldwide. DCM allows for a formal (Bayesian statistical analysis of cortical network connectivity, based upon realistic biophysical models of brain responses. It is this particular feature of DCM – the unique combination of generative models with optimization techniques based upon (variational Bayesian principles – that furnishes a novel way to characterize functional brain architectures. In particular, it provides answers to questions about how the brain is wired and how it responds to different experimental manipulations. For a review of the general role of neural fields in SPM the reader can consult e.g. see [1]. Neural fields have a long and illustrious history in mathematical
Remote sensing of the Canadian Arctic: Modelling biophysical variables
Liu, Nanfeng
It is anticipated that Arctic vegetation will respond in a variety of ways to altered temperature and precipitation patterns expected with climate change, including changes in phenology, productivity, biomass, cover and net ecosystem exchange. Remote sensing provides data and data processing methodologies for monitoring and assessing Arctic vegetation over large areas. The goal of this research was to explore the potential of hyperspectral and high spatial resolution multispectral remote sensing data for modelling two important Arctic biophysical variables: Percent Vegetation Cover (PVC) and the fraction of Absorbed Photosynthetically Active Radiation (fAPAR). A series of field experiments were conducted to collect PVC and fAPAR at three Canadian Arctic sites: (1) Sabine Peninsula, Melville Island, NU; (2) Cape Bounty Arctic Watershed Observatory (CBAWO), Melville Island, NU; and (3) Apex River Watershed (ARW), Baffin Island, NU. Linear relationships between biophysical variables and Vegetation Indices (VIs) were examined at different spatial scales using field spectra (for the Sabine Peninsula site) and high spatial resolution satellite data (for the CBAWO and ARW sites). At the Sabine Peninsula site, hyperspectral VIs exhibited a better performance for modelling PVC than multispectral VIs due to their capacity for sampling fine spectral features. The optimal hyperspectral bands were located at important spectral features observed in Arctic vegetation spectra, including leaf pigment absorption in the red wavelengths and at the red-edge, leaf water absorption in the near infrared, and leaf cellulose and lignin absorption in the shortwave infrared. At the CBAWO and ARW sites, field PVC and fAPAR exhibited strong correlations (R2 > 0.70) with the NDVI (Normalized Difference Vegetation Index) derived from high-resolution WorldView-2 data. Similarly, high spatial resolution satellite-derived fAPAR was correlated to MODIS fAPAR (R2 = 0.68), with a systematic
MODEL SELECTION FOR SPECTROPOLARIMETRIC INVERSIONS
International Nuclear Information System (INIS)
Asensio Ramos, A.; Manso Sainz, R.; Martínez González, M. J.; Socas-Navarro, H.; Viticchié, B.; Orozco Suárez, D.
2012-01-01
Inferring magnetic and thermodynamic information from spectropolarimetric observations relies on the assumption of a parameterized model atmosphere whose parameters are tuned by comparison with observations. Often, the choice of the underlying atmospheric model is based on subjective reasons. In other cases, complex models are chosen based on objective reasons (for instance, the necessity to explain asymmetries in the Stokes profiles) but it is not clear what degree of complexity is needed. The lack of an objective way of comparing models has, sometimes, led to opposing views of the solar magnetism because the inferred physical scenarios are essentially different. We present the first quantitative model comparison based on the computation of the Bayesian evidence ratios for spectropolarimetric observations. Our results show that there is not a single model appropriate for all profiles simultaneously. Data with moderate signal-to-noise ratios (S/Ns) favor models without gradients along the line of sight. If the observations show clear circular and linear polarization signals above the noise level, models with gradients along the line are preferred. As a general rule, observations with large S/Ns favor more complex models. We demonstrate that the evidence ratios correlate well with simple proxies. Therefore, we propose to calculate these proxies when carrying out standard least-squares inversions to allow for model comparison in the future.
Gradient Models in Molecular Biophysics: Progress, Challenges, Opportunities.
Bardhan, Jaydeep P
2013-12-01
In the interest of developing a bridge between researchers modeling materials and those modeling biological molecules, we survey recent progress in developing nonlocal-dielectric continuum models for studying the behavior of proteins and nucleic acids. As in other areas of science, continuum models are essential tools when atomistic simulations (e.g. molecular dynamics) are too expensive. Because biological molecules are essentially all nanoscale systems, the standard continuum model, involving local dielectric response, has basically always been dubious at best. The advanced continuum theories discussed here aim to remedy these shortcomings by adding features such as nonlocal dielectric response, and nonlinearities resulting from dielectric saturation. We begin by describing the central role of electrostatic interactions in biology at the molecular scale, and motivate the development of computationally tractable continuum models using applications in science and engineering. For context, we highlight some of the most important challenges that remain and survey the diverse theoretical formalisms for their treatment, highlighting the rigorous statistical mechanics that support the use and improvement of continuum models. We then address the development and implementation of nonlocal dielectric models, an approach pioneered by Dogonadze, Kornyshev, and their collaborators almost forty years ago. The simplest of these models is just a scalar form of gradient elasticity, and here we use ideas from gradient-based modeling to extend the electrostatic model to include additional length scales. The paper concludes with a discussion of open questions for model development, highlighting the many opportunities for the materials community to leverage its physical, mathematical, and computational expertise to help solve one of the most challenging questions in molecular biology and biophysics.
Gradient Models in Molecular Biophysics: Progress, Challenges, Opportunities
Bardhan, Jaydeep P.
2014-01-01
In the interest of developing a bridge between researchers modeling materials and those modeling biological molecules, we survey recent progress in developing nonlocal-dielectric continuum models for studying the behavior of proteins and nucleic acids. As in other areas of science, continuum models are essential tools when atomistic simulations (e.g. molecular dynamics) are too expensive. Because biological molecules are essentially all nanoscale systems, the standard continuum model, involving local dielectric response, has basically always been dubious at best. The advanced continuum theories discussed here aim to remedy these shortcomings by adding features such as nonlocal dielectric response, and nonlinearities resulting from dielectric saturation. We begin by describing the central role of electrostatic interactions in biology at the molecular scale, and motivate the development of computationally tractable continuum models using applications in science and engineering. For context, we highlight some of the most important challenges that remain and survey the diverse theoretical formalisms for their treatment, highlighting the rigorous statistical mechanics that support the use and improvement of continuum models. We then address the development and implementation of nonlocal dielectric models, an approach pioneered by Dogonadze, Kornyshev, and their collaborators almost forty years ago. The simplest of these models is just a scalar form of gradient elasticity, and here we use ideas from gradient-based modeling to extend the electrostatic model to include additional length scales. The paper concludes with a discussion of open questions for model development, highlighting the many opportunities for the materials community to leverage its physical, mathematical, and computational expertise to help solve one of the most challenging questions in molecular biology and biophysics. PMID:25505358
Gradient models in molecular biophysics: progress, challenges, opportunities
Bardhan, Jaydeep P.
2013-12-01
In the interest of developing a bridge between researchers modeling materials and those modeling biological molecules, we survey recent progress in developing nonlocal-dielectric continuum models for studying the behavior of proteins and nucleic acids. As in other areas of science, continuum models are essential tools when atomistic simulations (e.g., molecular dynamics) are too expensive. Because biological molecules are essentially all nanoscale systems, the standard continuum model, involving local dielectric response, has basically always been dubious at best. The advanced continuum theories discussed here aim to remedy these shortcomings by adding nonlocal dielectric response. We begin by describing the central role of electrostatic interactions in biology at the molecular scale, and motivate the development of computationally tractable continuum models using applications in science and engineering. For context, we highlight some of the most important challenges that remain, and survey the diverse theoretical formalisms for their treatment, highlighting the rigorous statistical mechanics that support the use and improvement of continuum models. We then address the development and implementation of nonlocal dielectric models, an approach pioneered by Dogonadze, Kornyshev, and their collaborators almost 40 years ago. The simplest of these models is just a scalar form of gradient elasticity, and here we use ideas from gradient-based modeling to extend the electrostatic model to include additional length scales. The review concludes with a discussion of open questions for model development, highlighting the many opportunities for the materials community to leverage its physical, mathematical, and computational expertise to help solve one of the most challenging questions in molecular biology and biophysics.
Automatic Flight Controller With Model Inversion
Meyer, George; Smith, G. Allan
1992-01-01
Automatic digital electronic control system based on inverse-model-follower concept being developed for proposed vertical-attitude-takeoff-and-landing airplane. Inverse-model-follower control places inverse mathematical model of dynamics of controlled plant in series with control actuators of controlled plant so response of combination of model and plant to command is unity. System includes feedback to compensate for uncertainties in mathematical model and disturbances imposed from without.
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
Biophysical model of prokaryotic diversity in geothermal hot springs.
Klales, Anna; Duncan, James; Nett, Elizabeth Janus; Kane, Suzanne Amador
2012-02-01
Recent studies of photosynthetic bacteria living in geothermal hot spring environments have revealed surprisingly complex ecosystems with an unexpected level of genetic diversity. One case of particular interest involves the distribution along hot spring thermal gradients of genetically distinct bacterial strains that differ in their preferred temperatures for reproduction and photosynthesis. In such systems, a single variable, temperature, defines the relevant environmental variation. In spite of this, each region along the thermal gradient exhibits multiple strains of photosynthetic bacteria adapted to several distinct thermal optima, rather than a single thermal strain adapted to the local environmental temperature. Here we analyze microbiology data from several ecological studies to show that the thermal distribution data exhibit several universal features independent of location and specific bacterial strain. These include the distribution of optimal temperatures of different thermal strains and the functional dependence of the net population density on temperature. We present a simple population dynamics model of these systems that is highly constrained by biophysical data and by physical features of the environment. This model can explain in detail the observed thermal population distributions, as well as certain features of population dynamics observed in laboratory studies of the same organisms. © 2012 American Physical Society
Wake Vortex Inverse Model User's Guide
Lai, David; Delisi, Donald
2008-01-01
NorthWest Research Associates (NWRA) has developed an inverse model for inverting landing aircraft vortex data. The data used for the inversion are the time evolution of the lateral transport position and vertical position of both the port and starboard vortices. The inverse model performs iterative forward model runs using various estimates of vortex parameters, vertical crosswind profiles, and vortex circulation as a function of wake age. Forward model predictions of lateral transport and altitude are then compared with the observed data. Differences between the data and model predictions guide the choice of vortex parameter values, crosswind profile and circulation evolution in the next iteration. Iterations are performed until a user-defined criterion is satisfied. Currently, the inverse model is set to stop when the improvement in the rms deviation between the data and model predictions is less than 1 percent for two consecutive iterations. The forward model used in this inverse model is a modified version of the Shear-APA model. A detailed description of this forward model, the inverse model, and its validation are presented in a different report (Lai, Mellman, Robins, and Delisi, 2007). This document is a User's Guide for the Wake Vortex Inverse Model. Section 2 presents an overview of the inverse model program. Execution of the inverse model is described in Section 3. When executing the inverse model, a user is requested to provide the name of an input file which contains the inverse model parameters, the various datasets, and directories needed for the inversion. A detailed description of the list of parameters in the inversion input file is presented in Section 4. A user has an option to save the inversion results of each lidar track in a mat-file (a condensed data file in Matlab format). These saved mat-files can be used for post-inversion analysis. A description of the contents of the saved files is given in Section 5. An example of an inversion input
Statistical and Biophysical Models for Predicting Total and Outdoor Water Use in Los Angeles
Mini, C.; Hogue, T. S.; Pincetl, S.
2012-04-01
Modeling water demand is a complex exercise in the choice of the functional form, techniques and variables to integrate in the model. The goal of the current research is to identify the determinants that control total and outdoor residential water use in semi-arid cities and to utilize that information in the development of statistical and biophysical models that can forecast spatial and temporal urban water use. The City of Los Angeles is unique in its highly diverse socio-demographic, economic and cultural characteristics across neighborhoods, which introduces significant challenges in modeling water use. Increasing climate variability also contributes to uncertainties in water use predictions in urban areas. Monthly individual water use records were acquired from the Los Angeles Department of Water and Power (LADWP) for the 2000 to 2010 period. Study predictors of residential water use include socio-demographic, economic, climate and landscaping variables at the zip code level collected from US Census database. Climate variables are estimated from ground-based observations and calculated at the centroid of each zip code by inverse-distance weighting method. Remotely-sensed products of vegetation biomass and landscape land cover are also utilized. Two linear regression models were developed based on the panel data and variables described: a pooled-OLS regression model and a linear mixed effects model. Both models show income per capita and the percentage of landscape areas in each zip code as being statistically significant predictors. The pooled-OLS model tends to over-estimate higher water use zip codes and both models provide similar RMSE values.Outdoor water use was estimated at the census tract level as the residual between total water use and indoor use. This residual is being compared with the output from a biophysical model including tree and grass cover areas, climate variables and estimates of evapotranspiration at very high spatial resolution. A
Forward modeling. Route to electromagnetic inversion
Energy Technology Data Exchange (ETDEWEB)
Groom, R; Walker, P [PetRos EiKon Incorporated, Ontario (Canada)
1996-05-01
Inversion of electromagnetic data is a topical subject in the literature, and much time has been devoted to understanding the convergence properties of various inverse methods. The relative lack of success of electromagnetic inversion techniques is partly attributable to the difficulties in the kernel forward modeling software. These difficulties come in two broad classes: (1) Completeness and robustness, and (2) convergence, execution time and model simplicity. If such problems exist in the forward modeling kernel, it was demonstrated that inversion can fail to generate reasonable results. It was suggested that classical inversion techniques, which are based on minimizing a norm of the error between data and the simulated data, will only be successful when these difficulties in forward modeling kernels are properly dealt with. 4 refs., 5 figs.
Multiscattering inversion for low-model wavenumbers
Alkhalifah, Tariq Ali; Wu, Zedong
2016-01-01
A successful full-waveform inversion implementation updates the low-wavenumber model components first for a proper description of the wavefield propagation and slowly adds the high wavenumber potentially scattering parts of the model. The low
Multiscattering inversion for low-model wavenumbers
Alkhalifah, Tariq Ali
2016-09-21
A successful full-waveform inversion implementation updates the low-wavenumber model components first for a proper description of the wavefield propagation and slowly adds the high wavenumber potentially scattering parts of the model. The low-wavenumber components can be extracted from the transmission parts of the recorded wavefield emanating directly from the source or the transmission parts from the single- or double-scattered wavefield computed from a predicted scatter field acting as secondary sources.We use a combined inversion of data modeled from the source and those corresponding to single and double scattering to update the velocity model and the component of the velocity (perturbation) responsible for the single and double scattering. The combined inversion helps us access most of the potential model wavenumber information that may be embedded in the data. A scattering-angle filter is used to divide the gradient of the combined inversion, so initially the high-wavenumber (low-scattering-angle) components of the gradient are directed to the perturbation model and the low-wavenumber (highscattering- angle) components are directed to the velocity model. As our background velocity matures, the scatteringangle divide is slowly lowered to allow for more of the higher wavenumbers to contribute the velocity model. Synthetic examples including the Marmousi model are used to demonstrate the additional illumination and improved velocity inversion obtained when including multiscattered energy. © 2016 Society of Exploration Geophysicists.
Zur, Hadas; Tuller, Tamir
2016-01-01
mRNA translation is the fundamental process of decoding the information encoded in mRNA molecules by the ribosome for the synthesis of proteins. The centrality of this process in various biomedical disciplines such as cell biology, evolution and biotechnology, encouraged the development of dozens of mathematical and computational models of translation in recent years. These models aimed at capturing various biophysical aspects of the process. The objective of this review is to survey these models, focusing on those based and/or validated on real large-scale genomic data. We consider aspects such as the complexity of the models, the biophysical aspects they regard and the predictions they may provide. Furthermore, we survey the central systems biology discoveries reported on their basis. This review demonstrates the fundamental advantages of employing computational biophysical translation models in general, and discusses the relative advantages of the different approaches and the challenges in the field. PMID:27591251
[Crop geometry identification based on inversion of semiempirical BRDF models].
Zhao, Chun-jiang; Huang, Wen-jiang; Mu, Xu-han; Wang, Jin-diz; Wang, Ji-hua
2009-09-01
With the rapid development of remote sensing technology, the application of remote sensing has extended from single view angle to multi-view angles. It was studied for the qualitative and quantitative effect of average leaf angle (ALA) on crop canopy reflected spectrum. Effect of ALA on canopy reflected spectrum can not be ignored with inversion of leaf area index (LAI) and monitoring of crop growth condition by remote sensing technology. Investigations of the effect of erective and horizontal varieties were conducted by bidirectional canopy reflected spectrum and semiempirical bidirectional reflectance distribution function (BRDF) models. The sensitive analysis was done based on the weight for the volumetric kernel (fvol), the weight for the geometric kernel (fgeo), and the weight for constant corresponding to isotropic reflectance (fiso) at red band (680 nm) and near infrared band (800 nm). By combining the weights of the red and near-infrared bands, the semiempirical models can obtain structural information by retrieving biophysical parameters from the physical BRDF model and a number of bidirectional observations. So, it will allow an on-site and non-sampling mode of crop ALA identification, which is useful for using remote sensing for crop growth monitoring and for improving the LAI inversion accuracy, and it will help the farmers in guiding the fertilizer and irrigation management in the farmland without a priori knowledge.
A note on the roles of quantum and mechanical models in social biophysics.
Takahashi, Taiki; Kim, Song-Ju; Naruse, Makoto
2017-11-01
Recent advances in the applications of quantum models into various disciplines such as cognitive science, social sciences, economics, and biology witnessed enormous achievements and possible future progress. In this paper, we propose one of the most promising directions in the applications of quantum models: the combination of quantum and mechanical models in social biophysics. The possible resulting discipline may be called as experimental quantum social biophysics and could foster our understandings of the relationships between the society and individuals. Copyright © 2017 Elsevier Ltd. All rights reserved.
Atmospheric inverse modeling via sparse reconstruction
Hase, Nils; Miller, Scot M.; Maaß, Peter; Notholt, Justus; Palm, Mathias; Warneke, Thorsten
2017-10-01
Many applications in atmospheric science involve ill-posed inverse problems. A crucial component of many inverse problems is the proper formulation of a priori knowledge about the unknown parameters. In most cases, this knowledge is expressed as a Gaussian prior. This formulation often performs well at capturing smoothed, large-scale processes but is often ill equipped to capture localized structures like large point sources or localized hot spots. Over the last decade, scientists from a diverse array of applied mathematics and engineering fields have developed sparse reconstruction techniques to identify localized structures. In this study, we present a new regularization approach for ill-posed inverse problems in atmospheric science. It is based on Tikhonov regularization with sparsity constraint and allows bounds on the parameters. We enforce sparsity using a dictionary representation system. We analyze its performance in an atmospheric inverse modeling scenario by estimating anthropogenic US methane (CH4) emissions from simulated atmospheric measurements. Different measures indicate that our sparse reconstruction approach is better able to capture large point sources or localized hot spots than other methods commonly used in atmospheric inversions. It captures the overall signal equally well but adds details on the grid scale. This feature can be of value for any inverse problem with point or spatially discrete sources. We show an example for source estimation of synthetic methane emissions from the Barnett shale formation.
Directory of Open Access Journals (Sweden)
Jessica Melbourne-Thomas
2011-09-01
Full Text Available Transdisciplinary approaches that consider both socioeconomic and biophysical processes are central to understanding and managing rapid change in coral reef systems worldwide. To date, there have been limited attempts to couple the two sets of processes in dynamic models for coral reefs, and these attempts are confined to reef systems in developed countries. We present an approach to coupling existing biophysical and socioeconomic models for coral reef systems in the Mexican state of Quintana Roo. The biophysical model is multiscale, using dynamic equations to capture local-scale ecological processes on individual reefs, with reefs connected at regional scales by the ocean transport of larval propagules. The agent-based socioeconomic model simulates changes in tourism, fisheries, and urbanization in the Quintana Roo region. Despite differences in the formulation and currencies of the two models, we were able to successfully modify and integrate them to synchronize and define information flows and feedbacks between them. A preliminary evaluation of the coupled model system indicates that the model gives reasonable predictions for fisheries and ecological variables and can be used to examine scenarios for future social-ecological change in Quintana Roo. We provide recommendations for where efforts might usefully be focused in future attempts to integrate models of biophysical and socioeconomic processes, based on the limitations of our coupled system.
Direct Scaling of Leaf-Resolving Biophysical Models from Leaves to Canopies
Bailey, B.; Mahaffee, W.; Hernandez Ochoa, M.
2017-12-01
Recent advances in the development of biophysical models and high-performance computing have enabled rapid increases in the level of detail that can be represented by simulations of plant systems. However, increasingly detailed models typically require increasingly detailed inputs, which can be a challenge to accurately specify. In this work, we explore the use of terrestrial LiDAR scanning data to accurately specify geometric inputs for high-resolution biophysical models that enables direct up-scaling of leaf-level biophysical processes. Terrestrial LiDAR scans generate "clouds" of millions of points that map out the geometric structure of the area of interest. However, points alone are often not particularly useful in generating geometric model inputs, as additional data processing techniques are required to provide necessary information regarding vegetation structure. A new method was developed that directly reconstructs as many leaves as possible that are in view of the LiDAR instrument, and uses a statistical backfilling technique to ensure that the overall leaf area and orientation distribution matches that of the actual vegetation being measured. This detailed structural data is used to provide inputs for leaf-resolving models of radiation, microclimate, evapotranspiration, and photosynthesis. Model complexity is afforded by utilizing graphics processing units (GPUs), which allows for simulations that resolve scales ranging from leaves to canopies. The model system was used to explore how heterogeneity in canopy architecture at various scales affects scaling of biophysical processes from leaves to canopies.
Modeling of uncertainties in statistical inverse problems
International Nuclear Information System (INIS)
Kaipio, Jari
2008-01-01
In all real world problems, the models that tie the measurements to the unknowns of interest, are at best only approximations for reality. While moderate modeling and approximation errors can be tolerated with stable problems, inverse problems are a notorious exception. Typical modeling errors include inaccurate geometry, unknown boundary and initial data, properties of noise and other disturbances, and simply the numerical approximations of the physical models. In principle, the Bayesian approach to inverse problems, in which all uncertainties are modeled as random variables, is capable of handling these uncertainties. Depending on the type of uncertainties, however, different strategies may be adopted. In this paper we give an overview of typical modeling errors and related strategies within the Bayesian framework.
Rubin, Andrew
2014-01-01
This book presents concise descriptions and analysis of the classical and modern models used in mathematical biophysics. The authors ask the question "what new information can be provided by the models that cannot be obtained directly from experimental data?" Actively developing fields such as regulatory mechanisms in cells and subcellular systems and electron transport and energy transport in membranes are addressed together with more classical topics such as metabolic processes, nerve conduction and heart activity, chemical kinetics, population dynamics, and photosynthesis. The main approach is to describe biological processes using different mathematical approaches necessary to reveal characteristic features and properties of simulated systems. With the emergence of powerful mathematics software packages such as MAPLE, Mathematica, Mathcad, and MatLab, these methodologies are now accessible to a wide audience. Provides succinct but authoritative coverage of a broad array of biophysical topics and models Wr...
Modelling and inversion of local magnetic anomalies
International Nuclear Information System (INIS)
Quesnel, Y; Langlais, B; Sotin, C; Galdéano, A
2008-01-01
We present a method—named as MILMA for modelling and inversion of local magnetic anomalies—that combines forward and inverse modelling of aeromagnetic data to characterize both magnetization properties and location of unconstrained local sources. Parameters of simple-shape magnetized bodies (cylinder, prism or sphere) are first adjusted by trial and error to predict the signal. Their parameters provide a priori information for inversion of the measurements. Here, a generalized nonlinear approach with a least-squares criterion is adopted to seek the best parameters of the sphere (dipole). This inversion step allows the model to be more objectively adjusted to fit the magnetic signal. The validity of the MILMA method is demonstrated through synthetic and real cases using aeromagnetic measurements. Tests with synthetic data reveal accurate results in terms of depth source, whatever be the number of sources. The MILMA method is then used with real measurements to constrain the properties of the magnetized units of the Champtoceaux complex (France). The resulting parameters correlate with the crustal structure and properties revealed by other geological and geophysical surveys in the same area. The MILMA method can therefore be used to investigate the properties of poorly constrained lithospheric magnetized sources
Tectonic forward modelling of positive inversion structures
Energy Technology Data Exchange (ETDEWEB)
Brandes, C. [Leibniz Univ. Hannover (Germany). Inst. fuer Geologie; Schmidt, C. [Landesamt fuer Bergbau, Energie und Geologie (LBEG), Hannover (Germany)
2013-08-01
Positive tectonic inversion structures are common features that were recognized in many deformed sedimentary basins (Lowell, 1995). They are characterized by a two phase fault evolution, where initial normal faulting was followed by reverse faulting along the same fault, accompanied by the development of hanging wall deformation. Analysing the evolution of such inversion structures is important for understanding the tectonics of sedimentary basins and the formation of hydrocarbon traps. We used a 2D tectonic forward modelling approach to simulate the stepwise structural evolution of inversion structures in cross-section. The modelling was performed with the software FaultFold Forward v. 6, which is based on trishear kinematics (Zehnder and Allmendinger, 2000). Key aspect of the study was to derive the controlling factors for the geometry of inversion structures. The simulation results show, that the trishear approach is able to reproduce the geometry of tectonic inversion structures in a realistic way. This implies that inversion structures are simply fault-related folds that initiated as extensional fault-propagation folds, which were subsequently transformed into compressional fault-propagation folds when the stress field changed. The hanging wall deformation is a consequence of the decrease in slip towards the tip line of the fault. Trishear angle and propagation-to-slip ratio are the key controlling factors for the geometry of the fault-related deformation. We tested trishear angles in the range of 30 - 60 and propagation-to-slip ratios between 1 and 2 in increments of 0.1. Small trishear angles and low propagation-to-slip ratios produced tight folds, whereas large trishear angles and high propagation-to-slip ratios led to more open folds with concentric shapes. This has a direct effect on the size and geometry of potential hydrocarbon traps. The 2D simulations can be extended to a pseudo 3D approach, where a set of parallel cross-sections is used to describe
Inverse hydrochemical models of aqueous extracts tests
Energy Technology Data Exchange (ETDEWEB)
Zheng, L.; Samper, J.; Montenegro, L.
2008-10-10
Aqueous extract test is a laboratory technique commonly used to measure the amount of soluble salts of a soil sample after adding a known mass of distilled water. Measured aqueous extract data have to be re-interpreted in order to infer porewater chemical composition of the sample because porewater chemistry changes significantly due to dilution and chemical reactions which take place during extraction. Here we present an inverse hydrochemical model to estimate porewater chemical composition from measured water content, aqueous extract, and mineralogical data. The model accounts for acid-base, redox, aqueous complexation, mineral dissolution/precipitation, gas dissolution/ex-solution, cation exchange and surface complexation reactions, of which are assumed to take place at local equilibrium. It has been solved with INVERSE-CORE{sup 2D} and been tested with bentonite samples taken from FEBEX (Full-scale Engineered Barrier EXperiment) in situ test. The inverse model reproduces most of the measured aqueous data except bicarbonate and provides an effective, flexible and comprehensive method to estimate porewater chemical composition of clays. Main uncertainties are related to kinetic calcite dissolution and variations in CO2(g) pressure.
Coexistence between silent and bursting states in a biophysical Hodgkin-Huxley-type of model
DEFF Research Database (Denmark)
Stankevich, Nataliya; Mosekilde, Erik
2017-01-01
in a slightly modified, biophysical model that describe the dynamics of pancreatic beta-cells. To realize this form of coexistence, we have introduced an additional voltage-dependent potassium current that is activated in the region around the original, unstable equilibrium point. It is interesting to note...... that this modification also leads the model to display a blue-sky catastrophe in the transition region between chaotic and bursting states....
DEFF Research Database (Denmark)
Hartwigsen, Gesa; Bergmann, Til Ole; Herz, Damian Marc
2015-01-01
these approaches advance the scientific potential of NTBS as an interventional tool in cognitive neuroscience. (i) Leveraging the anatomical information provided by structural imaging, the electric field distribution in the brain can be modeled and simulated. Biophysical modeling approaches generate testable...... predictions regarding the impact of interindividual variations in cortical anatomy on the injected electric fields or the influence of the orientation of current flow on the physiological stimulation effects. (ii) Functional brain mapping of the spatiotemporal neural dynamics during cognitive tasks can...
Broadbent, A. M.; Georgescu, M.; Krayenhoff, E. S.; Sailor, D.
2017-12-01
Utility-scale solar power plants are a rapidly growing component of the solar energy sector. Utility-scale photovoltaic (PV) solar power generation in the United States has increased by 867% since 2012 (EIA, 2016). This expansion is likely to continue as the cost PV technologies decrease. While most agree that solar power can decrease greenhouse gas emissions, the biophysical effects of PV systems on surface energy balance (SEB), and implications for surface climate, are not well understood. To our knowledge, there has never been a detailed observational study of SEB at a utility-scale solar array. This study presents data from an eddy covariance observational tower, temporarily placed above a utility-scale PV array in Southern Arizona. Comparison of PV SEB with a reference (unmodified) site, shows that solar panels can alter the SEB and near surface climate. SEB observations are used to develop and validate a new and more complete SEB PV model. In addition, the PV model is compared to simpler PV modelling methods. The simpler PV models produce differing results to our newly developed model and cannot capture the more complex processes that influence PV SEB. Finally, hypothetical scenarios of PV expansion across the continental United States (CONUS) were developed using various spatial mapping criteria. CONUS simulations of PV expansion reveal regional variability in biophysical effects of PV expansion. The study presents the first rigorous and validated simulations of the biophysical effects of utility-scale PV arrays.
Simple biophysical model of tumor evasion from immune system control
D'Onofrio, Alberto; Ciancio, Armando
2011-09-01
The competitive nonlinear interplay between a tumor and the host's immune system is not only very complex but is also time-changing. A fundamental aspect of this issue is the ability of the tumor to slowly carry out processes that gradually allow it to become less harmed and less susceptible to recognition by the immune system effectors. Here we propose a simple epigenetic escape mechanism that adaptively depends on the interactions per time unit between cells of the two systems. From a biological point of view, our model is based on the concept that a tumor cell that has survived an encounter with a cytotoxic T-lymphocyte (CTL) has an information gain that it transmits to the other cells of the neoplasm. The consequence of this information increase is a decrease in both the probabilities of being killed and of being recognized by a CTL. We show that the mathematical model of this mechanism is formally equal to an evolutionary imitation game dynamics. Numerical simulations of transitory phases complement the theoretical analysis. Implications of the interplay between the above mechanisms and the delivery of immunotherapies are also illustrated.
Kolotii, Andrii; Kussul, Nataliia; Skakun, Sergii; Shelestov, Andrii; Ostapenko, Vadim; Oliinyk, Tamara
2015-04-01
Efficient and timely crop monitoring and yield forecasting are important tasks for ensuring of stability and sustainable economic development [1]. As winter crops pay prominent role in agriculture of Ukraine - the main focus of this study is concentrated on winter wheat. In our previous research [2, 3] it was shown that usage of biophysical parameters of crops such as FAPAR (derived from Geoland-2 portal as for SPOT Vegetation data) is far more efficient for crop yield forecasting to NDVI derived from MODIS data - for available data. In our current work efficiency of usage such biophysical parameters as LAI, FAPAR, FCOVER (derived from SPOT Vegetation and PROBA-V data at resolution of 1 km and simulated within WOFOST model) and NDVI product (derived from MODIS) for winter wheat monitoring and yield forecasting is estimated. As the part of crop monitoring workflow (vegetation anomaly detection, vegetation indexes and products analysis) and yield forecasting SPIRITS tool developed by JRC is used. Statistics extraction is done for landcover maps created in SRI within FP-7 SIGMA project. Efficiency of usage satellite based and modelled with WOFOST model biophysical products is estimated. [1] N. Kussul, S. Skakun, A. Shelestov, O. Kussul, "Sensor Web approach to Flood Monitoring and Risk Assessment", in: IGARSS 2013, 21-26 July 2013, Melbourne, Australia, pp. 815-818. [2] F. Kogan, N. Kussul, T. Adamenko, S. Skakun, O. Kravchenko, O. Kryvobok, A. Shelestov, A. Kolotii, O. Kussul, and A. Lavrenyuk, "Winter wheat yield forecasting in Ukraine based on Earth observation, meteorological data and biophysical models," International Journal of Applied Earth Observation and Geoinformation, vol. 23, pp. 192-203, 2013. [3] Kussul O., Kussul N., Skakun S., Kravchenko O., Shelestov A., Kolotii A, "Assessment of relative efficiency of using MODIS data to winter wheat yield forecasting in Ukraine", in: IGARSS 2013, 21-26 July 2013, Melbourne, Australia, pp. 3235 - 3238.
Integrating Economic Models with Biophysical Models in the Willamette Water 2100 Project
Jaeger, W. K.; Plantinga, A.
2013-12-01
This paper highlights the human system modeling components for Willamette Water 2100, a comprehensive, highly integrated study of hydrological, ecological, and human factors affecting water scarcity in the Willamette River Basin (WRB). The project is developing a spatiotemporal simulation model to predict future trajectories of water scarcity, and to evaluate mitigation policies. Economic models of land use and water use are the main human system models in WW2100. Water scarcity depends on both supply and demand for water, and varies greatly across time and space (Jaeger et al., 2013). Thus, the locations of human water use can have enormous influence on where and when water is used, and hence where water scarcity may arise. Modeling the locations of human uses of water (e.g., urban versus agricultural) as well as human values and choices, are the principal quantitative ways that social science can contribute to research of this kind. Our models are empirically-based models of human resource allocation. Each model reflects private behavior (choices by households, farms, firms), institutions (property rights, laws, markets, regulations), public infrastructure (dams, canals, highways), and also 'external drivers' that influence the local economy (migration, population growth, national markets and policies). This paper describes the main model components, emphasizing similarities between human and biophysical components of the overall project, and the model's linkages and feedbacks relevant to our predictions of changes in water scarcity between now and 2100. Results presented include new insights from individual model components as well as available results from the integrated system model. Issues include water scarcity and water quality (temperature) for out-of-stream and instream uses, the impact of urban expansion on water use and potential flood damage. Changes in timing and variability of spring discharge with climate change, as well as changes in human uses of
Biophysical studies of cholesterol in unsaturated phospholipid model membranes
Williams, Justin Adam
Cellular membranes contain a staggering diversity of lipids. The lipids are heterogeneously distributed to create regions, or domains, whose physical properties differ from the bulk membrane and play an essential role in modulating the function of resident proteins. Many basic questions pertaining to the formation of these lateral assemblies remain. This research employs model membranes of well-defined composition to focus on the potential role of polyunsaturated fatty acids (PUFAs) and their interaction with cholesterol (chol) in restructuring the membrane environment. Omega-3 (n-3) PUFAs are the main bioactive components of fish oil, whose consumption alleviates a variety of health problems by a molecular mechanism that is unclear. We hypothesize that the incorporation of PUFAs into membrane lipids and the effect they have on molecular organization may be, in part, responsible. Chol is a major constituent in the plasma membrane of mammals. It determines the arrangement and collective properties of neighboring lipids, driving the formation of domains via differential affinity for different lipids. The molecular organization of 1-[2H31]palmitoyl-2-eicosapentaenoylphosphatidylcholine (PEPC-d31) and 1-[2H31]palmitoyl-2-docosahexaenoylphosphatidylcholine (PDPC-d31) in membranes with sphingomyelin (SM) and chol (1:1:1 mol) was compared by solid-state 2H NMR spectroscopy. Eicosapentaenoic (EPA) and docosahexaenoic (DHA) acids are the two major n-3 PUFAs found in fish oil, while PEPC-d31 and PDPC-d31 are phospholipids containing the respective PUFAs at the sn-2 position and a perdeuterated palmitic acid at the sn-1 position. Analysis of spectra recorded as a function of temperature indicates that in both cases, formation of PUFA-rich (less ordered) and SM-rich (more ordered) domains occurred. A surprisingly substantial proportion of PUFA was found to infiltrate the more ordered domain. There was almost twice as much DHA (65%) as EPA (30%). The implication is that n-3
Predicting the Presence of Scyphozoan Jellyfish in the Gulf of Mexico Using a Biophysical Model
Aleksa, K. T.; Nero, R. W.; Wiggert, J. D.; Graham, W. M.
2016-02-01
The study and quantification of jellyfish (cnidarian medusae and ctenophores) is difficult due to their fragile body plan and a composition similar to their environment. The development of a predictive biophysical jellyfish model would be the first of its kind for the Gulf of Mexico and could provide assistance in ecological research and human interactions. In this study, the collection data of two scyphozoan medusae, Chrysaora quinquecirrha and Aurelia spp., were extracted from SEAMAP trawling surveys and were used to determine biophysical predictors for the presence of large jellyfish medusae in the Gulf of Mexico. Both in situ and remote sensing measurements from 2003 to 2013 were obtained. Logistic regressions were then applied to 27 biophysical parameters derived from these data to explore and determine significant predictors for the presence of medusae. Significant predictors identified by this analysis included water temperature, chlorophyll a, turbidity, distance from shore, and salinity. Future application for this model include foraging assessment of gelatinous predators as well as possible near real time monitoring of the distribution and movement of these medusae in the Gulf of Mexico.
Representing biophysical landscape interactions in soil models by bridging disciplines and scales.
van der Ploeg, M. J.; Carranza, C.; Teixeira da Silva, R.; te Brake, B.; Baartman, J.; Robinson, D.
2017-12-01
The combination of climate change, population growth and soil threats including carbon loss, biodiversity decline and erosion, increasingly confront the global community (Schwilch et al., 2016). One major challenge in studying processes involved in soil threats, landscape resilience, ecosystem stability, sustainable land management and resulting economic consequences, is that it is an interdisciplinary field (Pelletier et al., 2012). Less stringent scientific disciplinary boundaries are therefore important (Liu et al., 2007), because as a result of disciplinary focus, ambiguity may arise on the understanding of landscape interactions. This is especially true in the interaction between a landscape's physical and biological processes (van der Ploeg et al. 2012). Biophysical landscape interactions are those biotic and abiotic processes in a landscape that have an influence on the developments within and evolution of a landscape. An important aspect in biophysical landscape interactions is the differences in scale related to the various processes that play a role in these systems. Moreover, the interplay between the physical landscape and the occurring vegetation, which often co-evolve, and the resulting heterogeneity and emerging patterns are the reason why it is so challenging to establish a theoretical basis to describe biophysical processes in landscapes (e.g. te Brake et al. 2013, Robinson et al. 2016). Another complicating factor is the response of vegetation to changing environmental conditions, including a possible, and often unknown, time-lag (e.g. Metzger et al., 2009). An integrative description for modelling biophysical interactions has been a long standing goal in soil science (Vereecken et al., 2016). We need the development of soil models that are more focused on networks, connectivity and feedbacks incorporating the most important aspects of our detailed mechanistic modelling (Paola & Leeder, 2011). Additionally, remote sensing measurement techniques
Constraint on Parameters of Inverse Compton Scattering Model for ...
Indian Academy of Sciences (India)
B2319+60, two parameters of inverse Compton scattering model, the initial Lorentz factor and the factor of energy loss of relativistic particles are constrained. Key words. Pulsar—inverse Compton scattering—emission mechanism. 1. Introduction. Among various kinds of models for pulsar radio emission, the inverse ...
Stochastic inverse problems: Models and metrics
International Nuclear Information System (INIS)
Sabbagh, Elias H.; Sabbagh, Harold A.; Murphy, R. Kim; Aldrin, John C.; Annis, Charles; Knopp, Jeremy S.
2015-01-01
In past work, we introduced model-based inverse methods, and applied them to problems in which the anomaly could be reasonably modeled by simple canonical shapes, such as rectangular solids. In these cases the parameters to be inverted would be length, width and height, as well as the occasional probe lift-off or rotation. We are now developing a formulation that allows more flexibility in modeling complex flaws. The idea consists of expanding the flaw in a sequence of basis functions, and then solving for the expansion coefficients of this sequence, which are modeled as independent random variables, uniformly distributed over their range of values. There are a number of applications of such modeling: 1. Connected cracks and multiple half-moons, which we have noted in a POD set. Ideally we would like to distinguish connected cracks from one long shallow crack. 2. Cracks of irregular profile and shape which have appeared in cold work holes during bolt-hole eddy-current inspection. One side of such cracks is much deeper than other. 3. L or C shaped crack profiles at the surface, examples of which have been seen in bolt-hole cracks. By formulating problems in a stochastic sense, we are able to leverage the stochastic global optimization algorithms in NLSE, which is resident in VIC-3D®, to answer questions of global minimization and to compute confidence bounds using the sensitivity coefficient that we get from NLSE. We will also address the issue of surrogate functions which are used during the inversion process, and how they contribute to the quality of the estimation of the bounds
Stochastic inverse problems: Models and metrics
Sabbagh, Elias H.; Sabbagh, Harold A.; Murphy, R. Kim; Aldrin, John C.; Annis, Charles; Knopp, Jeremy S.
2015-03-01
In past work, we introduced model-based inverse methods, and applied them to problems in which the anomaly could be reasonably modeled by simple canonical shapes, such as rectangular solids. In these cases the parameters to be inverted would be length, width and height, as well as the occasional probe lift-off or rotation. We are now developing a formulation that allows more flexibility in modeling complex flaws. The idea consists of expanding the flaw in a sequence of basis functions, and then solving for the expansion coefficients of this sequence, which are modeled as independent random variables, uniformly distributed over their range of values. There are a number of applications of such modeling: 1. Connected cracks and multiple half-moons, which we have noted in a POD set. Ideally we would like to distinguish connected cracks from one long shallow crack. 2. Cracks of irregular profile and shape which have appeared in cold work holes during bolt-hole eddy-current inspection. One side of such cracks is much deeper than other. 3. L or C shaped crack profiles at the surface, examples of which have been seen in bolt-hole cracks. By formulating problems in a stochastic sense, we are able to leverage the stochastic global optimization algorithms in NLSE, which is resident in VIC-3D®, to answer questions of global minimization and to compute confidence bounds using the sensitivity coefficient that we get from NLSE. We will also address the issue of surrogate functions which are used during the inversion process, and how they contribute to the quality of the estimation of the bounds.
Brown, Malcolm
2009-01-01
Inversions are fascinating phenomena. They are reversals of the normal or expected order. They occur across a wide variety of contexts. What do inversions have to do with learning spaces? The author suggests that they are a useful metaphor for the process that is unfolding in higher education with respect to education. On the basis of…
Directory of Open Access Journals (Sweden)
Daniel Mellem
Full Text Available Mitochondria are essential for the energy production of eukaryotic cells. During aging mitochondria run through various processes which change their quality in terms of activity, health and metabolic supply. In recent years, many of these processes such as fission and fusion of mitochondria, mitophagy, mitochondrial biogenesis and energy consumption have been subject of research. Based on numerous experimental insights, it was possible to qualify mitochondrial behaviour in computational simulations. Here, we present a new biophysical model based on the approach of Figge et al. in 2012. We introduce exponential decay and growth laws for each mitochondrial process to derive its time-dependent probability during the aging of cells. All mitochondrial processes of the original model are mathematically and biophysically redefined and additional processes are implemented: Mitochondrial fission and fusion is separated into a metabolic outer-membrane part and a protein-related inner-membrane part, a quality-dependent threshold for mitophagy and mitochondrial biogenesis is introduced and processes for activity-dependent internal oxidative stress as well as mitochondrial repair mechanisms are newly included. Our findings reveal a decrease of mitochondrial quality and a fragmentation of the mitochondrial network during aging. Additionally, the model discloses a quality increasing mechanism due to the interplay of the mitophagy and biogenesis cycle and the fission and fusion cycle of mitochondria. It is revealed that decreased mitochondrial repair can be a quality saving process in aged cells. Furthermore, the model finds strategies to sustain the quality of the mitochondrial network in cells with high production rates of reactive oxygen species due to large energy demands. Hence, the model adds new insights to biophysical mechanisms of mitochondrial aging and provides novel understandings of the interdependency of mitochondrial processes.
Theoretical study on the inverse modeling of deep body temperature measurement
International Nuclear Information System (INIS)
Huang, Ming; Chen, Wenxi
2012-01-01
We evaluated the theoretical aspects of monitoring the deep body temperature distribution with the inverse modeling method. A two-dimensional model was built based on anatomical structure to simulate the human abdomen. By integrating biophysical and physiological information, the deep body temperature distribution was estimated from cutaneous surface temperature measurements using an inverse quasilinear method. Simulations were conducted with and without the heat effect of blood perfusion in the muscle and skin layers. The results of the simulations showed consistently that the noise characteristics and arrangement of the temperature sensors were the major factors affecting the accuracy of the inverse solution. With temperature sensors of 0.05 °C systematic error and an optimized 16-sensor arrangement, the inverse method could estimate the deep body temperature distribution with an average absolute error of less than 0.20 °C. The results of this theoretical study suggest that it is possible to reconstruct the deep body temperature distribution with the inverse method and that this approach merits further investigation. (paper)
Confidence bands for inverse regression models
International Nuclear Information System (INIS)
Birke, Melanie; Bissantz, Nicolai; Holzmann, Hajo
2010-01-01
We construct uniform confidence bands for the regression function in inverse, homoscedastic regression models with convolution-type operators. Here, the convolution is between two non-periodic functions on the whole real line rather than between two periodic functions on a compact interval, since the former situation arguably arises more often in applications. First, following Bickel and Rosenblatt (1973 Ann. Stat. 1 1071–95) we construct asymptotic confidence bands which are based on strong approximations and on a limit theorem for the supremum of a stationary Gaussian process. Further, we propose bootstrap confidence bands based on the residual bootstrap and prove consistency of the bootstrap procedure. A simulation study shows that the bootstrap confidence bands perform reasonably well for moderate sample sizes. Finally, we apply our method to data from a gel electrophoresis experiment with genetically engineered neuronal receptor subunits incubated with rat brain extract
Bagstad, Kenneth J.; Reed, James; Semmens, Darius J.; Sherrouse, Ben C.; Troy, Austin
2016-01-01
Through extensive research, ecosystem services have been mapped using both survey-based and biophysical approaches, but comparative mapping of public values and those quantified using models has been lacking. In this paper, we mapped hot and cold spots for perceived and modeled ecosystem services by synthesizing results from a social-values mapping study of residents living near the Pike–San Isabel National Forest (PSI), located in the Southern Rocky Mountains, with corresponding biophysically modeled ecosystem services. Social-value maps for the PSI were developed using the Social Values for Ecosystem Services tool, providing statistically modeled continuous value surfaces for 12 value types, including aesthetic, biodiversity, and life-sustaining values. Biophysically modeled maps of carbon sequestration and storage, scenic viewsheds, sediment regulation, and water yield were generated using the Artificial Intelligence for Ecosystem Services tool. Hotspots for both perceived and modeled services were disproportionately located within the PSI’s wilderness areas. Additionally, we used regression analysis to evaluate spatial relationships between perceived biodiversity and cultural ecosystem services and corresponding biophysical model outputs. Our goal was to determine whether publicly valued locations for aesthetic, biodiversity, and life-sustaining values relate meaningfully to results from corresponding biophysical ecosystem service models. We found weak relationships between perceived and biophysically modeled services, indicating that public perception of ecosystem service provisioning regions is limited. We believe that biophysical and social approaches to ecosystem service mapping can serve as methodological complements that can advance ecosystem services-based resource management, benefitting resource managers by showing potential locations of synergy or conflict between areas supplying ecosystem services and those valued by the public.
Similar Biophysical Abnormalities in Glomeruli and Podocytes from Two Distinct Models.
Embry, Addie E; Liu, Zhenan; Henderson, Joel M; Byfield, F Jefferson; Liu, Liping; Yoon, Joonho; Wu, Zhenzhen; Cruz, Katrina; Moradi, Sara; Gillombardo, C Barton; Hussain, Rihanna Z; Doelger, Richard; Stuve, Olaf; Chang, Audrey N; Janmey, Paul A; Bruggeman, Leslie A; Miller, R Tyler
2018-03-23
Background FSGS is a pattern of podocyte injury that leads to loss of glomerular function. Podocytes support other podocytes and glomerular capillary structure, oppose hemodynamic forces, form the slit diaphragm, and have mechanical properties that permit these functions. However, the biophysical characteristics of glomeruli and podocytes in disease remain unclear. Methods Using microindentation, atomic force microscopy, immunofluorescence microscopy, quantitative RT-PCR, and a three-dimensional collagen gel contraction assay, we studied the biophysical and structural properties of glomeruli and podocytes in chronic (Tg26 mice [HIV protein expression]) and acute (protamine administration [cytoskeletal rearrangement]) models of podocyte injury. Results Compared with wild-type glomeruli, Tg26 glomeruli became progressively more deformable with disease progression, despite increased collagen content. Tg26 podocytes had disordered cytoskeletons, markedly abnormal focal adhesions, and weaker adhesion; they failed to respond to mechanical signals and exerted minimal traction force in three-dimensional collagen gels. Protamine treatment had similar but milder effects on glomeruli and podocytes. Conclusions Reduced structural integrity of Tg26 podocytes causes increased deformability of glomerular capillaries and limits the ability of capillaries to counter hemodynamic force, possibly leading to further podocyte injury. Loss of normal podocyte mechanical integrity could injure neighboring podocytes due to the absence of normal biophysical signals required for podocyte maintenance. The severe defects in podocyte mechanical behavior in the Tg26 model may explain why Tg26 glomeruli soften progressively, despite increased collagen deposition, and may be the basis for the rapid course of glomerular diseases associated with severe podocyte injury. In milder injury (protamine), similar processes occur but over a longer time. Copyright © 2018 by the American Society of Nephrology.
Simulation of Tillage Systems Impact on Soil Biophysical Properties Using the SALUS Model
Directory of Open Access Journals (Sweden)
Luigi Sartori
2011-02-01
Full Text Available A sustainable land management has been defined as the management system that allows for production, while minimizing risk, maintaining quality of soil and water. Tillage systems can significantly decrease soil carbon storage and influence the soil environment of a crop. Crop growth models can be useful tools in evaluating the impact of different tillage systems on soil biophysical properties and on the growth and final yield of the crops. The objectives of this paper were i to illustrate the SALUS model and its tillage component; ii to evaluate the effects of different tillage systems on water infiltration and time to ponding, iii to simulate the effect of tillage systems on some soil biophysical properties. The SALUS (System Approach to Land Use Sustainability model is designed to simulate continuous crop, soil, water and nutrient conditions under different tillage and crop residues management strategies for multiple years. Predictions of changes in surface residue, bulk density, runoff, drainage and evaporation were consistent with expected behaviours of these parameters as described in the literature. The experiment to estimate the time to ponding curve under different tillage system confirmed the theory and showed the beneficial effects of the residue on soil surface with respect to water infiltration. It also showed that the no-tillage system is a more appropriate system to adopt in areas characterized by high intensity rainfall.
Simulation of Tillage Systems Impact on Soil Biophysical Properties Using the SALUS Model
Directory of Open Access Journals (Sweden)
Bruno Basso
2006-12-01
Full Text Available A sustainable land management has been defined as the management system that allows for production, while minimizing risk, maintaining quality of soil and water. Tillage systems can significantly decrease soil carbon storage and influence the soil environment of a crop. Crop growth models can be useful tools in evaluating the impact of different tillage systems on soil biophysical properties and on the growth and final yield of the crops. The objectives of this paper were i to illustrate the SALUS model and its tillage component; ii to evaluate the effects of different tillage systems on water infiltration and time to ponding, iii to simulate the effect of tillage systems on some soil biophysical properties. The SALUS (System Approach to Land Use Sustainability model is designed to simulate continuous crop, soil, water and nutrient conditions under different tillage and crop residues management strategies for multiple years. Predictions of changes in surface residue, bulk density, runoff, drainage and evaporation were consistent with expected behaviours of these parameters as described in the literature. The experiment to estimate the time to ponding curve under different tillage system confirmed the theory and showed the beneficial effects of the residue on soil surface with respect to water infiltration. It also showed that the no-tillage system is a more appropriate system to adopt in areas characterized by high intensity rainfall.
Voxel inversion of airborne electromagnetic data for improved model integration
Fiandaca, Gianluca; Auken, Esben; Kirkegaard, Casper; Vest Christiansen, Anders
2014-05-01
Inversion of electromagnetic data has migrated from single site interpretations to inversions including entire surveys using spatial constraints to obtain geologically reasonable results. Though, the model space is usually linked to the actual observation points. For airborne electromagnetic (AEM) surveys the spatial discretization of the model space reflects the flight lines. On the contrary, geological and groundwater models most often refer to a regular voxel grid, not correlated to the geophysical model space, and the geophysical information has to be relocated for integration in (hydro)geological models. We have developed a new geophysical inversion algorithm working directly in a voxel grid disconnected from the actual measuring points, which then allows for informing directly geological/hydrogeological models. The new voxel model space defines the soil properties (like resistivity) on a set of nodes, and the distribution of the soil properties is computed everywhere by means of an interpolation function (e.g. inverse distance or kriging). Given this definition of the voxel model space, the 1D forward responses of the AEM data are computed as follows: 1) a 1D model subdivision, in terms of model thicknesses, is defined for each 1D data set, creating "virtual" layers. 2) the "virtual" 1D models at the sounding positions are finalized by interpolating the soil properties (the resistivity) in the center of the "virtual" layers. 3) the forward response is computed in 1D for each "virtual" model. We tested the new inversion scheme on an AEM survey carried out with the SkyTEM system close to Odder, in Denmark. The survey comprises 106054 dual mode AEM soundings, and covers an area of approximately 13 km X 16 km. The voxel inversion was carried out on a structured grid of 260 X 325 X 29 xyz nodes (50 m xy spacing), for a total of 2450500 inversion parameters. A classical spatially constrained inversion (SCI) was carried out on the same data set, using 106054
International Nuclear Information System (INIS)
Inoue, Y.; Olioso, A.
2004-01-01
Abstract Information on the ecological and physiological status of crops is essential for growth diagnostics and yield prediction. Within-field or between-field spatial information is required, especially with the recent trend toward precision agriculture, which seeks the efficient use of agrochemicals, water, and energy. The study of carbon and nitrogen cycles as well as environmental management on local and regional scales requires assessment of the spatial variability of biophysical and ecophysiological variables, scaling up of which is also needed for scientific and decision-making purposes. Remote sensing has great potential for these applications because it enables wide-area non-destructive, and real-time acquisition of information about ecophysiological conditions of vegetation. With recent advances in sensor technology, a variety of electromagnetic signatures, such as hyperspectral reflectance, thermal-infrared temperature, and microwave backscattering coefficients, can be acquired for both plants and ecosystems using ground-based, airborne, and satellite platforms. Their spatial and temporal resolutions have both recently been improved. This article reviews the state of the art in the remote sensing of plant ecophysiological data, with special emphasis on the synergy between remote sensing signatures and biophysical and ecophysiological process models. Several case studies for the optical, thermal, and microwave domains have demonstrated the potential of this synergistic linkage. Remote sensing and process modeling methods complement each other when combined synergistically. Further research on this approach is needed f or a wide range of ecophysiological and ecosystem studies, as well as for practical crop management
On The Development of Biophysical Models for Space Radiation Risk Assessment
Cucinotta, F. A.; Dicello, J. F.
1999-01-01
Experimental techniques in molecular biology are being applied to study biological risks from space radiation. The use of molecular assays presents a challenge to biophysical models which in the past have relied on descriptions of energy deposition and phenomenological treatments of repair. We describe a biochemical kinetics model of cell cycle control and DNA damage response proteins in order to model cellular responses to radiation exposures. Using models of cyclin-cdk, pRB, E2F's, p53, and GI inhibitors we show that simulations of cell cycle populations and GI arrest can be described by our biochemical approach. We consider radiation damaged DNA as a substrate for signal transduction processes and consider a dose and dose-rate reduction effectiveness factor (DDREF) for protein expression.
Benchmarking sensitivity of biophysical processes to leaf area changes in land surface models
Forzieri, Giovanni; Duveiller, Gregory; Georgievski, Goran; Li, Wei; Robestson, Eddy; Kautz, Markus; Lawrence, Peter; Ciais, Philippe; Pongratz, Julia; Sitch, Stephen; Wiltshire, Andy; Arneth, Almut; Cescatti, Alessandro
2017-04-01
Land surface models (LSM) are widely applied as supporting tools for policy-relevant assessment of climate change and its impact on terrestrial ecosystems, yet knowledge of their performance skills in representing the sensitivity of biophysical processes to changes in vegetation density is still limited. This is particularly relevant in light of the substantial impacts on regional climate associated with the changes in leaf area index (LAI) following the observed global greening. Benchmarking LSMs on the sensitivity of the simulated processes to vegetation density is essential to reduce their uncertainty and improve the representation of these effects. Here we present a novel benchmark system to assess model capacity in reproducing land surface-atmosphere energy exchanges modulated by vegetation density. Through a collaborative effort of different modeling groups, a consistent set of land surface energy fluxes and LAI dynamics has been generated from multiple LSMs, including JSBACH, JULES, ORCHIDEE, CLM4.5 and LPJ-GUESS. Relationships of interannual variations of modeled surface fluxes to LAI changes have been analyzed at global scale across different climatological gradients and compared with satellite-based products. A set of scoring metrics has been used to assess the overall model performances and a detailed analysis in the climate space has been provided to diagnose possible model errors associated to background conditions. Results have enabled us to identify model-specific strengths and deficiencies. An overall best performing model does not emerge from the analyses. However, the comparison with other models that work better under certain metrics and conditions indicates that improvements are expected to be potentially achievable. A general amplification of the biophysical processes mediated by vegetation is found across the different land surface schemes. Grasslands are characterized by an underestimated year-to-year variability of LAI in cold climates
Werdell, P. Jeremy; Ooesler, Collin S.
2012-01-01
The daily, synoptic images provided by satellite ocean color instruments provide viable data streams for observing changes in the biogeochemistrY of marine ecosystems. Ocean reflectance inversion models (ORMs) provide a common mechanism for inverting the "color" of the water observed a satellite into marine inherent optical properties (lOPs) through a combination of empiricism and radiative transfer theory. lOPs, namely the spectral absorption and scattering characteristics of ocean water and its dissolved and particulate constituents, describe the contents of the upper ocean, information critical for furthering scientific understanding of biogeochemical oceanic processes. Many recent studies inferred marine particle sizes and discriminated between phytoplankton functional groups using remotely-sensed lOPs. While all demonstrated the viability of their approaches, few described the vertical distributions of the water column constituents under consideration and, thus, failed to report the biophysical conditions under which their model performed (e.g., the depth and thickness of the phytoplankton bloom(s)). We developed an ORM to remotely identifY Noctiluca miliaris and other phytoplankton functional types using satellite ocean color data records collected in the northern Arabian Sea. Here, we present results from analyses designed to evaluate the applicability and sensitivity of the ORM to varied biophysical conditions. Specifically, we: (1) synthesized a series of vertical profiles of spectral inherent optical properties that represent a wide variety of bio-optical conditions for the northern Arabian Sea under aN Miliaris bloom; (2) generated spectral remote-sensing reflectances from these profiles using Hydrolight; and, (3) applied the ORM to the synthesized reflectances to estimate the relative concentrations of diatoms and N Miliaris for each example. By comparing the estimates from the inversion model to those from synthesized vertical profiles, we were able to
Nonlinear adaptive inverse control via the unified model neural network
Jeng, Jin-Tsong; Lee, Tsu-Tian
1999-03-01
In this paper, we propose a new nonlinear adaptive inverse control via a unified model neural network. In order to overcome nonsystematic design and long training time in nonlinear adaptive inverse control, we propose the approximate transformable technique to obtain a Chebyshev Polynomials Based Unified Model (CPBUM) neural network for the feedforward/recurrent neural networks. It turns out that the proposed method can use less training time to get an inverse model. Finally, we apply this proposed method to control magnetic bearing system. The experimental results show that the proposed nonlinear adaptive inverse control architecture provides a greater flexibility and better performance in controlling magnetic bearing systems.
Inverse Modelling Problems in Linear Algebra Undergraduate Courses
Martinez-Luaces, Victor E.
2013-01-01
This paper will offer an analysis from a theoretical point of view of mathematical modelling, applications and inverse problems of both causation and specification types. Inverse modelling problems give the opportunity to establish connections between theory and practice and to show this fact, a simple linear algebra example in two different…
Directory of Open Access Journals (Sweden)
Henrik eLindén
2014-01-01
Full Text Available Electrical extracellular recordings, i.e., recordings of the electrical potentials in the extracellular medium between cells, have been a main work-horse in electrophysiology for almost a century. The high-frequency part of the signal (>=500 Hz, i.e., themulti-unit activity (MUA, contains information about the firing of action potentials in surrounding neurons, while the low-frequency part, the local field potential (LFP, contains information about how these neurons integrate synaptic inputs. As the recorded extracellular signals arise from multiple neural processes, their interpretation is typically ambiguous and difficult. Fortunately, a precise biophysical modeling scheme linking activity at the cellular level and the recorded signal has been established: the extracellular potential can be calculated as a weighted sum of all transmembrane currents in all cells located in the vicinity of the electrode. This computational scheme can considerably aid the modeling and analysis of MUA and LFP signals.Here, we describe LFPy, an open source Python package for numerical simulations of extracellular potentials. LFPy consists of a set of easy-to-use classes for defining cells, synapses and recording electrodes as Python objects, implementing this biophysical modeling scheme. It runs on top of the widely used NEURON simulation environment, which allows for flexible usage of both new and existing cell models.Further, calculation of extracellular potentials using the line-source-method is efficiently implemented.We describe the theoretical framework underlying the extracellular potential calculations and illustrate by examples how LFPy can be used both for simulating LFPs, i.e., synaptic contributions from single cells as well a populations of cells, and MUAs, i.e., extracellular signatures of action potentials.
Lindén, Henrik; Hagen, Espen; Lęski, Szymon; Norheim, Eivind S; Pettersen, Klas H; Einevoll, Gaute T
2013-01-01
Electrical extracellular recordings, i.e., recordings of the electrical potentials in the extracellular medium between cells, have been a main work-horse in electrophysiology for almost a century. The high-frequency part of the signal (≳500 Hz), i.e., the multi-unit activity (MUA), contains information about the firing of action potentials in surrounding neurons, while the low-frequency part, the local field potential (LFP), contains information about how these neurons integrate synaptic inputs. As the recorded extracellular signals arise from multiple neural processes, their interpretation is typically ambiguous and difficult. Fortunately, a precise biophysical modeling scheme linking activity at the cellular level and the recorded signal has been established: the extracellular potential can be calculated as a weighted sum of all transmembrane currents in all cells located in the vicinity of the electrode. This computational scheme can considerably aid the modeling and analysis of MUA and LFP signals. Here, we describe LFPy, an open source Python package for numerical simulations of extracellular potentials. LFPy consists of a set of easy-to-use classes for defining cells, synapses and recording electrodes as Python objects, implementing this biophysical modeling scheme. It runs on top of the widely used NEURON simulation environment, which allows for flexible usage of both new and existing cell models. Further, calculation of extracellular potentials using the line-source-method is efficiently implemented. We describe the theoretical framework underlying the extracellular potential calculations and illustrate by examples how LFPy can be used both for simulating LFPs, i.e., synaptic contributions from single cells as well a populations of cells, and MUAs, i.e., extracellular signatures of action potentials.
Directory of Open Access Journals (Sweden)
N. Ghilain
2012-08-01
Full Text Available Monitoring evapotranspiration over land is highly dependent on the surface state and vegetation dynamics. Data from spaceborn platforms are desirable to complement estimations from land surface models. The success of daily evapotranspiration monitoring at continental scale relies on the availability, quality and continuity of such data. The biophysical variables derived from SEVIRI on board the geostationary satellite Meteosat Second Generation (MSG and distributed by the Satellite Application Facility on Land surface Analysis (LSA-SAF are particularly interesting for such applications, as they aimed at providing continuous and consistent daily time series in near-real time over Africa, Europe and South America. In this paper, we compare them to monthly vegetation parameters from a database commonly used in numerical weather predictions (ECOCLIMAP-I, showing the benefits of the new daily products in detecting the spatial and temporal (seasonal and inter-annual variability of the vegetation, especially relevant over Africa. We propose a method to handle Leaf Area Index (LAI and Fractional Vegetation Cover (FVC products for evapotranspiration monitoring with a land surface model at 3–5 km spatial resolution. The method is conceived to be applicable for near-real time processes at continental scale and relies on the use of a land cover map. We assess the impact of using LSA-SAF biophysical variables compared to ECOCLIMAP-I on evapotranspiration estimated by the land surface model H-TESSEL. Comparison with in-situ observations in Europe and Africa shows an improved estimation of the evapotranspiration, especially in semi-arid climates. Finally, the impact on the land surface modelled evapotranspiration is compared over a north–south transect with a large gradient of vegetation and climate in Western Africa using LSA-SAF radiation forcing derived from remote sensing. Differences are highlighted. An evaluation against remote sensing derived land
Paulsen, H.; Ilyina, T.; Six, K. D.
2016-02-01
Marine nitrogen fixers play a fundamental role in the oceanic nitrogen and carbon cycles by providing a major source of `new' nitrogen to the euphotic zone that supports biological carbon export and sequestration. Furthermore, nitrogen fixers may regionally have a direct impact on ocean physics and hence the climate system as they form extensive surface mats which can increase light absorption and surface albedo and reduce the momentum input by wind. Resulting alterations in temperature and stratification may feed back on nitrogen fixers' growth itself.We incorporate nitrogen fixers as a prognostic 3D tracer in the ocean biogeochemical component (HAMOCC) of the Max Planck Institute Earth system model and assess for the first time the impact of related bio-physical feedbacks on biogeochemistry and the climate system.The model successfully reproduces recent estimates of global nitrogen fixation rates, as well as the observed distribution of nitrogen fixers, covering large parts of the tropical and subtropical oceans. First results indicate that including bio-physical feedbacks has considerable effects on the upper ocean physics in this region. Light absorption by nitrogen fixers leads locally to surface heating, subsurface cooling, and mixed layer depth shoaling in the subtropical gyres. As a result, equatorial upwelling is increased, leading to surface cooling at the equator. This signal is damped by the effect of the reduced wind stress due to the presence of cyanobacteria mats, which causes a reduction in the wind-driven circulation, and hence a reduction in equatorial upwelling. The increase in surface albedo due to nitrogen fixers has only inconsiderable effects. The response of nitrogen fixers' growth to the alterations in temperature and stratification varies regionally. Simulations with the fully coupled Earth system model are in progress to assess the implications of the biologically induced changes in upper ocean physics for the global climate system.
Directory of Open Access Journals (Sweden)
Peter Vestergaard-Poulsen
Full Text Available Chronic stress has detrimental effects on physiology, learning and memory and is involved in the development of anxiety and depressive disorders. Besides changes in synaptic formation and neurogenesis, chronic stress also induces dendritic remodeling in the hippocampus, amygdala and the prefrontal cortex. Investigations of dendritic remodeling during development and treatment of stress are currently limited by the invasive nature of histological and stereological methods. Here we show that high field diffusion-weighted MRI combined with quantitative biophysical modeling of the hippocampal dendritic loss in 21 day restraint stressed rats highly correlates with former histological findings. Our study strongly indicates that diffusion-weighted MRI is sensitive to regional dendritic loss and thus a promising candidate for non-invasive studies of dendritic plasticity in chronic stress and stress-related disorders.
Multi-scattering inversion for low model wavenumbers
Alkhalifah, Tariq Ali; Wu, Zedong
2015-01-01
modeled from the source and those corresponding to single and double scattering to update both the velocity model and the component of the velocity (perturbation) responsible for the single and double scattering. The combined inversion helps us access most
Stochastic forward and inverse groundwater flow and solute transport modeling
Janssen, G.M.C.M.
2008-01-01
Keywords: calibration, inverse modeling, stochastic modeling, nonlinear biodegradation, stochastic-convective, advective-dispersive, travel time, network design, non-Gaussian distribution, multimodal distribution, representers
This thesis offers three new approaches that contribute
Data-Driven Model Order Reduction for Bayesian Inverse Problems
Cui, Tiangang; Youssef, Marzouk; Willcox, Karen
2014-01-01
One of the major challenges in using MCMC for the solution of inverse problems is the repeated evaluation of computationally expensive numerical models. We develop a data-driven projection- based model order reduction technique to reduce
Roeth, Anjali A; Slabu, Ioana; Baumann, Martin; Alizai, Patrick H; Schmeding, Maximilian; Guentherodt, Gernot; Schmitz-Rode, Thomas; Neumann, Ulf P
2017-01-01
Superparamagnetic iron oxide nanoparticles (SPION) may be used for local tumor treatment by coupling them to a drug and accumulating them locally with magnetic field traps, that is, a combination of permanent magnets and coils. Thereafter, an alternating magnetic field generates heat which may be used to release the thermosensitively bound drug and for hyperthermia. Until today, only superficial tumors can be treated with this method. Our aim was to transfer this method into an endoscopic setting to also reach the majority of tumors located inside the body. To find the ideal endoscopic magnetic field trap, which accumulates the most SPION, we first developed a biophysical model considering anatomical as well as physical conditions. Entities of choice were esophageal and prostate cancer. The magnetic susceptibilities of different porcine and rat tissues were measured with a superconducting quantum interference device. All tissues showed diamagnetic behavior. The evaluation of clinical data (computed tomography scan, endosonography, surgical reports, pathological evaluation) of patients gave insight into the topographical relationship between the tumor and its surroundings. Both were used to establish the biophysical model of the tumors and their surroundings, closely mirroring the clinical situation, in which we could virtually design, place and evaluate different electromagnetic coil configurations to find optimized magnetic field traps for each tumor entity. By simulation, we could show that the efficiency of the magnetic field traps can be enhanced by 38-fold for prostate and 8-fold for esophageal cancer. Therefore, our approach of endoscopic targeting is an improvement of the magnetic drug-targeting setups for SPION tumor therapy as it holds the possibility of reaching tumors inside the body in a minimal-invasive way. Future animal experiments must prove these findings in vivo.
Application Of Shared Gamma And Inverse-Gaussian Frailty Models ...
African Journals Online (AJOL)
Shared Gamma and Inverse-Gaussian Frailty models are used to analyze the survival times of patients who are clustered according to cancer/tumor types under Parametric Proportional Hazard framework. The result of the ... However, no evidence is strong enough for preference of either Gamma or Inverse Gaussian Frailty.
Reservoir Modeling Combining Geostatistics with Markov Chain Monte Carlo Inversion
DEFF Research Database (Denmark)
Zunino, Andrea; Lange, Katrine; Melnikova, Yulia
2014-01-01
We present a study on the inversion of seismic reflection data generated from a synthetic reservoir model. Our aim is to invert directly for rock facies and porosity of the target reservoir zone. We solve this inverse problem using a Markov chain Monte Carlo (McMC) method to handle the nonlinear...
Investigating Irregularly Patterned Deep Brain Stimulation Signal Design Using Biophysical Models
Directory of Open Access Journals (Sweden)
Samantha Rose Summerson
2015-06-01
Full Text Available Parkinson’s disease (PD is a neurodegenerative disorder which follows from cell loss of dopaminergic neurons in the substantia nigra pars compacta (SNc, a nucleus in the basal ganglia (BG. Deep brain stimulation (DBS is an electrical therapy that modulates the pathological activity to treat the motor symptoms of PD. Although this therapy is currently used in clinical practice, the sufficient conditions for therapeutic efficacy are unknown. In this work we develop a model of critical motor circuit structures in the brain using biophysical cell models as the base components and then evaluate performance of different DBS signals in this model to perform comparative studies of their efficacy. Biological models are an important tool for gaining insights into neural function and, in this case, serve as effective tools for investigating innovative new DBS paradigms. Experiments were performed using the hemi-parkinsonian rodent model to test the same set of signals, verifying the obedience of the model to physiological trends. We show that antidromic spiking from DBS of the subthalamic nucleus (STN has a significant impact on cortical neural activity, which is frequency dependent and additionally modulated by the regularity of the stimulus pulse train used. Irregular spacing between stimulus pulses, where the amount of variability added is bounded, is shown to increase diversification of response of basal ganglia neurons and reduce entropic noise in cortical neurons, which may be fundamentally important to restoration of information flow in the motor circuit.
Hati, Sanchita; Bhattacharyya, Sudeep
2016-01-01
A project-based biophysical chemistry laboratory course, which is offered to the biochemistry and molecular biology majors in their senior year, is described. In this course, the classroom study of the structure-function of biomolecules is integrated with the discovery-guided laboratory study of these molecules using computer modeling and…
CICAAR - Convolutive ICA with an Auto-Regressive Inverse Model
DEFF Research Database (Denmark)
Dyrholm, Mads; Hansen, Lars Kai
2004-01-01
We invoke an auto-regressive IIR inverse model for convolutive ICA and derive expressions for the likelihood and its gradient. We argue that optimization will give a stable inverse. When there are more sensors than sources the mixing model parameters are estimated in a second step by least square...... estimation. We demonstrate the method on synthetic data and finally separate speech and music in a real room recording....
Directory of Open Access Journals (Sweden)
Roeth AA
2017-08-01
Full Text Available Anjali A Roeth,1,* Ioana Slabu,2,* Martin Baumann,2 Patrick H Alizai,1 Maximilian Schmeding,1 Gernot Guentherodt,3 Thomas Schmitz-Rode,2 Ulf P Neumann1 1Department of General, Visceral and Transplant Surgery, University Hospital RWTH Aachen, 2Institute of Applied Medical Engineering, Helmholtz-Institute Aachen, RWTH Aachen, Aachen, 3Institute of Physics A, RWTH Aachen University, Aachen, Germany *These authors contributed equally to this work Abstract: Superparamagnetic iron oxide nanoparticles (SPION may be used for local tumor treatment by coupling them to a drug and accumulating them locally with magnetic field traps, that is, a combination of permanent magnets and coils. Thereafter, an alternating magnetic field generates heat which may be used to release the thermosensitively bound drug and for hyperthermia. Until today, only superficial tumors can be treated with this method. Our aim was to transfer this method into an endoscopic setting to also reach the majority of tumors located inside the body. To find the ideal endoscopic magnetic field trap, which accumulates the most SPION, we first developed a biophysical model considering anatomical as well as physical conditions. Entities of choice were esophageal and prostate cancer. The magnetic susceptibilities of different porcine and rat tissues were measured with a superconducting quantum interference device. All tissues showed diamagnetic behavior. The evaluation of clinical data (computed tomography scan, endosonography, surgical reports, pathological evaluation of patients gave insight into the topographical relationship between the tumor and its surroundings. Both were used to establish the biophysical model of the tumors and their surroundings, closely mirroring the clinical situation, in which we could virtually design, place and evaluate different electromagnetic coil configurations to find optimized magnetic field traps for each tumor entity. By simulation, we could show that the
FIACH: A biophysical model for automatic retrospective noise control in fMRI.
Tierney, Tim M; Weiss-Croft, Louise J; Centeno, Maria; Shamshiri, Elhum A; Perani, Suejen; Baldeweg, Torsten; Clark, Christopher A; Carmichael, David W
2016-01-01
Different noise sources in fMRI acquisition can lead to spurious false positives and reduced sensitivity. We have developed a biophysically-based model (named FIACH: Functional Image Artefact Correction Heuristic) which extends current retrospective noise control methods in fMRI. FIACH can be applied to both General Linear Model (GLM) and resting state functional connectivity MRI (rs-fcMRI) studies. FIACH is a two-step procedure involving the identification and correction of non-physiological large amplitude temporal signal changes and spatial regions of high temporal instability. We have demonstrated its efficacy in a sample of 42 healthy children while performing language tasks that include overt speech with known activations. We demonstrate large improvements in sensitivity when FIACH is compared with current methods of retrospective correction. FIACH reduces the confounding effects of noise and increases the study's power by explaining significant variance that is not contained within the commonly used motion parameters. The method is particularly useful in detecting activations in inferior temporal regions which have proven problematic for fMRI. We have shown greater reproducibility and robustness of fMRI responses using FIACH in the context of task induced motion. In a clinical setting this will translate to increasing the reliability and sensitivity of fMRI used for the identification of language lateralisation and eloquent cortex. FIACH can benefit studies of cognitive development in young children, patient populations and older adults. Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.
A biophysical model of endocannabinoid-mediated short term depression in hippocampal inhibition.
Directory of Open Access Journals (Sweden)
Margarita Zachariou
Full Text Available Memories are believed to be represented in the synaptic pathways of vastly interconnected networks of neurons. The plasticity of synapses, that is, their strengthening and weakening depending on neuronal activity, is believed to be the basis of learning and establishing memories. An increasing number of studies indicate that endocannabinoids have a widespread action on brain function through modulation of synaptic transmission and plasticity. Recent experimental studies have characterised the role of endocannabinoids in mediating both short- and long-term synaptic plasticity in various brain regions including the hippocampus, a brain region strongly associated with cognitive functions, such as learning and memory. Here, we present a biophysically plausible model of cannabinoid retrograde signalling at the synaptic level and investigate how this signalling mediates depolarisation induced suppression of inhibition (DSI, a prominent form of short-term synaptic depression in inhibitory transmission in hippocampus. The model successfully captures many of the key characteristics of DSI in the hippocampus, as observed experimentally, with a minimal yet sufficient mathematical description of the major signalling molecules and cascades involved. More specifically, this model serves as a framework to test hypotheses on the factors determining the variability of DSI and investigate under which conditions it can be evoked. The model reveals the frequency and duration bands in which the post-synaptic cell can be sufficiently stimulated to elicit DSI. Moreover, the model provides key insights on how the state of the inhibitory cell modulates DSI according to its firing rate and relative timing to the post-synaptic activation. Thus, it provides concrete suggestions to further investigate experimentally how DSI modulates and is modulated by neuronal activity in the brain. Importantly, this model serves as a stepping stone for future deciphering of the role of
Inverse modelling for flow and transport in porous media
International Nuclear Information System (INIS)
Giudici, M.
2004-01-01
The problem of parameter identification for flow and transport model in porous media is discussed in this communication. First, a general framework for the development and application of environmental models is discussed. Then the forward and inverse problems for discrete models are described in detail, introducing fundamental concepts (uniqueness, identifiability, stability, conditioning). The importance of model scales is reviewed and is shown its link with the stability and conditioning issues. Finally some remarks are given to the use of several independent sets of data in inverse modelling
Moal, Iain H; Barradas-Bautista, Didier; Jiménez-García, Brian; Torchala, Mieczyslaw; van der Velde, Arjan; Vreven, Thom; Weng, Zhiping; Bates, Paul A; Fernández-Recio, Juan
2017-06-15
In order to function, proteins frequently bind to one another and form 3D assemblies. Knowledge of the atomic details of these structures helps our understanding of how proteins work together, how mutations can lead to disease, and facilitates the designing of drugs which prevent or mimic the interaction. Atomic modeling of protein-protein interactions requires the selection of near-native structures from a set of docked poses based on their calculable properties. By considering this as an information retrieval problem, we have adapted methods developed for Internet search ranking and electoral voting into IRaPPA, a pipeline integrating biophysical properties. The approach enhances the identification of near-native structures when applied to four docking methods, resulting in a near-native appearing in the top 10 solutions for up to 50% of complexes benchmarked, and up to 70% in the top 100. IRaPPA has been implemented in the SwarmDock server ( http://bmm.crick.ac.uk/∼SwarmDock/ ), pyDock server ( http://life.bsc.es/pid/pydockrescoring/ ) and ZDOCK server ( http://zdock.umassmed.edu/ ), with code available on request. moal@ebi.ac.uk. Supplementary data are available at Bioinformatics online. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com
Tuckwell, Henry C
2013-06-01
Serotonergic neurons of the dorsal raphe nuclei, with their extensive innervation of nearly the whole brain have important modulatory effects on many cognitive and physiological processes. They play important roles in clinical depression and other psychiatric disorders. In order to quantify the effects of serotonergic transmission on target cells it is desirable to construct computational models and to this end these it is necessary to have details of the biophysical and spike properties of the serotonergic neurons. Here several basic properties are reviewed with data from several studies since the 1960s to the present. The quantities included are input resistance, resting membrane potential, membrane time constant, firing rate, spike duration, spike and afterhyperpolarization (AHP) amplitude, spike threshold, cell capacitance, soma and somadendritic areas. The action potentials of these cells are normally triggered by a combination of sodium and calcium currents which may result in autonomous pacemaker activity. We here analyse the mechanisms of high-threshold calcium spikes which have been demonstrated in these cells the presence of TTX (tetrodotoxin). The parameters for calcium dynamics required to give calcium spikes are quite different from those for regular spiking which suggests the involvement of restricted parts of the soma-dendritic surface as has been found, for example, in hippocampal neurons. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.
Cerebellar supervised learning revisited: biophysical modeling and degrees-of-freedom control.
Kawato, Mitsuo; Kuroda, Shinya; Schweighofer, Nicolas
2011-10-01
The biophysical models of spike-timing-dependent plasticity have explored dynamics with molecular basis for such computational concepts as coincidence detection, synaptic eligibility trace, and Hebbian learning. They overall support different learning algorithms in different brain areas, especially supervised learning in the cerebellum. Because a single spine is physically very small, chemical reactions at it are essentially stochastic, and thus sensitivity-longevity dilemma exists in the synaptic memory. Here, the cascade of excitable and bistable dynamics is proposed to overcome this difficulty. All kinds of learning algorithms in different brain regions confront with difficult generalization problems. For resolution of this issue, the control of the degrees-of-freedom can be realized by changing synchronicity of neural firing. Especially, for cerebellar supervised learning, the triangle closed-loop circuit consisting of Purkinje cells, the inferior olive nucleus, and the cerebellar nucleus is proposed as a circuit to optimally control synchronous firing and degrees-of-freedom in learning. Copyright © 2011 Elsevier Ltd. All rights reserved.
Shirshin, Evgeny A; Nikonova, Elena E; Kuzminov, Fedor I; Sluchanko, Nikolai N; Elanskaya, Irina V; Gorbunov, Maxim Y; Fadeev, Victor V; Friedrich, Thomas; Maksimov, Eugene G
2017-09-01
Non-photochemical quenching (NPQ) is a mechanism responsible for high light tolerance in photosynthetic organisms. In cyanobacteria, NPQ is realized by the interplay between light-harvesting complexes, phycobilisomes (PBs), a light sensor and effector of NPQ, the photoactive orange carotenoid protein (OCP), and the fluorescence recovery protein (FRP). Here, we introduced a biophysical model, which takes into account the whole spectrum of interactions between PBs, OCP, and FRP and describes the experimental PBs fluorescence kinetics, unraveling interaction rate constants between the components involved and their relative concentrations in the cell. We took benefit from the possibility to reconstruct the photoprotection mechanism and its parts in vitro, where most of the parameters could be varied, to develop the model and then applied it to describe the NPQ kinetics in the Synechocystis sp. PCC 6803 mutant lacking photosystems. Our analyses revealed that while an excess of the OCP over PBs is required to obtain substantial PBs fluorescence quenching in vitro, in vivo the OCP/PBs ratio is less than unity, due to higher local concentration of PBs, which was estimated as ~10 -5 M, compared to in vitro experiments. The analysis of PBs fluorescence recovery on the basis of the generalized model of enzymatic catalysis resulted in determination of the FRP concentration in vivo close to 10% of the OCP concentration. Finally, the possible role of the FRP oligomeric state alteration in the kinetics of PBs fluorescence was shown. This paper provides the most comprehensive model of the OCP-induced PBs fluorescence quenching to date and the results are important for better understanding of the regulatory molecular mechanisms underlying NPQ in cyanobacteria.
Batmanov, Kirill; Wang, Junbai
2017-09-18
DNA shape readout is an important mechanism of transcription factor target site recognition, in addition to the sequence readout. Several machine learning-based models of transcription factor-DNA interactions, considering DNA shape features, have been developed in recent years. Here, we present a new biophysical model of protein-DNA interactions by integrating the DNA shape properties. It is based on the neighbor dinucleotide dependency model BayesPI2, where new parameters are restricted to a subspace spanned by the dinucleotide form of DNA shape features. This allows a biophysical interpretation of the new parameters as a position-dependent preference towards specific DNA shape features. Using the new model, we explore the variation of DNA shape preferences in several transcription factors across various cancer cell lines and cellular conditions. The results reveal that there are DNA shape variations at FOXA1 (Forkhead Box Protein A1) binding sites in steroid-treated MCF7 cells. The new biophysical model is useful for elucidating the finer details of transcription factor-DNA interaction, as well as for predicting cancer mutation effects in the future.
REMOTE-SENSING-BASED BIOPHYSICAL MODELS FOR ESTIMATING LAI OF IRRIGATED CROPS IN MURRY DARLING BASIN
Directory of Open Access Journals (Sweden)
I. Wittamperuma
2012-07-01
Full Text Available Remote sensing is a rapid and reliable method for estimating crop growth data from individual plant to crops in irrigated agriculture ecosystem. The LAI is one of the important biophysical parameter for determining vegetation health, biomass, photosynthesis and evapotranspiration (ET for the modelling of crop yield and water productivity. Ground measurement of this parameter is tedious and time-consuming due to heterogeneity across the landscape over time and space. This study deals with the development of remote-sensing based empirical relationships for the estimation of ground-based LAI (LAIG using NDVI, modelled with and without atmospheric correction models for three irrigated crops (corn, wheat and rice grown in irrigated farms within Coleambally Irrigation Area (CIA which is located in southern Murray Darling basin, NSW in Australia. Extensive ground truthing campaigns were carried out to measure crop growth and to collect field samples of LAI using LAI- 2000 Plant Canopy Analyser and reflectance using CROPSCAN Multi Spectral Radiometer at several farms within the CIA. A Set of 12 cloud free Landsat 5 TM satellite images for the period of 2010-11 were downloaded and regression analysis was carried out to analyse the co-relationships between satellite and ground measured reflectance and to check the reliability of data sets for the crops. Among all the developed regression relationships between LAI and NDVI, the atmospheric correction process has significantly improved the relationship between LAI and NDVI for Landsat 5 TM images. The regression analysis also shows strong correlations for corn and wheat but weak correlations for rice which is currently being investigated.
The Impact of Forest Density on Forest Height Inversion Modeling from Polarimetric InSAR Data
Directory of Open Access Journals (Sweden)
Changcheng Wang
2016-03-01
Full Text Available Forest height is of great significance in analyzing the carbon cycle on a global or a local scale and in reconstructing the accurate forest underlying terrain. Major algorithms for estimating forest height, such as the three-stage inversion process, are depending on the random-volume-over-ground (RVoG model. However, the RVoG model is characterized by a lot of parameters, which influence its applicability in forest height retrieval. Forest density, as an important biophysical parameter, is one of those main influencing factors. However, its influence to the RVoG model has been ignored in relating researches. For this paper, we study the applicability of the RVoG model in forest height retrieval with different forest densities, using the simulated and real Polarimetric Interferometric SAR data. P-band ESAR datasets of the European Space Agency (ESA BioSAR 2008 campaign were selected for experiments. The test site was located in Krycklan River catchment in Northern Sweden. The experimental results show that the forest density clearly affects the inversion accuracy of forest height and ground phase. For the four selected forest stands, with the density increasing from 633 to 1827 stems/Ha, the RMSEs of inversion decrease from 4.6 m to 3.1 m. The RVoG model is not quite applicable for forest height retrieval especially in sparsely vegetated areas. We conclude that the forest stand density is positively related to the estimation accuracy of the ground phase, but negatively correlates to the ground-to-volume scattering ratio.
Why operational risk modelling creates inverse incentives
Doff, R.
2015-01-01
Operational risk modelling has become commonplace in large international banks and is gaining popularity in the insurance industry as well. This is partly due to financial regulation (Basel II, Solvency II). This article argues that operational risk modelling is fundamentally flawed, despite efforts
Artificial Neural Network Modeling of an Inverse Fluidized Bed ...
African Journals Online (AJOL)
A Radial Basis Function neural network has been successfully employed for the modeling of the inverse fluidized bed reactor. In the proposed model, the trained neural network represents the kinetics of biological decomposition of pollutants in the reactor. The neural network has been trained with experimental data ...
Data-Driven Model Order Reduction for Bayesian Inverse Problems
Cui, Tiangang
2014-01-06
One of the major challenges in using MCMC for the solution of inverse problems is the repeated evaluation of computationally expensive numerical models. We develop a data-driven projection- based model order reduction technique to reduce the computational cost of numerical PDE evaluations in this context.
Multi-scattering inversion for low model wavenumbers
Alkhalifah, Tariq Ali
2015-08-19
A successful full wavenumber inversion (FWI) implementation updates the low wavenumber model components first for proper wavefield propagation description, and slowly adds the high-wavenumber potentially scattering parts of the model. The low-wavenumber components can be extracted from the transmission parts of the recorded data given by direct arrivals or the transmission parts of the single and double-scattering wave-fields developed from a predicted scatter field. We develop a combined inversion of data modeled from the source and those corresponding to single and double scattering to update both the velocity model and the component of the velocity (perturbation) responsible for the single and double scattering. The combined inversion helps us access most of the potential model wavenumber information that may be embedded in the data. A scattering angle filter is used to divide the gradient of the combined inversion so initially the high wavenumber (low scattering angle) components of the gradient is directed to the perturbation model and the low wavenumber (high scattering angle) components to the velocity model. As our background velocity matures, the scattering angle divide is slowly lowered to allow for more of the higher wavenumbers to contribute the velocity model.
International Nuclear Information System (INIS)
Anon.
1979-01-01
Summaries of research projects conducted during 1978 and 1979 are presented. The structural biophysics group explores the high-resolution structure of biological macromolecules and cell organelles. Specific subject areas include: the basic characteristics of photosynthesis in plants; the chemical composition of individual fly ash particles at the site of their damaging action in tissues; direct analysis of frozen-hydrated biological samples by scanning electron microscopy; yeast genetics; the optical activity of DNA aggregates; measurement and characterization of lipoproteins; function of lipoproteins; and the effect of radiation and pollutants on mammalian cells
Data inversion in coupled subsurface flow and geomechanics models
International Nuclear Information System (INIS)
Iglesias, Marco A; McLaughlin, Dennis
2012-01-01
We present an inverse modeling approach to estimate petrophysical and elastic properties of the subsurface. The aim is to use the fully coupled geomechanics-flow model of Girault et al (2011 Math. Models Methods Appl. Sci. 21 169–213) to jointly invert surface deformation and pressure data from wells. We use a functional-analytic framework to construct a forward operator (parameter-to-output map) that arises from the geomechanics-flow model of Girault et al. Then, we follow a deterministic approach to pose the inverse problem of finding parameter estimates from measurements of the output of the forward operator. We prove that this inverse problem is ill-posed in the sense of stability. The inverse problem is then regularized with the implementation of the Newton-conjugate gradient (CG) algorithm of Hanke (1997 Numer. Funct. Anal. Optim. 18 18–971). For a consistent application of the Newton-CG scheme, we establish the differentiability of the forward map and characterize the adjoint of its linearization. We provide assumptions under which the theory of Hanke ensures convergence and regularizing properties of the Newton-CG scheme. These properties are verified in our numerical experiments. In addition, our synthetic experiments display the capabilities of the proposed inverse approach to estimate parameters of the subsurface by means of data inversion. In particular, the added value of measurements of surface deformation in the estimation of absolute permeability is quantified with respect to the standard history matching approach of inverting production data with flow models. The proposed methodology can be potentially used to invert satellite geodetic data (e.g. InSAR and GPS) in combination with production data for optimal monitoring and characterization of the subsurface. (paper)
Hybrid Adaptive Flight Control with Model Inversion Adaptation
Nguyen, Nhan
2011-01-01
This study investigates a hybrid adaptive flight control method as a design possibility for a flight control system that can enable an effective adaptation strategy to deal with off-nominal flight conditions. The hybrid adaptive control blends both direct and indirect adaptive control in a model inversion flight control architecture. The blending of both direct and indirect adaptive control provides a much more flexible and effective adaptive flight control architecture than that with either direct or indirect adaptive control alone. The indirect adaptive control is used to update the model inversion controller by an on-line parameter estimation of uncertain plant dynamics based on two methods. The first parameter estimation method is an indirect adaptive law based on the Lyapunov theory, and the second method is a recursive least-squares indirect adaptive law. The model inversion controller is therefore made to adapt to changes in the plant dynamics due to uncertainty. As a result, the modeling error is reduced that directly leads to a decrease in the tracking error. In conjunction with the indirect adaptive control that updates the model inversion controller, a direct adaptive control is implemented as an augmented command to further reduce any residual tracking error that is not entirely eliminated by the indirect adaptive control.
Heeding the waveform inversion nonlinearity by unwrapping the model and data
Alkhalifah, Tariq Ali; Choi, Yun Seok
2012-01-01
Unlike traveltime inversion, waveform inversion provides relatively higher-resolution inverted models. This feature, however, comes at the cost of introducing complex nonlinearity to the inversion operator complicating the convergence process. We
Inverse Gaussian model for small area estimation via Gibbs sampling
African Journals Online (AJOL)
We present a Bayesian method for estimating small area parameters under an inverse Gaussian model. The method is extended to estimate small area parameters for finite populations. The Gibbs sampler is proposed as a mechanism for implementing the Bayesian paradigm. We illustrate the method by application to ...
Influence of seeing effects on cloud model inversions
Czech Academy of Sciences Publication Activity Database
Tziotziou, K.; Heinzel, Petr; Tsiropoula, G.
2007-01-01
Roč. 472, č. 1 (2007), s. 287-292 ISSN 0004-6361 Institutional research plan: CEZ:AV0Z10030501 Keywords : cloud model * inversions * seeing effects Subject RIV: BN - Astronomy, Celestial Mechanics, Astrophysics Impact factor: 4.259, year: 2007
Robertson Lain, L; Bernard, S; Evers-King, H
2014-07-14
There is a pressing need for improved bio-optical models of high biomass waters as eutrophication of coastal and inland waters becomes an increasing problem. Seasonal boom conditions in the Southern Benguela and persistent harmful algal production in various inland waters in Southern Africa present valuable opportunities for the development of such modelling capabilities. The phytoplankton-dominated signal of these waters additionally addresses an increased interest in Phytoplankton Functional Type (PFT) analysis. To these ends, an initial validation of a new model of Equivalent Algal Populations (EAP) is presented here. This paper makes a first order comparison of two prominent phytoplankton Inherent Optical Property (IOP) models with the EAP model, which places emphasis on explicit bio-physical modelling of the phytoplankton population as a holistic determinant of inherent optical properties. This emphasis is shown to have an impact on the ability to retrieve the detailed phytoplankton spectral scattering information necessary for PFT applications and to successfully simulate reflectance across wide ranges of physical environments, biomass, and assemblage characteristics.
Boren, E. J.; Boschetti, L.; Johnson, D.
2017-12-01
Water plays a critical role in all plant physiological processes, including transpiration, photosynthesis, nutrient transportation, and maintenance of proper plant cell functions. Deficits in water content cause drought-induced stress conditions, such as constrained plant growth and cellular metabolism, while overabundance of water cause anoxic conditions which limit plant physiological processes and promote disease. Vegetation water content maps can provide agricultural producers key knowledge for improving production capacity and resiliency in agricultural systems while facilitating the ability to pinpoint, monitor, and resolve water scarcity issues. Radiative transfer model (RTM) inversion has been successfully applied to remotely sensed data to retrieve biophysical and canopy parameter estimates, including water content. The successful launch of the Landsat 8 Operational Land Imager (OLI) in 2012, Sentinel 2A Multispectral Instrument (MSI) in 2015, followed by Sentinel 2B in 2017, the systematic acquisition schedule and free data distribution policy provide the opportunity for water content estimation at a spatial and temporal scale that can meet the demands of potential operational users: combined, these polar-orbiting systems provide 10 m to 30 m multi-spectral global coverage up to every 3 days. The goal of the present research is to prototype the generation of a cropland canopy water content product, obtained from the newly developed Landsat 8 and Sentinel 2 atmospherically corrected HLS product, through the inversion of the leaf and canopy model PROSAIL5B. We assess the impact of a novel spatial and temporal stratification, where some parameters of the model are constrained by crop type and phenological phase, based on ancillary biophysical data, collected from various crop species grown in a controlled setting and under different water stress conditions. Canopy-level data, collected coincidently with satellite overpasses during four summer field campaigns
Numerical modeling of Harmonic Imaging and Pulse Inversion fields
Humphrey, Victor F.; Duncan, Tracy M.; Duck, Francis
2003-10-01
Tissue Harmonic Imaging (THI) and Pulse Inversion (PI) Harmonic Imaging exploit the harmonics generated as a result of nonlinear propagation through tissue to improve the performance of imaging systems. A 3D finite difference model, that solves the KZK equation in the frequency domain, is used to investigate the finite amplitude fields produced by rectangular transducers driven with short pulses and their inverses, in water and homogeneous tissue. This enables the characteristic of the fields and the effective PI field to be calculated. The suppression of the fundamental field in PI is monitored, and the suppression of side lobes and a reduction in the effective beamwidth for each field are calculated. In addition, the differences between the pulse and inverse pulse spectra resulting from the use of very short pulses are noted, and the differences in the location of the fundamental and second harmonic spectral peaks observed.
Bergamaschi, Peter; Karstens, Ute; Manning, Alistair J.; Saunois, Marielle; Tsuruta, Aki; Berchet, Antoine; Vermeulen, Alexander T.; Arnold, Tim; Janssens-Maenhout, Greet; Hammer, Samuel; Levin, Ingeborg; Schmidt, Martina; Ramonet, Michel; Lopez, Morgan; Lavric, Jost; Aalto, Tuula; Chen, Huilin; Feist, Dietrich G.; Gerbig, Christoph; Haszpra, László; Hermansen, Ove; Manca, Giovanni; Moncrieff, John; Meinhardt, Frank; Necki, Jaroslaw; Galkowski, Michal; O'Doherty, Simon; Paramonova, Nina; Scheeren, Hubertus A.; Steinbacher, Martin; Dlugokencky, Ed
2018-01-01
We present inverse modelling (top down) estimates of European methane (CH4) emissions for 2006-2012 based on a new quality-controlled and harmonised in situ data set from 18 European atmospheric monitoring stations. We applied an ensemble of seven inverse models and performed four inversion experiments, investigating the impact of different sets of stations and the use of a priori information on emissions. The inverse models infer total CH4 emissions of 26.8 (20.2-29.7) Tg CH4 yr-1 (mean, 10th and 90th percentiles from all inversions) for the EU-28 for 2006-2012 from the four inversion experiments. For comparison, total anthropogenic CH4 emissions reported to UNFCCC (bottom up, based on statistical data and emissions factors) amount to only 21.3 Tg CH4 yr-1 (2006) to 18.8 Tg CH4 yr-1 (2012). A potential explanation for the higher range of top-down estimates compared to bottom-up inventories could be the contribution from natural sources, such as peatlands, wetlands, and wet soils. Based on seven different wetland inventories from the Wetland and Wetland CH4 Inter-comparison of Models Project (WETCHIMP), total wetland emissions of 4.3 (2.3-8.2) Tg CH4 yr-1 from the EU-28 are estimated. The hypothesis of significant natural emissions is supported by the finding that several inverse models yield significant seasonal cycles of derived CH4 emissions with maxima in summer, while anthropogenic CH4 emissions are assumed to have much lower seasonal variability. Taking into account the wetland emissions from the WETCHIMP ensemble, the top-down estimates are broadly consistent with the sum of anthropogenic and natural bottom-up inventories. However, the contribution of natural sources and their regional distribution remain rather uncertain. Furthermore, we investigate potential biases in the inverse models by comparison with regular aircraft profiles at four European sites and with vertical profiles obtained during the Infrastructure for Measurement of the European Carbon
International Nuclear Information System (INIS)
Jacob, P.; Paretzke, H.G.; Roth, P.
2000-01-01
The Association Contract covers a range of research domains that are important to the Radiation Protection Research Action, especially in the areas 'Evaluation of Radiation Risks' and 'Understanding Radiation Mechanisms and Epidemiology'. Three research projects concentrate on radiation dosimetry research and two projects on the modelling of radiation carcinogenesis. The following list gives an overview on the topics and responsible scientific project leaders of the Association Contract: Study of radiation fields and dosimetry at aviation altitudes. Biokinetics and dosimetry of incorporated radionuclides. Dose reconstruction. Biophysical models for the induction of cancer by radiation. Experimental data for the induction of cancer by radiation of different qualities. (orig.)
International Nuclear Information System (INIS)
Anon.
1979-01-01
Summaries of research projects conducted during 1978 and 1979 are presented. The overall thrust of the research is aimed at understanding the effects of radiation on organisms. Specific subject areas include: the effects of heavy-particle beam nuclear interactions in tissue on dosimetry; tracer studies with radioactive fragments of heavy-ion beams; the effects of heavy/ions on human kidney cells and Chinese hamster cells; the response of a rhabdomyosarcoma tumor system in rats to heavy-ion beams; the use of heavy charged particles in radiotherapy of human cancer; heavy-ion radiography; the biological effects of high magnetic fields; central nervous system neurotoxicity; and biophysical studies on cell membranes
Two radiative inverse seesaw models, dark matter, and baryogenesis
International Nuclear Information System (INIS)
Baldes, Iason; Bell, Nicole F.; Petraki, Kalliopi; Volkas, Raymond R.
2013-01-01
The inverse seesaw mechanism allows the neutrino masses to be generated by new physics at an experimentally accessible scale, even with O(1) Yukawa couplings. In the inverse seesaw scenario, the smallness of neutrino masses is linked to the smallness of a lepton number violating parameter. This parameter may arise radiatively. In this paper, we study the cosmological implications of two contrasting radiative inverse seesaw models, one due to Ma and the other to Law and McDonald. The former features spontaneous, the latter explicit lepton number violation. First, we examine the effect of the lepton-number violating interactions introduced in these models on the baryon asymmetry of the universe. We investigate under what conditions a pre-existing baryon asymmetry does not get washed out. While both models allow a baryon asymmetry to survive only once the temperature has dropped below the mass of their heaviest fields, the Ma model can create the baryon asymmetry through resonant leptogenesis. Then we investigate the viability of the dark matter candidates arising within these models, and explore the prospects for direct detection. We find that the Law/McDonald model allows a simple dark matter scenario similar to the Higgs portal, while in the Ma model the simplest cold dark matter scenario would tend to overclose the universe
Polynomial model inversion control: numerical tests and applications
Novara, Carlo
2015-01-01
A novel control design approach for general nonlinear systems is described in this paper. The approach is based on the identification of a polynomial model of the system to control and on the on-line inversion of this model. Extensive simulations are carried out to test the numerical efficiency of the approach. Numerical examples of applicative interest are presented, concerned with control of the Duffing oscillator, control of a robot manipulator and insulin regulation in a type 1 diabetic p...
Fundamental Concepts in Biophysics Volume 1
Jue, Thomas
2009-01-01
HANDBOOK OF MODERN BIOPHYSICS Series Editor Thomas Jue, PhD Handbook of Modern Biophysics brings current biophysics topics into focus, so that biology, medical, engineering, mathematics, and physical-science students or researchers can learn fundamental concepts and the application of new techniques in addressing biomedical challenges. Chapters explicate the conceptual framework of the physics formalism and illustrate the biomedical applications. With the addition of problem sets, guides to further study, and references, the interested reader can continue to explore independently the ideas presented. Volume I: Fundamental Concepts in Biophysics Editor Thomas Jue, PhD In Fundamental Concepts in Biophysics, prominent professors have established a foundation for the study of biophysics related to the following topics: Mathematical Methods in Biophysics Quantum Mechanics Basic to Biophysical Methods Computational Modeling of Receptor–Ligand Binding and Cellular Signaling Processes Fluorescence Spectroscopy Elec...
Energy Technology Data Exchange (ETDEWEB)
Dobranszky, G.
2005-12-15
Stratigraphic modeling aims at rebuilding the history of the sedimentary basins by simulating the processes of erosion, transport and deposit of sediments using physical models. The objective is to determine the location of the bed-rocks likely to contain the organic matter, the location of the porous rocks that could trap the hydrocarbons during their migration and the location of the impermeable rocks likely to seal the reservoir. The model considered within this thesis is based on a multi-lithological diffusive transport model and applies to large scales of time and space. Due to the complexity of the phenomena and scales considered, none of the model parameters is directly measurable. Therefore it is essential to inverse them. The standard approach, which consists in inverting all the parameters by minimizing a cost function using a gradient method, proved very sensitive to the choice of the parameterization, to the weights given to the various terms of the cost function (hearing on data of very diverse nature) and to the numerical noise. These observations led us to give up this method and to carry out the in-version step by step by decoupling the parameters. This decoupling is not obtained by fixing the parameters but by making several assumptions on the model resulting in a range of reduced but relevant models. In this thesis, we show how these models enable us to inverse all the parameters in a robust and interactive way. (author)
High effective inverse dynamics modelling for dual-arm robot
Shen, Haoyu; Liu, Yanli; Wu, Hongtao
2018-05-01
To deal with the problem of inverse dynamics modelling for dual arm robot, a recursive inverse dynamics modelling method based on decoupled natural orthogonal complement is presented. In this model, the concepts and methods of Decoupled Natural Orthogonal Complement matrices are used to eliminate the constraint forces in the Newton-Euler kinematic equations, and the screws is used to express the kinematic and dynamics variables. On this basis, the paper has developed a special simulation program with symbol software of Mathematica and conducted a simulation research on the a dual-arm robot. Simulation results show that the proposed method based on decoupled natural orthogonal complement can save an enormous amount of CPU time that was spent in computing compared with the recursive Newton-Euler kinematic equations and the results is correct and reasonable, which can verify the reliability and efficiency of the method.
Biophysical radiosensitization
International Nuclear Information System (INIS)
Vladescu, C.; Apetroae, M.
1983-01-01
Experimental studies on normal and tumor-bearing rats revealed that chronic treatment with hydroquinone (5 mg/kg/day) inhibited catalase activity in liver, spleen, blood, and H 18R tumor. 3 H-hydroquinone (1.5 μCi/g body weight) showed tumor specificity, with maximum radioactivity in the tumor at 1 h after administration. The biological half-time of 3 H-hydroquinone in the tumor was 2 h, but there seems to exist a longer component, since 24 h after administration, some 30% of the maximum radioactivity could be detected in the tumor. Hydroquinone treatment produces a specific inhibition of catalase in the tumor and a higher degree of oxygenation at this level. These findings support the assumption that the mechanism of action of hydroquinone as an anticancer agent is achieved mainly via peroxide production. The oxygenation of the hypoxic tumoral tissue is done at non-toxic levels of hydroquinone, through a natural and specific biophysical pathway, recommanding hydroquinone for combined anticancer treatment (radiotherapy and chemotherapy). (orig.)
Anatomy of Higgs mass in supersymmetric inverse seesaw models
Energy Technology Data Exchange (ETDEWEB)
Chun, Eung Jin, E-mail: ejchun@kias.re.kr [Korea Institute for Advanced Study, Seoul 130-722 (Korea, Republic of); Mummidi, V. Suryanarayana, E-mail: soori9@cts.iisc.ernet.in [Centre for High Energy Physics, Indian Institute of Science, Bangalore 560012 (India); Vempati, Sudhir K., E-mail: vempati@cts.iisc.ernet.in [Centre for High Energy Physics, Indian Institute of Science, Bangalore 560012 (India)
2014-09-07
We compute the one loop corrections to the CP-even Higgs mass matrix in the supersymmetric inverse seesaw model to single out the different cases where the radiative corrections from the neutrino sector could become important. It is found that there could be a significant enhancement in the Higgs mass even for Dirac neutrino masses of O(30) GeV if the left-handed sneutrino soft mass is comparable or larger than the right-handed neutrino mass. In the case where right-handed neutrino masses are significantly larger than the supersymmetry breaking scale, the corrections can utmost account to an upward shift of 3 GeV. For very heavy multi TeV sneutrinos, the corrections replicate the stop corrections at 1-loop. We further show that general gauge mediation with inverse seesaw model naturally accommodates a 125 GeV Higgs with TeV scale stops.
Effects of induced stress on seismic forward modelling and inversion
Tromp, Jeroen; Trampert, Jeannot
2018-05-01
We demonstrate how effects of induced stress may be incorporated in seismic modelling and inversion. Our approach is motivated by the accommodation of pre-stress in global seismology. Induced stress modifies both the equation of motion and the constitutive relationship. The theory predicts that induced pressure linearly affects the unstressed isotropic moduli with a slope determined by their adiabatic pressure derivatives. The induced deviatoric stress produces anisotropic compressional and shear wave speeds; the latter result in shear wave splitting. For forward modelling purposes, we determine the weak form of the equation of motion under induced stress. In the context of the inverse problem, we determine induced stress sensitivity kernels, which may be used for adjoint tomography. The theory is illustrated by considering 2-D propagation of SH waves and related Fréchet derivatives based on a spectral-element method.
Inverse Modeling of Emissions and their Time Profiles
Czech Academy of Sciences Publication Activity Database
Resler, Jaroslav; Eben, Kryštof; Juruš, Pavel; Liczki, Jitka
2010-01-01
Roč. 1, č. 4 (2010), s. 288-295 ISSN 1309-1042 R&D Projects: GA MŽP SP/1A4/107/07 Grant - others:COST(XE) ES0602 Institutional research plan: CEZ:AV0Z10300504 Keywords : 4DVar * inverse modeling * diurnal time profile of emission * CMAQ adjoint * satellite observations Subject RIV: DG - Athmosphere Sciences, Meteorology
Sparse optimization for inverse problems in atmospheric modelling
Czech Academy of Sciences Publication Activity Database
Adam, Lukáš; Branda, Martin
2016-01-01
Roč. 79, č. 3 (2016), s. 256-266 ISSN 1364-8152 R&D Projects: GA MŠk(CZ) 7F14287 Institutional support: RVO:67985556 Keywords : Inverse modelling * Sparse optimization * Integer optimization * Least squares * European tracer experiment * Free Matlab codes Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 4.404, year: 2016 http://library.utia.cas.cz/separaty/2016/MTR/adam-0457037.pdf
Core flow inversion tested with numerical dynamo models
Rau, Steffen; Christensen, Ulrich; Jackson, Andrew; Wicht, Johannes
2000-05-01
We test inversion methods of geomagnetic secular variation data for the pattern of fluid flow near the surface of the core with synthetic data. These are taken from self-consistent 3-D models of convection-driven magnetohydrodynamic dynamos in rotating spherical shells, which generate dipole-dominated magnetic fields with an Earth-like morphology. We find that the frozen-flux approximation, which is fundamental to all inversion schemes, is satisfied to a fair degree in the models. In order to alleviate the non-uniqueness of the inversion, usually a priori conditions are imposed on the flow; for example, it is required to be purely toroidal or geostrophic. Either condition is nearly satisfied by our model flows near the outer surface. However, most of the surface velocity field lies in the nullspace of the inversion problem. Nonetheless, the a priori constraints reduce the nullspace, and by inverting the magnetic data with either one of them we recover a significant part of the flow. With the geostrophic condition the correlation coefficient between the inverted and the true velocity field can reach values of up to 0.65, depending on the choice of the damping parameter. The correlation is significant at the 95 per cent level for most spherical harmonic degrees up to l=26. However, it degrades substantially, even at long wavelengths, when we truncate the magnetic data sets to l currents, similar to those seen in core-flow models derived from geomagnetic data, occur in the equatorial region. However, the true flow does not contain this flow component. The results suggest that some meaningful information on the core-flow pattern can be retrieved from secular variation data, but also that the limited resolution of the magnetic core field could produce serious artefacts.
Efficient Stochastic Inversion Using Adjoint Models and Kernel-PCA
Energy Technology Data Exchange (ETDEWEB)
Thimmisetty, Charanraj A. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States). Center for Applied Scientific Computing; Zhao, Wenju [Florida State Univ., Tallahassee, FL (United States). Dept. of Scientific Computing; Chen, Xiao [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States). Center for Applied Scientific Computing; Tong, Charles H. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States). Center for Applied Scientific Computing; White, Joshua A. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States). Atmospheric, Earth and Energy Division
2017-10-18
Performing stochastic inversion on a computationally expensive forward simulation model with a high-dimensional uncertain parameter space (e.g. a spatial random field) is computationally prohibitive even when gradient information can be computed efficiently. Moreover, the ‘nonlinear’ mapping from parameters to observables generally gives rise to non-Gaussian posteriors even with Gaussian priors, thus hampering the use of efficient inversion algorithms designed for models with Gaussian assumptions. In this paper, we propose a novel Bayesian stochastic inversion methodology, which is characterized by a tight coupling between the gradient-based Langevin Markov Chain Monte Carlo (LMCMC) method and a kernel principal component analysis (KPCA). This approach addresses the ‘curse-of-dimensionality’ via KPCA to identify a low-dimensional feature space within the high-dimensional and nonlinearly correlated parameter space. In addition, non-Gaussian posterior distributions are estimated via an efficient LMCMC method on the projected low-dimensional feature space. We will demonstrate this computational framework by integrating and adapting our recent data-driven statistics-on-manifolds constructions and reduction-through-projection techniques to a linear elasticity model.
Inverse modeling of geochemical and mechanical compaction in sedimentary basins
Colombo, Ivo; Porta, Giovanni Michele; Guadagnini, Alberto
2015-04-01
We study key phenomena driving the feedback between sediment compaction processes and fluid flow in stratified sedimentary basins formed through lithification of sand and clay sediments after deposition. Processes we consider are mechanic compaction of the host rock and the geochemical compaction due to quartz cementation in sandstones. Key objectives of our study include (i) the quantification of the influence of the uncertainty of the model input parameters on the model output and (ii) the application of an inverse modeling technique to field scale data. Proper accounting of the feedback between sediment compaction processes and fluid flow in the subsurface is key to quantify a wide set of environmentally and industrially relevant phenomena. These include, e.g., compaction-driven brine and/or saltwater flow at deep locations and its influence on (a) tracer concentrations observed in shallow sediments, (b) build up of fluid overpressure, (c) hydrocarbon generation and migration, (d) subsidence due to groundwater and/or hydrocarbons withdrawal, and (e) formation of ore deposits. Main processes driving the diagenesis of sediments after deposition are mechanical compaction due to overburden and precipitation/dissolution associated with reactive transport. The natural evolution of sedimentary basins is characterized by geological time scales, thus preventing direct and exhaustive measurement of the system dynamical changes. The outputs of compaction models are plagued by uncertainty because of the incomplete knowledge of the models and parameters governing diagenesis. Development of robust methodologies for inverse modeling and parameter estimation under uncertainty is therefore crucial to the quantification of natural compaction phenomena. We employ a numerical methodology based on three building blocks: (i) space-time discretization of the compaction process; (ii) representation of target output variables through a Polynomial Chaos Expansion (PCE); and (iii) model
Three-dimensional magnetotelluric axial anisotropic forward modeling and inversion
Cao, Hui; Wang, Kunpeng; Wang, Tao; Hua, Boguang
2018-06-01
Magnetotelluric (MT) data has been widely used to image underground electrical structural. However, when the significant axial resistivity anisotropy presents, how this influences three-dimensional MT data has not been resolved clearly yet. We here propose a scheme for three-dimensional modeling of MT data in presence of axial anisotropic resistivity, where the electromagnetic fields are decomposed into primary and secondary components. A 3D staggered-grid finite difference method is then used to resolve the resulting 3D governing equations. Numerical tests have completed to validate the correctness and accuracy of the present algorithm. A limited-memory Broyden-Fletcher-Goldfarb-Shanno method is then utilized to realize the 3D MT axial anisotropic inversion. The testing results show that, compared to the results of isotropic resistivity inversion, taking account the axial anisotropy can much improve the inverted results.
International Nuclear Information System (INIS)
Paretzke, H.G.; Ballarini, F.; Brugmans, M.
2000-01-01
The overall project is organised into seven work packages. WP1 concentrates on the development of mechanistic, quantitative models for radiation oncogenesis using selected data sets from radiation epidemiology and from experimental animal studies. WP2 concentrates on the development of mechanistic, mathematical models for the induction of chromosome aberrations. WP3 develops mechanistic models for radiation mutagenesis, particularly using the HPRT-mutation as a paradigm. WP4 will develop mechanistic models for damage and repair of DNA, and compare these with experimentally derived data. WP5 concentrates on the improvement of our knowledge on the chemical reaction pathways of initial radiation chemical species in particular those that migrate to react with the DNA and on their simulation in track structure codes. WP6 models by track structure simulation codes the production of initial physical and chemical species, within DNA, water and other components of mammalian cells, in the tracks of charged particles following the physical processes of energy transfer, migration, absorption, and decay of excited states. WP7 concentrates on the determination of the start spectra of those tracks considered in WP6 for different impinging radiation fields and different irradiated biological objects. (orig.)
Inverse Analysis and Modeling for Tunneling Thrust on Shield Machine
Directory of Open Access Journals (Sweden)
Qian Zhang
2013-01-01
Full Text Available With the rapid development of sensor and detection technologies, measured data analysis plays an increasingly important role in the design and control of heavy engineering equipment. The paper proposed a method for inverse analysis and modeling based on mass on-site measured data, in which dimensional analysis and data mining techniques were combined. The method was applied to the modeling of the tunneling thrust on shield machines and an explicit expression for thrust prediction was established. Combined with on-site data from a tunneling project in China, the inverse identification of model coefficients was carried out using the multiple regression method. The model residual was analyzed by statistical methods. By comparing the on-site data and the model predicted results in the other two projects with different tunneling conditions, the feasibility of the model was discussed. The work may provide a scientific basis for the rational design and control of shield tunneling machines and also a new way for mass on-site data analysis of complex engineering systems with nonlinear, multivariable, time-varying characteristics.
Theoretical Molecular Biophysics
Scherer, Philipp
2010-01-01
"Theoretical Molecular Biophysics" is an advanced study book for students, shortly before or after completing undergraduate studies, in physics, chemistry or biology. It provides the tools for an understanding of elementary processes in biology, such as photosynthesis on a molecular level. A basic knowledge in mechanics, electrostatics, quantum theory and statistical physics is desirable. The reader will be exposed to basic concepts in modern biophysics such as entropic forces, phase separation, potentials of mean force, proton and electron transfer, heterogeneous reactions coherent and incoherent energy transfer as well as molecular motors. Basic concepts such as phase transitions of biopolymers, electrostatics, protonation equilibria, ion transport, radiationless transitions as well as energy- and electron transfer are discussed within the frame of simple models.
Joint Inversion Modelling of Geophysical Data From Lough Neagh Basin
Vozar, J.; Moorkamp, M.; Jones, A. G.; Rath, V.; Muller, M. R.
2015-12-01
Multi-dimensional modelling of geophysical data collected in the Lough Neagh Basin is presented in the frame of the IRETHERM project. The Permo-Triassic Lough Neagh Basin, situated in the southeastern part of Northern Ireland, exhibits elevated geothermal gradient (~30 °C/km) in the exploratory drilled boreholes. This is taken to indicate good geothermal exploitation potential in the Sherwood Sandstone aquifer for heating, and possibly even electricity production, purposes. We have used a 3-D joint inversion framework for modelling the magnetotelluric (MT) and gravity data collected to the north of the Lough Neagh to derive robust subsurface geological models. Comprehensive supporting geophysical and geological data (e.g. borehole logs and reflection seismic images) have been used in order to analyze and model the MT and gravity data. The geophysical data sets were provided by the Geological Survey of Northern Ireland (GSNI). Considering correct objective function weighting in favor of noise-free MT response functions is particularly important in joint inversion. There is no simple way how to correct distortion effects the 3-D responses as can be done in 1-D or 2-D case. We have used the Tellus Project airborne EM data to constrain magnetotelluric data and correct them for near surface effects. The shallow models from airborne data are used to constrain the uppermost part of 3-D inversion model. Preliminary 3-D joint inversion modeling reveals that the Sherwood Sandstone Group and the Permian Sandstone Formation are imaged as a conductive zone at the depth range of 500 m to 2000 m with laterally varying thickness, depth, and conductance. The conductive target sediments become shallower and thinner to the north and they are laterally continuous. To obtain better characterization of thermal transport properties of investigated area we used porosity and resistivity data from the Annaghmore and Ballymacilroy boreholes to estimate the relations between porosity
2016-09-01
PROPERTIES OF PHYSICAL FITNESS UNIFORMS AND MODELED HEAT STRAIN AND THERMAL COMFORT DISCLAIMER The opinions or assertions contained herein are the...SHIRTS: COMPARISON OF SPECTROPHOTOMETRIC AND OTHER BIOPHYSICAL PROPERTIES OF PHYSICAL FITNESS UNIFORMS AND MODELED HEAT STRAIN AND THERMAL COMFORT ...the impact of the environment on the wearer. To model these impacts on human thermal sensation (e.g., thermal comfort ) and thermoregulatory
Sebek, Jan; Albin, Nathan; Bortel, Radoslav; Natarajan, Bala; Prakash, Punit
2016-05-01
Computational models of microwave ablation (MWA) are widely used during the design optimization of novel devices and are under consideration for patient-specific treatment planning. The objective of this study was to assess the sensitivity of computational models of MWA to tissue biophysical properties. The Morris method was employed to assess the global sensitivity of the coupled electromagnetic-thermal model, which was implemented with the finite element method (FEM). The FEM model incorporated temperature dependencies of tissue physical properties. The variability of the model was studied using six different outputs to characterize the size and shape of the ablation zone, as well as impedance matching of the ablation antenna. Furthermore, the sensitivity results were statistically analyzed and absolute influence of each input parameter was quantified. A framework for systematically incorporating model uncertainties for treatment planning was suggested. A total of 1221 simulations, incorporating 111 randomly sampled starting points, were performed. Tissue dielectric parameters, specifically relative permittivity, effective conductivity, and the threshold temperature at which they transitioned to lower values (i.e., signifying desiccation), were identified as the most influential parameters for the shape of the ablation zone and antenna impedance matching. Of the thermal parameters considered in this study, the nominal blood perfusion rate and the temperature interval across which the tissue changes phase were identified as the most influential. The latent heat of tissue water vaporization and the volumetric heat capacity of the vaporized tissue were recognized as the least influential parameters. Based on the evaluation of absolute changes, the most important parameter (perfusion) had approximately 40.23 times greater influence on ablation area than the least important parameter (volumetric heat capacity of vaporized tissue). Another significant input parameter
Incorporating modelled subglacial hydrology into inversions for basal drag
Directory of Open Access Journals (Sweden)
C. P. Koziol
2017-12-01
Full Text Available A key challenge in modelling coupled ice-flow–subglacial hydrology is initializing the state and parameters of the system. We address this problem by presenting a workflow for initializing these values at the start of a summer melt season. The workflow depends on running a subglacial hydrology model for the winter season, when the system is not forced by meltwater inputs, and ice velocities can be assumed constant. Key parameters of the winter run of the subglacial hydrology model are determined from an initial inversion for basal drag using a linear sliding law. The state of the subglacial hydrology model at the end of winter is incorporated into an inversion of basal drag using a non-linear sliding law which is a function of water pressure. We demonstrate this procedure in the Russell Glacier area and compare the output of the linear sliding law with two non-linear sliding laws. Additionally, we compare the modelled winter hydrological state to radar observations and find that it is in line with summer rather than winter observations.
A nonlinear inversion for the velocity background and perturbation models
Wu, Zedong
2015-08-19
Reflected waveform inversion (RWI) provides a method to reduce the nonlinearity of the standard full waveform inversion (FWI) by inverting for the single scattered wavefield obtained using an image. However, current RWI methods usually neglect diving waves, which is an important source of information for extracting the long wavelength components of the velocity model. Thus, we propose a new optimization problem through breaking the velocity model into the background and the perturbation in the wave equation directly. In this case, the perturbed model is no longer the single scattering model, but includes all scattering. We optimize both components simultaneously, and thus, the objective function is nonlinear with respect to both the background and perturbation. The new introduced w can absorb the non-smooth update of background naturally. Application to the Marmousi model with frequencies that start at 5 Hz shows that this method can converge to the accurate velocity starting from a linearly increasing initial velocity. Application to the SEG2014 demonstrates the versatility of the approach.
Humanoid Walking Robot: Modeling, Inverse Dynamics, and Gain Scheduling Control
Directory of Open Access Journals (Sweden)
Elvedin Kljuno
2010-01-01
Full Text Available This article presents reference-model-based control design for a 10 degree-of-freedom bipedal walking robot, using nonlinear gain scheduling. The main goal is to show concentrated mass models can be used for prediction of the required joint torques for a bipedal walking robot. Relatively complicated architecture, high DOF, and balancing requirements make the control task of these robots difficult. Although linear control techniques can be used to control bipedal robots, nonlinear control is necessary for better performance. The emphasis of this work is to show that the reference model can be a bipedal walking model with concentrated mass at the center of gravity, which removes the problems related to design of a pseudo-inverse system. Another significance of this approach is the reduced calculation requirements due to the simplified procedure of nominal joint torques calculation. Kinematic and dynamic analysis is discussed including results for joint torques and ground force necessary to implement a prescribed walking motion. This analysis is accompanied by a comparison with experimental data. An inverse plant and a tracking error linearization-based controller design approach is described. We propose a novel combination of a nonlinear gain scheduling with a concentrated mass model for the MIMO bipedal robot system.
Biophysics of protein evolution and evolutionary protein biophysics
Sikosek, Tobias; Chan, Hue Sun
2014-01-01
The study of molecular evolution at the level of protein-coding genes often entails comparing large datasets of sequences to infer their evolutionary relationships. Despite the importance of a protein's structure and conformational dynamics to its function and thus its fitness, common phylogenetic methods embody minimal biophysical knowledge of proteins. To underscore the biophysical constraints on natural selection, we survey effects of protein mutations, highlighting the physical basis for marginal stability of natural globular proteins and how requirement for kinetic stability and avoidance of misfolding and misinteractions might have affected protein evolution. The biophysical underpinnings of these effects have been addressed by models with an explicit coarse-grained spatial representation of the polypeptide chain. Sequence–structure mappings based on such models are powerful conceptual tools that rationalize mutational robustness, evolvability, epistasis, promiscuous function performed by ‘hidden’ conformational states, resolution of adaptive conflicts and conformational switches in the evolution from one protein fold to another. Recently, protein biophysics has been applied to derive more accurate evolutionary accounts of sequence data. Methods have also been developed to exploit sequence-based evolutionary information to predict biophysical behaviours of proteins. The success of these approaches demonstrates a deep synergy between the fields of protein biophysics and protein evolution. PMID:25165599
Retrieving global aerosol sources from satellites using inverse modeling
Directory of Open Access Journals (Sweden)
O. Dubovik
2008-01-01
Full Text Available Understanding aerosol effects on global climate requires knowing the global distribution of tropospheric aerosols. By accounting for aerosol sources, transports, and removal processes, chemical transport models simulate the global aerosol distribution using archived meteorological fields. We develop an algorithm for retrieving global aerosol sources from satellite observations of aerosol distribution by inverting the GOCART aerosol transport model.
The inversion is based on a generalized, multi-term least-squares-type fitting, allowing flexible selection and refinement of a priori algorithm constraints. For example, limitations can be placed on retrieved quantity partial derivatives, to constrain global aerosol emission space and time variability in the results. Similarities and differences between commonly used inverse modeling and remote sensing techniques are analyzed. To retain the high space and time resolution of long-period, global observational records, the algorithm is expressed using adjoint operators.
Successful global aerosol emission retrievals at 2°×2.5 resolution were obtained by inverting GOCART aerosol transport model output, assuming constant emissions over the diurnal cycle, and neglecting aerosol compositional differences. In addition, fine and coarse mode aerosol emission sources were inverted separately from MODIS fine and coarse mode aerosol optical thickness data, respectively. These assumptions are justified, based on observational coverage and accuracy limitations, producing valuable aerosol source locations and emission strengths. From two weeks of daily MODIS observations during August 2000, the global placement of fine mode aerosol sources agreed with available independent knowledge, even though the inverse method did not use any a priori information about aerosol sources, and was initialized with a "zero aerosol emission" assumption. Retrieving coarse mode aerosol emissions was less successful
Alloy design as an inverse problem of cluster expansion models
DEFF Research Database (Denmark)
Larsen, Peter Mahler; Kalidindi, Arvind R.; Schmidt, Søren
2017-01-01
Central to a lattice model of an alloy system is the description of the energy of a given atomic configuration, which can be conveniently developed through a cluster expansion. Given a specific cluster expansion, the ground state of the lattice model at 0 K can be solved by finding the configurat......Central to a lattice model of an alloy system is the description of the energy of a given atomic configuration, which can be conveniently developed through a cluster expansion. Given a specific cluster expansion, the ground state of the lattice model at 0 K can be solved by finding...... the inverse problem in terms of energetically distinct configurations, using a constraint satisfaction model to identify constructible configurations, and show that a convex hull can be used to identify ground states. To demonstrate the approach, we solve for all ground states for a binary alloy in a 2D...
Inverse geothermal modelling applied to Danish sedimentary basins
Poulsen, Søren E.; Balling, Niels; Bording, Thue S.; Mathiesen, Anders; Nielsen, Søren B.
2017-10-01
This paper presents a numerical procedure for predicting subsurface temperatures and heat-flow distribution in 3-D using inverse calibration methodology. The procedure is based on a modified version of the groundwater code MODFLOW by taking advantage of the mathematical similarity between confined groundwater flow (Darcy's law) and heat conduction (Fourier's law). Thermal conductivity, heat production and exponential porosity-depth relations are specified separately for the individual geological units of the model domain. The steady-state temperature model includes a model-based transient correction for the long-term palaeoclimatic thermal disturbance of the subsurface temperature regime. Variable model parameters are estimated by inversion of measured borehole temperatures with uncertainties reflecting their quality. The procedure facilitates uncertainty estimation for temperature predictions. The modelling procedure is applied to Danish onshore areas containing deep sedimentary basins. A 3-D voxel-based model, with 14 lithological units from surface to 5000 m depth, was built from digital geological maps derived from combined analyses of reflection seismic lines and borehole information. Matrix thermal conductivity of model lithologies was estimated by inversion of all available deep borehole temperature data and applied together with prescribed background heat flow to derive the 3-D subsurface temperature distribution. Modelled temperatures are found to agree very well with observations. The numerical model was utilized for predicting and contouring temperatures at 2000 and 3000 m depths and for two main geothermal reservoir units, the Gassum (Lower Jurassic-Upper Triassic) and Bunter/Skagerrak (Triassic) reservoirs, both currently utilized for geothermal energy production. Temperature gradients to depths of 2000-3000 m are generally around 25-30 °C km-1, locally up to about 35 °C km-1. Large regions have geothermal reservoirs with characteristic temperatures
Inverse modeling with RZWQM2 to predict water quality
Nolan, Bernard T.; Malone, Robert W.; Ma, Liwang; Green, Christopher T.; Fienen, Michael N.; Jaynes, Dan B.
2011-01-01
This chapter presents guidelines for autocalibration of the Root Zone Water Quality Model (RZWQM2) by inverse modeling using PEST parameter estimation software (Doherty, 2010). Two sites with diverse climate and management were considered for simulation of N losses by leaching and in drain flow: an almond [Prunus dulcis (Mill.) D.A. Webb] orchard in the San Joaquin Valley, California and the Walnut Creek watershed in central Iowa, which is predominantly in corn (Zea mays L.)–soybean [Glycine max (L.) Merr.] rotation. Inverse modeling provides an objective statistical basis for calibration that involves simultaneous adjustment of model parameters and yields parameter confidence intervals and sensitivities. We describe operation of PEST in both parameter estimation and predictive analysis modes. The goal of parameter estimation is to identify a unique set of parameters that minimize a weighted least squares objective function, and the goal of predictive analysis is to construct a nonlinear confidence interval for a prediction of interest by finding a set of parameters that maximizes or minimizes the prediction while maintaining the model in a calibrated state. We also describe PEST utilities (PAR2PAR, TSPROC) for maintaining ordered relations among model parameters (e.g., soil root growth factor) and for post-processing of RZWQM2 outputs representing different cropping practices at the Iowa site. Inverse modeling provided reasonable fits to observed water and N fluxes and directly benefitted the modeling through: (i) simultaneous adjustment of multiple parameters versus one-at-a-time adjustment in manual approaches; (ii) clear indication by convergence criteria of when calibration is complete; (iii) straightforward detection of nonunique and insensitive parameters, which can affect the stability of PEST and RZWQM2; and (iv) generation of confidence intervals for uncertainty analysis of parameters and model predictions. Composite scaled sensitivities, which
A nonlinear inversion for the velocity background and perturbation models
Wu, Zedong; Alkhalifah, Tariq Ali
2015-01-01
Reflected waveform inversion (RWI) provides a method to reduce the nonlinearity of the standard full waveform inversion (FWI) by inverting for the single scattered wavefield obtained using an image. However, current RWI methods usually neglect
Incorporating model parameter uncertainty into inverse treatment planning
International Nuclear Information System (INIS)
Lian Jun; Xing Lei
2004-01-01
Radiobiological treatment planning depends not only on the accuracy of the models describing the dose-response relation of different tumors and normal tissues but also on the accuracy of tissue specific radiobiological parameters in these models. Whereas the general formalism remains the same, different sets of model parameters lead to different solutions and thus critically determine the final plan. Here we describe an inverse planning formalism with inclusion of model parameter uncertainties. This is made possible by using a statistical analysis-based frameset developed by our group. In this formalism, the uncertainties of model parameters, such as the parameter a that describes tissue-specific effect in the equivalent uniform dose (EUD) model, are expressed by probability density function and are included in the dose optimization process. We found that the final solution strongly depends on distribution functions of the model parameters. Considering that currently available models for computing biological effects of radiation are simplistic, and the clinical data used to derive the models are sparse and of questionable quality, the proposed technique provides us with an effective tool to minimize the effect caused by the uncertainties in a statistical sense. With the incorporation of the uncertainties, the technique has potential for us to maximally utilize the available radiobiology knowledge for better IMRT treatment
An inverse problem for a mathematical model of aquaponic agriculture
Bobak, Carly; Kunze, Herb
2017-01-01
Aquaponic agriculture is a sustainable ecosystem that relies on a symbiotic relationship between fish and macrophytes. While the practice has been growing in popularity, relatively little mathematical models exist which aim to study the system processes. In this paper, we present a system of ODEs which aims to mathematically model the population and concetrations dynamics present in an aquaponic environment. Values of the parameters in the system are estimated from the literature so that simulated results can be presented to illustrate the nature of the solutions to the system. As well, a brief sensitivity analysis is performed in order to identify redundant parameters and highlight those which may need more reliable estimates. Specifically, an inverse problem with manufactured data for fish and plants is presented to demonstrate the ability of the collage theorem to recover parameter estimates.
Bayesian inversion using a geologically realistic and discrete model space
Jaeggli, C.; Julien, S.; Renard, P.
2017-12-01
Since the early days of groundwater modeling, inverse methods play a crucial role. Many research and engineering groups aim to infer extensive knowledge of aquifer parameters from a sparse set of observations. Despite decades of dedicated research on this topic, there are still several major issues to be solved. In the hydrogeological framework, one is often confronted with underground structures that present very sharp contrasts of geophysical properties. In particular, subsoil structures such as karst conduits, channels, faults, or lenses, strongly influence groundwater flow and transport behavior of the underground. For this reason it can be essential to identify their location and shape very precisely. Unfortunately, when inverse methods are specially trained to consider such complex features, their computation effort often becomes unaffordably high. The following work is an attempt to solve this dilemma. We present a new method that is, in some sense, a compromise between the ergodicity of Markov chain Monte Carlo (McMC) methods and the efficient handling of data by the ensemble based Kalmann filters. The realistic and complex random fields are generated by a Multiple-Point Statistics (MPS) tool. Nonetheless, it is applicable with any conditional geostatistical simulation tool. Furthermore, the algorithm is independent of any parametrization what becomes most important when two parametric systems are equivalent (permeability and resistivity, speed and slowness, etc.). When compared to two existing McMC schemes, the computational effort was divided by a factor of 12.
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...
International Nuclear Information System (INIS)
1992-11-01
The report comprises 18 papers held at the 2nd Biophysical Work Meeting, 11 - 13 September 1991 in Schlema, Germany. The history of biophysics in Germany particularly of radiation biophysics and radon research, measurements of the radiation effects of radon and the derivation of limits, radon balneotherapy and consequences of uranium ore mining are dealt with. (orig.) [de
International Nuclear Information System (INIS)
1978-01-01
The main subject on the biophysics meeting was the biophysics of membranes with practical subjects from photosynthesis and the transfer processes on membranes. In radiation biophysics, problems of radiation sensitisation, immunological problems after radiation exposure, the oxygen effect and inhibitory processes in RNS synthesis after radiation exposure were discussed with a view to tumour therapy. (AJ) [de
Anthropomorphic Coding of Speech and Audio: A Model Inversion Approach
Directory of Open Access Journals (Sweden)
W. Bastiaan Kleijn
2005-06-01
Full Text Available Auditory modeling is a well-established methodology that provides insight into human perception and that facilitates the extraction of signal features that are most relevant to the listener. The aim of this paper is to provide a tutorial on perceptual speech and audio coding using an invertible auditory model. In this approach, the audio signal is converted into an auditory representation using an invertible auditory model. The auditory representation is quantized and coded. Upon decoding, it is then transformed back into the acoustic domain. This transformation converts a complex distortion criterion into a simple one, thus facilitating quantization with low complexity. We briefly review past work on auditory models and describe in more detail the components of our invertible model and its inversion procedure, that is, the method to reconstruct the signal from the output of the auditory model. We summarize attempts to use the auditory representation for low-bit-rate coding. Our approach also allows the exploitation of the inherent redundancy of the human auditory system for the purpose of multiple description (joint source-channel coding.
Natural vs. artificial groundwater recharge, quantification through inverse modeling
Directory of Open Access Journals (Sweden)
H. Hashemi
2013-02-01
Full Text Available Estimating the change in groundwater recharge from an introduced artificial recharge system is important in order to evaluate future water availability. This paper presents an inverse modeling approach to quantify the recharge contribution from both an ephemeral river channel and an introduced artificial recharge system based on floodwater spreading in arid Iran. The study used the MODFLOW-2000 to estimate recharge for both steady- and unsteady-state conditions. The model was calibrated and verified based on the observed hydraulic head in observation wells and model precision, uncertainty, and model sensitivity were analyzed in all modeling steps. The results showed that in a normal year without extreme events, the floodwater spreading system is the main contributor to recharge with 80% and the ephemeral river channel with 20% of total recharge in the studied area. Uncertainty analysis revealed that the river channel recharge estimation represents relatively more uncertainty in comparison to the artificial recharge zones. The model is also less sensitive to the river channel. The results show that by expanding the artificial recharge system, the recharge volume can be increased even for small flood events, while the recharge through the river channel increases only for major flood events.
UCODE, a computer code for universal inverse modeling
Poeter, E.P.; Hill, M.C.
1999-01-01
This article presents the US Geological Survey computer program UCODE, which was developed in collaboration with the US Army Corps of Engineers Waterways Experiment Station and the International Ground Water Modeling Center of the Colorado School of Mines. UCODE performs inverse modeling, posed as a parameter-estimation problem, using nonlinear regression. Any application model or set of models can be used; the only requirement is that they have numerical (ASCII or text only) input and output files and that the numbers in these files have sufficient significant digits. Application models can include preprocessors and postprocessors as well as models related to the processes of interest (physical, chemical and so on), making UCODE extremely powerful for model calibration. Estimated parameters can be defined flexibly with user-specified functions. Observations to be matched in the regression can be any quantity for which a simulated equivalent value can be produced, thus simulated equivalent values are calculated using values that appear in the application model output files and can be manipulated with additive and multiplicative functions, if necessary. Prior, or direct, information on estimated parameters also can be included in the regression. The nonlinear regression problem is solved by minimizing a weighted least-squares objective function with respect to the parameter values using a modified Gauss-Newton method. Sensitivities needed for the method are calculated approximately by forward or central differences and problems and solutions related to this approximation are discussed. Statistics are calculated and printed for use in (1) diagnosing inadequate data or identifying parameters that probably cannot be estimated with the available data, (2) evaluating estimated parameter values, (3) evaluating the model representation of the actual processes and (4) quantifying the uncertainty of model simulated values. UCODE is intended for use on any computer operating
Unified dark energy-dark matter model with inverse quintessence
Energy Technology Data Exchange (ETDEWEB)
Ansoldi, Stefano [ICRA — International Center for Relativistic Astrophysics, INFN — Istituto Nazionale di Fisica Nucleare, and Dipartimento di Matematica e Informatica, Università degli Studi di Udine, via delle Scienze 206, I-33100 Udine (UD) (Italy); Guendelman, Eduardo I., E-mail: ansoldi@fulbrightmail.org, E-mail: guendel@bgu.ac.il [Department of Physics, Ben-Gurion University of the Negeev, Beer-Sheva 84105 (Israel)
2013-05-01
We consider a model where both dark energy and dark matter originate from the coupling of a scalar field with a non-canonical kinetic term to, both, a metric measure and a non-metric measure. An interacting dark energy/dark matter scenario can be obtained by introducing an additional scalar that can produce non constant vacuum energy and associated variations in dark matter. The phenomenology is most interesting when the kinetic term of the additional scalar field is ghost-type, since in this case the dark energy vanishes in the early universe and then grows with time. This constitutes an ''inverse quintessence scenario'', where the universe starts from a zero vacuum energy density state, instead of approaching it in the future.
Determination of hydraulic properties of unsaturated soil via inverse modeling
International Nuclear Information System (INIS)
Kodesova, R.
2004-01-01
The method for determining the hydraulic properties of unsaturated soil with inverse modeling is presented. A modified cone penetrometer has been designed to inject water into the soil through a screen, and measure the progress of the wetting front with two tensiometer rings positioned above the screen. Cumulative inflow and pressure head readings are analyzed to obtain estimates of the hydraulic parameters describing K(h) and θ(h). Optimization results for tests at one side are used to demonstrate the possibility to evaluate either the wetting branches of the soil hydraulic properties, or the wetting and drying curves simultaneously, via analysis of different parts of the experiment. The optimization results are compared to the results of standard laboratory and field methods. (author)
Unified dark energy-dark matter model with inverse quintessence
International Nuclear Information System (INIS)
Ansoldi, Stefano; Guendelman, Eduardo I.
2013-01-01
We consider a model where both dark energy and dark matter originate from the coupling of a scalar field with a non-canonical kinetic term to, both, a metric measure and a non-metric measure. An interacting dark energy/dark matter scenario can be obtained by introducing an additional scalar that can produce non constant vacuum energy and associated variations in dark matter. The phenomenology is most interesting when the kinetic term of the additional scalar field is ghost-type, since in this case the dark energy vanishes in the early universe and then grows with time. This constitutes an ''inverse quintessence scenario'', where the universe starts from a zero vacuum energy density state, instead of approaching it in the future
Electron electric dipole moment in Inverse Seesaw models
Energy Technology Data Exchange (ETDEWEB)
Abada, Asmaa; Toma, Takashi [Laboratoire de Physique Théorique, CNRS, University Paris-Sud, Université Paris-Saclay,91405 Orsay (France)
2016-08-11
We consider the contribution of sterile neutrinos to the electric dipole moment of charged leptons in the most minimal realisation of the Inverse Seesaw mechanism, in which the Standard Model is extended by two right-handed neutrinos and two sterile fermion states. Our study shows that the two pairs of (heavy) pseudo-Dirac mass eigenstates can give significant contributions to the electron electric dipole moment, lying close to future experimental sensitivity if their masses are above the electroweak scale. The major contribution comes from two-loop diagrams with pseudo-Dirac neutrino states running in the loops. In our analysis we further discuss the possibility of having a successful leptogenesis in this framework, compatible with a large electron electric dipole moment.
Electron electric dipole moment in Inverse Seesaw models
International Nuclear Information System (INIS)
Abada, Asmaa; Toma, Takashi
2016-01-01
We consider the contribution of sterile neutrinos to the electric dipole moment of charged leptons in the most minimal realisation of the Inverse Seesaw mechanism, in which the Standard Model is extended by two right-handed neutrinos and two sterile fermion states. Our study shows that the two pairs of (heavy) pseudo-Dirac mass eigenstates can give significant contributions to the electron electric dipole moment, lying close to future experimental sensitivity if their masses are above the electroweak scale. The major contribution comes from two-loop diagrams with pseudo-Dirac neutrino states running in the loops. In our analysis we further discuss the possibility of having a successful leptogenesis in this framework, compatible with a large electron electric dipole moment.
The Inverse Problem of Identification of Hydrogen Permeability Model
Directory of Open Access Journals (Sweden)
Yury V. Zaika
2018-01-01
Full Text Available One of the technological challenges for hydrogen materials science is the currently active search for structural materials with important applications (including the ITER project and gas-separation plants. One had to estimate the parameters of diffusion and sorption to numerically model the different scenarios and experimental conditions of the material usage (including extreme ones. The article presents boundary value problems of hydrogen permeability and thermal desorption with dynamical boundary conditions. A numerical method is developed for TDS spectrum simulation, where only integration of a nonlinear system of low order ordinary differential equations is required. The main final output of the article is a noise-resistant algorithm for solving the inverse problem of parametric identification for the aggregated experiment where desorption and diffusion are dynamically interrelated (without the artificial division of studies into the diffusion limited regime (DLR and the surface limited regime (SLR.
Farasat, Iman; Salis, Howard M.
2016-01-01
The ability to precisely modify genomes and regulate specific genes will greatly accelerate several medical and engineering applications. The CRISPR/Cas9 (Type II) system binds and cuts DNA using guide RNAs, though the variables that control its on-target and off-target activity remain poorly characterized. Here, we develop and parameterize a system-wide biophysical model of Cas9-based genome editing and gene regulation to predict how changing guide RNA sequences, DNA superhelical densities, Cas9 and crRNA expression levels, organisms and growth conditions, and experimental conditions collectively control the dynamics of dCas9-based binding and Cas9-based cleavage at all DNA sites with both canonical and non-canonical PAMs. We combine statistical thermodynamics and kinetics to model Cas9:crRNA complex formation, diffusion, site selection, reversible R-loop formation, and cleavage, using large amounts of structural, biochemical, expression, and next-generation sequencing data to determine kinetic parameters and develop free energy models. Our results identify DNA supercoiling as a novel mechanism controlling Cas9 binding. Using the model, we predict Cas9 off-target binding frequencies across the lambdaphage and human genomes, and explain why Cas9’s off-target activity can be so high. With this improved understanding, we propose several rules for designing experiments for minimizing off-target activity. We also discuss the implications for engineering dCas9-based genetic circuits. PMID:26824432
International Nuclear Information System (INIS)
1988-01-01
The Department of Energy's Office of Health and Environmental Research and the Commission of the European Communities (CEC) Radiation Protection Program support the majority of Research in the Field of Radiobiological Modeling. This field of science develops models based on scientifically sound principles to predict biological response (at the cellular, molecular, and animal level) to exposure to low level ionizing radiation. Biophysical models are an important tool for estimating response of ionizing radiation at low doses and dose rates. Generally speaking, the biophysical models can be classified into two groups: (1) mechanistic models and (2) phenomenological models. Mechanistic models are based on some assumptions about the physical, chemical, or biological mechanisms of action in association with radiobiological data whereas the phenomenological models are based solely on available experimental data on radiobiological effects with less emphasis on mechanisms of action. There are a number of these models which are being developed. Since model builders rely on radiobiological data available in the literature either to develop mechanistic or phenomenological models, it is essential that a critical evaluation of existing radiobiological data be made and data that is generally considered good and most appropriate for biophysical modeling be identified. A Workshop jointly sponsored by the DOE and the CEC was held at Oak Ridge, Tennessee from June 23--25, 1988, to review the data available from physical and chemical, cellular and molecular and animal studies with ionizing radiation
Directory of Open Access Journals (Sweden)
R. Locatelli
2013-10-01
Full Text Available A modelling experiment has been conceived to assess the impact of transport model errors on methane emissions estimated in an atmospheric inversion system. Synthetic methane observations, obtained from 10 different model outputs from the international TransCom-CH4 model inter-comparison exercise, are combined with a prior scenario of methane emissions and sinks, and integrated into the three-component PYVAR-LMDZ-SACS (PYthon VARiational-Laboratoire de Météorologie Dynamique model with Zooming capability-Simplified Atmospheric Chemistry System inversion system to produce 10 different methane emission estimates at the global scale for the year 2005. The same methane sinks, emissions and initial conditions have been applied to produce the 10 synthetic observation datasets. The same inversion set-up (statistical errors, prior emissions, inverse procedure is then applied to derive flux estimates by inverse modelling. Consequently, only differences in the modelling of atmospheric transport may cause differences in the estimated fluxes. In our framework, we show that transport model errors lead to a discrepancy of 27 Tg yr−1 at the global scale, representing 5% of total methane emissions. At continental and annual scales, transport model errors are proportionally larger than at the global scale, with errors ranging from 36 Tg yr−1 in North America to 7 Tg yr−1 in Boreal Eurasia (from 23 to 48%, respectively. At the model grid-scale, the spread of inverse estimates can reach 150% of the prior flux. Therefore, transport model errors contribute significantly to overall uncertainties in emission estimates by inverse modelling, especially when small spatial scales are examined. Sensitivity tests have been carried out to estimate the impact of the measurement network and the advantage of higher horizontal resolution in transport models. The large differences found between methane flux estimates inferred in these different configurations highly
Temporal rainfall estimation using input data reduction and model inversion
Wright, A. J.; Vrugt, J. A.; Walker, J. P.; Pauwels, V. R. N.
2016-12-01
Floods are devastating natural hazards. To provide accurate, precise and timely flood forecasts there is a need to understand the uncertainties associated with temporal rainfall and model parameters. The estimation of temporal rainfall and model parameter distributions from streamflow observations in complex dynamic catchments adds skill to current areal rainfall estimation methods, allows for the uncertainty of rainfall input to be considered when estimating model parameters and provides the ability to estimate rainfall from poorly gauged catchments. Current methods to estimate temporal rainfall distributions from streamflow are unable to adequately explain and invert complex non-linear hydrologic systems. This study uses the Discrete Wavelet Transform (DWT) to reduce rainfall dimensionality for the catchment of Warwick, Queensland, Australia. The reduction of rainfall to DWT coefficients allows the input rainfall time series to be simultaneously estimated along with model parameters. The estimation process is conducted using multi-chain Markov chain Monte Carlo simulation with the DREAMZS algorithm. The use of a likelihood function that considers both rainfall and streamflow error allows for model parameter and temporal rainfall distributions to be estimated. Estimation of the wavelet approximation coefficients of lower order decomposition structures was able to estimate the most realistic temporal rainfall distributions. These rainfall estimates were all able to simulate streamflow that was superior to the results of a traditional calibration approach. It is shown that the choice of wavelet has a considerable impact on the robustness of the inversion. The results demonstrate that streamflow data contains sufficient information to estimate temporal rainfall and model parameter distributions. The extent and variance of rainfall time series that are able to simulate streamflow that is superior to that simulated by a traditional calibration approach is a
Roy, S; Fox, M R; Curcic, J; Schwizer, W; Pal, A
2012-07-01
The function and structure of the gastro-esophageal junction (GEJ) determine its efficacy as a reflux barrier. This study presents a novel methodology for the quantitative assessment of GEJ and proximal gastric morphology from magnetic resonance (MR) imaging. Based on this data we propose a new conceptualization of the hypothesis that a flap valve mechanism contributes to reflux protection. 3D models of the GEJ and proximal stomach were reconstructed from MR images in 12 healthy volunteers during respiration and on eating a test meal to maximum satiation. A rotating plane analysis measured the gastro-esophageal insertion angle and span of contact. An ellipsoid fit provided quantitative assessment of gastric shape and orientation relative to a fixed anatomical reference point. Position of the esophageal insertion on the 'gastric ellipse' was noted. An ellipsoid-cylinder model was designed to analyze the relationships among parameters describing the GEJ morphology. The insertion angle became more acute on expiration, but did not change with meal ingestion. In contrast the span of contact did not vary with respiration, but increased with gastric filling. Changes in gastric morphology with distension further augmented the span of gastro-esophageal contact in almost 70% of the studies. Novel MR imaging and biophysical analysis of the GEJ and proximal stomach provide a quantitative description of structures contributing to the reflux barrier. Changes in these parameters during respiration and on eating support the hypothesis that structural components of a functional 'flap valve' like mechanism contribute to reflux protection. © 2012 Blackwell Publishing Ltd.
Forecasting wind-driven wildfires using an inverse modelling approach
Directory of Open Access Journals (Sweden)
O. Rios
2014-06-01
Full Text Available A technology able to rapidly forecast wildfire dynamics would lead to a paradigm shift in the response to emergencies, providing the Fire Service with essential information about the ongoing fire. This paper presents and explores a novel methodology to forecast wildfire dynamics in wind-driven conditions, using real-time data assimilation and inverse modelling. The forecasting algorithm combines Rothermel's rate of spread theory with a perimeter expansion model based on Huygens principle and solves the optimisation problem with a tangent linear approach and forward automatic differentiation. Its potential is investigated using synthetic data and evaluated in different wildfire scenarios. The results show the capacity of the method to quickly predict the location of the fire front with a positive lead time (ahead of the event in the order of 10 min for a spatial scale of 100 m. The greatest strengths of our method are lightness, speed and flexibility. We specifically tailor the forecast to be efficient and computationally cheap so it can be used in mobile systems for field deployment and operativeness. Thus, we put emphasis on producing a positive lead time and the means to maximise it.
Directory of Open Access Journals (Sweden)
J. F. Meirink
2008-11-01
Full Text Available A four-dimensional variational (4D-Var data assimilation system for inverse modelling of atmospheric methane emissions is presented. The system is based on the TM5 atmospheric transport model. It can be used for assimilating large volumes of measurements, in particular satellite observations and quasi-continuous in-situ observations, and at the same time it enables the optimization of a large number of model parameters, specifically grid-scale emission rates. Furthermore, the variational method allows to estimate uncertainties in posterior emissions. Here, the system is applied to optimize monthly methane emissions over a 1-year time window on the basis of surface observations from the NOAA-ESRL network. The results are rigorously compared with an analogous inversion by Bergamaschi et al. (2007, which was based on the traditional synthesis approach. The posterior emissions as well as their uncertainties obtained in both inversions show a high degree of consistency. At the same time we illustrate the advantage of 4D-Var in reducing aggregation errors by optimizing emissions at the grid scale of the transport model. The full potential of the assimilation system is exploited in Meirink et al. (2008, who use satellite observations of column-averaged methane mixing ratios to optimize emissions at high spatial resolution, taking advantage of the zooming capability of the TM5 model.
Elements of a dialogue between nonlinear models in condensed matter and biophysics
International Nuclear Information System (INIS)
Bishop, A.R.; Lomdahl, P.S.; Kerr, W.C.
1985-01-01
We indicate some of the emerging thematic connections between strongly nonlinear effects in condensed matter and biological materials. These are illustrated with model studies of: (1) structural phase transitions in anisotropic lattices; and (2) finite temperature effects on self-trapped states in vibron-phonon models of α-helix proteins. 13 refs., 8 figs
Affordable and personalized lighting using inverse modeling and virtual sensors
Basu, Chandrayee; Chen, Benjamin; Richards, Jacob; Dhinakaran, Aparna; Agogino, Alice; Martin, Rodney
2014-03-01
Wireless sensor networks (WSN) have great potential to enable personalized intelligent lighting systems while reducing building energy use by 50%-70%. As a result WSN systems are being increasingly integrated in state-ofart intelligent lighting systems. In the future these systems will enable participation of lighting loads as ancillary services. However, such systems can be expensive to install and lack the plug-and-play quality necessary for user-friendly commissioning. In this paper we present an integrated system of wireless sensor platforms and modeling software to enable affordable and user-friendly intelligent lighting. It requires ⇠ 60% fewer sensor deployments compared to current commercial systems. Reduction in sensor deployments has been achieved by optimally replacing the actual photo-sensors with real-time discrete predictive inverse models. Spatially sparse and clustered sub-hourly photo-sensor data captured by the WSN platforms are used to develop and validate a piece-wise linear regression of indoor light distribution. This deterministic data-driven model accounts for sky conditions and solar position. The optimal placement of photo-sensors is performed iteratively to achieve the best predictability of the light field desired for indoor lighting control. Using two weeks of daylight and artificial light training data acquired at the Sustainability Base at NASA Ames, the model was able to predict the light level at seven monitored workstations with 80%-95% accuracy. We estimate that 10% adoption of this intelligent wireless sensor system in commercial buildings could save 0.2-0.25 quads BTU of energy nationwide.
Modelling and genetic algorithm based optimisation of inverse supply chain
Bányai, T.
2009-04-01
(Recycling of household appliances with emphasis on reuse options). The purpose of this paper is the presentation of a possible method for avoiding the unnecessary environmental risk and landscape use through unprovoked large supply chain of collection systems of recycling processes. In the first part of the paper the author presents the mathematical model of recycling related collection systems (applied especially for wastes of electric and electronic products) and in the second part of the work a genetic algorithm based optimisation method will be demonstrated, by the aid of which it is possible to determine the optimal structure of the inverse supply chain from the point of view economical, ecological and logistic objective functions. The model of the inverse supply chain is based on a multi-level, hierarchical collection system. In case of this static model it is assumed that technical conditions are permanent. The total costs consist of three parts: total infrastructure costs, total material handling costs and environmental risk costs. The infrastructure-related costs are dependent only on the specific fixed costs and the specific unit costs of the operation points (collection, pre-treatment, treatment, recycling and reuse plants). The costs of warehousing and transportation are represented by the material handling related costs. The most important factors determining the level of environmental risk cost are the number of out of time recycled (treated or reused) products, the number of supply chain objects and the length of transportation routes. The objective function is the minimization of the total cost taking into consideration the constraints. However a lot of research work discussed the design of supply chain [8], but most of them concentrate on linear cost functions. In the case of this model non-linear cost functions were used. The non-linear cost functions and the possible high number of objects of the inverse supply chain leaded to the problem of choosing a
A biophysical model of the mitochondrial respiratory system and oxidative phosphorylation.
Directory of Open Access Journals (Sweden)
Daniel A Beard
2005-09-01
Full Text Available A computational model for the mitochondrial respiratory chain that appropriately balances mass, charge, and free energy transduction is introduced and analyzed based on a previously published set of data measured on isolated cardiac mitochondria. The basic components included in the model are the reactions at complexes I, III, and IV of the electron transport system, ATP synthesis at F1F0 ATPase, substrate transporters including adenine nucleotide translocase and the phosphate-hydrogen co-transporter, and cation fluxes across the inner membrane including fluxes through the K+/H+ antiporter and passive H+ and K+ permeation. Estimation of 16 adjustable parameter values is based on fitting model simulations to nine independent data curves. The identified model is further validated by comparison to additional datasets measured from mitochondria isolated from rat heart and liver and observed at low oxygen concentration. To obtain reasonable fits to the available data, it is necessary to incorporate inorganic-phosphate-dependent activation of the dehydrogenase activity and the electron transport system. Specifically, it is shown that a model incorporating phosphate-dependent activation of complex III is able to reasonably reproduce the observed data. The resulting validated and verified model provides a foundation for building larger and more complex systems models and investigating complex physiological and pathophysiological interactions in cardiac energetics.
A Biophysical Model of the Mitochondrial Respiratory System and Oxidative Phosphorylation.
Directory of Open Access Journals (Sweden)
2005-09-01
Full Text Available A computational model for the mitochondrial respiratory chain that appropriately balances mass, charge, and free energy transduction is introduced and analyzed based on a previously published set of data measured on isolated cardiac mitochondria. The basic components included in the model are the reactions at complexes I, III, and IV of the electron transport system, ATP synthesis at F(1F(0 ATPase, substrate transporters including adenine nucleotide translocase and the phosphate-hydrogen co-transporter, and cation fluxes across the inner membrane including fluxes through the K/H antiporter and passive H and K permeation. Estimation of 16 adjustable parameter values is based on fitting model simulations to nine independent data curves. The identified model is further validated by comparison to additional datasets measured from mitochondria isolated from rat heart and liver and observed at low oxygen concentration. To obtain reasonable fits to the available data, it is necessary to incorporate inorganic-phosphate-dependent activation of the dehydrogenase activity and the electron transport system. Specifically, it is shown that a model incorporating phosphate-dependent activation of complex III is able to reasonably reproduce the observed data. The resulting validated and verified model provides a foundation for building larger and more complex systems models and investigating complex physiological and pathophysiological interactions in cardiac energetics.
Moraru, Ion C; Tai, An; Erickson, Beth; Li, X Allen
2014-01-01
We analyzed recent clinical data obtained from chemoradiation of unresectable, locally advanced pancreatic cancer (LAPC) in order to examine possible benefits from radiation therapy dose escalation. A modified linear quadratic model was used to fit clinical tumor response and survival data of chemoradiation treatments for LAPC reported from 20 institutions. Biophysical radiosensitivity parameters were extracted from the fits. Examination of the clinical data demonstrated an enhancement in tumor response with higher irradiation dose, an important clinical result for palliation and quality of life. Little indication of improvement in 1-year survival with increased radiation dose was observed. Possible dose escalation schemes are proposed based on calculations of the biologically effective dose required for a 50% tumor response rate. Based on the evaluation of tumor response data, the escalation of radiation dose presents potential clinical benefits which when combined with normal tissue complication analyses may result in improved treatment outcome for locally advanced pancreatic cancer patients. Copyright © 2014 American Society for Radiation Oncology. Published by Elsevier Inc. All rights reserved.
Abdellah, Marwan; Bilgili, Ahmet; Eilemann, Stefan; Shillcock, Julian; Markram, Henry; Schü rmann, Felix
2017-01-01
to visualize the results of their virtual experiments that are performed in computer simulations, or in silico. The impact of the presented pipeline opens novel avenues for assisting the neuroscientists to build biologically accurate models of the brain
Nölting, Bengt
2006-01-01
Incorporating recent dramatic advances, this textbook presents a fresh and timely introduction to modern biophysical methods. An array of new, faster and higher-power biophysical methods now enables scientists to examine the mysteries of life at a molecular level. This innovative text surveys and explains the ten key biophysical methods, including those related to biophysical nanotechnology, scanning probe microscopy, X-ray crystallography, ion mobility spectrometry, mass spectrometry, proteomics, and protein folding and structure. Incorporating much information previously unavailable in tutorial form, Nölting employs worked examples and 267 illustrations to fully detail the techniques and their underlying mechanisms. Methods in Modern Biophysics is written for advanced undergraduate and graduate students, postdocs, researchers, lecturers and professors in biophysics, biochemistry and related fields. Special features in the 2nd edition: • Illustrates the high-resolution methods for ultrashort-living protei...
Nölting, Bengt
2010-01-01
Incorporating recent dramatic advances, this textbook presents a fresh and timely introduction to modern biophysical methods. An array of new, faster and higher-power biophysical methods now enables scientists to examine the mysteries of life at a molecular level. This innovative text surveys and explains the ten key biophysical methods, including those related to biophysical nanotechnology, scanning probe microscopy, X-ray crystallography, ion mobility spectrometry, mass spectrometry, proteomics, and protein folding and structure. Incorporating much information previously unavailable in tutorial form, Nölting employs worked examples and about 270 illustrations to fully detail the techniques and their underlying mechanisms. Methods in Modern Biophysics is written for advanced undergraduate and graduate students, postdocs, researchers, lecturers, and professors in biophysics, biochemistry and related fields. Special features in the 3rd edition: Introduces rapid partial protein ladder sequencing - an important...
Biophysical Model of Cortical Network Activity and the Influence of Electrical Stimulation
2015-11-13
model, multicompartment model, subdural cortical stimulation, anode, cathode, epilepsy REPORT DOCUMENTATION PAGE 11. SPONSOR/MONITOR’S REPORT NUMBER(S...and axon orientation in respect to the electrode position. 4) A single stimulation pulse causes a sequence of action potentials ectopically generated...Bergey, P.J. Franaszczuk. Phase-dependent stimulation effects on bursting activity in a neural network cortical simulation, Epilepsy Research (07 2008
Exploring the Red Sea seasonal ecosystem functioning using a three-dimensional biophysical model
Triantafyllou, G.; Yao, F.; Petihakis, G.; Tsiaras, K. P.; Raitsos, D. E.; Hoteit, Ibrahim
2014-01-01
The Red Sea exhibits complex hydrodynamic and biogeochemical dynamics, which vary both in time and space. These dynamics have been explored through the development and application of a 3-D ecosystem model. The simulation system comprises two off-line coupled submodels: the MIT General Circulation Model (MITgcm) and the European Regional Seas Ecosystem Model (ERSEM), both adapted for the Red Sea. The results from an annual simulation under climatological forcing are presented. Simulation results are in good agreement with satellite and in situ data illustrating the role of the physical processes in determining the evolution and variability of the Red Sea ecosystem. The model was able to reproduce the main features of the Red Sea ecosystem functioning, including the exchange with the Gulf of Aden, which is a major driving mechanism for the whole Red Sea ecosystem and the winter overturning taking place in the north. Some model limitations, mainly related to the dynamics of the extended reef system located in the southern part of the Red Sea, which is not currently represented in the model, still need to be addressed.
Selection of carbon beam therapy: biophysical models of carbon beam therapy.
Matsufuji, Naruhiro
2018-03-01
Variation in the relative biological effectiveness (RBE) within the irradiation field of a carbon beam makes carbon-ion radiotherapy unique and advantageous in delivering the therapeutic dose to a deep-seated tumor, while sparing surrounding normal tissues. However, it is crucial to consider the RBE, not only in designing the dose distribution during treatment planning, but also in analyzing the clinical response retrospectively. At the National Institute of Radiological Sciences, the RBE model was established based on the response of human salivary gland cells. The response was originally handled with a linear-quadratic model, and later with a microdosimetric kinetic model. Retrospective analysis with a tumor-control probability model of non-small cell cancer treatment revealed a steep dose response in the tumor, and that the RBE of the tumor was adequately estimated using the model. A commonly used normal tissue complication probability model has not yet fully been accountable for the variable RBE of carbon ions; however, analysis of rectum injury after prostate cancer treatment suggested a highly serial-organ structure for the rectum, and a steep dose response similar to that observed for tumors.
Exploring the Red Sea seasonal ecosystem functioning using a three-dimensional biophysical model
Triantafyllou, G.
2014-03-01
The Red Sea exhibits complex hydrodynamic and biogeochemical dynamics, which vary both in time and space. These dynamics have been explored through the development and application of a 3-D ecosystem model. The simulation system comprises two off-line coupled submodels: the MIT General Circulation Model (MITgcm) and the European Regional Seas Ecosystem Model (ERSEM), both adapted for the Red Sea. The results from an annual simulation under climatological forcing are presented. Simulation results are in good agreement with satellite and in situ data illustrating the role of the physical processes in determining the evolution and variability of the Red Sea ecosystem. The model was able to reproduce the main features of the Red Sea ecosystem functioning, including the exchange with the Gulf of Aden, which is a major driving mechanism for the whole Red Sea ecosystem and the winter overturning taking place in the north. Some model limitations, mainly related to the dynamics of the extended reef system located in the southern part of the Red Sea, which is not currently represented in the model, still need to be addressed.
Abdellah, Marwan
2017-02-15
Background We present a visualization pipeline capable of accurate rendering of highly scattering fluorescent neocortical neuronal models. The pipeline is mainly developed to serve the computational neurobiology community. It allows the scientists to visualize the results of their virtual experiments that are performed in computer simulations, or in silico. The impact of the presented pipeline opens novel avenues for assisting the neuroscientists to build biologically accurate models of the brain. These models result from computer simulations of physical experiments that use fluorescence imaging to understand the structural and functional aspects of the brain. Due to the limited capabilities of the current visualization workflows to handle fluorescent volumetric datasets, we propose a physically-based optical model that can accurately simulate light interaction with fluorescent-tagged scattering media based on the basic principles of geometric optics and Monte Carlo path tracing. We also develop an automated and efficient framework for generating dense fluorescent tissue blocks from a neocortical column model that is composed of approximately 31000 neurons. Results Our pipeline is used to visualize a virtual fluorescent tissue block of 50 μm3 that is reconstructed from the somatosensory cortex of juvenile rat. The fluorescence optical model is qualitatively analyzed and validated against experimental emission spectra of different fluorescent dyes from the Alexa Fluor family. Conclusion We discussed a scientific visualization pipeline for creating images of synthetic neocortical neuronal models that are tagged virtually with fluorescent labels on a physically-plausible basis. The pipeline is applied to analyze and validate simulation data generated from neuroscientific in silico experiments.
2011-01-01
This editorial celebrates the re-launch of PMC Biophysics previously published by PhysMath Central, in its new format as BMC Biophysics published by BioMed Central with an expanded scope and Editorial Board. BMC Biophysics will fill its own niche in the BMC series alongside complementary companion journals including BMC Bioinformatics, BMC Medical Physics, BMC Structural Biology and BMC Systems Biology. PMID:21595996
Vologodskii, Alexander
2015-01-01
Surveying the last sixty years of research, this book describes the physical properties of DNA in the context of its biological functioning. It is designed to enable both students and researchers of molecular biology, biochemistry and physics to better understand the biophysics of DNA, addressing key questions and facilitating further research. The chapters integrate theoretical and experimental approaches, emphasising throughout the importance of a quantitative knowledge of physical properties in building and analysing models of DNA functioning. For example, the book shows how the relationship between DNA mechanical properties and the sequence specificity of DNA-protein binding can be analyzed quantitatively by using our current knowledge of the physical and structural properties of DNA. Theoretical models and experimental methods in the field are critically considered to enable the reader to engage effectively with the current scientific literature on the physical properties of DNA.
Thomas, Yoann; Dumas, Franck; Andréfouët, Serge
2016-12-01
The black-lip pearl oyster (Pinctada margaritifera) is cultured extensively to produce black pearls, especially in French Polynesia atoll lagoons. This aquaculture relies on spat collection, a process that experiences spatial and temporal variability and needs to be optimized by understanding which factors influence recruitment. Here, we investigate the sensitivity of P. margaritifera larval dispersal to both physical and biological factors in the lagoon of Ahe atoll. Coupling a validated 3D larval dispersal model, a bioenergetics larval growth model following the Dynamic Energy Budget (DEB) theory, and a population dynamics model, the variability of lagoon-scale connectivity patterns and recruitment potential is investigated. The relative contribution of reared and wild broodstock to the lagoon-scale recruitment potential is also investigated. Sensitivity analyses pointed out the major effect of the broodstock population structure as well as the sensitivity to larval mortality rate and inter-individual growth variability to larval supply and to the subsequent settlement potential. The application of the growth model clarifies how trophic conditions determine the larval supply and connectivity patterns. These results provide new cues to understand the dynamics of bottom-dwelling populations in atoll lagoons, their recruitment, and discuss how to take advantage of these findings and numerical models for pearl oyster management.
Microstrip natural wave spectrum mathematical model using partial inversion method
International Nuclear Information System (INIS)
Pogarsky, S.A.; Litvinenko, L.N.; Prosvirnin, S.L.
1995-01-01
It is generally agreed that both microstrip lines itself and different discontinuities based on microstrips are the most difficult problem for accurate electrodynamic analysis. Over the last years much has been published about principles and accurate (or full wave) methods of microstrip lines investigations. The growing interest for this problem may be explained by the microstrip application in the millimeter-wave range for purpose of realizing interconnects and a variety of passive components. At these higher operating rating frequencies accurate component modeling becomes more critical. A creation, examination and experimental verification of the accurate method for planar electrodynamical structures natural wave spectrum investigations are the objects of this manuscript. The moment method with partial inversion operator method using may be considered as a basical way for solving this problem. This method is outlook for accurate analysis of different planar discontinuities in microstrip: such as step discontinuities, microstrip turns, Y- and X-junctions and etc., substrate space steps dielectric constants and other anisotropy types
Shell Model Far From Stability: Island of Inversion Mergers
Nowacki, F.; Poves, A.
2018-02-01
In this study we propose a common mechanism for the disappearance of shell closures far from stabilty. With the use of Large Scale Shell Model calculations (SM-CI), we predict that the region of deformation which comprises the heaviest Chromium and Iron isotopes at and beyond N=40 will merge with a new one at N=50 in an astonishing parallel to the N=20 and N=28 case in the Neon and Magnesium isotopes. We propose a valence space including the full pf-shell for the protons and the full sdg shell for the neutrons, which represents a come-back of the the harmonic oscillator shells in the very neutron rich regime. Our calculations preserve the doubly magic nature of the ground state of 78Ni, which, however, exhibits a well deformed prolate band at low excitation energy, providing a striking example of shape coexistence far from stability. This new Island of Inversion (IoI) adds to the four well documented ones at N=8, 20, 28 and 40.
Malerba, Paola; Straudi, Sofia; Fregni, Felipe; Bazhenov, Maxim; Basaglia, Nino
2017-01-01
Stroke is a leading cause of worldwide disability, and up to 75% of survivors suffer from some degree of arm paresis. Recently, rehabilitation of stroke patients has focused on recovering motor skills by taking advantage of use-dependent neuroplasticity, where high-repetition of goal-oriented movement is at times combined with non-invasive brain stimulation, such as transcranial direct current stimulation (tDCS). Merging the two approaches is thought to provide outlasting clinical gains, by enhancing synaptic plasticity and motor relearning in the motor cortex primary area. However, this general approach has shown mixed results across the stroke population. In particular, stroke location has been found to correlate with the likelihood of success, which suggests that different patients might require different protocols. Understanding how motor rehabilitation and stimulation interact with ongoing neural dynamics is crucial to optimize rehabilitation strategies, but it requires theoretical and computational models to consider the multiple levels at which this complex phenomenon operate. In this work, we argue that biophysical models of cortical dynamics are uniquely suited to address this problem. Specifically, biophysical models can predict treatment efficacy by introducing explicit variables and dynamics for damaged connections, changes in neural excitability, neurotransmitters, neuromodulators, plasticity mechanisms, and repetitive movement, which together can represent brain state, effect of incoming stimulus, and movement-induced activity. In this work, we hypothesize that effects of tDCS depend on ongoing neural activity and that tDCS effects on plasticity may be also related to enhancing inhibitory processes. We propose a model design for each step of this complex system, and highlight strengths and limitations of the different modeling choices within our approach. Our theoretical framework proposes a change in paradigm, where biophysical models can contribute
Forbes, K. A.; Kienzle, S. W.; Coburn, C. A.; Byrne, J. M.
2006-12-01
Multiple threats, including intensification of agricultural production, non-renewable resource extraction and climate change, are threatening Southern Alberta's water supply. The objective of this research is to calibrate/evaluate the Agricultural Catchments Research Unit (ACRU) agrohydrological model; with the end goal of forecasting the impacts of a changing environment on water quantity. The strength of this model is the intensive multi-layered soil water budgeting routine that integrates water movement between the surface and atmosphere. The ACRU model was parameterized using data from Environment Canada's climate database for a twenty year period (1984-2004) and was used to simulate streamflow for Beaver Creek. The simulated streamflow was compared to Environment Canada's historical streamflow database to validate the model output. The Beaver Creek Watershed, located in the Porcupine Hills southwestern Alberta, Canada contains a heterogeneous cover of deciduous, coniferous, native prairie grasslands and forage crops. In a catchment with highly diversified land cover, canopy architecture cannot be overlooked in rainfall interception parameterization. Preliminary testing of ACRU suggests that streamflows were sensitive to varied levels of leaf area index (LAI), a representative fraction of canopy foliage. Further testing using remotely sensed LAI's will provide a more accurate representation of canopy foliage and ultimately best represent this important element of the hydrological cycle and the associated processes which govern the natural hydrology of the Beaver Creek watershed.
An exploratory modeling study on bio-physical processes associated with ENSO
Park, Jong-Yeon; Kug, Jong-Seong; Park, Young-Gyu
2014-05-01
Variability of marine phytoplankton associated with El Niño-Southern Oscillation (ENSO) and potential biological feedbacks onto ENSO are investigated by performing coupled ocean/biogeochemical model experiments forced by realistic surface winds from 1951 to 2010. The ocean model used in this study is the MOM4, which is coupled to a biogeochemical model, called TOPAZ (Tracers in the Ocean with Allometric Zooplankton). In general, it is shown that MOM4-TOPAZ mimics the observed main features of phytoplankton variability associated with ENSO. By comparing the actively coupled MOM4-TOPAZ experiment with the ocean model experiments using prescribed chlorophyll concentrations, potential impacts of phytoplankton on ENSO are evaluated. We found that chlorophyll generally increases mean sea surface temperature (SST) and decreases subsurface temperature by altering the penetration of solar radiation. However, as the chlorophyll concentration increases, the equatorial Pacific SST decreases due to the enhanced upwelling of the cooler subsurface water with shoaling of mixed layer and thermocline. The presence of chlorophyll generally intensifies ENSO amplitude by changing the ocean basic state. On the other hand, interactively varying chlorophyll associated with the ENSO tends to reduce ENSO amplitude. Therefore, the two biological effects on SST are competing against each other regarding the SST variance in the equatorial Pacific.
Striatal Function Explored Through a Biophysical Model of a Medium Spiny Neuron
Guthrie, Martin
2006-01-01
The basal ganglia are a dynamic neural network of telencephalic subcortical nuclei, involved in adaptive control of behaviour. There has been much experimental evidence on the anatomy and physiology of the basal ganglia published over the last 25 years showing that the basal ganglia are involved in the learning of many adaptive behaviours, including motor planning, working memory and cognitive functions. Current qualitative basal ganglia models of the box and arrow type, whi...
A Biophysical Model of Electrical Activity in Human β-Cells.
Pedersen, Morten Gram
2010-01-01
Electrical activity in pancreatic β-cells plays a pivotal role in glucose-stimulated insulin secretion by coupling metabolism to calcium-triggered exocytosis. Mathematical models based on rodent data have helped in understanding the mechanisms underlying the electrophysiological patterns observed in laboratory animals. However, human β-cells differ in several aspects, and in particular in their electrophysiological characteristics, from rodent β-cells. Hence, from a clinical perspective and t...
Inverse modeling of FIB milling by dose profile optimization
International Nuclear Information System (INIS)
Lindsey, S.; Waid, S.; Hobler, G.; Wanzenböck, H.D.; Bertagnolli, E.
2014-01-01
FIB technologies possess a unique ability to form topographies that are difficult or impossible to generate with binary etching through typical photo-lithography. The ability to arbitrarily vary the spatial dose distribution and therefore the amount of milling opens possibilities for the production of a wide range of functional structures with applications in biology, chemistry, and optics. However in practice, the realization of these goals is made difficult by the angular dependence of the sputtering yield and redeposition effects that vary as the topography evolves. An inverse modeling algorithm that optimizes dose profiles, defined as the superposition of time invariant pixel dose profiles (determined from the beam parameters and pixel dwell times), is presented. The response of the target to a set of pixel dwell times in modeled by numerical continuum simulations utilizing 1st and 2nd order sputtering and redeposition, the resulting surfaces are evaluated with respect to a target topography in an error minimization routine. Two algorithms for the parameterization of pixel dwell times are presented, a direct pixel dwell time method, and an abstracted method that uses a refineable piecewise linear cage function to generate pixel dwell times from a minimal number of parameters. The cage function method demonstrates great flexibility and efficiency as compared to the direct fitting method with performance enhancements exceeding ∼10× as compared to direct fitting for medium to large simulation sets. Furthermore, the refineable nature of the cage function enables solutions to adapt to the desired target function. The optimization algorithm, although working with stationary dose profiles, is demonstrated to be applicable also outside the quasi-static approximation. Experimental data confirms the viability of the solutions for 5 × 7 μm deep lens like structures defined by 90 pixel dwell times
Inferior olive mirrors joint dynamics to implement an inverse controller.
Alvarez-Icaza, Rodrigo; Boahen, Kwabena
2012-10-01
To produce smooth and coordinated motion, our nervous systems need to generate precisely timed muscle activation patterns that, due to axonal conduction delay, must be generated in a predictive and feedforward manner. Kawato proposed that the cerebellum accomplishes this by acting as an inverse controller that modulates descending motor commands to predictively drive the spinal cord such that the musculoskeletal dynamics are canceled out. This and other cerebellar theories do not, however, account for the rich biophysical properties expressed by the olivocerebellar complex's various cell types, making these theories difficult to verify experimentally. Here we propose that a multizonal microcomplex's (MZMC) inferior olivary neurons use their subthreshold oscillations to mirror a musculoskeletal joint's underdamped dynamics, thereby achieving inverse control. We used control theory to map a joint's inverse model onto an MZMC's biophysics, and we used biophysical modeling to confirm that inferior olivary neurons can express the dynamics required to mirror biomechanical joints. We then combined both techniques to predict how experimentally injecting current into the inferior olive would affect overall motor output performance. We found that this experimental manipulation unmasked a joint's natural dynamics, as observed by motor output ringing at the joint's natural frequency, with amplitude proportional to the amount of current. These results support the proposal that the cerebellum-in particular an MZMC-is an inverse controller; the results also provide a biophysical implementation for this controller and allow one to make an experimentally testable prediction.
Directory of Open Access Journals (Sweden)
Tetsuya Uetake
2014-08-01
Full Text Available Promoting environmentally friendly farming products is crucial to meeting consumer demand. Although governments implement policy measures to improve the environmental performance of the agriculture sector, theirimpacts are difficult to assess. This study analyses the performance of agri‐environmental policies in Japan, by using the OECD’s policy impact model and reference level framework. In particular, it identifies the environmental impacts of three simulated agri‐environmental policies based on farms’ characteristics. The results suggest that a policy mix of regulation and an incentive payment would reduce environmental impacts, suggesting that targeted approaches could improve the cost‐effectiveness of agri‐environmental policies.
DEFF Research Database (Denmark)
Oh, Geok Lian
properties such as the elastic wave speeds and soil densities. One processing method is casting the estimation problem into an inverse problem to solve for the unknown material parameters. The forward model for the seismic signals used in the literatures include ray tracing methods that consider only...... density values of the discretized ground medium, which leads to time-consuming computations and instability behaviour of the inversion process. In addition, the geophysics inverse problem is generally ill-posed due to non-exact forward model that introduces errors. The Bayesian inversion method through...... the first arrivals of the reflected compressional P-waves from the subsurface structures, or 3D elastic wave models that model all the seismic wave components. The ray tracing forward model formulation is linear, whereas the full 3D elastic wave model leads to a nonlinear inversion problem. In this Ph...
Liu, Yi; Zhang, He; Liu, Siwei; Lin, Fuchang
2018-05-01
The J-A (Jiles-Atherton) model is widely used to describe the magnetization characteristics of magnetic cores in a low-frequency alternating field. However, this model is deficient in the quantitative analysis of the eddy current loss and residual loss in a high-frequency magnetic field. Based on the decomposition of magnetization intensity, an inverse J-A model is established which uses magnetic flux density B as an input variable. Static and dynamic core losses under high frequency excitation are separated based on the inverse J-A model. Optimized parameters of the inverse J-A model are obtained based on particle swarm optimization. The platform for the pulsed magnetization characteristic test is designed and constructed. The hysteresis curves of ferrite and Fe-based nanocrystalline cores at high magnetization rates are measured. The simulated and measured hysteresis curves are presented and compared. It is found that the inverse J-A model can be used to describe the magnetization characteristics at high magnetization rates and to separate the static loss and dynamic loss accurately.
Risk evaluation of uranium mining: A geochemical inverse modelling approach
Rillard, J.; Zuddas, P.; Scislewski, A.
2011-12-01
It is well known that uranium extraction operations can increase risks linked to radiation exposure. The toxicity of uranium and associated heavy metals is the main environmental concern regarding exploitation and processing of U-ore. In areas where U mining is planned, a careful assessment of toxic and radioactive element concentrations is recommended before the start of mining activities. A background evaluation of harmful elements is important in order to prevent and/or quantify future water contamination resulting from possible migration of toxic metals coming from ore and waste water interaction. Controlled leaching experiments were carried out to investigate processes of ore and waste (leached ore) degradation, using samples from the uranium exploitation site located in Caetité-Bahia, Brazil. In experiments in which the reaction of waste with water was tested, we found that the water had low pH and high levels of sulphates and aluminium. On the other hand, in experiments in which ore was tested, the water had a chemical composition comparable to natural water found in the region of Caetité. On the basis of our experiments, we suggest that waste resulting from sulphuric acid treatment can induce acidification and salinization of surface and ground water. For this reason proper storage of waste is imperative. As a tool to evaluate the risks, a geochemical inverse modelling approach was developed to estimate the water-mineral interaction involving the presence of toxic elements. We used a method earlier described by Scislewski and Zuddas 2010 (Geochim. Cosmochim. Acta 74, 6996-7007) in which the reactive surface area of mineral dissolution can be estimated. We found that the reactive surface area of rock parent minerals is not constant during time but varies according to several orders of magnitude in only two months of interaction. We propose that parent mineral heterogeneity and particularly, neogenic phase formation may explain the observed variation of the
An integrated Biophysical CGE model to provide Sustainable Development Goal insights
Sanchez, Marko; Cicowiez, Martin; Howells, Mark; Zepeda, Eduardo
2016-04-01
Future projected changes in the energy system will inevitably result in changes to the level of appropriation of environmental resources, particularly land and water, and this will have wider implications for environmental sustainability, and may affect other sectors of the economy. An integrated climate, land, energy and water (CLEW) system will provide useful insights, particularly with regard to the environmental sustainability. However, it will require adequate integration with other tools to detect economic impacts and broaden the scope for policy analysis. A computable general equilibrium (CGE) model is a well suited tool to channel impacts, as detected in a CLEW analysis, onto all sectors of the economy, and evaluate trade-offs and synergies, including those of possible policy responses. This paper will show an application of such integration in a single-country CGE model with the following key characteristics. Climate is partly exogenous (as proxied by temperature and rainfall) and partly endogenous (as proxied by emissions generated by different sectors) and has an impact on endogenous variables such as land productivity and labor productivity. Land is a factor of production used in agricultural and forestry activities which can be of various types if land use alternatives (e.g., deforestation) are to be considered. Energy is an input to the production process of all economic sectors and a consumption good for households. Because it is possible to allow for substitution among different energy sources (e.g. renewable vs non-renewable) in the generation of electricity, the production process of energy products can consider the use of natural resources such as oil and water. Water, data permitting, can be considered as an input into the production process of agricultural sectors, which is particularly relevant in case of irrigation. It can also be considered as a determinant of total factor productivity in hydro-power generation. The integration of a CLEW
Losada, David E.; Barreiro, Alvaro
2003-01-01
Proposes an approach to incorporate term similarity and inverse document frequency into a logical model of information retrieval. Highlights include document representation and matching; incorporating term similarity into the measure of distance; new algorithms for implementation; inverse document frequency; and logical versus classical models of…
A Direct inverse model to determine permeability fields from pressure and flow rate measurements
Brouwer, G.K.; Fokker, P.A.; Wilschut, F.; Zijl, W.
2008-01-01
The determination of the permeability field from pressure and flow rate measurements in wells is a key problem in reservoir engineering. This paper presents a Double Constraint method for inverse modeling that is an example of direct inverse modeling. The method is used with a standard
Comparison of inverse modeling results with measured and interpolated hydraulic head data
International Nuclear Information System (INIS)
Jacobson, E.A.
1986-12-01
Inverse modeling of aquifers involves identification of effective parameters, such as transmissivities, based on hydraulic head data. The result of inverse modeling is a calibrated ground water flow model that reproduces the measured hydraulic head data as closely as is statistically possible. An inverse method that includes prior information about the parameters (i.e., kriged log transmissivity) was applied to the Avra Valley aquifer of southern Arizona using hydraulic heads obtained in three ways: measured at well locations, estimated at nodes by hand contouring, and estimated at nodes by kriging. Hand contouring yields only estimates of hydraulic head at node points, whereas kriging yields hydraulic head estimates at node points and their corresponding estimation errors. A comparison of the three inverse applications indicates the variations in the ground water flow model caused by the different treatments of the hydraulic head data. Estimates of hydraulic head computed by all three inverse models were more representative of the measured or interpolated hydraulic heads than those computed using the kriged estimates of log transmissivity. The large-scale trends in the estimates of log transmissivity determined by the three inverse models were generally similar except in the southern portion of the study area. The hydraulic head values and gradients produced by the three inverse models were similar in the interior of the study area, while the major differences between the inverse models occurred along the boundaries. 17 refs., 18 figs., 1 tab
International Nuclear Information System (INIS)
Chang, Qiang; Zuo, Li
2013-01-01
Spatial gradients of surrounding chemoattractants are the key factors in determining the directionality of eukaryotic cell movement. Thus, it is important for cells to accurately measure the spatial gradients of surrounding chemoattractants. Here, we study the precision of sensing the spatial gradients of multiple chemoattractants using cooperative receptor clusters. Cooperative receptors on cells are modeled as an Ising chain of Monod–Wyman–Changeux clusters subject to multiple chemical-gradient fields to study the physical limits of multiple chemoattractants spatial gradients sensing. We found that eukaryotic cells cannot sense each chemoattractant gradient individually. Instead, cells can only sense a weighted sum of surrounding chemical gradients. Moreover, the precision of sensing one chemical gradient is signicantly affected by coexisting chemoattractant concentrations. These findings can provide a further insight into the role of chemoattractants in immune response and help develop novel treatments for inflammatory diseases. (paper)
Directory of Open Access Journals (Sweden)
Anshuman Dixit
Full Text Available Deciphering functional mechanisms of the Hsp90 chaperone machinery is an important objective in cancer biology aiming to facilitate discovery of targeted anti-cancer therapies. Despite significant advances in understanding structure and function of molecular chaperones, organizing molecular principles that control the relationship between conformational diversity and functional mechanisms of the Hsp90 activity lack a sufficient quantitative characterization. We combined molecular dynamics simulations, principal component analysis, the energy landscape model and structure-functional analysis of Hsp90 regulatory interactions to systematically investigate functional dynamics of the molecular chaperone. This approach has identified a network of conserved regions common to the Hsp90 chaperones that could play a universal role in coordinating functional dynamics, principal collective motions and allosteric signaling of Hsp90. We have found that these functional motifs may be utilized by the molecular chaperone machinery to act collectively as central regulators of Hsp90 dynamics and activity, including the inter-domain communications, control of ATP hydrolysis, and protein client binding. These findings have provided support to a long-standing assertion that allosteric regulation and catalysis may have emerged via common evolutionary routes. The interaction networks regulating functional motions of Hsp90 may be determined by the inherent structural architecture of the molecular chaperone. At the same time, the thermodynamics-based "conformational selection" of functional states is likely to be activated based on the nature of the binding partner. This mechanistic model of Hsp90 dynamics and function is consistent with the notion that allosteric networks orchestrating cooperative protein motions can be formed by evolutionary conserved and sparsely connected residue clusters. Hence, allosteric signaling through a small network of distantly connected
Modeling the biophysical impacts of global change in mountain biosphere reserves
Bugmann, H.K.M.; Bjornsen, F. Ewert; Haeberli, W.; Guisan, Antoine; Fagre, Daniel B.; Kaab, A.
2007-01-01
Mountains and mountain societies provide a wide range of goods and services to humanity, but they are particularly sensitive to the effects of global environmental change. Thus, the definition of appropriate management regimes that maintain the multiple functions of mountain regions in a time of greatly changing climatic, economic, and societal drivers constitutes a significant challenge. Management decisions must be based on a sound understanding of the future dynamics of these systems. The present article reviews the elements required for an integrated effort to project the impacts of global change on mountain regions, and recommends tools that can be used at 3 scientific levels (essential, improved, and optimum). The proposed strategy is evaluated with respect to UNESCO's network of Mountain Biosphere Reserves (MBRs), with the intention of implementing it in other mountain regions as well. First, methods for generating scenarios of key drivers of global change are reviewed, including land use/land cover and climate change. This is followed by a brief review of the models available for projecting the impacts of these scenarios on (1) cryospheric systems, (2) ecosystem structure and diversity, and (3) ecosystem functions such as carbon and water relations. Finally, the cross-cutting role of remote sensing techniques is evaluated with respect to both monitoring and modeling efforts. We conclude that a broad range of techniques is available for both scenario generation and impact assessments, many of which can be implemented without much capacity building across many or even most MBRs. However, to foster implementation of the proposed strategy, further efforts are required to establish partnerships between scientists and resource managers in mountain areas.
Theoretical molecular biophysics
Scherer, Philipp O J
2017-01-01
This book gives an introduction to molecular biophysics. It starts from material properties at equilibrium related to polymers, dielectrics and membranes. Electronic spectra are developed for the understanding of elementary dynamic processes in photosynthesis including proton transfer and dynamics of molecular motors. Since the molecular structures of functional groups of bio-systems were resolved, it has become feasible to develop a theory based on the quantum theory and statistical physics with emphasis on the specifics of the high complexity of bio-systems. This introduction to molecular aspects of the field focuses on solvable models. Elementary biological processes provide as special challenge the presence of partial disorder in the structure which does not destroy the basic reproducibility of the processes. Apparently the elementary molecular processes are organized in a way to optimize the efficiency. Learning from nature by means exploring the relation between structure and function may even help to b...
DEFF Research Database (Denmark)
Hansen, Thomas Mejer; Cordua, Knud Skou; Holm Jacobsen, Bo
2014-01-01
forward models, can be more than an order of magnitude larger than the measurement uncertainty. We also found that the modeling error is strongly linked to the spatial variability of the assumed velocity field, i.e., the a priori velocity model.We discovered some general tools by which the modeling error...... synthetic ground-penetrating radar crosshole tomographic inverse problems. Ignoring the modeling error can lead to severe artifacts, which erroneously appear to be well resolved in the solution of the inverse problem. Accounting for the modeling error leads to a solution of the inverse problem consistent...
Necpálová, Magdalena; Anex, Robert P.; Fienen, Michael N.; Del Grosso, Stephen J.; Castellano, Michael J.; Sawyer, John E.; Iqbal, Javed; Pantoja, Jose L.; Barker, Daniel W.
2015-01-01
The ability of biogeochemical ecosystem models to represent agro-ecosystems depends on their correct integration with field observations. We report simultaneous calibration of 67 DayCent model parameters using multiple observation types through inverse modeling using the PEST parameter estimation software. Parameter estimation reduced the total sum of weighted squared residuals by 56% and improved model fit to crop productivity, soil carbon, volumetric soil water content, soil temperature, N2O, and soil3NO− compared to the default simulation. Inverse modeling substantially reduced predictive model error relative to the default model for all model predictions, except for soil 3NO− and 4NH+. Post-processing analyses provided insights into parameter–observation relationships based on parameter correlations, sensitivity and identifiability. Inverse modeling tools are shown to be a powerful way to systematize and accelerate the process of biogeochemical model interrogation, improving our understanding of model function and the underlying ecosystem biogeochemical processes that they represent.
Modeling the biophysical effects in a carbon beam delivery line by using Monte Carlo simulations
Cho, Ilsung; Yoo, SeungHoon; Cho, Sungho; Kim, Eun Ho; Song, Yongkeun; Shin, Jae-ik; Jung, Won-Gyun
2016-09-01
The Relative biological effectiveness (RBE) plays an important role in designing a uniform dose response for ion-beam therapy. In this study, the biological effectiveness of a carbon-ion beam delivery system was investigated using Monte Carlo simulations. A carbon-ion beam delivery line was designed for the Korea Heavy Ion Medical Accelerator (KHIMA) project. The GEANT4 simulation tool kit was used to simulate carbon-ion beam transport into media. An incident energy carbon-ion beam with energy in the range between 220 MeV/u and 290 MeV/u was chosen to generate secondary particles. The microdosimetric-kinetic (MK) model was applied to describe the RBE of 10% survival in human salivary-gland (HSG) cells. The RBE weighted dose was estimated as a function of the penetration depth in the water phantom along the incident beam's direction. A biologically photon-equivalent Spread Out Bragg Peak (SOBP) was designed using the RBE-weighted absorbed dose. Finally, the RBE of mixed beams was predicted as a function of the depth in the water phantom.
Glaser, Roland
2012-01-01
Biophysics is the science of physical principles underlying all processes of life, including the dynamics and kinetics of biological systems. This fully revised 2nd English edition is an introductory text that spans all steps of biological organization, from the molecular, to the organism level, as well as influences of environmental factors. In response to the enormous progress recently made, especially in theoretical and molecular biophysics, the author has updated the text, integrating new results and developments concerning protein folding and dynamics, molecular aspects of membrane assembly and transport, noise-enhanced processes, and photo-biophysics. The advances made in theoretical biology in the last decade call for a fully new conception of the corresponding sections. Thus, the book provides the background needed for fundamental training in biophysics and, in addition, offers a great deal of advanced biophysical knowledge.
Directory of Open Access Journals (Sweden)
Bao-Tian Huang
2017-01-01
Full Text Available Purpose. The consistency for predicting local control (LC data using biophysical models for stereotactic body radiotherapy (SBRT treatment of lung cancer is unclear. This study aims to compare the results calculated from different models using the treatment planning data. Materials and Methods. Treatment plans were designed for 17 patients diagnosed with primary non-small cell lung cancer (NSCLC using 5 different fraction schemes. The Martel model, Ohri model, and the Tai model were used to predict the 2-year LC value. The Gucken model, Santiago model, and the Tai model were employed to estimate the 3-year LC data. Results. We found that the employed models resulted in completely different LC prediction except for the Gucken and the Santiago models which exhibited quite similar 3-year LC data. The predicted 2-year and 3-year LC values in different models were not only associated with the dose normalization but also associated with the employed fraction schemes. The greatest difference predicted by different models was up to 15.0%. Conclusions. Our results show that different biophysical models influence the LC prediction and the difference is not only correlated to the dose normalization but also correlated to the employed fraction schemes.
Souty, F.; Brunelle, T.; Dumas, P.; Dorin, B.; Ciais, P.; Crassous, R.; Müller, C.; Bondeau, A.
2012-10-01
Interactions between food demand, biomass energy and forest preservation are driving both food prices and land-use changes, regionally and globally. This study presents a new model called Nexus Land-Use version 1.0 which describes these interactions through a generic representation of agricultural intensification mechanisms within agricultural lands. The Nexus Land-Use model equations combine biophysics and economics into a single coherent framework to calculate crop yields, food prices, and resulting pasture and cropland areas within 12 regions inter-connected with each other by international trade. The representation of cropland and livestock production systems in each region relies on three components: (i) a biomass production function derived from the crop yield response function to inputs such as industrial fertilisers; (ii) a detailed representation of the livestock production system subdivided into an intensive and an extensive component, and (iii) a spatially explicit distribution of potential (maximal) crop yields prescribed from the Lund-Postdam-Jena global vegetation model for managed Land (LPJmL). The economic principles governing decisions about land-use and intensification are adapted from the Ricardian rent theory, assuming cost minimisation for farmers. In contrast to the other land-use models linking economy and biophysics, crops are aggregated as a representative product in calories and intensification for the representative crop is a non-linear function of chemical inputs. The model equations and parameter values are first described in details. Then, idealised scenarios exploring the impact of forest preservation policies or rising energy price on agricultural intensification are described, and their impacts on pasture and cropland areas are investigated.
Directory of Open Access Journals (Sweden)
F. Souty
2012-10-01
Full Text Available Interactions between food demand, biomass energy and forest preservation are driving both food prices and land-use changes, regionally and globally. This study presents a new model called Nexus Land-Use version 1.0 which describes these interactions through a generic representation of agricultural intensification mechanisms within agricultural lands. The Nexus Land-Use model equations combine biophysics and economics into a single coherent framework to calculate crop yields, food prices, and resulting pasture and cropland areas within 12 regions inter-connected with each other by international trade. The representation of cropland and livestock production systems in each region relies on three components: (i a biomass production function derived from the crop yield response function to inputs such as industrial fertilisers; (ii a detailed representation of the livestock production system subdivided into an intensive and an extensive component, and (iii a spatially explicit distribution of potential (maximal crop yields prescribed from the Lund-Postdam-Jena global vegetation model for managed Land (LPJmL. The economic principles governing decisions about land-use and intensification are adapted from the Ricardian rent theory, assuming cost minimisation for farmers. In contrast to the other land-use models linking economy and biophysics, crops are aggregated as a representative product in calories and intensification for the representative crop is a non-linear function of chemical inputs. The model equations and parameter values are first described in details. Then, idealised scenarios exploring the impact of forest preservation policies or rising energy price on agricultural intensification are described, and their impacts on pasture and cropland areas are investigated.
Cousquer, Yohann; Pryet, Alexandre; Atteia, Olivier; Ferré, Ty P. A.; Delbart, Célestine; Valois, Rémi; Dupuy, Alain
2018-03-01
The inverse problem of groundwater models is often ill-posed and model parameters are likely to be poorly constrained. Identifiability is improved if diverse data types are used for parameter estimation. However, some models, including detailed solute transport models, are further limited by prohibitive computation times. This often precludes the use of concentration data for parameter estimation, even if those data are available. In the case of surface water-groundwater (SW-GW) models, concentration data can provide SW-GW mixing ratios, which efficiently constrain the estimate of exchange flow, but are rarely used. We propose to reduce computational limits by simulating SW-GW exchange at a sink (well or drain) based on particle tracking under steady state flow conditions. Particle tracking is used to simulate advective transport. A comparison between the particle tracking surrogate model and an advective-dispersive model shows that dispersion can often be neglected when the mixing ratio is computed for a sink, allowing for use of the particle tracking surrogate model. The surrogate model was implemented to solve the inverse problem for a real SW-GW transport problem with heads and concentrations combined in a weighted hybrid objective function. The resulting inversion showed markedly reduced uncertainty in the transmissivity field compared to calibration on head data alone.
Homogenization of Electromagnetic and Seismic Wavefields for Joint Inverse Modeling
Newman, G. A.; Commer, M.; Petrov, P.; Um, E. S.
2011-12-01
A significant obstacle in developing a robust joint imaging technology exploiting seismic and electromagnetic (EM) wave fields is the resolution at which these different geophysical measurements sense the subsurface. Imaging of seismic reflection data is an order of magnitude finer in resolution and scale compared to images produced with EM data. A consistent joint image of the subsurface geophysical attributes (velocity, electrical conductivity) requires/demands the different geophysical data types be similar in their resolution of the subsurface. The superior resolution of seismic data results from the fact that the energy propagates as a wave, while propagation of EM energy is diffusive and attenuates with distance. On the other hand, the complexity of the seismic wave field can be a significant problem due to high reflectivity of the subsurface and the generation of multiple scattering events. While seismic wave fields have been very useful in mapping the subsurface for energy resources, too much scattering and too many reflections can lead to difficulties in imaging and interpreting seismic data. To overcome these obstacles a formulation for joint imaging of seismic and EM wave fields is introduced, where each data type is matched in resolution. In order to accomplish this, seismic data are first transformed into the Laplace-Fourier Domain, which changes the modeling of the seismic wave field from wave propagation to diffusion. Though high frequency information (reflectivity) is lost with this transformation, several benefits follow: (1) seismic and EM data can be easily matched in resolution, governed by the same physics of diffusion, (2) standard least squares inversion works well with diffusive type problems including both transformed seismic and EM, (3) joint imaging of seismic and EM data may produce better starting velocity models critical for successful reverse time migration or full waveform imaging of seismic data (non transformed) and (4
Rosas-Carbajal, M.; Linde, N.; Kalscheuer, T.; Vrugt, J.A.
2014-01-01
Probabilistic inversion methods based on Markov chain Monte Carlo (MCMC) simulation are well suited to quantify parameter and model uncertainty of nonlinear inverse problems. Yet, application of such methods to CPU-intensive forward models can be a daunting task, particularly if the parameter space
On the modeling and inversion of seismic data
Stolk, C.C.
2000-01-01
In this thesis we investigate some mathematical questions related to the inversion of seismic data. In Chapter 2 we review results in the literature and give some new results on wave equations with coefficients that are just bounded and measurable. We show that these equations have unique
Directory of Open Access Journals (Sweden)
Pablo Martínez-Cañada
2018-01-01
Full Text Available Despite half-a-century of research since the seminal work of Hubel and Wiesel, the role of the dorsal lateral geniculate nucleus (dLGN in shaping the visual signals is not properly understood. Placed on route from retina to primary visual cortex in the early visual pathway, a striking feature of the dLGN circuit is that both the relay cells (RCs and interneurons (INs not only receive feedforward input from retinal ganglion cells, but also a prominent feedback from cells in layer 6 of visual cortex. This feedback has been proposed to affect synchronicity and other temporal properties of the RC firing. It has also been seen to affect spatial properties such as the center-surround antagonism of thalamic receptive fields, i.e., the suppression of the response to very large stimuli compared to smaller, more optimal stimuli. Here we explore the spatial effects of cortical feedback on the RC response by means of a a comprehensive network model with biophysically detailed, single-compartment and multicompartment neuron models of RCs, INs and a population of orientation-selective layer 6 simple cells, consisting of pyramidal cells (PY. We have considered two different arrangements of synaptic feedback from the ON and OFF zones in the visual cortex to the dLGN: phase-reversed ('push-pull' and phase-matched ('push-push', as well as different spatial extents of the corticothalamic projection pattern. Our simulation results support that a phase-reversed arrangement provides a more effective way for cortical feedback to provide the increased center-surround antagonism seen in experiments both for flashing spots and, even more prominently, for patch gratings. This implies that ON-center RCs receive direct excitation from OFF-dominated cortical cells and indirect inhibitory feedback from ON-dominated cortical cells. The increased center-surround antagonism in the model is accompanied by spatial focusing, i.e., the maximum RC response occurs for smaller stimuli
Martínez-Cañada, Pablo; Halnes, Geir; Fyhn, Marianne
2018-01-01
Despite half-a-century of research since the seminal work of Hubel and Wiesel, the role of the dorsal lateral geniculate nucleus (dLGN) in shaping the visual signals is not properly understood. Placed on route from retina to primary visual cortex in the early visual pathway, a striking feature of the dLGN circuit is that both the relay cells (RCs) and interneurons (INs) not only receive feedforward input from retinal ganglion cells, but also a prominent feedback from cells in layer 6 of visual cortex. This feedback has been proposed to affect synchronicity and other temporal properties of the RC firing. It has also been seen to affect spatial properties such as the center-surround antagonism of thalamic receptive fields, i.e., the suppression of the response to very large stimuli compared to smaller, more optimal stimuli. Here we explore the spatial effects of cortical feedback on the RC response by means of a a comprehensive network model with biophysically detailed, single-compartment and multicompartment neuron models of RCs, INs and a population of orientation-selective layer 6 simple cells, consisting of pyramidal cells (PY). We have considered two different arrangements of synaptic feedback from the ON and OFF zones in the visual cortex to the dLGN: phase-reversed (‘push-pull’) and phase-matched (‘push-push’), as well as different spatial extents of the corticothalamic projection pattern. Our simulation results support that a phase-reversed arrangement provides a more effective way for cortical feedback to provide the increased center-surround antagonism seen in experiments both for flashing spots and, even more prominently, for patch gratings. This implies that ON-center RCs receive direct excitation from OFF-dominated cortical cells and indirect inhibitory feedback from ON-dominated cortical cells. The increased center-surround antagonism in the model is accompanied by spatial focusing, i.e., the maximum RC response occurs for smaller stimuli when
Directory of Open Access Journals (Sweden)
YanBin Liu
2017-01-01
Full Text Available The inversion design approach is a very useful tool for the complex multiple-input-multiple-output nonlinear systems to implement the decoupling control goal, such as the airplane model and spacecraft model. In this work, the flight control law is proposed using the neural-based inversion design method associated with the nonlinear compensation for a general longitudinal model of the airplane. First, the nonlinear mathematic model is converted to the equivalent linear model based on the feedback linearization theory. Then, the flight control law integrated with this inversion model is developed to stabilize the nonlinear system and relieve the coupling effect. Afterwards, the inversion control combined with the neural network and nonlinear portion is presented to improve the transient performance and attenuate the uncertain effects on both external disturbances and model errors. Finally, the simulation results demonstrate the effectiveness of this controller.
Spaiardi, Paolo; Tavazzani, Elisa; Manca, Marco; Milesi, Veronica; Russo, Giancarlo; Prigioni, Ivo; Marcotti, Walter; Magistretti, Jacopo; Masetto, Sergio
2017-11-01
Vestibular type I and type II hair cells and their afferent fibres send information to the brain regarding the position and movement of the head. The characteristic feature of type I hair cells is the expression of a low-voltage-activated outward rectifying K + current, I K,L , whose biophysical properties and molecular identity are still largely unknown. In vitro, the afferent nerve calyx surrounding type I hair cells causes unstable intercellular K + concentrations, altering the biophysical properties of I K,L . We found that in the absence of the calyx, I K,L in type I hair cells exhibited unique biophysical activation properties, which were faithfully reproduced by an allosteric channel gating scheme. These results form the basis for a molecular and pharmacological identification of I K,L . Type I and type II hair cells are the sensory receptors of the mammalian vestibular epithelia. Type I hair cells are characterized by their basolateral membrane being enveloped in a single large afferent nerve terminal, named the calyx, and by the expression of a low-voltage-activated outward rectifying K + current, I K,L . The biophysical properties and molecular profile of I K,L are still largely unknown. By using the patch-clamp whole-cell technique, we examined the voltage- and time-dependent properties of I K,L in type I hair cells of the mouse semicircular canal. We found that the biophysical properties of I K,L were affected by an unstable K + equilibrium potential (V eq K + ). Both the outward and inward K + currents shifted V eq K + consistent with K + accumulation or depletion, respectively, in the extracellular space, which we attributed to a residual calyx attached to the basolateral membrane of the hair cells. We therefore optimized the hair cell dissociation protocol in order to isolate mature type I hair cells without their calyx. In these cells, the uncontaminated I K,L showed a half-activation at -79.6 mV and a steep voltage dependence (2.8 mV). I K,L also
Directory of Open Access Journals (Sweden)
Lingen Chen
2012-01-01
Full Text Available A thermodynamic model of an open combined regenerative Brayton and inverse Brayton cycles with regeneration before the inverse cycle is established in this paper by using thermodynamic optimization theory. The flow processes of the working fluid with the pressure drops and the size constraint of the real power plant are modeled. There are 13 flow resistances encountered by the working fluid stream for the cycle model. Four of these, the friction through the blades and vanes of the compressors and the turbines, are related to the isentropic efficiencies. The remaining nine flow resistances are always present because of the changes in flow cross-section at the compressor inlet of the top cycle, regenerator inlet and outlet, combustion chamber inlet and outlet, turbine outlet of the top cycle, turbine outlet of the bottom cycle, heat exchanger inlet, and compressor inlet of the bottom cycle. These resistances associated with the flow through various cross-sectional areas are derived as functions of the compressor inlet relative pressure drop of the top cycle, and control the air flow rate, the net power output and the thermal efficiency. The analytical formulae about the power output, efficiency and other coefficients are derived with 13 pressure drop losses. It is found that the combined cycle with regenerator can reach higher thermal efficiency but smaller power output than those of the base combined cycle at small compressor inlet relative pressure drop of the top cycle.
Advanced Techniques in Biophysics
Arrondo, José Luis R
2006-01-01
Technical advancements are basic elements in our life. In biophysical studies, new applications and improvements in well-established techniques are being implemented every day. This book deals with advancements produced not only from a technical point of view, but also from new approaches that are being taken in the study of biophysical samples, such as nanotechniques or single-cell measurements. This book constitutes a privileged observatory for reviewing novel applications of biophysical techniques that can help the reader enter an area where the technology is progressing quickly and where a comprehensive explanation is not always to be found.
An Inverse Problem Formulation Methodology for Stochastic Models
2010-05-02
form the surveillance data Infection control measures were implemented in the form of health care worker hand - hygiene before and after patients contact...manuscript derives from our interest in understanding the spread of infectious diseases in particular, nosocomial infections , in order to prevent major...given by the inverse of the parameter of the exponential distribution. A hand - hygiene policy applied to health care workers on isolated VRE colonized
Alkhalifah, Tariq Ali
2012-09-25
Traveltime inversion focuses on the geometrical features of the waveform (traveltimes), which is generally smooth, and thus, tends to provide averaged (smoothed) information of the model. On other hand, general waveform inversion uses additional elements of the wavefield including amplitudes to extract higher resolution information, but this comes at the cost of introducing non-linearity to the inversion operator, complicating the convergence process. We use unwrapped phase-based objective functions in waveform inversion as a link between the two general types of inversions in a domain in which such contributions to the inversion process can be easily identified and controlled. The instantaneous traveltime is a measure of the average traveltime of the energy in a trace as a function of frequency. It unwraps the phase of wavefields yielding far less non-linearity in the objective function than that experienced with conventional wavefields, yet it still holds most of the critical wavefield information in its frequency dependency. However, it suffers from non-linearity introduced by the model (or reflectivity), as reflections from independent events in our model interact with each other. Unwrapping the phase of such a model can mitigate this non-linearity as well. Specifically, a simple modification to the inverted domain (or model), can reduce the effect of the model-induced non-linearity and, thus, make the inversion more convergent. Simple numerical examples demonstrate these assertions.
Alkhalifah, Tariq Ali; Choi, Yun Seok
2012-01-01
Traveltime inversion focuses on the geometrical features of the waveform (traveltimes), which is generally smooth, and thus, tends to provide averaged (smoothed) information of the model. On other hand, general waveform inversion uses additional elements of the wavefield including amplitudes to extract higher resolution information, but this comes at the cost of introducing non-linearity to the inversion operator, complicating the convergence process. We use unwrapped phase-based objective functions in waveform inversion as a link between the two general types of inversions in a domain in which such contributions to the inversion process can be easily identified and controlled. The instantaneous traveltime is a measure of the average traveltime of the energy in a trace as a function of frequency. It unwraps the phase of wavefields yielding far less non-linearity in the objective function than that experienced with conventional wavefields, yet it still holds most of the critical wavefield information in its frequency dependency. However, it suffers from non-linearity introduced by the model (or reflectivity), as reflections from independent events in our model interact with each other. Unwrapping the phase of such a model can mitigate this non-linearity as well. Specifically, a simple modification to the inverted domain (or model), can reduce the effect of the model-induced non-linearity and, thus, make the inversion more convergent. Simple numerical examples demonstrate these assertions.
Houborg, Rasmus
2015-01-19
Leaf area index (LAI) and leaf chlorophyll content (Chll) represent key biophysical and biochemical controls on water, energy and carbon exchange processes in the terrestrial biosphere. In combination, LAI and Chll provide critical information on vegetation density, vitality and photosynthetic potentials. However, simultaneous retrieval of LAI and Chll from space observations is extremely challenging. Regularization strategies are required to increase the robustness and accuracy of retrieved properties and enable more reliable separation of soil, leaf and canopy parameters. To address these challenges, the REGularized canopy reFLECtance model (REGFLEC) inversion system was refined to incorporate enhanced techniques for exploiting ancillary LAI and temporal information derived from multiple satellite scenes. In this current analysis, REGFLEC is applied to a time-series of Landsat data.A novel aspect of the REGFLEC approach is the fact that no site-specific data are required to calibrate the model, which may be run in a largely automated fashion using information extracted entirely from image-based and other widely available datasets. Validation results, based upon in-situ LAI and Chll observations collected over maize and soybean fields in central Nebraska for the period 2001-2005, demonstrate Chll retrieval with a relative root-mean-square-deviation (RMSD) on the order of 19% (RMSD=8.42μgcm-2). While Chll retrievals were clearly influenced by the version of the leaf optical properties model used (PROSPECT), the application of spatio-temporal regularization constraints was shown to be critical for estimating Chll with sufficient accuracy. REGFLEC also reproduced the dynamics of in-situ measured LAI well (r2 =0.85), but estimates were biased low, particularly over maize (LAI was underestimated by ~36 %). This disparity may be attributed to differences between effective and true LAI caused by significant foliage clumping not being properly accounted for in the canopy
Inverse problems in the design, modeling and testing of engineering systems
Alifanov, Oleg M.
1991-01-01
Formulations, classification, areas of application, and approaches to solving different inverse problems are considered for the design of structures, modeling, and experimental data processing. Problems in the practical implementation of theoretical-experimental methods based on solving inverse problems are analyzed in order to identify mathematical models of physical processes, aid in input data preparation for design parameter optimization, help in design parameter optimization itself, and to model experiments, large-scale tests, and real tests of engineering systems.
Dilmahamod, A. F.; Hermes, J. C.; Reason, C. J. C.
2016-02-01
The biological variability of the upwelling region of the Seychelles-Chagos Thermocline Ridge (SCTR), both at surface and subsurface levels, is investigated using monthly outputs of a coupled biophysical model from 1958 to 2011. Owing to its large spatial distribution and sensitivity to climate variability, the SCTR is studied as three distinct regions; namely, sub-regions 1 (western; 5°S-12°S, 55°E-65°E), 2 (central; 5°S-12°S, 65°E-75°E) and 3 (eastern; 5°S-12°S, 75°E-90°E). Surface and subsurface chlorophyll-a (Chl-a) exhibit completely different response mechanisms in sub-region 3 compared to sub-regions 1 and 2 during El Niño Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) events. During the intense 1997/1998 ENSO-IOD event, the high Chl-a tongue observed in the eastern Indian Ocean induces an increase in surface concentration in sub-region 3, whose subsurface variability is also substantially less (more) impacted by downwelling (upwelling) Rossby waves generated by El Niño (La Niña) forcing. After filtering out the annual signal, wavelet analysis of surface Chl-a revealed a significant 6 month periodicity in sub-regions 1 and 2 whereas a 5-year signal dominated in sub-region 3. The latter suggests that sub-region 3 is more prone to different ENSO/IOD influences, due to its proximity to the eastern Indian Ocean. In the unfiltered data, the subsurface Chl-a in sub-region 3 exhibits a strong signal near 1 year, with sub-regions 1 and 2 having a pronounced 6-year and 5-year signals respectively. These analyses show that the SCTR cannot be investigated as a single homogeneous region due to its large spatial distribution and different response mechanisms to climate events. Furthermore, changes in SST, thermocline depth, winds and Chl-a before and after the 1976-1977 climate shift differed across the SCTR, further highlighting the heterogeneity of this sensitive region in the Indian Ocean.
2013-01-01
The Encyclopedia of Biophysics is envisioned both as an easily accessible source of information and as an introductory guide to the scientific literature. It includes entries describing both Techniques and Systems. In the Techniques entries, each of the wide range of methods which fall under the heading of Biophysics are explained in detail, together with the value and the limitations of the information each provides. Techniques covered range from diffraction (X-ray, electron and neutron) through a wide range of spectroscopic methods (X-ray, optical, EPR, NMR) to imaging (from electron microscopy to live cell imaging and MRI), as well as computational and simulation approaches. In the Systems entries, biophysical approaches to specific biological systems or problems – from protein and nucleic acid structure to membranes, ion channels and receptors – are described. These sections, which place emphasis on the integration of the different techniques, therefore provide an inroad into Biophysics from a biolo...
Ding, Deng
Intensive human-environment interactions are taking place in Midwestern agricultural systems. An integrated modeling framework is suitable for predicting dynamics of key variables of the socio-economic, biophysical, hydrological processes as well as exploring the potential transitions of system states in response to changes of the driving factors. The purpose of this dissertation is to address issues concerning the interacting processes and consequent changes in land use, water balance, and water quality using an integrated modeling framework. This dissertation is composed of three studies in the same agricultural watershed, the Clear Creek watershed in East-Central Iowa. In the first study, a parsimonious hydrologic model, the Threshold-Exceedance-Lagrangian Model (TELM), is further developed into RS-TELM (Remote Sensing TELM) to integrate remote sensing vegetation data for estimating evapotranspiration. The goodness of fit of RS-TELM is comparable to a well-calibrated SWAT (Soil and Water Assessment Tool) and even slightly superior in capturing intra-seasonal variability of stream flow. The integration of RS LAI (Leaf Area Index) data improves the model's performance especially over the agriculture dominated landscapes. The input of rainfall datasets with spatially explicit information plays a critical role in increasing the model's goodness of fit. In the second study, an agent-based model is developed to simulate farmers' decisions on crop type and fertilizer application in response to commodity and biofuel crop prices. The comparison between simulated crop land percentage and crop rotations with satellite-based land cover data suggest that farmers may be underestimating the effects that continuous corn production has on yields (yield drag). The simulation results given alternative market scenarios based on a survey of agricultural land owners and operators in the Clear Creek Watershed show that, farmers see cellulosic biofuel feedstock production in the form
Sun, Y.; Hou, Z.; Huang, M.; Tian, F.; Leung, L. Ruby
2013-12-01
This study demonstrates the possibility of inverting hydrologic parameters using surface flux and runoff observations in version 4 of the Community Land Model (CLM4). Previous studies showed that surface flux and runoff calculations are sensitive to major hydrologic parameters in CLM4 over different watersheds, and illustrated the necessity and possibility of parameter calibration. Both deterministic least-square fitting and stochastic Markov-chain Monte Carlo (MCMC)-Bayesian inversion approaches are evaluated by applying them to CLM4 at selected sites with different climate and soil conditions. The unknowns to be estimated include surface and subsurface runoff generation parameters and vadose zone soil water parameters. We find that using model parameters calibrated by the sampling-based stochastic inversion approaches provides significant improvements in the model simulations compared to using default CLM4 parameter values, and that as more information comes in, the predictive intervals (ranges of posterior distributions) of the calibrated parameters become narrower. In general, parameters that are identified to be significant through sensitivity analyses and statistical tests are better calibrated than those with weak or nonlinear impacts on flux or runoff observations. Temporal resolution of observations has larger impacts on the results of inverse modeling using heat flux data than runoff data. Soil and vegetation cover have important impacts on parameter sensitivities, leading to different patterns of posterior distributions of parameters at different sites. Overall, the MCMC-Bayesian inversion approach effectively and reliably improves the simulation of CLM under different climates and environmental conditions. Bayesian model averaging of the posterior estimates with different reference acceptance probabilities can smooth the posterior distribution and provide more reliable parameter estimates, but at the expense of wider uncertainty bounds.
Lu, Xiao-Ping; Huang, Xiang-Jie; Ip, Wing-Huen; Hsia, Chi-Hao
2018-04-01
In the lightcurve inversion process where asteroid's physical parameters such as rotational period, pole orientation and overall shape are searched, the numerical calculations of the synthetic photometric brightness based on different shape models are frequently implemented. Lebedev quadrature is an efficient method to numerically calculate the surface integral on the unit sphere. By transforming the surface integral on the Cellinoid shape model to that on the unit sphere, the lightcurve inversion process based on the Cellinoid shape model can be remarkably accelerated. Furthermore, Matlab codes of the lightcurve inversion process based on the Cellinoid shape model are available on Github for free downloading. The photometric models, i.e., the scattering laws, also play an important role in the lightcurve inversion process, although the shape variations of asteroids dominate the morphologies of the lightcurves. Derived from the radiative transfer theory, the Hapke model can describe the light reflectance behaviors from the viewpoint of physics, while there are also many empirical models in numerical applications. Numerical simulations are implemented for the comparison of the Hapke model with the other three numerical models, including the Lommel-Seeliger, Minnaert, and Kaasalainen models. The results show that the numerical models with simple function expressions can fit well with the synthetic lightcurves generated based on the Hapke model; this good fit implies that they can be adopted in the lightcurve inversion process for asteroids to improve the numerical efficiency and derive similar results to those of the Hapke model.
Inverting reflections using full-waveform inversion with inaccurate starting models
AlTheyab, Abdullah; Schuster, Gerard T.
2015-01-01
We present a method for inverting seismic reflections using full-waveform inversion (FWI) with inaccurate starting models. For a layered medium, near-offset reflections (with zero angle of incidence) are unlikely to be cycle-skipped regardless
CSIR Research Space (South Africa)
Evers-King, H
2014-05-01
Full Text Available phytoplankton functional type descriptors within known confidence limits from remotely sensed data has become a major objective to extend the use of ocean colour data beyond chlorophyll a retrievals. Here, a new forward and inverse modelling structure...
Application of Lead Field Theory and Computerized Thorax Modeling for the ECG Inverse Problem
National Research Council Canada - National Science Library
Puurtinen, H
2001-01-01
.... In this study, one anatomically detailed 3D FDM model of the human thorax as a volume conductor was employed for forward and inverse estimation of ECG potentials and cardiac sources, respectively...
EEG-distributed inverse solutions for a spherical head model
Riera, J. J.; Fuentes, M. E.; Valdés, P. A.; Ohárriz, Y.
1998-08-01
The theoretical study of the minimum norm solution to the MEG inverse problem has been carried out in previous papers for the particular case of spherical symmetry. However, a similar study for the EEG is remarkably more difficult due to the very complicated nature of the expression relating the voltage differences on the scalp to the primary current density (PCD) even for this simple symmetry. This paper introduces the use of the electric lead field (ELF) on the dyadic formalism in the spherical coordinate system to overcome such a drawback using an expansion of the ELF in terms of longitudinal and orthogonal vector fields. This approach allows us to represent EEG Fourier coefficients on a 2-sphere in terms of a current multipole expansion. The choice of a suitable basis for the Hilbert space of the PCDs on the brain region allows the current multipole moments to be related by spatial transfer functions to the PCD spectral coefficients. Properties of the most used distributed inverse solutions are explored on the basis of these results. Also, a part of the ELF null space is completely characterized and those spherical components of the PCD which are possible silent candidates are discussed.
Irving, J.; Koepke, C.; Elsheikh, A. H.
2017-12-01
Bayesian solutions to geophysical and hydrological inverse problems are dependent upon a forward process model linking subsurface parameters to measured data, which is typically assumed to be known perfectly in the inversion procedure. However, in order to make the stochastic solution of the inverse problem computationally tractable using, for example, Markov-chain-Monte-Carlo (MCMC) methods, fast approximations of the forward model are commonly employed. This introduces model error into the problem, which has the potential to significantly bias posterior statistics and hamper data integration efforts if not properly accounted for. Here, we present a new methodology for addressing the issue of model error in Bayesian solutions to hydrogeophysical inverse problems that is geared towards the common case where these errors cannot be effectively characterized globally through some parametric statistical distribution or locally based on interpolation between a small number of computed realizations. Rather than focusing on the construction of a global or local error model, we instead work towards identification of the model-error component of the residual through a projection-based approach. In this regard, pairs of approximate and detailed model runs are stored in a dictionary that grows at a specified rate during the MCMC inversion procedure. At each iteration, a local model-error basis is constructed for the current test set of model parameters using the K-nearest neighbour entries in the dictionary, which is then used to separate the model error from the other error sources before computing the likelihood of the proposed set of model parameters. We demonstrate the performance of our technique on the inversion of synthetic crosshole ground-penetrating radar traveltime data for three different subsurface parameterizations of varying complexity. The synthetic data are generated using the eikonal equation, whereas a straight-ray forward model is assumed in the inversion
Large-scale 3-D modeling by integration of resistivity models and borehole data through inversion
DEFF Research Database (Denmark)
Foged, N.; Marker, Pernille Aabye; Christiansen, A. V.
2014-01-01
resistivity and the clay fraction. Through inversion we use the lithological data and the resistivity data to determine the optimum spatially distributed translator function. Applying the translator function we get a 3-D clay fraction model, which holds information from the resistivity data set...... and the borehole data set in one variable. Finally, we use k-means clustering to generate a 3-D model of the subsurface structures. We apply the procedure to the Norsminde survey in Denmark, integrating approximately 700 boreholes and more than 100 000 resistivity models from an airborne survey...
Large-scale inverse model analyses employing fast randomized data reduction
Lin, Youzuo; Le, Ellen B.; O'Malley, Daniel; Vesselinov, Velimir V.; Bui-Thanh, Tan
2017-08-01
When the number of observations is large, it is computationally challenging to apply classical inverse modeling techniques. We have developed a new computationally efficient technique for solving inverse problems with a large number of observations (e.g., on the order of 107 or greater). Our method, which we call the randomized geostatistical approach (RGA), is built upon the principal component geostatistical approach (PCGA). We employ a data reduction technique combined with the PCGA to improve the computational efficiency and reduce the memory usage. Specifically, we employ a randomized numerical linear algebra technique based on a so-called "sketching" matrix to effectively reduce the dimension of the observations without losing the information content needed for the inverse analysis. In this way, the computational and memory costs for RGA scale with the information content rather than the size of the calibration data. Our algorithm is coded in Julia and implemented in the MADS open-source high-performance computational framework (http://mads.lanl.gov). We apply our new inverse modeling method to invert for a synthetic transmissivity field. Compared to a standard geostatistical approach (GA), our method is more efficient when the number of observations is large. Most importantly, our method is capable of solving larger inverse problems than the standard GA and PCGA approaches. Therefore, our new model inversion method is a powerful tool for solving large-scale inverse problems. The method can be applied in any field and is not limited to hydrogeological applications such as the characterization of aquifer heterogeneity.
Soft-sensing Modeling Based on MLS-SVM Inversion for L-lysine Fermentation Processes
Directory of Open Access Journals (Sweden)
Bo Wang
2015-06-01
Full Text Available A modeling approach 63 based on multiple output variables least squares support vector machine (MLS-SVM inversion is presented by a combination of inverse system and support vector machine theory. Firstly, a dynamic system model is developed based on material balance relation of a fed-batch fermentation process, with which it is analyzed whether an inverse system exists or not, and into which characteristic information of a fermentation process is introduced to set up an extended inversion model. Secondly, an initial extended inversion model is developed off-line by the use of the fitting capacity of MLS-SVM; on-line correction is made by the use of a differential evolution (DE algorithm on the basis of deviation information. Finally, a combined pseudo-linear system is formed by means of a serial connection of a corrected extended inversion model behind the L-lysine fermentation processes; thereby crucial biochemical parameters of a fermentation process could be predicted on-line. The simulation experiment shows that this soft-sensing modeling method features very high prediction precision and can predict crucial biochemical parameters of L-lysine fermentation process very well.
Energy Technology Data Exchange (ETDEWEB)
Privette, J.L.
1994-12-31
The angular distribution of radiation scattered by the earth surface contains information on the structural and optical properties of the surface. Potentially, this information may be retrieved through the inversion of surface bidirectional reflectance distribution function (BRDF) models. This report details the limitations and efficient application of BRDF model inversions using data from ground- and satellite-based sensors. A turbid medium BRDF model, based on the discrete ordinates solution to the transport equation, was used to quantify the sensitivity of top-of-canopy reflectance to vegetation and soil parameters. Results were used to define parameter sets for inversions. Using synthetic reflectance values, the invertibility of the model was investigated for different optimization algorithms, surface and sampling conditions. Inversions were also conducted with field data from a ground-based radiometer. First, a soil BRDF model was inverted for different soil and sampling conditions. A condition-invariant solution was determined and used as the lower boundary condition in canopy model inversions. Finally, a scheme was developed to improve the speed and accuracy of inversions.
Metamodel-based inverse method for parameter identification: elastic-plastic damage model
Huang, Changwu; El Hami, Abdelkhalak; Radi, Bouchaïb
2017-04-01
This article proposed a metamodel-based inverse method for material parameter identification and applies it to elastic-plastic damage model parameter identification. An elastic-plastic damage model is presented and implemented in numerical simulation. The metamodel-based inverse method is proposed in order to overcome the disadvantage in computational cost of the inverse method. In the metamodel-based inverse method, a Kriging metamodel is constructed based on the experimental design in order to model the relationship between material parameters and the objective function values in the inverse problem, and then the optimization procedure is executed by the use of a metamodel. The applications of the presented material model and proposed parameter identification method in the standard A 2017-T4 tensile test prove that the presented elastic-plastic damage model is adequate to describe the material's mechanical behaviour and that the proposed metamodel-based inverse method not only enhances the efficiency of parameter identification but also gives reliable results.
Campbell, Stefan F.; Kaneshige, John T.
2010-01-01
Presented here is a Predictor-Based Model Reference Adaptive Control (PMRAC) architecture for a generic transport aircraft. At its core, this architecture features a three-axis, non-linear, dynamic-inversion controller. Command inputs for this baseline controller are provided by pilot roll-rate, pitch-rate, and sideslip commands. This paper will first thoroughly present the baseline controller followed by a description of the PMRAC adaptive augmentation to this control system. Results are presented via a full-scale, nonlinear simulation of NASA s Generic Transport Model (GTM).
Inverse problem for the mean-field monomer-dimer model with attractive interaction
International Nuclear Information System (INIS)
Contucci, Pierluigi; Luzi, Rachele; Vernia, Cecilia
2017-01-01
The inverse problem method is tested for a class of monomer-dimer statistical mechanics models that contain also an attractive potential and display a mean-field critical point at a boundary of a coexistence line. The inversion is obtained by analytically identifying the parameters in terms of the correlation functions and via the maximum-likelihood method. The precision is tested in the whole phase space and, when close to the coexistence line, the algorithm is used together with a clustering method to take care of the underlying possible ambiguity of the inversion. (paper)
Isomorphs in the phase diagram of a model liquid without inverse power law repulsion
DEFF Research Database (Denmark)
Veldhorst, Arnold Adriaan; Bøhling, Lasse; Dyre, J. C.
2012-01-01
scattering function are calculated. The results are shown to reflect a hidden scale invariance; despite its exponential repulsion the Buckingham potential is well approximated by an inverse power-law plus a linear term in the region of the first peak of the radial distribution function. As a consequence...... the dynamics of the viscous Buckingham liquid is mimicked by a corresponding model with purely repulsive inverse-power-law interactions. The results presented here closely resemble earlier results for Lennard-Jones type liquids, demonstrating that the existence of strong correlations and isomorphs does...... not depend critically on the mathematical form of the repulsion being an inverse power law....
Energy Technology Data Exchange (ETDEWEB)
Aguilo Valentin, Miguel Alejandro [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
2016-07-01
This study presents a new nonlinear programming formulation for the solution of inverse problems. First, a general inverse problem formulation based on the compliance error functional is presented. The proposed error functional enables the computation of the Lagrange multipliers, and thus the first order derivative information, at the expense of just one model evaluation. Therefore, the calculation of the Lagrange multipliers does not require the solution of the computationally intensive adjoint problem. This leads to significant speedups for large-scale, gradient-based inverse problems.
Modeling and inverse feedforward control for conducting polymer actuators with hysteresis
International Nuclear Information System (INIS)
Wang, Xiangjiang; Alici, Gursel; Tan, Xiaobo
2014-01-01
Conducting polymer actuators are biocompatible with a small footprint, and operate in air or liquid media under low actuation voltages. This makes them excellent actuators for macro- and micro-manipulation devices, however, their positioning ability or accuracy is adversely affected by their hysteresis non-linearity under open-loop control strategies. In this paper, we establish a hysteresis model for conducting polymer actuators, based on a rate-independent hysteresis model known as the Duhem model. The hysteresis model is experimentally identified and integrated with the linear dynamics of the actuator. This combined model is inverted to control the displacement of the tri-layer actuators considered in this study, without using any external feedback. The inversion requires an inverse hysteresis model which was experimentally identified using an inverse neural network model. Experimental results show that the position tracking errors are reduced by more than 50% when the hysteresis inverse model is incorporated into an inversion-based feedforward controller, indicating the potential of the proposed method in enabling wider use of such smart actuators. (paper)
On the feasibility of inversion methods based on models of urban sky glow
International Nuclear Information System (INIS)
Kolláth, Z.; Kránicz, B.
2014-01-01
Multi-wavelength imaging luminance photometry of sky glow provides a huge amount of information on light pollution. However, the understanding of the measured data involves the combination of different processes and data of radiation transfer, atmospheric physics and atmospheric constitution. State-of-the-art numerical radiation transfer models provide the possibility to define an inverse problem to obtain information on the emission intensity distribution of a city and perhaps the physical properties of the atmosphere. We provide numerical tests on the solvability and feasibility of such procedures. - Highlights: • A method of urban sky glow inversion is introduced based on Monte-Carlo calculations. • Imaging photometry can provide enough information for basic inversions. • The inversion technique can be used to construct maps of light pollution. • The inclusion of multiple scattering in the models plays an important role
CSIR Research Space (South Africa)
Robertson Lain, L
2014-07-01
Full Text Available (PFT) analysis. To these ends, an initial validation of a new model of Equivalent Algal Populations (EAP) is presented here. This paper makes a first order comparison of two prominent phytoplankton Inherent Optical Property (IOP) models with the EAP...
Houborg, Rasmus
2015-10-14
Accurate retrieval of canopy biophysical and leaf biochemical constituents from space observations is critical to diagnosing the functioning and condition of vegetation canopies across spatio-temporal scales. Retrieved vegetation characteristics may serve as important inputs to precision farming applications and as constraints in spatially and temporally distributed model simulations of water and carbon exchange processes. However significant challenges remain in the translation of composite remote sensing signals into useful biochemical, physiological or structural quantities and treatment of confounding factors in spectrum-trait relations. Bands in the red-edge spectrum have particular potential for improving the robustness of retrieved vegetation properties. The development of observationally based vegetation retrieval capacities, effectively constrained by the enhanced information content afforded by bands in the red-edge, is a needed investment towards optimizing the benefit of current and future satellite sensor systems. In this study, a REGularized canopy reFLECtance model (REGFLEC) for joint leaf chlorophyll (Chll) and leaf area index (LAI) retrieval is extended to sensor systems with a band in the red-edge region for the first time. Application to time-series of 5 m resolution multi-spectral RapidEye data is demonstrated over an irrigated agricultural region in central Saudi Arabia, showcasing the value of satellite-derived crop information at this fine scale for precision management. Validation against in-situ measurements in fields of alfalfa, Rhodes grass, carrot and maize indicate improved accuracy of retrieved vegetation properties when exploiting red-edge information in the model inversion process. © (2015) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE).
Inverse modelling of national and European CH4 emissions using the atmospheric zoom model TM5
Directory of Open Access Journals (Sweden)
P. Bergamaschi
2005-01-01
Full Text Available A synthesis inversion based on the atmospheric zoom model TM5 is used to derive top-down estimates of CH4 emissions from individual European countries for the year 2001. We employ a model zoom over Europe with 1° × 1° resolution that is two-way nested into the global model domain (with resolution of 6° × 4°. This approach ensures consistent boundary conditions for the zoom domain and thus European top-down estimates consistent with global CH4 observations. The TM5 model, driven by ECMWF analyses, simulates synoptic scale events at most European and global sites fairly well, and the use of high-frequency observations allows exploiting the information content of individual synoptic events. A detailed source attribution is presented for a comprehensive set of 56 monitoring sites, assigning the atmospheric signal to the emissions of individual European countries and larger global regions. The available observational data put significant constraints on emissions from different regions. Within Europe, in particular several Western European countries are well constrained. The inversion results suggest up to 50-90% higher anthropogenic CH4 emissions in 2001 for Germany, France and UK compared to reported UNFCCC values (EEA, 2003. A recent revision of the German inventory, however, resulted in an increase of reported CH4 emissions by 68.5% (EEA, 2004, being now in very good agreement with our top-down estimate. The top-down estimate for Finland is distinctly smaller than the a priori estimate, suggesting much smaller CH4 emissions from Finnish wetlands than derived from the bottom-up inventory. The EU-15 totals are relatively close to UNFCCC values (within 4-30% and appear very robust for different inversion scenarios.
Global Monthly CO2 Flux Inversion Based on Results of Terrestrial Ecosystem Modeling
Deng, F.; Chen, J.; Peters, W.; Krol, M.
2008-12-01
Most of our understanding of the sources and sinks of atmospheric CO2 has come from inverse studies of atmospheric CO2 concentration measurements. However, the number of currently available observation stations and our ability to simulate the diurnal planetary boundary layer evolution over continental regions essentially limit the number of regions that can be reliably inverted globally, especially over continental areas. In order to overcome these restrictions, a nested inverse modeling system was developed based on the Bayesian principle for estimating carbon fluxes of 30 regions in North America and 20 regions for the rest of the globe. Inverse modeling was conducted in monthly steps using CO2 concentration measurements of 5 years (2000 - 2005) with the following two models: (a) An atmospheric transport model (TM5) is used to generate the transport matrix where the diurnal variation n of atmospheric CO2 concentration is considered to enhance the use of the afternoon-hour average CO2 concentration measurements over the continental sites. (b) A process-based terrestrial ecosystem model (BEPS) is used to produce hourly step carbon fluxes, which could minimize the limitation due to our inability to solve the inverse problem in a high resolution, as the background of our inversion. We will present our recent results achieved through a combination of the bottom-up modeling with BEPS and the top-down modeling based on TM5 driven by offline meteorological fields generated by the European Centre for Medium Range Weather Forecast (ECMFW).
Lain, Lisl Robertson; Bernard, Stewart; Matthews, Mark W
2016-11-28
We regret that the Rrs spectra shown for the EAP modelled high biomass validation in Fig. 7 [Opt. Express, 22, 16745 (2014)] are incorrect. They are corrected here. The closest match of modelled to measured effective diameter is for a generalised 16 μm dinoflagellate population and not a 12 μm one as previously stated. These corrections do not affect the discussion or the conclusions of the paper.
A model reduction approach to numerical inversion for a parabolic partial differential equation
International Nuclear Information System (INIS)
Borcea, Liliana; Druskin, Vladimir; Zaslavsky, Mikhail; Mamonov, Alexander V
2014-01-01
We propose a novel numerical inversion algorithm for the coefficients of parabolic partial differential equations, based on model reduction. The study is motivated by the application of controlled source electromagnetic exploration, where the unknown is the subsurface electrical resistivity and the data are time resolved surface measurements of the magnetic field. The algorithm presented in this paper considers inversion in one and two dimensions. The reduced model is obtained with rational interpolation in the frequency (Laplace) domain and a rational Krylov subspace projection method. It amounts to a nonlinear mapping from the function space of the unknown resistivity to the small dimensional space of the parameters of the reduced model. We use this mapping as a nonlinear preconditioner for the Gauss–Newton iterative solution of the inverse problem. The advantage of the inversion algorithm is twofold. First, the nonlinear preconditioner resolves most of the nonlinearity of the problem. Thus the iterations are less likely to get stuck in local minima and the convergence is fast. Second, the inversion is computationally efficient because it avoids repeated accurate simulations of the time-domain response. We study the stability of the inversion algorithm for various rational Krylov subspaces, and assess its performance with numerical experiments. (paper)
A model reduction approach to numerical inversion for a parabolic partial differential equation
Borcea, Liliana; Druskin, Vladimir; Mamonov, Alexander V.; Zaslavsky, Mikhail
2014-12-01
We propose a novel numerical inversion algorithm for the coefficients of parabolic partial differential equations, based on model reduction. The study is motivated by the application of controlled source electromagnetic exploration, where the unknown is the subsurface electrical resistivity and the data are time resolved surface measurements of the magnetic field. The algorithm presented in this paper considers inversion in one and two dimensions. The reduced model is obtained with rational interpolation in the frequency (Laplace) domain and a rational Krylov subspace projection method. It amounts to a nonlinear mapping from the function space of the unknown resistivity to the small dimensional space of the parameters of the reduced model. We use this mapping as a nonlinear preconditioner for the Gauss-Newton iterative solution of the inverse problem. The advantage of the inversion algorithm is twofold. First, the nonlinear preconditioner resolves most of the nonlinearity of the problem. Thus the iterations are less likely to get stuck in local minima and the convergence is fast. Second, the inversion is computationally efficient because it avoids repeated accurate simulations of the time-domain response. We study the stability of the inversion algorithm for various rational Krylov subspaces, and assess its performance with numerical experiments.
DEFF Research Database (Denmark)
Gillet, N.; Jault, D.; Finlay, Chris
2013-01-01
Inferring the core dynamics responsible for the observed geomagnetic secular variation requires knowledge of the magnetic field at the core-mantle boundary together with its associated model covariances. However, most currently available field models have been built using regularization conditions...... variation error model in core flow inversions and geomagnetic data assimilation studies....
DEFF Research Database (Denmark)
Gillet, Nicolas; Jault, D.; Finlay, Chris
2013-01-01
Inferring the core dynamics responsible for the observed geomagnetic secular variation requires knowledge of the magnetic field at the core mantle boundary together with its associated model covariances. However, all currently available field models have been built using regularization conditions...... variation error model in core flow inversions and geomagnetic data assimilation studies....
Inverse modeling and animation of growing single-stemmed trees at interactive rates
S. Rudnick; L. Linsen; E.G. McPherson
2007-01-01
For city planning purposes, animations of growing trees of several species can be used to deduce which species may best fit a particular environment. The models used for the animation must conform to real measured data. We present an approach for inverse modeling to fit global growth parameters. The model comprises local production rules, which are iteratively and...
DEFF Research Database (Denmark)
Tordrup, Karl Woldum; Poulsen, Uffe Vestergaard; Nielsen, Carsten
2017-01-01
We use a modular approach to develop a TRNSYS model for a district heating facility by applying inverse modelling to one year of operational data for individual components. We assemble the components into a single TRNSYS model for the full system using the accumulation tanks as a central hub conn...
Identification of Loss-of-Coolant Accidents in LWRs by Inverse Models
International Nuclear Information System (INIS)
Cholewa, Wojciech; Frid, Wiktor; Bednarski, Marcin
2004-01-01
This paper describes a novel diagnostic method based on inverse models that could be applied to identification of transients and accidents in nuclear power plants. In particular, it is shown that such models could be successfully applied to identification of loss-of-coolant accidents (LOCAs). This is demonstrated for LOCA scenarios for a boiling water reactor. Two classes of inverse models are discussed: local models valid only in a selected neighborhood of an unknown element in the data set, representing a state of a considered object, and global models, in the form of partially unilateral models, valid over the whole learning data set. An interesting and useful property of local inverse models is that they can be considered as example-based models, i.e., models that are spanned on particular sets of pattern data. It is concluded that the optimal diagnostic method should combine the advantages of both models, i.e., the high quality of results obtained from a local inverse model and the information about the confidence interval for the expected output provided by a partially unilateral model
DEFF Research Database (Denmark)
Yoon, Daeung; Zhdanov, Michael; Cai, Hongzhu
2015-01-01
One of the major problems in the modeling and inversion of marine controlled source electromagnetic (MCSEM) data is related to the need for accurate representation of very complex geoelectrical models typical for marine environment. At the same time, the corresponding forward modeling algorithms...
Inverse modeling of the terrestrial carbon flux in China with flux covariance among inverted regions
Wang, H.; Jiang, F.; Chen, J. M.; Ju, W.; Wang, H.
2011-12-01
Quantitative understanding of the role of ocean and terrestrial biosphere in the global carbon cycle, their response and feedback to climate change is required for the future projection of the global climate. China has the largest amount of anthropogenic CO2 emission, diverse terrestrial ecosystems and an unprecedented rate of urbanization. Thus information on spatial and temporal distributions of the terrestrial carbon flux in China is of great importance in understanding the global carbon cycle. We developed a nested inversion with focus in China. Based on Transcom 22 regions for the globe, we divide China and its neighboring countries into 17 regions, making 39 regions in total for the globe. A Bayesian synthesis inversion is made to estimate the terrestrial carbon flux based on GlobalView CO2 data. In the inversion, GEOS-Chem is used as the transport model to develop the transport matrix. A terrestrial ecosystem model named BEPS is used to produce the prior surface flux to constrain the inversion. However, the sparseness of available observation stations in Asia poses a challenge to the inversion for the 17 small regions. To obtain additional constraint on the inversion, a prior flux covariance matrix is constructed using the BEPS model through analyzing the correlation in the net carbon flux among regions under variable climate conditions. The use of the covariance among different regions in the inversion effectively extends the information content of CO2 observations to more regions. The carbon flux over the 39 land and ocean regions are inverted for the period from 2004 to 2009. In order to investigate the impact of introducing the covariance matrix with non-zero off-diagonal values to the inversion, the inverted terrestrial carbon flux over China is evaluated against ChinaFlux eddy-covariance observations after applying an upscaling methodology.
Gu, Xiaoyu; Yu, Yang; Li, Jianchun; Li, Yancheng
2017-10-01
Magnetorheological elastomer (MRE) base isolations have attracted considerable attention over the last two decades thanks to its self-adaptability and high-authority controllability in semi-active control realm. Due to the inherent nonlinearity and hysteresis of the devices, it is challenging to obtain a reasonably complicated mathematical model to describe the inverse dynamics of MRE base isolators and hence to realise control synthesis of the MRE base isolation system. Two aims have been achieved in this paper: i) development of an inverse model for MRE base isolator based on optimal general regression neural network (GRNN); ii) numerical and experimental validation of a real-time semi-active controlled MRE base isolation system utilising LQR controller and GRNN inverse model. The superiority of GRNN inverse model lays in fewer input variables requirement, faster training process and prompt calculation response, which makes it suitable for online training and real-time control. The control system is integrated with a three-storey shear building model and control performance of the MRE base isolation system is compared with bare building, passive-on isolation system and passive-off isolation system. Testing results show that the proposed GRNN inverse model is able to reproduce desired control force accurately and the MRE base isolation system can effectively suppress the structural responses when compared to the passive isolation system.
Heeding the waveform inversion nonlinearity by unwrapping the model and data
Alkhalifah, Tariq Ali
2012-01-01
Unlike traveltime inversion, waveform inversion provides relatively higher-resolution inverted models. This feature, however, comes at the cost of introducing complex nonlinearity to the inversion operator complicating the convergence process. We use unwrapped-phase-based objective functions to reduce such nonlinearity in a domain in which the high-frequency component is given by the traveltime inversion. Such information is packaged in a frequency-dependent attribute (or traveltime) that can be easily manipulated at different frequencies. It unwraps the phase of the wavefield yielding far less nonlinearity in the objective function than those experienced with the conventional misfit objective function, and yet it still holds most of the critical waveform information in its frequency dependency. However, it suffers from nonlinearity introduced by the model (or reflectivity), as events interact with each other (something like cross talk). This stems from the sinusoidal nature of the band-limited reflectivity model. Unwrapping the phase for such a model can mitigate this nonlinearity as well. Specifically, a simple modification to the inverted domain (or model), can reduce the effect of the model-induced nonlinearity and, thus, make the inversion more convergent. Simple examples are used to highlight such features.
Kumaravelu, Karthik; Brocker, David T; Grill, Warren M
2016-04-01
Electrical stimulation of sub-cortical brain regions (the basal ganglia), known as deep brain stimulation (DBS), is an effective treatment for Parkinson's disease (PD). Chronic high frequency (HF) DBS in the subthalamic nucleus (STN) or globus pallidus interna (GPi) reduces motor symptoms including bradykinesia and tremor in patients with PD, but the therapeutic mechanisms of DBS are not fully understood. We developed a biophysical network model comprising of the closed loop cortical-basal ganglia-thalamus circuit representing the healthy and parkinsonian rat brain. The network properties of the model were validated by comparing responses evoked in basal ganglia (BG) nuclei by cortical (CTX) stimulation to published experimental results. A key emergent property of the model was generation of low-frequency network oscillations. Consistent with their putative pathological role, low-frequency oscillations in model BG neurons were exaggerated in the parkinsonian state compared to the healthy condition. We used the model to quantify the effectiveness of STN DBS at different frequencies in suppressing low-frequency oscillatory activity in GPi. Frequencies less than 40 Hz were ineffective, low-frequency oscillatory power decreased gradually for frequencies between 50 Hz and 130 Hz, and saturated at frequencies higher than 150 Hz. HF STN DBS suppressed pathological oscillations in GPe/GPi both by exciting and inhibiting the firing in GPe/GPi neurons, and the number of GPe/GPi neurons influenced was greater for HF stimulation than low-frequency stimulation. Similar to the frequency dependent suppression of pathological oscillations, STN DBS also normalized the abnormal GPi spiking activity evoked by CTX stimulation in a frequency dependent fashion with HF being the most effective. Therefore, therapeutic HF STN DBS effectively suppresses pathological activity by influencing the activity of a greater proportion of neurons in the output nucleus of the BG.
International Nuclear Information System (INIS)
Bemski, G.
1980-03-01
Recent progress in biophysics is reviewed, and three examples of the use of physical techniques and ideas in biological research are given. The first one deals with the oxygen transporting protein-hemoglobin, the second one with photosynthesis, and the third one with image formation, using nuclear magnetic resonance. (Author) [pt
Biophysics of molecular gastronomy.
Brenner, Michael P; Sörensen, Pia M
2015-03-26
Chefs and scientists exploring biophysical processes have given rise to molecular gastronomy. In this Commentary, we describe how a scientific understanding of recipes and techniques facilitates the development of new textures and expands the flavor palette. The new dishes that result engage our senses in unexpected ways. PAPERCLIP. Copyright © 2015 Elsevier Inc. All rights reserved.
Biophysical Cancer Transformation Pathway
Czech Academy of Sciences Publication Activity Database
Pokorný, Jiří
2009-01-01
Roč. 28, č. 2 (2009), s. 105-123 ISSN 1536-8378 Institutional research plan: CEZ:AV0Z20670512 Keywords : Biophysics * Cancer * Electromagnetic fields Subject RIV: JA - Electronics ; Optoelectronics, Electrical Engineering Impact factor: 0.729, year: 2009
Inverse Problems in Geosciences: Modelling the Rock Properties of an Oil Reservoir
DEFF Research Database (Denmark)
Lange, Katrine
. We have developed and implemented the Frequency Matching method that uses the closed form expression of the a priori probability density function to formulate an inverse problem and compute the maximum a posteriori solution to it. Other methods for computing models that simultaneously fit data...... of the subsurface of the reservoirs. Hence the focus of this work has been on acquiring models of spatial parameters describing rock properties of the subsurface using geostatistical a priori knowledge and available geophysical data. Such models are solutions to often severely under-determined, inverse problems...
pyGIMLi: An open-source library for modelling and inversion in geophysics
Rücker, Carsten; Günther, Thomas; Wagner, Florian M.
2017-12-01
Many tasks in applied geosciences cannot be solved by single measurements, but require the integration of geophysical, geotechnical and hydrological methods. Numerical simulation techniques are essential both for planning and interpretation, as well as for the process understanding of modern geophysical methods. These trends encourage open, simple, and modern software architectures aiming at a uniform interface for interdisciplinary and flexible modelling and inversion approaches. We present pyGIMLi (Python Library for Inversion and Modelling in Geophysics), an open-source framework that provides tools for modelling and inversion of various geophysical but also hydrological methods. The modelling component supplies discretization management and the numerical basis for finite-element and finite-volume solvers in 1D, 2D and 3D on arbitrarily structured meshes. The generalized inversion framework solves the minimization problem with a Gauss-Newton algorithm for any physical forward operator and provides opportunities for uncertainty and resolution analyses. More general requirements, such as flexible regularization strategies, time-lapse processing and different sorts of coupling individual methods are provided independently of the actual methods used. The usage of pyGIMLi is first demonstrated by solving the steady-state heat equation, followed by a demonstration of more complex capabilities for the combination of different geophysical data sets. A fully coupled hydrogeophysical inversion of electrical resistivity tomography (ERT) data of a simulated tracer experiment is presented that allows to directly reconstruct the underlying hydraulic conductivity distribution of the aquifer. Another example demonstrates the improvement of jointly inverting ERT and ultrasonic data with respect to saturation by a new approach that incorporates petrophysical relations in the inversion. Potential applications of the presented framework are manifold and include time
The inverse niche model for food webs with parasites
Warren, Christopher P.; Pascual, Mercedes; Lafferty, Kevin D.; Kuris, Armand M.
2010-01-01
Although parasites represent an important component of ecosystems, few field and theoretical studies have addressed the structure of parasites in food webs. We evaluate the structure of parasitic links in an extensive salt marsh food web, with a new model distinguishing parasitic links from non-parasitic links among free-living species. The proposed model is an extension of the niche model for food web structure, motivated by the potential role of size (and related metabolic rates) in structuring food webs. The proposed extension captures several properties observed in the data, including patterns of clustering and nestedness, better than does a random model. By relaxing specific assumptions, we demonstrate that two essential elements of the proposed model are the similarity of a parasite's hosts and the increasing degree of parasite specialization, along a one-dimensional niche axis. Thus, inverting one of the basic rules of the original model, the one determining consumers' generality appears critical. Our results support the role of size as one of the organizing principles underlying niche space and food web topology. They also strengthen the evidence for the non-random structure of parasitic links in food webs and open the door to addressing questions concerning the consequences and origins of this structure.
Regime transitions in near-surface temperature inversions : a conceptual model
van de Wiel, B.J.H.; Vignon, E.; Baas, P.; Bosveld, F.C.; de Roode, S.R.; Moene, A.F.; Genthon, C.; van der Linden, Steven J.A.; van Hooft, J. Antoon; van Hooijdonk, I.G.S.
2017-01-01
A conceptual model is used in combination with observational analysis to understand regime transitions of near-surface temperature inversions at night as well as in Arctic conditions. The model combines a surface energy budget with a bulk parameterization for turbulent heat transport. Energy fluxes
Data and modelling requirements for CO2 inversions using high-frequency data
International Nuclear Information System (INIS)
Law, R.M.; Rayner, P.J.; Steele, L.P.; Enting, I.G.
2003-01-01
We explore the future possibilities for CO 2 source estimation from atmospheric concentration data by performing synthetic data experiments. Synthetic data are used to test seasonal CO 2 inversions using high-frequency data. Monthly CO 2 sources over the Australian region are calculated for inversions with data at 4-hourly frequency and averaged over 1 d, 2.5 d, 5 d, 12.17 d and 1 month. The inversion quality, as determined by bias and uncertainty, is degraded when averaging over longer periods. This shows the value of the strong but relatively short-lived signals present in high-frequency records that are removed in averaged and particularly filtered records. Sensitivity tests are performed in which the synthetic data are 'corrupted' to simulate systematic measurement errors such as intercalibration differences or to simulate transport modelling errors. The inversion is also used to estimate the effect of calibration offsets between sites. We find that at short data-averaging periods the inversion is reasonably robust to measurement-type errors. For transport-type errors, the best results are achieved for synoptic (2-5 d) timescales. Overall the tests indicate that improved source estimates should be possible by incorporating continuous measurements into CO 2 inversions
Fukuda, J.; Johnson, K. M.
2009-12-01
Studies utilizing inversions of geodetic data for the spatial distribution of coseismic slip on faults typically present the result as a single fault plane and slip distribution. Commonly the geometry of the fault plane is assumed to be known a priori and the data are inverted for slip. However, sometimes there is not strong a priori information on the geometry of the fault that produced the earthquake and the data is not always strong enough to completely resolve the fault geometry. We develop a method to solve for the full posterior probability distribution of fault slip and fault geometry parameters in a Bayesian framework using Monte Carlo methods. The slip inversion problem is particularly challenging because it often involves multiple data sets with unknown relative weights (e.g. InSAR, GPS), model parameters that are related linearly (slip) and nonlinearly (fault geometry) through the theoretical model to surface observations, prior information on model parameters, and a regularization prior to stabilize the inversion. We present the theoretical framework and solution method for a Bayesian inversion that can handle all of these aspects of the problem. The method handles the mixed linear/nonlinear nature of the problem through combination of both analytical least-squares solutions and Monte Carlo methods. We first illustrate and validate the inversion scheme using synthetic data sets. We then apply the method to inversion of geodetic data from the 2003 M6.6 San Simeon, California earthquake. We show that the uncertainty in strike and dip of the fault plane is over 20 degrees. We characterize the uncertainty in the slip estimate with a volume around the mean fault solution in which the slip most likely occurred. Slip likely occurred somewhere in a volume that extends 5-10 km in either direction normal to the fault plane. We implement slip inversions with both traditional, kinematic smoothing constraints on slip and a simple physical condition of uniform stress
Micromechanical modeling and inverse identification of damage using cohesive approaches
International Nuclear Information System (INIS)
Blal, Nawfal
2013-01-01
In this study a micromechanical model is proposed for a collection of cohesive zone models embedded between two each elements of a standard cohesive-volumetric finite element method. An equivalent 'matrix-inclusions' composite is proposed as a representation of the cohesive-volumetric discretization. The overall behaviour is obtained using homogenization approaches (Hashin Shtrikman scheme and the P. Ponte Castaneda approach). The derived model deals with elastic, brittle and ductile materials. It is available whatever the triaxiality loading rate and the shape of the cohesive law, and leads to direct relationships between the overall material properties and the local cohesive parameters and the mesh density. First, rigorous bounds on the normal and tangential cohesive stiffnesses are obtained leading to a suitable control of the inherent artificial elastic loss induced by intrinsic cohesive models. Second, theoretical criteria on damageable and ductile cohesive parameters are established (cohesive peak stress, critical separation, cohesive failure energy,... ). These criteria allow a practical calibration of the cohesive zone parameters as function of the overall material properties and the mesh length. The main interest of such calibration is its promising capacity to lead to a mesh-insensitive overall response in surface damage. (author) [fr
Directory of Open Access Journals (Sweden)
A.A. Fahmy
2013-12-01
Full Text Available This paper presents a new neuro-fuzzy controller for robot manipulators. First, an inductive learning technique is applied to generate the required inverse modeling rules from input/output data recorded in the off-line structure learning phase. Second, a fully differentiable fuzzy neural network is developed to construct the inverse dynamics part of the controller for the online parameter learning phase. Finally, a fuzzy-PID-like incremental controller was employed as Feedback servo controller. The proposed control system was tested using dynamic model of a six-axis industrial robot. The control system showed good results compared to the conventional PID individual joint controller.
Inverse mathematical modelling and identification in metal powder compaction process
International Nuclear Information System (INIS)
Gakwaya, A.; Hrairi, M.; Guillot, M.
2000-01-01
An online assessment of the quality of advanced integrated computer aided manufacturing systems require the knowledge of accurate and reliable non-linear constitutive material behavior. This paper is concerned with material parameter identification based on experimental data for which non uniform distribution of stresses and deformation within the volume of the specimen is considered. Both geometric and material non linearities as well interfacial frictional contact are taken into account during the simulation. Within the framework of finite deformation theory, a multisurface multiplicative plasticity model for metal powder compaction process is presented. The model is seen to involve several parameters which are not always activated by a single state variable even though it may be technologically important in assessing the final product quality and manufacturing performance. The resulting expressions are presented in spatial setting and gradient based descent method utilizing the modified Levenberg-Marquardt scheme is used for the minimization of least square functional so as to obtain the best agreement between relevant experimental data and simulated data in a specified energy norm. The identification of a subset of material parameters of the cap model for stainless steel powder compaction is performed. The obtained parameters are validated through a simulation of an industrial part manufacturing case. A very good agreement between simulated final density and measured density is obtained thus demonstrating the practical usefulness of the proposed approach. (author)
Modeling and inversion Matlab algorithms for resistivity, induced polarization and seismic data
Karaoulis, M.; Revil, A.; Minsley, B. J.; Werkema, D. D.
2011-12-01
M. Karaoulis (1), D.D. Werkema (3), A. Revil (1,2), A., B. Minsley (4), (1) Colorado School of Mines, Dept. of Geophysics, Golden, CO, USA. (2) ISTerre, CNRS, UMR 5559, Université de Savoie, Equipe Volcan, Le Bourget du Lac, France. (3) U.S. EPA, ORD, NERL, ESD, CMB, Las Vegas, Nevada, USA . (4) USGS, Federal Center, Lakewood, 10, 80225-0046, CO. Abstract We propose 2D and 3D forward modeling and inversion package for DC resistivity, time domain induced polarization (IP), frequency-domain IP, and seismic refraction data. For the resistivity and IP case, discretization is based on rectangular cells, where each cell has as unknown resistivity in the case of DC modelling, resistivity and chargeability in the time domain IP modelling, and complex resistivity in the spectral IP modelling. The governing partial-differential equations are solved with the finite element method, which can be applied to both real and complex variables that are solved for. For the seismic case, forward modeling is based on solving the eikonal equation using a second-order fast marching method. The wavepaths are materialized by Fresnel volumes rather than by conventional rays. This approach accounts for complicated velocity models and is advantageous because it considers frequency effects on the velocity resolution. The inversion can accommodate data at a single time step, or as a time-lapse dataset if the geophysical data are gathered for monitoring purposes. The aim of time-lapse inversion is to find the change in the velocities or resistivities of each model cell as a function of time. Different time-lapse algorithms can be applied such as independent inversion, difference inversion, 4D inversion, and 4D active time constraint inversion. The forward algorithms are benchmarked against analytical solutions and inversion results are compared with existing ones. The algorithms are packaged as Matlab codes with a simple Graphical User Interface. Although the code is parallelized for multi
Evangeliou, Nikolaos; Hamburger, Thomas; Cozic, Anne; Balkanski, Yves; Stohl, Andreas
2017-07-01
This paper describes the results of an inverse modeling study for the determination of the source term of the radionuclides 134Cs, 137Cs and 131I released after the Chernobyl accident. The accident occurred on 26 April 1986 in the Former Soviet Union and released about 1019 Bq of radioactive materials that were transported as far away as the USA and Japan. Thereafter, several attempts to assess the magnitude of the emissions were made that were based on the knowledge of the core inventory and the levels of the spent fuel. More recently, when modeling tools were further developed, inverse modeling techniques were applied to the Chernobyl case for source term quantification. However, because radioactivity is a sensitive topic for the public and attracts a lot of attention, high-quality measurements, which are essential for inverse modeling, were not made available except for a few sparse activity concentration measurements far from the source and far from the main direction of the radioactive fallout. For the first time, we apply Bayesian inversion of the Chernobyl source term using not only activity concentrations but also deposition measurements from the most recent public data set. These observations refer to a data rescue attempt that started more than 10 years ago, with a final goal to provide available measurements to anyone interested. In regards to our inverse modeling results, emissions of 134Cs were estimated to be 80 PBq or 30-50 % higher than what was previously published. From the released amount of 134Cs, about 70 PBq were deposited all over Europe. Similar to 134Cs, emissions of 137Cs were estimated as 86 PBq, on the same order as previously reported results. Finally, 131I emissions of 1365 PBq were found, which are about 10 % less than the prior total releases. The inversion pushes the injection heights of the three radionuclides to higher altitudes (up to about 3 km) than previously assumed (≈ 2.2 km) in order to better match both concentration
Directory of Open Access Journals (Sweden)
N. Evangeliou
2017-07-01
Full Text Available This paper describes the results of an inverse modeling study for the determination of the source term of the radionuclides 134Cs, 137Cs and 131I released after the Chernobyl accident. The accident occurred on 26 April 1986 in the Former Soviet Union and released about 1019 Bq of radioactive materials that were transported as far away as the USA and Japan. Thereafter, several attempts to assess the magnitude of the emissions were made that were based on the knowledge of the core inventory and the levels of the spent fuel. More recently, when modeling tools were further developed, inverse modeling techniques were applied to the Chernobyl case for source term quantification. However, because radioactivity is a sensitive topic for the public and attracts a lot of attention, high-quality measurements, which are essential for inverse modeling, were not made available except for a few sparse activity concentration measurements far from the source and far from the main direction of the radioactive fallout. For the first time, we apply Bayesian inversion of the Chernobyl source term using not only activity concentrations but also deposition measurements from the most recent public data set. These observations refer to a data rescue attempt that started more than 10 years ago, with a final goal to provide available measurements to anyone interested. In regards to our inverse modeling results, emissions of 134Cs were estimated to be 80 PBq or 30–50 % higher than what was previously published. From the released amount of 134Cs, about 70 PBq were deposited all over Europe. Similar to 134Cs, emissions of 137Cs were estimated as 86 PBq, on the same order as previously reported results. Finally, 131I emissions of 1365 PBq were found, which are about 10 % less than the prior total releases. The inversion pushes the injection heights of the three radionuclides to higher altitudes (up to about 3 km than previously assumed (≈ 2.2 km in order
Chu, Dezhang; Lawson, Gareth L; Wiebe, Peter H
2016-05-01
The linear inversion commonly used in fisheries and zooplankton acoustics assumes a constant inversion kernel and ignores the uncertainties associated with the shape and behavior of the scattering targets, as well as other relevant animal parameters. Here, errors of the linear inversion due to uncertainty associated with the inversion kernel are quantified. A scattering model-based nonlinear inversion method is presented that takes into account the nonlinearity of the inverse problem and is able to estimate simultaneously animal abundance and the parameters associated with the scattering model inherent to the kernel. It uses sophisticated scattering models to estimate first, the abundance, and second, the relevant shape and behavioral parameters of the target organisms. Numerical simulations demonstrate that the abundance, size, and behavior (tilt angle) parameters of marine animals (fish or zooplankton) can be accurately inferred from the inversion by using multi-frequency acoustic data. The influence of the singularity and uncertainty in the inversion kernel on the inversion results can be mitigated by examining the singular values for linear inverse problems and employing a non-linear inversion involving a scattering model-based kernel.
Model study of the compact gravity reconstruction; Juryoku inversion `CGR` no model kento
Energy Technology Data Exchange (ETDEWEB)
Ishii, Y; Muraoka, A [Sogo Geophysical Exploration Co. Ltd., Tokyo (Japan)
1996-05-01
An examination was made on gravity inversion using a compact gravity reconstruction (CGR) method in gravity tomography analysis. In a model analysis, an analytical region of 100m{times}50m was divided into cells of 10m{times}10m, on the assumption that two density anomalous bodies with a density difference of 1.0g/cm{sup 3} existed with one shallow and the other deep density distribution. The result of the analysis revealed that, in a linear analysis by a general inverse matrix, blurs and blotting were plenty with a tendency of making gravity anomaly attributable to an anomalous distribution of shallow density; that CGR provided a large effect in making a clear contrast of an anomalous part; that, where structures of shallow and deep density anomalies existed, the analysis by CGR was inferior in the restoration of a deep structure with errors enlarged; that, if a gravity traverse was taken long compared with the distribution depth of density anomalies, the analytical precision of a deep part was improved; that an analytical convergence was better with the restriction of density difference given on the large side than on the small side; and so on. 3 refs., 10 figs.
Sabbagh, Harold A; Sabbagh, Elias H; Aldrin, John C; Knopp, Jeremy S
2013-01-01
Computational Electromagnetics and Model-Based Inversion: A Modern Paradigm for Eddy Current Nondestructive Evaluation describes the natural marriage of the computer to eddy-current NDE. Three distinct topics are emphasized in the book: (a) fundamental mathematical principles of volume-integral equations as a subset of computational electromagnetics, (b) mathematical algorithms applied to signal-processing and inverse scattering problems, and (c) applications of these two topics to problems in which real and model data are used. By showing how mathematics and the computer can solve problems more effectively than current analog practices, this book defines the modern technology of eddy-current NDE. This book will be useful to advanced students and practitioners in the fields of computational electromagnetics, electromagnetic inverse-scattering theory, nondestructive evaluation, materials evaluation and biomedical imaging. Users of eddy-current NDE technology in industries as varied as nuclear power, aerospace,...
Inverse geothermal modelling applied to Danish sedimentary basins
DEFF Research Database (Denmark)
Poulsen, Soren E.; Balling, Niels; Bording, Thue S.
2017-01-01
. The numerical model was utilized for predicting and contouring temperatures at 2000 and 3000 m depths and for two main geothermal reservoir units, the Gassum (Lower Jurassic-Upper Triassic) and Bunter/Skagerrak (Triassic) reservoirs, both currently utilized for geothermal energy production. Temperature...... gradients to depths of 2000-3000 m are generally around 25-30. degrees C km(-1), locally up to about 35. degrees C km(-1). Large regions have geothermal reservoirs with characteristic temperatures ranging from ca. 40-50. degrees C, at 1000-1500 m depth, to ca. 80-110. degrees C, at 2500-3500 m, however...
Puglisi, Joseph D
2007-01-01
This volume is a collection of articles from the proceedings of the ISSBMR 7th Course: Structure and Biophysics - New Technologies for Current Challenges in Biology and Beyond. This NATO Advanced Institute (ASI) was held in Erice at the Ettore Majorana Foundation and Centre for Scientific Culture on 22 June through 3 July 2005. The ASI brought together a diverse group of experts in the fields of Structural Biology, Biophysics and Physics. Prominent lecturers, from seven different countries, and students from around the world participated in the NATO ASI organized by Professors Joseph Puglisi (Stanford University, USA) and Alexander Arseniev (Moscow, RU). Advances in nuclear magnetic resonance spectroscopy (NMR) and x-ray crystallography have allowed the three-dimensional structures of many biological macromolecules and their complexes, including the ribosome and RNA polymerase to be solved. Fundamental principles of NMR spectroscopy and dynamics, x-ray crystallography, computation and experimental dynamics we...
Cotteril, Rodney
2002-01-01
Biophysics: An Introduction, is a concise balanced introduction to this subject. Written in an accessible and readable style, the book takes a fresh, modern approach with the author successfully combining key concepts and theory with relevant applications and examples drawn from the field as a whole. Beginning with a brief introduction to the origins of biophysics, the book takes the reader through successive levels of complexity, from atoms to molecules, structures, systems and ultimately to the behaviour of organisms. The book also includes extensive coverage of biopolymers, biomembranes, biological energy, and nervous systems. The text not only explores basic ideas, but also discusses recent developments, such as protein folding, DNA/RNA conformations, molecular motors, optical tweezers and the biological origins of consciousness and intelligence.
Implementation of a Monte Carlo based inverse planning model for clinical IMRT with MCNP code
International Nuclear Information System (INIS)
He, Tongming Tony
2003-01-01
Inaccurate dose calculations and limitations of optimization algorithms in inverse planning introduce systematic and convergence errors to treatment plans. This work was to implement a Monte Carlo based inverse planning model for clinical IMRT aiming to minimize the aforementioned errors. The strategy was to precalculate the dose matrices of beamlets in a Monte Carlo based method followed by the optimization of beamlet intensities. The MCNP 4B (Monte Carlo N-Particle version 4B) code was modified to implement selective particle transport and dose tallying in voxels and efficient estimation of statistical uncertainties. The resulting performance gain was over eleven thousand times. Due to concurrent calculation of multiple beamlets of individual ports, hundreds of beamlets in an IMRT plan could be calculated within a practical length of time. A finite-sized point source model provided a simple and accurate modeling of treatment beams. The dose matrix calculations were validated through measurements in phantoms. Agreements were better than 1.5% or 0.2 cm. The beamlet intensities were optimized using a parallel platform based optimization algorithm that was capable of escape from local minima and preventing premature convergence. The Monte Carlo based inverse planning model was applied to clinical cases. The feasibility and capability of Monte Carlo based inverse planning for clinical IMRT was demonstrated. Systematic errors in treatment plans of a commercial inverse planning system were assessed in comparison with the Monte Carlo based calculations. Discrepancies in tumor doses and critical structure doses were up to 12% and 17%, respectively. The clinical importance of Monte Carlo based inverse planning for IMRT was demonstrated
Inverse modelling of thermal histories with apatite fission tracks
International Nuclear Information System (INIS)
El Lmrani, A.; Zine El Abidine, H.; Limouri, M.; Essaid, A.; POupeau, G.
1998-01-01
The problem of modelling thermal histories lies in the exploration of a time-temperature space, usually so broad, in order to identify the optimal paths. For overcoming this difficulty, many approaches were proposed, using linear and non-linear optimisation algorithms. Generally, these approaches do not take into account the experimental data (fission track age [FTA] and fission track length distribution [FTLD]) to better aim the search strategy. The present work shows that experimental data hold some precious information, for which it should be known how to extract it. In fact, it allows us to tighten the time-temperature space of search, supposed to contain the optimal solutions. A genetic algorithm is also used in this work to perform the search for these optimal solutions. (authors)
Biophysical shunt theory for neuropsychopathology: Part I.
Naisberg, Y; Avnon, M; Weizman, A
1995-11-01
We present a new model of the origin of schizophrenia based on biophysical ionic shunts in neuronal (electrical) pathways. Microstructural and molecular evidence is presented for the way in which changes in the neuronal membrane ionic channels may facilitate membrane property rearrangement, leading to a change in the density and composition of the ion channel charge which in turn causes a change in ionic flow orientation and distribution. We suggest that, under abnormal conditions, ionic flow shunts are created which redirect the biophysical collateral neuronal (electrical) pathways, resulting in psychiatric signs and symptoms. This model is complementary to the biological basis of schizophrenia.
National Research Council Canada - National Science Library
Hatch, Andrew G; Smith, Ralph C; De, Tathagata; Salapaka, Murti V
2005-01-01
.... In this paper, we illustrate the construction of inverse filters, based on homogenized energy models, which can be used to approximately linearize the piezoceramic transducer behavior for linear...
Directory of Open Access Journals (Sweden)
Benjamin L. Turner
2016-10-01
Full Text Available Agriculture-based irrigation communities of northern New Mexico have survived for centuries despite the arid environment in which they reside. These irrigation communities are threatened by regional population growth, urbanization, a changing demographic profile, economic development, climate change, and other factors. Within this context, we investigated the extent to which community resource management practices centering on shared resources (e.g., water for agricultural in the floodplains and grazing resources in the uplands and mutualism (i.e., shared responsibility of local residents to maintaining traditional irrigation policies and upholding cultural and spiritual observances embedded within the community structure influence acequia function. We used a system dynamics modeling approach as an interdisciplinary platform to integrate these systems, specifically the relationship between community structure and resource management. In this paper we describe the background and context of acequia communities in northern New Mexico and the challenges they face. We formulate a Dynamic Hypothesis capturing the endogenous feedbacks driving acequia community vitality. Development of the model centered on major stock-and-flow components, including linkages for hydrology, ecology, community, and economics. Calibration metrics were used for model evaluation, including statistical correlation of observed and predicted values and Theil inequality statistics. Results indicated that the model reproduced trends exhibited by the observed system. Sensitivity analyses of socio-cultural processes identified absentee decisions, cumulative income effect on time in agriculture, and land use preference due to time allocation, community demographic effect, effect of employment on participation, and farm size effect as key determinants of system behavior and response. Sensitivity analyses of biophysical parameters revealed that several key parameters (e.g., acres per
Inverse modeling of cloud-aerosol interactions -- Part 1: Detailed response surface analysis
Partridge, D.G.; Vrugt, J.A.; Tunved, P.; Ekman, A.M.L.; Gorea, D.; Sooroshian, A.
2011-01-01
New methodologies are required to probe the sensitivity of parameters describing cloud droplet activation. This paper presents an inverse modeling-based method for exploring cloud-aerosol interactions via response surfaces. The objective function, containing the difference between the measured and
Bayesian Uncertainty Quantification for Subsurface Inversion Using a Multiscale Hierarchical Model
Mondal, Anirban
2014-07-03
We consider a Bayesian approach to nonlinear inverse problems in which the unknown quantity is a random field (spatial or temporal). The Bayesian approach contains a natural mechanism for regularization in the form of prior information, can incorporate information from heterogeneous sources and provide a quantitative assessment of uncertainty in the inverse solution. The Bayesian setting casts the inverse solution as a posterior probability distribution over the model parameters. The Karhunen-Loeve expansion is used for dimension reduction of the random field. Furthermore, we use a hierarchical Bayes model to inject multiscale data in the modeling framework. In this Bayesian framework, we show that this inverse problem is well-posed by proving that the posterior measure is Lipschitz continuous with respect to the data in total variation norm. Computational challenges in this construction arise from the need for repeated evaluations of the forward model (e.g., in the context of MCMC) and are compounded by high dimensionality of the posterior. We develop two-stage reversible jump MCMC that has the ability to screen the bad proposals in the first inexpensive stage. Numerical results are presented by analyzing simulated as well as real data from hydrocarbon reservoir. This article has supplementary material available online. © 2014 American Statistical Association and the American Society for Quality.
An inverse dynamics model for the analysis, reconstruction and prediction of bipedal walking
Koopman, Hubertus F.J.M.; Grootenboer, H.J.; de Jongh, Henk J.; Huijing, P.A.J.B.M.; de Vries, J.
1995-01-01
Walking is a constrained movement which may best be observed during the double stance phase when both feet contact the floor. When analyzing a measured movement with an inverse dynamics model, a violation of these constrains will always occur due to measuring errors and deviations of the segments
Digital Repository Service at National Institute of Oceanography (India)
Tripathy, G.R.; Das, Anirban.
used methods, the Least Square Regression (LSR) and Inverse Modeling (IM), to determine the contributions of (i) solutes from different sources to global river water, and (ii) various rocks to a glacial till. The purpose of this exercise is to compare...
Justiniano, A.; Jaya, Y.; Diephuis, G.; Veenhof, R.; Pringle, T.
2015-01-01
The objective of the study is to characterise the Triassic massive stacked sandstone deposits of the Main Buntsandstein Subgroup at Block Q16 located in the West Netherlands Basin. The characterisation was carried out through combining rock-physics modelling and seismic inversion techniques. The
Evaluation of inverse modeling techniques for pinpointing water leakages at building constructions
Schijndel, van A.W.M.
2015-01-01
The location and nature of the moisture leakages are sometimes difficult to detect. Moreover, the relation between observed inside surface moisture patterns and where the moisture enters the construction is often not clear. The objective of this paper is to investigate inverse modeling techniques as
A Systematic and Numerically Efficient Procedure for Stable Dynamic Model Inversion of LTI Systems
George, K.; Verhaegen, M.; Scherpen, J.M.A.
1999-01-01
Output tracking via the novel Stable Dynamic model Inversion (SDI) technique, applicable to non-minimum phase systems, and which naturally takes into account the presence of noise in target time histories, is considered here. We are motivated by the typical need to replicate time signals in the
Inverse problem theory methods for data fitting and model parameter estimation
Tarantola, A
2002-01-01
Inverse Problem Theory is written for physicists, geophysicists and all scientists facing the problem of quantitative interpretation of experimental data. Although it contains a lot of mathematics, it is not intended as a mathematical book, but rather tries to explain how a method of acquisition of information can be applied to the actual world.The book provides a comprehensive, up-to-date description of the methods to be used for fitting experimental data, or to estimate model parameters, and to unify these methods into the Inverse Problem Theory. The first part of the book deals wi
Ojo, A. O.; Xie, Jun; Olorunfemi, M. O.
2018-01-01
To reduce ambiguity related to nonlinearities in the resistivity model-data relationships, an efficient direct-search scheme employing the Neighbourhood Algorithm (NA) was implemented to solve the 1-D resistivity problem. In addition to finding a range of best-fit models which are more likely to be global minimums, this method investigates the entire multi-dimensional model space and provides additional information about the posterior model covariance matrix, marginal probability density function and an ensemble of acceptable models. This provides new insights into how well the model parameters are constrained and make assessing trade-offs between them possible, thus avoiding some common interpretation pitfalls. The efficacy of the newly developed program is tested by inverting both synthetic (noisy and noise-free) data and field data from other authors employing different inversion methods so as to provide a good base for comparative performance. In all cases, the inverted model parameters were in good agreement with the true and recovered model parameters from other methods and remarkably correlate with the available borehole litho-log and known geology for the field dataset. The NA method has proven to be useful whilst a good starting model is not available and the reduced number of unknowns in the 1-D resistivity inverse problem makes it an attractive alternative to the linearized methods. Hence, it is concluded that the newly developed program offers an excellent complementary tool for the global inversion of the layered resistivity structure.
Double point source W-phase inversion: Real-time implementation and automated model selection
Nealy, Jennifer; Hayes, Gavin
2015-01-01
Rapid and accurate characterization of an earthquake source is an extremely important and ever evolving field of research. Within this field, source inversion of the W-phase has recently been shown to be an effective technique, which can be efficiently implemented in real-time. An extension to the W-phase source inversion is presented in which two point sources are derived to better characterize complex earthquakes. A single source inversion followed by a double point source inversion with centroid locations fixed at the single source solution location can be efficiently run as part of earthquake monitoring network operational procedures. In order to determine the most appropriate solution, i.e., whether an earthquake is most appropriately described by a single source or a double source, an Akaike information criterion (AIC) test is performed. Analyses of all earthquakes of magnitude 7.5 and greater occurring since January 2000 were performed with extended analyses of the September 29, 2009 magnitude 8.1 Samoa earthquake and the April 19, 2014 magnitude 7.5 Papua New Guinea earthquake. The AIC test is shown to be able to accurately select the most appropriate model and the selected W-phase inversion is shown to yield reliable solutions that match published analyses of the same events.
Identification of Constitutive Parameters Using Inverse Strategy Coupled to an ANN Model
International Nuclear Information System (INIS)
Aguir, H.; Chamekh, A.; BelHadjSalah, H.; Hambli, R.
2007-01-01
This paper deals with the identification of material parameters using an inverse strategy. In the classical methods, the inverse technique is generally coupled with a finite element code which leads to a long computing time. In this work an inverse strategy coupled with an ANN procedure is proposed. This method has the advantage of being faster than the classical one. To validate this approach an experimental plane tensile and bulge tests are used in order to identify material behavior. The ANN model is trained from finite element simulations of the two tests. In order to reduce the gap between the experimental responses and the numerical ones, the proposed method is coupled with an optimization procedure to identify material parameters for the AISI304. The identified material parameters are the hardening curve and the anisotropic coefficients
GEMSFITS: Code package for optimization of geochemical model parameters and inverse modeling
International Nuclear Information System (INIS)
Miron, George D.; Kulik, Dmitrii A.; Dmytrieva, Svitlana V.; Wagner, Thomas
2015-01-01
Highlights: • Tool for generating consistent parameters against various types of experiments. • Handles a large number of experimental data and parameters (is parallelized). • Has a graphical interface and can perform statistical analysis on the parameters. • Tested on fitting the standard state Gibbs free energies of aqueous Al species. • Example on fitting interaction parameters of mixing models and thermobarometry. - Abstract: GEMSFITS is a new code package for fitting internally consistent input parameters of GEM (Gibbs Energy Minimization) geochemical–thermodynamic models against various types of experimental or geochemical data, and for performing inverse modeling tasks. It consists of the gemsfit2 (parameter optimizer) and gfshell2 (graphical user interface) programs both accessing a NoSQL database, all developed with flexibility, generality, efficiency, and user friendliness in mind. The parameter optimizer gemsfit2 includes the GEMS3K chemical speciation solver ( (http://gems.web.psi.ch/GEMS3K)), which features a comprehensive suite of non-ideal activity- and equation-of-state models of solution phases (aqueous electrolyte, gas and fluid mixtures, solid solutions, (ad)sorption. The gemsfit2 code uses the robust open-source NLopt library for parameter fitting, which provides a selection between several nonlinear optimization algorithms (global, local, gradient-based), and supports large-scale parallelization. The gemsfit2 code can also perform comprehensive statistical analysis of the fitted parameters (basic statistics, sensitivity, Monte Carlo confidence intervals), thus supporting the user with powerful tools for evaluating the quality of the fits and the physical significance of the model parameters. The gfshell2 code provides menu-driven setup of optimization options (data selection, properties to fit and their constraints, measured properties to compare with computed counterparts, and statistics). The practical utility, efficiency, and
Approaches to highly parameterized inversion-A guide to using PEST for groundwater-model calibration
Doherty, John E.; Hunt, Randall J.
2010-01-01
Highly parameterized groundwater models can create calibration difficulties. Regularized inversion-the combined use of large numbers of parameters with mathematical approaches for stable parameter estimation-is becoming a common approach to address these difficulties and enhance the transfer of information contained in field measurements to parameters used to model that system. Though commonly used in other industries, regularized inversion is somewhat imperfectly understood in the groundwater field. There is concern that this unfamiliarity can lead to underuse, and misuse, of the methodology. This document is constructed to facilitate the appropriate use of regularized inversion for calibrating highly parameterized groundwater models. The presentation is directed at an intermediate- to advanced-level modeler, and it focuses on the PEST software suite-a frequently used tool for highly parameterized model calibration and one that is widely supported by commercial graphical user interfaces. A brief overview of the regularized inversion approach is provided, and techniques for mathematical regularization offered by PEST are outlined, including Tikhonov, subspace, and hybrid schemes. Guidelines for applying regularized inversion techniques are presented after a logical progression of steps for building suitable PEST input. The discussion starts with use of pilot points as a parameterization device and processing/grouping observations to form multicomponent objective functions. A description of potential parameter solution methodologies and resources available through the PEST software and its supporting utility programs follows. Directing the parameter-estimation process through PEST control variables is then discussed, including guidance for monitoring and optimizing the performance of PEST. Comprehensive listings of PEST control variables, and of the roles performed by PEST utility support programs, are presented in the appendixes.
Castro, Luísa; Aguiar, Paulo
2012-08-01
Phase precession is one of the most well known examples within the temporal coding hypothesis. Here we present a biophysical spiking model for phase precession in hippocampal CA1 which focuses on the interaction between place cells and local inhibitory interneurons. The model's functional block is composed of a place cell (PC) connected with a local inhibitory cell (IC) which is modulated by the population theta rhythm. Both cells receive excitatory inputs from the entorhinal cortex (EC). These inputs are both theta modulated and space modulated. The dynamics of the two neuron types are described by integrate-and-fire models with conductance synapses, and the EC inputs are described using non-homogeneous Poisson processes. Phase precession in our model is caused by increased drive to specific PC/IC pairs when the animal is in their place field. The excitation increases the IC's firing rate, and this modulates the PC's firing rate such that both cells precess relative to theta. Our model implies that phase coding in place cells may not be independent from rate coding. The absence of restrictive connectivity constraints in this model predicts the generation of phase precession in any network with similar architecture and subject to a clocking rhythm, independently of the involvement in spatial tasks.
Dettmer, Jan; Dosso, Stan E
2012-10-01
This paper develops a trans-dimensional approach to matched-field geoacoustic inversion, including interacting Markov chains to improve efficiency and an autoregressive model to account for correlated errors. The trans-dimensional approach and hierarchical seabed model allows inversion without assuming any particular parametrization by relaxing model specification to a range of plausible seabed models (e.g., in this case, the number of sediment layers is an unknown parameter). Data errors are addressed by sampling statistical error-distribution parameters, including correlated errors (covariance), by applying a hierarchical autoregressive error model. The well-known difficulty of low acceptance rates for trans-dimensional jumps is addressed with interacting Markov chains, resulting in a substantial increase in efficiency. The trans-dimensional seabed model and the hierarchical error model relax the degree of prior assumptions required in the inversion, resulting in substantially improved (more realistic) uncertainty estimates and a more automated algorithm. In particular, the approach gives seabed parameter uncertainty estimates that account for uncertainty due to prior model choice (layering and data error statistics). The approach is applied to data measured on a vertical array in the Mediterranean Sea.
Effect of recent observations on Asian CO2 flux estimates by transport model inversions
International Nuclear Information System (INIS)
Maksyutov, Shamil; Patra, Prabir K.; Machida, Toshinobu; Mukai, Hitoshi; Nakazawa, Takakiyo; Inoue, Gen
2003-01-01
We use an inverse model to evaluate the effects of the recent CO 2 observations over Asia on estimates of regional CO 2 sources and sinks. Global CO 2 flux distribution is evaluated using several atmospheric transport models, atmospheric CO 2 observations and a 'time-independent' inversion procedure adopted in the basic synthesis inversion by the Transcom-3 inverse model intercomparison project. In our analysis we include airborne and tower observations in Siberia, continuous monitoring and airborne observations over Japan, and airborne monitoring on regular flights on Tokyo-Sydney route. The inclusion of the new data reduces the uncertainty of the estimated regional CO 2 fluxes for Boreal Asia (Siberia), Temperate Asia and South-East Asia. The largest effect is observed for the emission/sink estimate for the Boreal Asia region, where introducing the observations in Siberia reduces the source uncertainty by almost half. It also produces an uncertainty reduction for Boreal North America. Addition of the Siberian airborne observations leads to projecting extra sinks in Boreal Asia of 0.2 Pg C/yr, and a smaller change for Europe. The Tokyo-Sydney observations reduce and constrain the Southeast Asian source
Validation of the Swiss methane emission inventory by atmospheric observations and inverse modelling
Directory of Open Access Journals (Sweden)
S. Henne
2016-03-01
Full Text Available Atmospheric inverse modelling has the potential to provide observation-based estimates of greenhouse gas emissions at the country scale, thereby allowing for an independent validation of national emission inventories. Here, we present a regional-scale inverse modelling study to quantify the emissions of methane (CH4 from Switzerland, making use of the newly established CarboCount-CH measurement network and a high-resolution Lagrangian transport model. In our reference inversion, prior emissions were taken from the "bottom-up" Swiss Greenhouse Gas Inventory (SGHGI as published by the Swiss Federal Office for the Environment in 2014 for the year 2012. Overall we estimate national CH4 emissions to be 196 ± 18 Gg yr−1 for the year 2013 (1σ uncertainty. This result is in close agreement with the recently revised SGHGI estimate of 206 ± 33 Gg yr−1 as reported in 2015 for the year 2012. Results from sensitivity inversions using alternative prior emissions, uncertainty covariance settings, large-scale background mole fractions, two different inverse algorithms (Bayesian and extended Kalman filter, and two different transport models confirm the robustness and independent character of our estimate. According to the latest SGHGI estimate the main CH4 source categories in Switzerland are agriculture (78 %, waste handling (15 % and natural gas distribution and combustion (6 %. The spatial distribution and seasonal variability of our posterior emissions suggest an overestimation of agricultural CH4 emissions by 10 to 20 % in the most recent SGHGI, which is likely due to an overestimation of emissions from manure handling. Urban areas do not appear as emission hotspots in our posterior results, suggesting that leakages from natural gas distribution are only a minor source of CH4 in Switzerland. This is consistent with rather low emissions of 8.4 Gg yr−1 reported by the SGHGI but inconsistent with the much higher value of 32 Gg yr−1 implied by the
Magnetotelluric Forward Modeling and Inversion In 3 -d Conductivity Model of The Vesuvio Volcano
Spichak, V.; Patella, D.
Three-dimensional forward modeling of MT fields in the simplified conductivity model of the Vesuvio volcano (T=0.1, 1, 10, 100 and 1000s) indicates that the best image of the magma chamber could be obtained basing on the pseudo-section of the determinant apparent resitivity phase as well as on the real and imaginary components of the electric field. Another important result of the studies conducted is that it was demonstrated the principal opportunity of detection and contouring the magma chamber by 2-D pseudo-sections constructed basing on the data transforms mentioned above. Bayesian three-dimensional inversion of synthetic MT data in the volcano model indicates that it is possible to determine the depth and vertical size of the magma chamber, however, simultaneous detection of the conductivity distribution inside the domain of search is of pure quality. However, if the geometrical parameters of the magma chamber are determined in advance, it becomes quite realistic to find out the conductivity distribution inside. The accuracy of such estimation strongly depends on the uncertainty in its prior value: the more narrow is the prior conductivity palette the closer could be the posterior conductivity distribution to the true one.
Modelling Inverse Gaussian Data with Censored Response Values: EM versus MCMC
Directory of Open Access Journals (Sweden)
R. S. Sparks
2011-01-01
Full Text Available Low detection limits are common in measure environmental variables. Building models using data containing low or high detection limits without adjusting for the censoring produces biased models. This paper offers approaches to estimate an inverse Gaussian distribution when some of the data used are censored because of low or high detection limits. Adjustments for the censoring can be made if there is between 2% and 20% censoring using either the EM algorithm or MCMC. This paper compares these approaches.
Inversion of the Jacobi-Porstendörfer Room Model for the Radon Progeny
Czech Academy of Sciences Publication Activity Database
Thomas, J.; Jílek, K.; Brabec, Marek
2010-01-01
Roč. 55, č. 4 (2010), s. 433-437 ISSN 0029-5922 Institutional research plan: CEZ:AV0Z10300504 Keywords : Jacobi room model * inversion and invariants of the model * unattached radon daughters * attachment rate * deposition rate Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 0.321, year: 2010 http://www.nukleonika.pl/www/back/full/vol55_2010/v55n4p433f.pdf
Partridge, Daniel; Morales, Ricardo; Stier, Philip
2015-04-01
Many previous studies have compared droplet activation parameterisations against adiabatic parcel models (e.g. Ghan et al., 2001). However, these have often involved comparisons for a limited number of parameter combinations based upon certain aerosol regimes. Recent studies (Morales et al., 2014) have used wider ranges when evaluating their parameterisations, however, no study has explored the full possible multi-dimensional parameter space that would be experienced by droplet activations within a global climate model (GCM). It is important to be able to efficiently highlight regions of the entire multi-dimensional parameter space in which we can expect the largest discrepancy between parameterisation and cloud parcel models in order to ascertain which regions simulated by a GCM can be expected to be a less accurate representation of the process of cloud droplet activation. This study provides a new, efficient, inverse modelling framework for comparing droplet activation parameterisations to more complex cloud parcel models. To achieve this we couple a Markov Chain Monte Carlo algorithm (Partridge et al., 2012) to two independent adiabatic cloud parcel models and four droplet activation parameterisations. This framework is computationally faster than employing a brute force Monte Carlo simulation, and allows us to transparently highlight which parameterisation provides the closest representation across all aerosol physiochemical and meteorological environments. The parameterisations are demonstrated to perform well for a large proportion of possible parameter combinations, however, for certain key parameters; most notably the vertical velocity and accumulation mode aerosol concentration, large discrepancies are highlighted. These discrepancies correspond for parameter combinations that result in very high/low simulated values of maximum supersaturation. By identifying parameter interactions or regimes within the multi-dimensional parameter space we hope to guide
Efficient non-negative constrained model-based inversion in optoacoustic tomography
International Nuclear Information System (INIS)
Ding, Lu; Luís Deán-Ben, X; Lutzweiler, Christian; Razansky, Daniel; Ntziachristos, Vasilis
2015-01-01
The inversion accuracy in optoacoustic tomography depends on a number of parameters, including the number of detectors employed, discrete sampling issues or imperfectness of the forward model. These parameters result in ambiguities on the reconstructed image. A common ambiguity is the appearance of negative values, which have no physical meaning since optical absorption can only be higher or equal than zero. We investigate herein algorithms that impose non-negative constraints in model-based optoacoustic inversion. Several state-of-the-art non-negative constrained algorithms are analyzed. Furthermore, an algorithm based on the conjugate gradient method is introduced in this work. We are particularly interested in investigating whether positive restrictions lead to accurate solutions or drive the appearance of errors and artifacts. It is shown that the computational performance of non-negative constrained inversion is higher for the introduced algorithm than for the other algorithms, while yielding equivalent results. The experimental performance of this inversion procedure is then tested in phantoms and small animals, showing an improvement in image quality and quantitativeness with respect to the unconstrained approach. The study performed validates the use of non-negative constraints for improving image accuracy compared to unconstrained methods, while maintaining computational efficiency. (paper)
Directory of Open Access Journals (Sweden)
Warsa
2014-07-01
Full Text Available Groundwater is an important economic source of water supply for drinking water and irrigation water for agriculture. Surface nuclear magnetic resonance (SNMR sounding is a relatively new geophysical method that can be used to determine the presence of culturally and economically important substances, such as subsurface water or hydrocarbon distribution. SNMR sounding allows the determination of water content and pore size distribution directly from the surface. The SNMR method is performed by stimulating an alternating current pulse through an antenna at the surface in order to confirm the existence of water in the subsurface. This paper reports the development of a 3-D forward modeling code for SNMR amplitudes and decay times, after which an improved 2-D and 3-D inversion algorithm is investigated, consisting of schemes for regularizing model parameterization. After briefly reviewing inversion schemes generally used in geophysics, the special properties of SNMR or magnetic resonance sounding (MRS inversion are evaluated. We present an extension of MRS to magnetic resonance tomography (MRT, i.e. an extension for 2-D and 3-D investigation, and the appropriate inversions.
Energy Technology Data Exchange (ETDEWEB)
Garreta, Vincent; Guiot, Joel; Hely, Christelle [CEREGE, UMR 6635, CNRS, Universite Aix-Marseille, Europole de l' Arbois, Aix-en-Provence (France); Miller, Paul A.; Sykes, Martin T. [Lund University, Department of Physical Geography and Ecosystems Analysis, Geobiosphere Science Centre, Lund (Sweden); Brewer, Simon [Universite de Liege, Institut d' Astrophysique et de Geophysique, Liege (Belgium); Litt, Thomas [University of Bonn, Paleontological Institute, Bonn (Germany)
2010-08-15
Climate reconstructions from data sensitive to past climates provide estimates of what these climates were like. Comparing these reconstructions with simulations from climate models allows to validate the models used for future climate prediction. It has been shown that for fossil pollen data, gaining estimates by inverting a vegetation model allows inclusion of past changes in carbon dioxide values. As a new generation of dynamic vegetation model is available we have developed an inversion method for one model, LPJ-GUESS. When this novel method is used with high-resolution sediment it allows us to bypass the classic assumptions of (1) climate and pollen independence between samples and (2) equilibrium between the vegetation, represented as pollen, and climate. Our dynamic inversion method is based on a statistical model to describe the links among climate, simulated vegetation and pollen samples. The inversion is realised thanks to a particle filter algorithm. We perform a validation on 30 modern European sites and then apply the method to the sediment core of Meerfelder Maar (Germany), which covers the Holocene at a temporal resolution of approximately one sample per 30 years. We demonstrate that reconstructed temperatures are constrained. The reconstructed precipitation is less well constrained, due to the dimension considered (one precipitation by season), and the low sensitivity of LPJ-GUESS to precipitation changes. (orig.)
Inverse modeling as a step in the calibration of the LBL-USGS site-scale model of Yucca Mountain
International Nuclear Information System (INIS)
Finsterle, S.; Bodvarsson, G.S.; Chen, G.
1995-05-01
Calibration of the LBL-USGS site-scale model of Yucca Mountain is initiated. Inverse modeling techniques are used to match the results of simplified submodels to the observed pressure, saturation, and temperature data. Hydrologic and thermal parameters are determined and compared to the values obtained from laboratory measurements and conventional field test analysis
Three-dimensional gravity modeling and focusing inversion using rectangular meshes.
Energy Technology Data Exchange (ETDEWEB)
Commer, M.
2011-03-01
Rectangular grid cells are commonly used for the geophysical modeling of gravity anomalies, owing to their flexibility in constructing complex models. The straightforward handling of cubic cells in gravity inversion algorithms allows for a flexible imposition of model regularization constraints, which are generally essential in the inversion of static potential field data. The first part of this paper provides a review of commonly used expressions for calculating the gravity of a right polygonal prism, both for gravity and gradiometry, where the formulas of Plouff and Forsberg are adapted. The formulas can be cast into general forms practical for implementation. In the second part, a weighting scheme for resolution enhancement at depth is presented. Modelling the earth using highly digitized meshes, depth weighting schemes are typically applied to the model objective functional, subject to minimizing the data misfit. The scheme proposed here involves a non-linear conjugate gradient inversion scheme with a weighting function applied to the non-linear conjugate gradient scheme's gradient vector of the objective functional. The low depth resolution due to the quick decay of the gravity kernel functions is counteracted by suppressing the search directions in the parameter space that would lead to near-surface concentrations of gravity anomalies. Further, a density parameter transformation function enabling the imposition of lower and upper bounding constraints is employed. Using synthetic data from models of varying complexity and a field data set, it is demonstrated that, given an adequate depth weighting function, the gravity inversion in the transform space can recover geologically meaningful models requiring a minimum of prior information and user interaction.
Full-model wavenumber inversion: An emphasis on the appropriate wavenumber continuation
Alkhalifah, Tariq Ali
2016-04-06
A model of the earth can be described using a Fourier basis represented by its wavenumber content. In full-waveform inversion (FWI), the wavenumber description of the model is natural because our Born-approximation-based velocity updates are made up of wavefields. Our objective in FWI is to access all the model wavenumbers available in our limited aperture and bandwidth recorded data that are not yet accurately present in the initial velocity model. To invert for those model wavenumbers, we need to locate their imprint in the data. Thus, I review the relation between the model wavenumber buildup and the inversion process. Specifically, I emphasize a focus on the model wavenumber components and identified their individual influence on the data. Missing the energy for a single vertical low-model wavenumber from the residual between the true Marmousi model and some initial linearly increasing velocity model produced a worse least-squares fit to the data than the initial model itself, in which all the residual model wavenumbers were missing. This stern realization validated the importance of wavenumber continuation, specifically starting from the low-model wavenumbers, to higher (resolution) wavenumbers, especially those attained in an order dictated by the scattering angle filter. A numerical Marmousi example determined the important role that the scattering angle filter played in managing the wavenumber continuation from low to high. An application on the SEG2014 blind test data set with frequencies lower than 7 Hz muted out further validated the versatility of the scattering angle filtering.
Full-model wavenumber inversion: An emphasis on the appropriate wavenumber continuation
Alkhalifah, Tariq Ali
2016-01-01
A model of the earth can be described using a Fourier basis represented by its wavenumber content. In full-waveform inversion (FWI), the wavenumber description of the model is natural because our Born-approximation-based velocity updates are made up of wavefields. Our objective in FWI is to access all the model wavenumbers available in our limited aperture and bandwidth recorded data that are not yet accurately present in the initial velocity model. To invert for those model wavenumbers, we need to locate their imprint in the data. Thus, I review the relation between the model wavenumber buildup and the inversion process. Specifically, I emphasize a focus on the model wavenumber components and identified their individual influence on the data. Missing the energy for a single vertical low-model wavenumber from the residual between the true Marmousi model and some initial linearly increasing velocity model produced a worse least-squares fit to the data than the initial model itself, in which all the residual model wavenumbers were missing. This stern realization validated the importance of wavenumber continuation, specifically starting from the low-model wavenumbers, to higher (resolution) wavenumbers, especially those attained in an order dictated by the scattering angle filter. A numerical Marmousi example determined the important role that the scattering angle filter played in managing the wavenumber continuation from low to high. An application on the SEG2014 blind test data set with frequencies lower than 7 Hz muted out further validated the versatility of the scattering angle filtering.
DEFF Research Database (Denmark)
Herckenrath, Daan; Fiandaca, G.; Auken, Esben
2013-01-01
hydrogeophysical inversion approaches to inform a field-scale groundwater model with time domain electromagnetic (TDEM) and electrical resistivity tomography (ERT) data. In a sequential hydrogeophysical inversion (SHI) a groundwater model is calibrated with geophysical data by coupling groundwater model parameters...... with the inverted geophysical models. We subsequently compare the SHI with a joint hydrogeophysical inversion (JHI). In the JHI, a geophysical model is simultaneously inverted with a groundwater model by coupling the groundwater and geophysical parameters to explicitly account for an established petrophysical...
DEFF Research Database (Denmark)
Herckenrath, Daan; Fiandaca, G.; Auken, Esben
2013-01-01
with the inverted geophysical models. We subsequently compare the SHI with a joint hydrogeophysical inversion (JHI). In the JHI, a geophysical model is simultaneously inverted with a groundwater model by coupling the groundwater and geophysical parameters to explicitly account for an established petrophysical...... hydrogeophysical inversion approaches to inform a field-scale groundwater model with time domain electromagnetic (TDEM) and electrical resistivity tomography (ERT) data. In a sequential hydrogeophysical inversion (SHI) a groundwater model is calibrated with geophysical data by coupling groundwater model parameters...
Inverse Optimization: A New Perspective on the Black-Litterman Model
Bertsimas, Dimitris; Gupta, Vishal; Paschalidis, Ioannis Ch.
2014-01-01
The Black-Litterman (BL) model is a widely used asset allocation model in the financial industry. In this paper, we provide a new perspective. The key insight is to replace the statistical framework in the original approach with ideas from inverse optimization. This insight allows us to significantly expand the scope and applicability of the BL model. We provide a richer formulation that, unlike the original model, is flexible enough to incorporate investor information on volatility and market dynamics. Equally importantly, our approach allows us to move beyond the traditional mean-variance paradigm of the original model and construct “BL”-type estimators for more general notions of risk such as coherent risk measures. Computationally, we introduce and study two new “BL”-type estimators and their corresponding portfolios: a Mean Variance Inverse Optimization (MV-IO) portfolio and a Robust Mean Variance Inverse Optimization (RMV-IO) portfolio. These two approaches are motivated by ideas from arbitrage pricing theory and volatility uncertainty. Using numerical simulation and historical backtesting, we show that both methods often demonstrate a better risk-reward tradeoff than their BL counterparts and are more robust to incorrect investor views. PMID:25382873
Inverse Optimization: A New Perspective on the Black-Litterman Model.
Bertsimas, Dimitris; Gupta, Vishal; Paschalidis, Ioannis Ch
2012-12-11
The Black-Litterman (BL) model is a widely used asset allocation model in the financial industry. In this paper, we provide a new perspective. The key insight is to replace the statistical framework in the original approach with ideas from inverse optimization. This insight allows us to significantly expand the scope and applicability of the BL model. We provide a richer formulation that, unlike the original model, is flexible enough to incorporate investor information on volatility and market dynamics. Equally importantly, our approach allows us to move beyond the traditional mean-variance paradigm of the original model and construct "BL"-type estimators for more general notions of risk such as coherent risk measures. Computationally, we introduce and study two new "BL"-type estimators and their corresponding portfolios: a Mean Variance Inverse Optimization (MV-IO) portfolio and a Robust Mean Variance Inverse Optimization (RMV-IO) portfolio. These two approaches are motivated by ideas from arbitrage pricing theory and volatility uncertainty. Using numerical simulation and historical backtesting, we show that both methods often demonstrate a better risk-reward tradeoff than their BL counterparts and are more robust to incorrect investor views.
Numerical modeling of axi-symmetrical cold forging process by ``Pseudo Inverse Approach''
Halouani, A.; Li, Y. M.; Abbes, B.; Guo, Y. Q.
2011-05-01
The incremental approach is widely used for the forging process modeling, it gives good strain and stress estimation, but it is time consuming. A fast Inverse Approach (IA) has been developed for the axi-symmetric cold forging modeling [1-2]. This approach exploits maximum the knowledge of the final part's shape and the assumptions of proportional loading and simplified tool actions make the IA simulation very fast. The IA is proved very useful for the tool design and optimization because of its rapidity and good strain estimation. However, the assumptions mentioned above cannot provide good stress estimation because of neglecting the loading history. A new approach called "Pseudo Inverse Approach" (PIA) was proposed by Batoz, Guo et al.. [3] for the sheet forming modeling, which keeps the IA's advantages but gives good stress estimation by taking into consideration the loading history. Our aim is to adapt the PIA for the cold forging modeling in this paper. The main developments in PIA are resumed as follows: A few intermediate configurations are generated for the given tools' positions to consider the deformation history; the strain increment is calculated by the inverse method between the previous and actual configurations. An incremental algorithm of the plastic integration is used in PIA instead of the total constitutive law used in the IA. An example is used to show the effectiveness and limitations of the PIA for the cold forging process modeling.
Biophysics and systems biology.
Noble, Denis
2010-03-13
Biophysics at the systems level, as distinct from molecular biophysics, acquired its most famous paradigm in the work of Hodgkin and Huxley, who integrated their equations for the nerve impulse in 1952. Their approach has since been extended to other organs of the body, notably including the heart. The modern field of computational biology has expanded rapidly during the first decade of the twenty-first century and, through its contribution to what is now called systems biology, it is set to revise many of the fundamental principles of biology, including the relations between genotypes and phenotypes. Evolutionary theory, in particular, will require re-assessment. To succeed in this, computational and systems biology will need to develop the theoretical framework required to deal with multilevel interactions. While computational power is necessary, and is forthcoming, it is not sufficient. We will also require mathematical insight, perhaps of a nature we have not yet identified. This article is therefore also a challenge to mathematicians to develop such insights.
Energy Technology Data Exchange (ETDEWEB)
Lehikoinen, A.; Huttunen, J.M.J.; Finsterle, S.; Kowalsky, M.B.; Kaipio, J.P.
2009-08-01
We propose an approach for imaging the dynamics of complex hydrological processes. The evolution of electrically conductive fluids in porous media is imaged using time-lapse electrical resistance tomography. The related dynamic inversion problem is solved using Bayesian filtering techniques, that is, it is formulated as a sequential state estimation problem in which the target is an evolving posterior probability density of the system state. The dynamical inversion framework is based on the state space representation of the system, which involves the construction of a stochastic evolution model and an observation model. The observation model used in this paper consists of the complete electrode model for ERT, with Archie's law relating saturations to electrical conductivity. The evolution model is an approximate model for simulating flow through partially saturated porous media. Unavoidable modeling and approximation errors in both the observation and evolution models are considered by computing approximate statistics for these errors. These models are then included in the construction of the posterior probability density of the estimated system state. This approximation error method allows the use of approximate - and therefore computationally efficient - observation and evolution models in the Bayesian filtering. We consider a synthetic example and show that the incorporation of an explicit model for the model uncertainties in the state space representation can yield better estimates than a frame-by-frame imaging approach.
Tadmor, Arbel
2009-03-01
In this work a biophysical model of Escherichia coli is presented that predicts growth rate and an effective cellular composition from an effective, coarse-grained representation of its genome. We assume that E. coli is in a state of balanced exponential steady-state growth, growing in a temporally and spatially constant environment, rich in resources. We apply this model to a series of past measurements, where the growth rate and rRNA-to-protein ratio have been measured for seven E. coli strains with an rRNA operon copy number ranging from one to seven (the wild-type copy number). These experiments show that growth rate markedly decreases for strains with fewer than six copies. Using the model, we were able to reproduce these measurements. We show that the model that best fits these data suggests that the volume fraction of macromolecules inside E. coli is not fixed when the rRNA operon copy number is varied. Moreover, the model predicts that increasing the copy number beyond seven results in a cytoplasm densely packed with ribosomes and proteins. Assuming that under such overcrowded conditions prolonged diffusion times tend to weaken binding affinities, the model predicts that growth rate will not increase substantially beyond the wild-type growth rate, as indicated by other experiments. Our model therefore suggests that changing the rRNA operon copy number of wild-type E. coli cells growing in a constant rich environment does not substantially increase their growth rate. Other observations regarding strains with an altered rRNA operon copy number, such as nucleoid compaction and the rRNA operon feedback response, appear to be qualitatively consistent with this model. In addition, we discuss possible design principles suggested by the model and propose further experiments to test its validity.
International Nuclear Information System (INIS)
Green, J.; Eccles, R.; Hegmann, G.; Morrison, L.; Salter, R.; van Egmond, T.; Vonk, P.; Ash, G.; Crowther, R.; Dance, T.; Edwards, W.; Veldman, W.
1997-02-01
An environmental assessment of the Cold Lake Expansion Project has been conducted to identify major issues of concern by public and government agencies, to determine means to eliminate or reduce those impacts, and to recommend any further efforts required to obtain missing information or monitor impacts. Volume 2 of the environmental impact assessment is divided into two parts. Part 1 (biophysical and resource use assessment) constitutes the primary environmental impact assessment document for the Cold Lake expansion project. It includes technical support documentation in regard to: (1) an assessment of noise impacts, (2) an assessment of greenhouse gas emissions, (3) a conceptual conservation and reclamation plan, (4) a historical resource impact assessment, and (5) a description of effects of oil spills on fish. Part 2 (impact model description) serves a reference document for part 1. It describes the approach taken in developing and assessing the impact models, discusses proposed methods for mitigation and management of residual impacts, and the recommended monitoring requirements for each of the major resource disciplines. The impact models describe the specific pathways through which impacts will occur as a result of interactions between project-related activities and important environmental components. 476 refs., 58 tabs., 23 figs
Research Note: Full-waveform inversion of the unwrapped phase of a model
Alkhalifah, Tariq Ali
2013-12-06
Reflections in seismic data induce serious non-linearity in the objective function of full- waveform inversion. Thus, without a good initial velocity model that can produce reflections within a half cycle of the frequency used in the inversion, convergence to a solution becomes difficult. As a result, we tend to invert for refracted events and damp reflections in data. Reflection induced non-linearity stems from cycle skipping between the imprint of the true model in observed data and the predicted model in synthesized data. Inverting for the phase of the model allows us to address this problem by avoiding the source of non-linearity, the phase wrapping phenomena. Most of the information related to the location (or depths) of interfaces is embedded in the phase component of a model, mainly influenced by the background model, while the velocity-contrast information (responsible for the reflection energy) is mainly embedded in the amplitude component. In combination with unwrapping the phase of data, which mitigates the non-linearity introduced by the source function, I develop a framework to invert for the unwrapped phase of a model, represented by the instantaneous depth, using the unwrapped phase of the data. The resulting gradient function provides a mechanism to non-linearly update the velocity model by applying mainly phase shifts to the model. In using the instantaneous depth as a model parameter, we keep track of the model properties unfazed by the wrapping phenomena. © 2013 European Association of Geoscientists & Engineers.
Chen, Mingjie; Izady, Azizallah; Abdalla, Osman A.; Amerjeed, Mansoor
2018-02-01
Bayesian inference using Markov Chain Monte Carlo (MCMC) provides an explicit framework for stochastic calibration of hydrogeologic models accounting for uncertainties; however, the MCMC sampling entails a large number of model calls, and could easily become computationally unwieldy if the high-fidelity hydrogeologic model simulation is time consuming. This study proposes a surrogate-based Bayesian framework to address this notorious issue, and illustrates the methodology by inverse modeling a regional MODFLOW model. The high-fidelity groundwater model is approximated by a fast statistical model using Bagging Multivariate Adaptive Regression Spline (BMARS) algorithm, and hence the MCMC sampling can be efficiently performed. In this study, the MODFLOW model is developed to simulate the groundwater flow in an arid region of Oman consisting of mountain-coast aquifers, and used to run representative simulations to generate training dataset for BMARS model construction. A BMARS-based Sobol' method is also employed to efficiently calculate input parameter sensitivities, which are used to evaluate and rank their importance for the groundwater flow model system. According to sensitivity analysis, insensitive parameters are screened out of Bayesian inversion of the MODFLOW model, further saving computing efforts. The posterior probability distribution of input parameters is efficiently inferred from the prescribed prior distribution using observed head data, demonstrating that the presented BMARS-based Bayesian framework is an efficient tool to reduce parameter uncertainties of a groundwater system.
Directory of Open Access Journals (Sweden)
I. Pison
2006-07-01
Full Text Available A new methodology for the inversion of anthropogenic emissions at a local scale is tested. The inversion constraints are provided by a kriging technique used in air quality forecast in the Paris area, which computes an analyzed concentration field from network measurements and the first-guess simulation of a CTM. The inverse developed here is based on the CHIMERE model and its adjoint to perform 4-D integration. The methodology is validated on synthetic cases inverting emission fluxes. It is shown that the information provided by the analyzed concentrations is sufficient to reach a mathematically acceptable solution to the optimization, even when little information is available in the measurements. As compared to the use of measurements alone or of measurements and a background matrix, the use of kriging leads to a more homogeneous distribution of the corrections, both in space and time. Moreover, it is then possible to double the accuracy of the inversion by performing two kriging-optimization cycles. Nevertheless, kriging analysis cannot compensate for a very important lack of information in the measurements.
Directory of Open Access Journals (Sweden)
Leszek Majkut
Full Text Available In the work, in order to solve the inverse problem, i.e. the problem of finding values of the additional quantities (mass, elasticity, the beam inverse model was proposed. Analysis of this model allows finding such a value of additional mass (elasticity as a function of its localization so that the free vibration frequency changes to desirable value. The criteria for choice of the “proper” pair (mass - its position, including the criterion allowing changing the position of the vibration node of the second mode of the free vibrations, were given. Analysis of the influence of uncertainties in the determination of the additional quantity value and its position on the desired free vibration frequency was carried out, too. The proposed beam inverse model can be employing to identification of the beam cracks. In such a case, the input quantity is free vibration frequency measured on the damaged object. Each determined free-vibration frequency allows determining the flexibility curve for the spring modeling crack as a function of its position. The searched parameters of the crack (its depth and position are indicated by the common point of two arbitrary curves. Accuracy of crack parameters determination depends on accuracy (uncertainty of frequency measurement. Only some regions containing the searched crack parameters can be obtained in such a situation.
Energy Technology Data Exchange (ETDEWEB)
Watanabe, T; Sassa, K [Kyoto University, Kyoto (Japan); Uesaka, S [Kyoto University, Kyoto (Japan). Faculty of Engineering
1996-10-01
The effect of initial models on full-wave inversion (FWI) analysis based on acoustic wave-equation was studied for elastic wave tomography of underground structures. At present, travel time inversion using initial motion travel time is generally used, and inverse analysis is conducted using the concept `ray,` assuming very high wave frequency. Although this method can derive stable solutions relatively unaffected by initial model, it uses only the data of initial motion travel time. FWI calculates theoretical waveform at each receiver using all of observed waveforms as data by wave equation modeling where 2-D underground structure is calculated by difference calculus under the assumption that wave propagation is described by wave equation of P wave. Although it is a weak point that FWI is easily affected by noises in an initial model and data, it is featured by high resolution of solutions. This method offers very excellent convergence as a proper initial model is used, resulting in sufficient performance, however, it is strongly affected by initial model. 2 refs., 7 figs., 1 tab.
Directory of Open Access Journals (Sweden)
Leszek Majkut
Full Text Available In the work, the problems of the beam structural modification through coupling the additional mass or elastic support, as well as the problem of diagnostics of the beam cracks, are discussed. The common feature for both problems is that the material parameters in each of the discussed cases change only in one point (additional mass, the support in one point, the crack described by the elastic joint. These systems, after determination of the value of additional element and its localization, should have a given natural vibration frequency. In order to solve the inverse problem, i.e. the problem of finding values of the additional quantities (mass, elasticity, the beam inverse model was proposed. Analysis of this model allows finding such a value of additional mass (elasticity as a function of its localization so that the system has the free vibration frequency, which is desired in the modification problem or measured on the object in the diagnostics.
Impact of petrophysical uncertainty on Bayesian hydrogeophysical inversion and model selection
Brunetti, Carlotta; Linde, Niklas
2018-01-01
Quantitative hydrogeophysical studies rely heavily on petrophysical relationships that link geophysical properties to hydrogeological properties and state variables. Coupled inversion studies are frequently based on the questionable assumption that these relationships are perfect (i.e., no scatter). Using synthetic examples and crosshole ground-penetrating radar (GPR) data from the South Oyster Bacterial Transport Site in Virginia, USA, we investigate the impact of spatially-correlated petrophysical uncertainty on inferred posterior porosity and hydraulic conductivity distributions and on Bayes factors used in Bayesian model selection. Our study shows that accounting for petrophysical uncertainty in the inversion (I) decreases bias of the inferred variance of hydrogeological subsurface properties, (II) provides more realistic uncertainty assessment and (III) reduces the overconfidence in the ability of geophysical data to falsify conceptual hydrogeological models.
Comparison of inverse dynamics calculated by two- and three-dimensional models during walking
DEFF Research Database (Denmark)
Alkjaer, T; Simonsen, E B; Dyhre-Poulsen, P
2001-01-01
recorded the subjects as they walked across two force plates. The subjects were invited to approach a walking speed of 4.5 km/h. The ankle, knee and hip joint moments in the sagittal plane were calculated by 2D and 3D inverse dynamics analysis and compared. Despite the uniform walking speed (4.53 km....../h) and similar footwear, relatively large inter-individual variations were found in the joint moment patterns during the stance phase. The differences between individuals were present in both the 2D and 3D analysis. For the entire sample of subjects the overall time course pattern of the ankle, knee and hip...... the magnitude of the joint moments calculated by 2D and 3D inverse dynamics but the inter-individual variation was not affected by the different models. The simpler 2D model seems therefore appropriate for human gait analysis. However, comparisons of gait data from different studies are problematic...
Liu, Long; Liu, Wei
2018-04-01
A forward modeling and inversion algorithm is adopted in order to determine the water injection plan in the oilfield water injection network. The main idea of the algorithm is shown as follows: firstly, the oilfield water injection network is inversely calculated. The pumping station demand flow is calculated. Then, forward modeling calculation is carried out for judging whether all water injection wells meet the requirements of injection allocation or not. If all water injection wells meet the requirements of injection allocation, calculation is stopped, otherwise the demand injection allocation flow rate of certain step size is reduced aiming at water injection wells which do not meet requirements, and next iterative operation is started. It is not necessary to list the algorithm into water injection network system algorithm, which can be realized easily. Iterative method is used, which is suitable for computer programming. Experimental result shows that the algorithm is fast and accurate.
Spin model for nontrivial types of magnetic order in inverse-perovskite antiferromagnets
Mochizuki, Masahito; Kobayashi, Masaya; Okabe, Reoya; Yamamoto, Daisuke
2018-02-01
Nontrivial magnetic orders in the inverse-perovskite manganese nitrides are theoretically studied by constructing a classical spin model describing the magnetic anisotropy and frustrated exchange interactions inherent in specific crystal and electronic structures of these materials. With a replica-exchange Monte Carlo technique, a theoretical analysis of this model reproduces the experimentally observed triangular Γ5 g and Γ4 g spin-ordered patterns and the systematic evolution of magnetic orders. Our Rapid Communication solves a 40-year-old problem of nontrivial magnetism for the inverse-perovskite manganese nitrides and provides a firm basis for clarifying the magnetism-driven negative thermal expansion phenomenon discovered in this class of materials.
Amalia, Junita; Purhadi, Otok, Bambang Widjanarko
2017-11-01
Poisson distribution is a discrete distribution with count data as the random variables and it has one parameter defines both mean and variance. Poisson regression assumes mean and variance should be same (equidispersion). Nonetheless, some case of the count data unsatisfied this assumption because variance exceeds mean (over-dispersion). The ignorance of over-dispersion causes underestimates in standard error. Furthermore, it causes incorrect decision in the statistical test. Previously, paired count data has a correlation and it has bivariate Poisson distribution. If there is over-dispersion, modeling paired count data is not sufficient with simple bivariate Poisson regression. Bivariate Poisson Inverse Gaussian Regression (BPIGR) model is mix Poisson regression for modeling paired count data within over-dispersion. BPIGR model produces a global model for all locations. In another hand, each location has different geographic conditions, social, cultural and economic so that Geographically Weighted Regression (GWR) is needed. The weighting function of each location in GWR generates a different local model. Geographically Weighted Bivariate Poisson Inverse Gaussian Regression (GWBPIGR) model is used to solve over-dispersion and to generate local models. Parameter estimation of GWBPIGR model obtained by Maximum Likelihood Estimation (MLE) method. Meanwhile, hypothesis testing of GWBPIGR model acquired by Maximum Likelihood Ratio Test (MLRT) method.
Kleynhans, E; Barton, M G; Conlong, D E; Terblanche, J S
2017-08-08
Understanding pest population dynamics and seasonal phenology is a critical component of modern integrated pest-management programs. Accurate forecasting allows timely, cost-effective interventions, including maximum efficacy of, for example, biological control and/or sterile insect technique. Due to the variation in life stage-related sensitivity toward climate, insect pest population abundance models are often not easily interpreted or lack direct relevance to management strategies in the field. Here we apply a process-based (biophysical) model that incorporates climate data with life stage-dependent physiology and life history to attempt to predict Eldana saccharina life stage and generation turnover in sugarcane fields. Fitness traits are modelled at two agricultural locations in South Africa that differ in average temperature (hereafter a cold and a warm site). We test whether the life stage population structures in the field entering winter and local climate during winter directly affect development rates, and therefore interact to determine the population dynamics and phenological responses of E. saccharina in subsequent spring and summer seasons. The model predicts that: (1) E. saccharina can cycle through more generations at the warm site where fewer hours of cold and heat stress are endured, and (2) at the cold site, overwintering as pupae (rather than larvae) confer higher relative fitness and fecundity in the subsequent summer adult moths. The model predictions were compared with a large dataset of field observations from scouting records. Model predictions for larval presence (or absence) generally overlapped well with positive (or negative) scout records. These results are important for integrated pest management strategies by providing a useful foundation for future population dynamics models, and are applicable to a variety of agricultural landscapes, but especially the sugarcane industry of South Africa.
Yavari, Fatemeh; Mahdavi, Shirin; Towhidkhah, Farzad; Ahmadi-Pajouh, Mohammad-Ali; Ekhtiari, Hamed; Darainy, Mohammad
2016-04-01
Despite several pieces of evidence, which suggest that the human brain employs internal models for motor control and learning, the location of these models in the brain is not yet clear. In this study, we used transcranial direct current stimulation (tDCS) to manipulate right cerebellar function, while subjects adapt to a visuomotor task. We investigated the effect of this manipulation on the internal forward and inverse models by measuring two kinds of behavior: generalization of training in one direction to neighboring directions (as a proxy for inverse models) and localization of the hand position after movement without visual feedback (as a proxy for forward model). The experimental results showed no effect of cerebellar tDCS on generalization, but significant effect on localization. These observations support the idea that the cerebellum is a possible brain region for internal forward, but not inverse model formation. We also used a realistic human head model to calculate current density distribution in the brain. The result of this model confirmed the passage of current through the cerebellum. Moreover, to further explain some observed experimental results, we modeled the visuomotor adaptation process with the help of a biologically inspired method known as population coding. The effect of tDCS was also incorporated in the model. The results of this modeling study closely match our experimental data and provide further evidence in line with the idea that tDCS manipulates FM's function in the cerebellum.
Quantum Nanobiology and Biophysical Chemistry
DEFF Research Database (Denmark)
2013-01-01
An introduction was provided in the first issue by way of an Editorial to this special two issue volume of Current Physical Chemistry – “Quantum Nanobiology and Biophysical Chemistry” [1]. The Guest Editors would like to thank all the authors and referees who have contributed to this second issue....... Wu et al. use density functional theory to explore the use of Ni/Fe bimetallic nanotechnology in the bioremediation of decabromo-diphenyl esters. Araújo-Chaves et al. explore the binding and reactivity of Mn(III) porphyrins in the membrane mimetic setting of model liposomal systems. Claussen et al....... demonstrate extremely low detection performance of acyl-homoserine lactone in a biologically relevant system using surface enhanced Raman spectroscopy. Sugihara and Bondar evaluate the influence of methyl-groups and the protein environment on retinal geometries in rhodopsin and bacteriorhodopsin, two...
Advanced Multivariate Inversion Techniques for High Resolution 3D Geophysical Modeling
2011-09-01
2005). We implemented a method to increase the usefulness of gravity data by filtering the Bouguer anomaly map. Though commonly applied 40 km 30 35...remove the long-wavelength components from the Bouguer gravity map we follow Tessema and Antoine (2004), who use an upward continuation method and...inversion of group velocities and gravity. (a) Top: Group velocities from a representative cell in the model. Bottom: Filtered Bouguer anomalies. (b
Polarimetry data inversion in conditions of tokamak plasma: Model based tomography concept
International Nuclear Information System (INIS)
Bieg, B.; Chrzanowski, J.; Kravtsov, Yu. A.; Mazon, D.
2015-01-01
Highlights: • Model based plasma tomography is presented. • Minimization procedure for the error function is suggested to be performed using the gradient method. • model based procedure of data inversion in the case of joint polarimetry–interferometry data. - Abstract: Model based plasma tomography is studied which fits a hypothetical multi-parameter plasma model to polarimetry and interferometry experimental data. Fitting procedure implies minimization of the error function, defined as a sum of squared differences between theoretical and empirical values. Minimization procedure for the function is suggested to be performed using the gradient method. Contrary to traditional tomography, which deals exclusively with observational data, model-based tomography (MBT) operates also with reasonable model of inhomogeneous plasma distribution and verifies which profile of a given class better fits experimental data. Model based tomography (MBT) restricts itself by definite class of models for instance power series, Fourier expansion etc. The basic equations of MBT are presented which generalize the equations of model based procedure of polarimetric data inversion in the case of joint polarimetry–interferometry data.
Polarimetry data inversion in conditions of tokamak plasma: Model based tomography concept
Energy Technology Data Exchange (ETDEWEB)
Bieg, B. [Maritime University of Szczecin, Waly Chrobrego 1-2, 70-500 Szczecin (Poland); Chrzanowski, J., E-mail: j.chrzanowski@am.szczecin.pl [Maritime University of Szczecin, Waly Chrobrego 1-2, 70-500 Szczecin (Poland); Kravtsov, Yu. A. [Maritime University of Szczecin, Waly Chrobrego 1-2, 70-500 Szczecin (Poland); Space Research Institute, Profsoyuznaya St. 82/34 Russian Academy of Science, Moscow 117997 (Russian Federation); Mazon, D. [CEA, IRFM, F-13108 Saint Paul-lez-Durance (France)
2015-10-15
Highlights: • Model based plasma tomography is presented. • Minimization procedure for the error function is suggested to be performed using the gradient method. • model based procedure of data inversion in the case of joint polarimetry–interferometry data. - Abstract: Model based plasma tomography is studied which fits a hypothetical multi-parameter plasma model to polarimetry and interferometry experimental data. Fitting procedure implies minimization of the error function, defined as a sum of squared differences between theoretical and empirical values. Minimization procedure for the function is suggested to be performed using the gradient method. Contrary to traditional tomography, which deals exclusively with observational data, model-based tomography (MBT) operates also with reasonable model of inhomogeneous plasma distribution and verifies which profile of a given class better fits experimental data. Model based tomography (MBT) restricts itself by definite class of models for instance power series, Fourier expansion etc. The basic equations of MBT are presented which generalize the equations of model based procedure of polarimetric data inversion in the case of joint polarimetry–interferometry data.
Smith, G. A.; Meyer, G.; Nordstrom, M.
1986-01-01
A new automatic flight control system concept suitable for aircraft with highly nonlinear aerodynamic and propulsion characteristics and which must operate over a wide flight envelope was investigated. This exact model follower inverts a complete nonlinear model of the aircraft as part of the feed-forward path. The inversion is accomplished by a Newton-Raphson trim of the model at each digital computer cycle time of 0.05 seconds. The combination of the inverse model and the actual aircraft in the feed-forward path alloys the translational and rotational regulators in the feedback path to be easily designed by linear methods. An explanation of the model inversion procedure is presented. An extensive set of simulation data for essentially the full flight envelope for a vertical attitude takeoff and landing aircraft (VATOL) is presented. These data demonstrate the successful, smooth, and precise control that can be achieved with this concept. The trajectory includes conventional flight from 200 to 900 ft/sec with path accelerations and decelerations, altitude changes of over 6000 ft and 2g and 3g turns. Vertical attitude maneuvering as a tail sitter along all axes is demonstrated. A transition trajectory from 200 ft/sec in conventional flight to stationary hover in the vertical attitude includes satisfactory operation through lift-cure slope reversal as attitude goes from horizontal to vertical at constant altitude. A vertical attitude takeoff from stationary hover to conventional flight is also demonstrated.
Fitz, Hartmut; Chang, Franklin
2017-09-01
Nativist theories have argued that language involves syntactic principles which are unlearnable from the input children receive. A paradigm case of these innate principles is the structure dependence of auxiliary inversion in complex polar questions (Chomsky, 1968, 1975, 1980). Computational approaches have focused on the properties of the input in explaining how children acquire these questions. In contrast, we argue that messages are structured in a way that supports structure dependence in syntax. We demonstrate this approach within a connectionist model of sentence production (Chang, 2009) which learned to generate a range of complex polar questions from a structured message without positive exemplars in the input. The model also generated different types of error in development that were similar in magnitude to those in children (e.g., auxiliary doubling, Ambridge, Rowland, & Pine, 2008; Crain & Nakayama, 1987). Through model comparisons we trace how meaning constraints and linguistic experience interact during the acquisition of auxiliary inversion. Our results suggest that auxiliary inversion rules in English can be acquired without innate syntactic principles, as long as it is assumed that speakers who ask complex questions express messages that are structured into multiple propositions. Copyright © 2017 Elsevier B.V. All rights reserved.
An Inverse Neural Controller Based on the Applicability Domain of RBF Network Models
Directory of Open Access Journals (Sweden)
Alex Alexandridis
2018-01-01
Full Text Available This paper presents a novel methodology of generic nature for controlling nonlinear systems, using inverse radial basis function neural network models, which may combine diverse data originating from various sources. The algorithm starts by applying the particle swarm optimization-based non-symmetric variant of the fuzzy means (PSO-NSFM algorithm so that an approximation of the inverse system dynamics is obtained. PSO-NSFM offers models of high accuracy combined with small network structures. Next, the applicability domain concept is suitably tailored and embedded into the proposed control structure in order to ensure that extrapolation is avoided in the controller predictions. Finally, an error correction term, estimating the error produced by the unmodeled dynamics and/or unmeasured external disturbances, is included to the control scheme to increase robustness. The resulting controller guarantees bounded input-bounded state (BIBS stability for the closed loop system when the open loop system is BIBS stable. The proposed methodology is evaluated on two different control problems, namely, the control of an experimental armature-controlled direct current (DC motor and the stabilization of a highly nonlinear simulated inverted pendulum. For each one of these problems, appropriate case studies are tested, in which a conventional neural controller employing inverse models and a PID controller are also applied. The results reveal the ability of the proposed control scheme to handle and manipulate diverse data through a data fusion approach and illustrate the superiority of the method in terms of faster and less oscillatory responses.
Pant, Anup D; Dorairaj, Syril K; Amini, Rouzbeh
2018-07-01
Quantifying the mechanical properties of the iris is important, as it provides insight into the pathophysiology of glaucoma. Recent ex vivo studies have shown that the mechanical properties of the iris are different in glaucomatous eyes as compared to normal ones. Notwithstanding the importance of the ex vivo studies, such measurements are severely limited for diagnosis and preclude development of treatment strategies. With the advent of detailed imaging modalities, it is possible to determine the in vivo mechanical properties using inverse finite element (FE) modeling. An inverse modeling approach requires an appropriate objective function for reliable estimation of parameters. In the case of the iris, numerous measurements such as iris chord length (CL) and iris concavity (CV) are made routinely in clinical practice. In this study, we have evaluated five different objective functions chosen based on the iris biometrics (in the presence and absence of clinical measurement errors) to determine the appropriate criterion for inverse modeling. Our results showed that in the absence of experimental measurement error, a combination of iris CL and CV can be used as the objective function. However, with the addition of measurement errors, the objective functions that employ a large number of local displacement values provide more reliable outcomes.
Juhojuntti, N. G.; Kamm, J.
2010-12-01
We present a layered-model approach to joint inversion of shallow seismic refraction and resistivity (DC) data, which we believe is a seldom tested method of addressing the problem. This method has been developed as we believe that for shallow sedimentary environments (roughly fairly simple 2D geometries, mainly for checking the validity of the calculations. The inversion generally converges towards the correct solution, although there could be stability problems if the starting model is too erroneous. We have also applied the code to field data from seismic refraction and multi-electrode resistivity measurements at typical sand-gravel groundwater reservoirs. The tests are promising, as the calculated depths agree fairly well with information from drilling and the velocity and resistivity values appear reasonable. Current work includes better regularization of the inversion as well as defining individual weight factors for the different datasets, as the present algorithm tends to constrain the depths mainly by using the seismic data. More complex synthetic examples will also be tested, including models addressing the seismic hidden-layer problem.
Modeling and forecasting foreign exchange daily closing prices with normal inverse Gaussian
Teneng, Dean
2013-09-01
We fit the normal inverse Gaussian(NIG) distribution to foreign exchange closing prices using the open software package R and select best models by Käärik and Umbleja (2011) proposed strategy. We observe that daily closing prices (12/04/2008 - 07/08/2012) of CHF/JPY, AUD/JPY, GBP/JPY, NZD/USD, QAR/CHF, QAR/EUR, SAR/CHF, SAR/EUR, TND/CHF and TND/EUR are excellent fits while EGP/EUR and EUR/GBP are good fits with a Kolmogorov-Smirnov test p-value of 0.062 and 0.08 respectively. It was impossible to estimate normal inverse Gaussian parameters (by maximum likelihood; computational problem) for JPY/CHF but CHF/JPY was an excellent fit. Thus, while the stochastic properties of an exchange rate can be completely modeled with a probability distribution in one direction, it may be impossible the other way around. We also demonstrate that foreign exchange closing prices can be forecasted with the normal inverse Gaussian (NIG) Lévy process, both in cases where the daily closing prices can and cannot be modeled by NIG distribution.
Han, B.; Flores, A. N.; Benner, S. G.
2017-12-01
In semiarid and arid regions where water supply is intensively managed, future water scarcity is a product of complex interactions between climate change and human activities. Evaluating future water scarcity under alternative scenarios of climate change, therefore, necessitates modeling approaches that explicitly represent the coupled biophysical and social processes responsible for the redistribution of water in these regions. At regional scales a particular challenge lies in adequately capturing not only the central tendencies of change in projections of climate change, but also the associated plausible range of variability in those projections. This study develops a framework that combines a stochastic weather generator, historical climate observations, and statistically downscaled General Circulation Model (GCM) projections. The method generates a large ensemble of daily climate realizations, avoiding deficiencies of using a few or mean values of individual GCM realizations. Three climate change scenario groups reflecting the historical, RCP4.5, and RCP8.5 future projections are developed. Importantly, the model explicitly captures the spatiotemporally varying irrigation activities as constrained by local water rights in a rapidly growing, semi-arid human-environment system in southwest Idaho. We use this modeling framework to project water use and scarcity patterns under the three future climate change scenarios. The model is built using the Envision alternative futures modeling framework. Climate projections for the region show future increases in both precipitation and temperature, especially under the RCP8.5 scenario. The increase of temperature has a direct influence on the increase of the irrigation water use and water scarcity, while the influence of increased precipitation on water use is less clear. The predicted changes are potentially useful in identifying areas in the watershed particularly sensitive to water scarcity, the relative importance of
Biophysical pathology in cancer transformation
Czech Academy of Sciences Publication Activity Database
Pokorný, Jiří; Pokorný, Jan
S1, Nov (2013), s. 1-9 ISSN 2324-9110 R&D Projects: GA ČR(CZ) GAP102/11/0649 Institutional support: RVO:68378271 ; RVO:67985882 Keywords : cancer biophysics * Warburg effect * reverse Warburg effect * biological electrodynamics * coherent states Subject RIV: BO - Biophysics
International Nuclear Information System (INIS)
Buecker, H.; Horneck, G.
1983-01-01
In a demonstration experiment bacterium sporules have been exposed to the space vacuum and to the solar radiation field at 254 nm, with the following results: 1) a short vacuum exposition of 1.3 h does not affect the vitality of the sporules, 2) the survival rate of humid sporules after UV-irradiation is consistent with terrestrial control samples, 3) after a simultaneous exposition to vacuum and solar UV-radiation the effect on the sporules is enhanced by a factor of ten as compared to the situation without vaccum exposition. Additional studies in biophysical simulation systems revealed, that the enhanced UV sensitivity is caused by the dehydration of the sporules. By this process the structure of the essential macromolecules in cell, such as DNA and proteins, is modified such that new photo-products can be formed. For these products the cells have no effective repair systems. (AJ) [de
Biophysics of radiation action
International Nuclear Information System (INIS)
Dertinger, H.
1984-01-01
Understanding the cellular response to ionizing radiation is not only necessary to meet the requirements of radioprotection, but also for medical application of radiation in cancer treatment. In terms of radiobiology, cancer therapy means the selective inactivation of malignant cells without affecting the normal healthy tissue. However, for several physical and biological reasons, this ideal situation is normally not attained. The elaboration of biophysical parameters that could be used to improve the selective sterilization of tumor cells has become one of the main activities of cellular radiobiology during the last two decades. Progress in this field has been facilitated by the development of tissue culture techniques allowing to grow and analyze cells in a synthetic nutrient medium. This chapter describes the physical and biological factors which determine cellular radiosensitivity and which are important to know for better understanding the cellular radiation action, in particular with reference to cancer treatment
Nicolini, Claudio
1986-01-01
Since the early times of the Greek philosophers Leucippus and Democritus, and later of the Roman philosopher Lucretius, a simple, fundamental idea emerged that brought the life sciences into the realm of the physical sciences. Atoms, after various interactions, were assumed to acquire stable configurations that corresponded either to the living or to the inanimate world. This simple and unitary theory, which has evolved in successive steps to our present time, remarkably maintained its validity despite several centuries of alternative vicissitudes, and is the foundation of modern biophysics. Some of the recent developments of this ancient idea are the discovery of the direct relationship between spatial structures and chemical activity of such molecules as methane and benzene, and the later discovery of the three-dimensional structure of double-helical DNA, and of its relationship with biological activity. The relationship between the structure of various macromolecules and the function of living cells was on...
American Option Pricing using GARCH models and the Normal Inverse Gaussian distribution
DEFF Research Database (Denmark)
Stentoft, Lars Peter
In this paper we propose a feasible way to price American options in a model with time varying volatility and conditional skewness and leptokurtosis using GARCH processes and the Normal Inverse Gaussian distribution. We show how the risk neutral dynamics can be obtained in this model, we interpret...... properties shows that there are important option pricing differences compared to the Gaussian case as well as to the symmetric special case. A large scale empirical examination shows that our model outperforms the Gaussian case for pricing options on three large US stocks as well as a major index...
Directory of Open Access Journals (Sweden)
A. Richter
2009-11-01
Full Text Available Tropospheric glyoxal and formaldehyde columns retrieved from the SCIAMACHY satellite instrument in 2005 are used with the IMAGESv2 global chemistry-transport model and its adjoint in a two-compound inversion scheme designed to estimate the continental source of glyoxal. The formaldehyde observations provide an important constraint on the production of glyoxal from isoprene in the model, since the degradation of isoprene constitutes an important source of both glyoxal and formaldehyde. Current modelling studies underestimate largely the observed glyoxal satellite columns, pointing to the existence of an additional land glyoxal source of biogenic origin. We include an extra glyoxal source in the model and we explore its possible distribution and magnitude through two inversion experiments. In the first case, the additional source is represented as a direct glyoxal emission, and in the second, as a secondary formation through the oxidation of an unspecified glyoxal precursor. Besides this extra source, the inversion scheme optimizes the primary glyoxal and formaldehyde emissions, as well as their secondary production from other identified non-methane volatile organic precursors of anthropogenic, pyrogenic and biogenic origin.
In the first inversion experiment, the additional direct source, estimated at 36 Tg/yr, represents 38% of the global continental source, whereas the contribution of isoprene is equally important (30%, the remainder being accounted for by anthropogenic (20% and pyrogenic fluxes. The inversion succeeds in reducing the underestimation of the glyoxal columns by the model, but it leads to a severe overestimation of glyoxal surface concentrations in comparison with in situ measurements. In the second scenario, the inferred total global continental glyoxal source is estimated at 108 Tg/yr, almost two times higher than the global a priori source. The extra secondary source is the largest contribution to the global glyoxal
Hydrochlorofluorocarbon and hydrofluorocarbon emissions in East Asia determined by inverse modeling
Directory of Open Access Journals (Sweden)
A. Stohl
2010-04-01
Full Text Available The emissions of three hydrochlorofluorocarbons, HCFC-22 (CHClF_{2}, HCFC-141b (CH_{3}CCl_{2}F and HCFC-142b (CH_{3}CClF_{2} and three hydrofluorocarbons, HFC-23 (CHF_{3}, HFC-134a (CH_{2}FCF_{3} and HFC-152a (CH_{3}CHF_{2} from four East Asian countries and the Taiwan region for the year 2008 are determined by inverse modeling. The inverse modeling is based on in-situ measurements of these halocarbons at the Japanese stations Cape Ochi-ishi and Hateruma, the Chinese station Shangdianzi and the South Korean station Gosan. For every station and every 3 h, 20-day backward calculations were made with the Lagrangian particle dispersion model FLEXPART. The model output, the measurement data, bottom-up emission information and corresponding uncertainties were fed into an inversion algorithm to determine the regional emission fluxes. The model captures the observed variation of halocarbon mixing ratios very well for the two Japanese stations but has difficulties explaining the large observed variability at Shangdianzi, which is partly caused by small-scale transport from Beijing that is not adequately captured by the model. Based on HFC-23 measurements, the inversion algorithm could successfully identify the locations of factories known to produce HCFC-22 and emit HFC-23 as an unintentional byproduct. This lends substantial credibility to the inversion method. We report national emissions for China, North Korea, South Korea and Japan, as well as emissions for the Taiwan region. Halocarbon emissions in China are much larger than the emissions in the other countries together and contribute a substantial fraction to the global emissions. Our estimates of Chinese emissions for the year 2008 are 65.3±6.6 kt/yr for HCFC-22 (17% of global emissions extrapolated from Montzka et al., 2009, 12.1±1.6 kt/yr for HCFC-141b (22%, 7.3±0.7 kt/yr for HCFC-142b (17%, 6.2±0.7 kt/yr for HFC
International Nuclear Information System (INIS)
Jacob, P.; Aragno, D.; Bailiff, I.K.
2000-01-01
The project Dose Reconstruction was conducted within the five work packages: - EPR with teeth, - Chromosome painting (FISH) in lymphocytes, - Luminescence methods, - Modelling, and - Evaluation. (orig.)
Wu, Zedong; Alkhalifah, Tariq Ali
2017-01-01
Reflection-waveform inversion (RWI) can help us reduce the nonlinearity of the standard full-waveform inversion (FWI) by inverting for the background velocity model using the wave-path of a single scattered wavefield to an image. However, current
TransCom N2O model inter-comparison, Part II : Atmospheric inversion estimates of N2O emissions
Thompson, R. L.; Ishijima, K.; Saikawa, E.; Corazza, M.; Karstens, U.; Patra, P. K.; Bergamaschi, P.; Chevallier, F.; Dlugokencky, E.; Prinn, R. G.; Weiss, R. F.; O'Doherty, S.; Fraser, P. J.; Steele, L. P.; Krummel, P. B.; Vermeulen, A.; Tohjima, Y.; Jordan, A.; Haszpra, L.; Steinbacher, M.; Van Der Laan, S.; Aalto, T.; Meinhardt, F.; Popa, Maria Elena; Moncrieff, J.; Bousquet, P.
2014-01-01
This study examines N2O emission estimates from 5 different atmospheric inversion frameworks. The 5 frameworks differ in the choice of atmospheric transport model, meteorological data, prior uncertainties and inversion method but use the same prior emissions and observation dataset. The mean
TransCom N2O model inter-comparison - Part 2 : Atmospheric inversion estimates of N2O emissions
Thompson, R. L.; Ishijima, K.; Saikawa, E.; Corazza, M.; Karstens, U.; Patra, P. K.; Bergamaschi, P.; Chevallier, F.; Dlugokencky, E.; Prinn, R. G.; Weiss, R. F.; O'Doherty, S.; Fraser, P. J.; Steele, L. P.; Krummel, P. B.; Vermeulen, A.; Tohjima, Y.; Jordan, A.; Haszpra, L.; Steinbacher, M.; Van Der Laan, S.; Aalto, T.; Meinhardt, F.; Popa, Maria Elena|info:eu-repo/dai/nl/375806407; Moncrieff, J.; Bousquet, P.
2014-01-01
This study examines N2O emission estimates from five different atmospheric inversion frameworks based on chemistry transport models (CTMs). The five frameworks differ in the choice of CTM, meteorological data, prior uncertainties and inversion method but use the same prior emissions and observation
Transient Inverse Calibration of the Site-Wide Groundwater Flow Model (ACM-2): FY03 Progress Report
International Nuclear Information System (INIS)
Vermeul, Vince R.; Bergeron, Marcel P.; Cole, C R.; Murray, Christopher J.; Nichols, William E.; Scheibe, Timothy D.; Thorne, Paul D.; Waichler, Scott R.; Xie, YuLong
2003-01-01
DOE and PNNL are working to strengthen the technical defensibility of the groundwater flow and transport model at the Hanford Site and to incorporate uncertainty into the model. One aspect of the initiative is developing and using a three-dimensional transient inverse model to estimate the hydraulic conductivities, specific yields, and other parameters using data from Hanford since 1943. The focus of the alternative conceptual model (ACM-2) inverse modeling initiative documented in this report was to address limitations identified in the ACM-1 model, complete the facies-based approach for representing the hydraulic conductivity distribution in the Hanford and middle Ringold Formations, develop the approach and implementation methodology for generating multiple ACMs based on geostatistical data analysis, and develop an approach for inverse modeling of these stochastic ACMs. The primary modifications to ACM-2 transient inverse model include facies-based zonation of Units 1 (Hanford ) and 5 (middle Ringold); an improved approach for handling run-on recharge from upland areas based on watershed modeling results; an improved approach for representing artificial discharges from site operations; and minor changes to the geologic conceptual model. ACM-2 is the first attempt to fully incorporate the facies-based approach to represent the hydrogeologic structure. Further refinement and additional improvements to overall model fit will be realized during future inverse simulations of groundwater flow and transport. In addition, preliminary work was completed on an approach and implementation for generating an inverse modeling of stochastic ACMs. These techniques were applied to assess the uncertainty in the facies-based zonation of the Hanford formation and the geological structure of Ringold mud units. The geostatistical analysis used a preliminary interpretation of the facies-based zonation that was not consistent with that used in ACM-2. Although the overall objective of
Truong-Loi, My-Linh; Saatchi, Sassan; Jaruwatanadilok, Sermsak
2012-01-01
A semi-empirical algorithm for the retrieval of soil moisture, root mean square (RMS) height and biomass from polarimetric SAR data is explained and analyzed in this paper. The algorithm is a simplification of the distorted Born model. It takes into account the physical scattering phenomenon and has three major components: volume, double-bounce and surface. This simplified model uses the three backscattering coefficients ( sigma HH, sigma HV and sigma vv) at low-frequency (P-band). The inversion process uses the Levenberg-Marquardt non-linear least-squares method to estimate the structural parameters. The estimation process is entirely explained in this paper, from initialization of the unknowns to retrievals. A sensitivity analysis is also done where the initial values in the inversion process are varying randomly. The results show that the inversion process is not really sensitive to initial values and a major part of the retrievals has a root-mean-square error lower than 5% for soil moisture, 24 Mg/ha for biomass and 0.49 cm for roughness, considering a soil moisture of 40%, roughness equal to 3cm and biomass varying from 0 to 500 Mg/ha with a mean of 161 Mg/ha
Exploring the Subtleties of Inverse Probability Weighting and Marginal Structural Models.
Breskin, Alexander; Cole, Stephen R; Westreich, Daniel
2018-05-01
Since being introduced to epidemiology in 2000, marginal structural models have become a commonly used method for causal inference in a wide range of epidemiologic settings. In this brief report, we aim to explore three subtleties of marginal structural models. First, we distinguish marginal structural models from the inverse probability weighting estimator, and we emphasize that marginal structural models are not only for longitudinal exposures. Second, we explore the meaning of the word "marginal" in "marginal structural model." Finally, we show that the specification of a marginal structural model can have important implications for the interpretation of its parameters. Each of these concepts have important implications for the use and understanding of marginal structural models, and thus providing detailed explanations of them may lead to better practices for the field of epidemiology.
Improving Atomic Force Microscopy Imaging by a Direct Inverse Asymmetric PI Hysteresis Model
Directory of Open Access Journals (Sweden)
Dong Wang
2015-02-01
Full Text Available A modified Prandtl–Ishlinskii (PI model, referred to as a direct inverse asymmetric PI (DIAPI model in this paper, was implemented to reduce the displacement error between a predicted model and the actual trajectory of a piezoelectric actuator which is commonly found in AFM systems. Due to the nonlinearity of the piezoelectric actuator, the standard symmetric PI model cannot precisely describe the asymmetric motion of the actuator. In order to improve the accuracy of AFM scans, two series of slope parameters were introduced in the PI model to describe both the voltage-increase-loop (trace and voltage-decrease-loop (retrace. A feedforward controller based on the DIAPI model was implemented to compensate hysteresis. Performance of the DIAPI model and the feedforward controller were validated by scanning micro-lenses and standard silicon grating using a custom-built AFM.
Directory of Open Access Journals (Sweden)
Birgül Kınalıbalaban
2013-08-01
Full Text Available In this study, the province of Gaziantep, Şehitkamil district, there are thought to be chrome-metallic mine in the village of Sofalıca the localization of gravity and economical method was to investigate whether it has a reserve. Approximately 189 hectares of land gravity measurements over the measurement point in the study area was 220. By differentiating regional and residual Bouguer gravity map of the generated residual maps were obtained on areas likely to be created on the source. The inversion method of pre-NTG was required for the selection model is the appropriate start. On the structure of polygon slices as a result of application received in the form of the inversion in the range of 125 meters and 450 meters long, 25 meters to 70 meters in thickness in the range of existence of geometric structures have been identified.
Non-unitary neutrino mixing and CP violation in the minimal inverse seesaw model
International Nuclear Information System (INIS)
Malinsky, Michal; Ohlsson, Tommy; Xing, Zhi-zhong; Zhang He
2009-01-01
We propose a simplified version of the inverse seesaw model, in which only two pairs of the gauge-singlet neutrinos are introduced, to interpret the observed neutrino mass hierarchy and lepton flavor mixing at or below the TeV scale. This 'minimal' inverse seesaw scenario (MISS) is technically natural and experimentally testable. In particular, we show that the effective parameters describing the non-unitary neutrino mixing matrix are strongly correlated in the MISS, and thus, their upper bounds can be constrained by current experimental data in a more restrictive way. The Jarlskog invariants of non-unitary CP violation are calculated, and the discovery potential of such new CP-violating effects in the near detector of a neutrino factory is discussed.
Inverse modeling for the determination of hydrogeological parameters of a two-phase system
International Nuclear Information System (INIS)
Finsterle, S.
1993-02-01
Investigations related to the disposal of radioactive wastes in Switzerland consider formations containing natural gas as potential rocks for a repository. Moreover, gas generation in the repository itself may lead to an unsaturated zone of significant extent and impact on the system's performance. The site characterization procedure requires the estimation of hydraulic properties being used as input parameters for a two-phase two-component numerical simulator. In this study, estimates of gas-related formation parameters are obtained by inverse modeling. Based on discrete observations of the system's state, model parameters can be estimated within the framework of a given conceptual model by means of optimization techniques. This study presents the theoretical background that related field data to the model parameters. A parameter estimation procedure is proposed and implemented in a computer code for automatic model calibration. This tool allows identification of key parameters affecting flow of water and gas in porous media. The inverse modeling approach is verified using data from a synthetic laboratory experiment. In addition, the Gas test performed at the Grimsel Test Site is analyzed in order to demonstrate the applicability of the proposed procedure when used with data from a real geologic environment. Estimation of hydrogeologic parameters by automatic model calibration improves the understanding of the two-phase flow processes and therefore increases the reliability of the subsequent simulation runs. (author) figs., tabs., refs
Inverse modeling for the determination of hydrogeological parameters of a two-phase system
International Nuclear Information System (INIS)
Finsterle, S.
1993-01-01
Investigations related to the disposal of radioactive wastes in Switzerland are dealing with formations containing natural gas as potential host rock for a repository. Moreover, gas generation in the repository itself may lead to an unsaturated zone of significant extent and impact on the system's performance. The site characterization procedure requires the estimation of hydraulic properties being used as input parameters for a two-phase two-component numerical simulator. In this study, estimates of gas related formation parameters are obtained by inverse modeling. Based on discrete observations of the system's state, model parameters can be estimated within the framework of a given conceptual model by means of optimization techniques. This study presents the theoretical background that relates field data to the model parameters. A parameter estimation procedure is proposed and implemented in a computer code for automatic model calibration. This tool allows to identify key parameters affecting flow of water and gas in porous media. The inverse modeling approach is verified using data from a synthetic laboratory experiment. In addition, the Gastest performed at the Grimsel Test Site is analyzed in order to demonstrate the applicability of the proposed procedure when used with data from a real geologic environment. Estimation of hydrogeologic parameters by automatic model calibration improves the understanding of the two-phase flow processes and therefore increases the reliability of the subsequent simulation runs. (author) figs., tabs., 100 refs
Energy Technology Data Exchange (ETDEWEB)
Kiryukhin, Alexey V. [Institute of Volcanology and Seismology FEB RAS, Piip-9, P-Kamchatsky 683006 (Russian Federation); Asaulova, Natalia P. [Kamchatskburgeotemia Enterprise, Krasheninnikova-1, Thermalny, Kamchatka 684035 (Russian Federation); Finsterle, Stefan [Lawrence Berkeley National Laboratory, MS 90-1116, One Cyclotron Road, Berkeley, CA 94720 (United States)
2008-10-15
A three-dimensional numerical model of the Pauzhetsky geothermal field has been developed based on a conceptual hydrogeological model of the system. It extends over a 13.6-km{sup 2} area and includes three layers: (1) a base layer with inflow; (2) a geothermal reservoir; and (3) an upper layer with discharge and recharge/infiltration areas. Using the computer program iTOUGH2 [Finsterle, S., 2004. Multiphase inverse modeling: review and iTOUGH2 applications. Vadose Zone J. 3, 747-762], the model is calibrated to a total of 13,675 calibration points, combining natural-state and 1960-2006 exploitation data. The principal model parameters identified and estimated by inverse modeling include the fracture permeability and fracture porosity of the geothermal reservoir, the initial natural upflow rate, the base-layer porosity, and the permeabilities of the infiltration zones. Heat and mass balances derived from the calibrated model helped identify the sources of the geothermal reserves in the field. With the addition of five make-up wells, simulation forecasts for the 2007-2032 period predict a sustainable average steam production of 29 kg/s, which is sufficient to maintain the generation of 6.8 MWe at the Pauzhetsky power plant. (author)
A Flexible framework for forward and inverse modeling of stormwater control measures
Aflaki, S.; Massoudieh, A.
2016-12-01
Models that allow for design considerations of green infrastructure (GI) practices to control stormwater runoff and associated contaminants have received considerable attention in recent years. While popular, generally, the GI models are relatively simplistic. However, GI model predictions are being relied upon by many municipalities and State/Local agencies to make decisions about grey vs. green infrastructure improvement planning. Adding complexity to GI modeling frameworks may preclude their use in simpler urban planning situations. Therefore, the goal here was to develop a sophisticated, yet flexible tool that could be used by design engineers and researchers to capture and explore the effect of design factors and properties of the media used in the performance of GI systems at a relatively small scale. We deemed it essential to have a flexible GI modeling tool that is capable of simulating GI system components and specific biophysical processes affecting contaminants such as reactions, and particle-associated transport accurately while maintaining a high degree of flexibly to account for the myriad of GI alternatives. The mathematical framework for a stand-alone GI performance assessment tool has been developed and will be demonstrated. The process-based model framework developed here can be used to model a diverse range of GI practices such as green roof, retention pond, bioretention, infiltration trench, permeable pavement and other custom-designed combinatory systems. Four demonstration applications covering a diverse range of systems will be presented. The example applications include a evaluating hydraulic performance of a complex bioretention system, hydraulic analysis of porous pavement system, flow colloid-facilitated transport, reactive transport and groundwater recharge underneath an infiltration pond and finally reactive transport and bed-sediment interactions in a wetland system will be presented.
International Nuclear Information System (INIS)
Kase, Yuki; Matsufuji, Naruhiro; Furusawa, Yoshiya; Kanai, Tatsuaki
2007-01-01
In a treatment planning of heavy-ion radiotherapy, it is necessary to estimate the biological effect of the heavy-ion beams. Physical dose should be associated with the relative biological effectiveness (RBE) at each point. Presently, carbon ion radiotherapy has been carried out at the National Institute Radiological Sciences (NIRS) in Japan and the Gesellschaft fuer Schwerionenforschung mbH (GSI) in Germany. Both facilities take individual approach for the calculation of the RBE value. At NIRS, the classical LQ model has been used while the local effect model (LEM) has been incorporated into the treatment planning system at GSI. The first aim of this study is to explain the RBE model of NIRS by the microdosimetric kinetic model (MKM). In addition, the clarification of similarities and differences between the MKM and the LEM was also investigated. (author)
Quintero-Chavarria, E.; Ochoa Gutierrez, L. H.
2016-12-01
Applications of the Self-potential Method in the fields of Hydrogeology and Environmental Sciences have had significant developments during the last two decades with a strong use on groundwater flows identification. Although only few authors deal with the forward problem's solution -especially in geophysics literature- different inversion procedures are currently being developed but in most cases they are compared with unconventional groundwater velocity fields and restricted to structured meshes. This research solves the forward problem based on the finite element method using the St. Venant's Principle to transform a point dipole, which is the field generated by a single vector, into a distribution of electrical monopoles. Then, two simple aquifer models were generated with specific boundary conditions and head potentials, velocity fields and electric potentials in the medium were computed. With the model's surface electric potential, the inverse problem is solved to retrieve the source of electric potential (vector field associated to groundwater flow) using deterministic and stochastic approaches. The first approach was carried out by implementing a Tikhonov regularization with a stabilized operator adapted to the finite element mesh while for the second a hierarchical Bayesian model based on Markov chain Monte Carlo (McMC) and Markov Random Fields (MRF) was constructed. For all implemented methods, the result between the direct and inverse models was contrasted in two ways: 1) shape and distribution of the vector field, and 2) magnitude's histogram. Finally, it was concluded that inversion procedures are improved when the velocity field's behavior is considered, thus, the deterministic method is more suitable for unconfined aquifers than confined ones. McMC has restricted applications and requires a lot of information (particularly in potentials fields) while MRF has a remarkable response especially when dealing with confined aquifers.
Baudracco, J; Lopez-Villalobos, N; Holmes, C W; Comeron, E A; Macdonald, K A; Barry, T N
2013-05-01
A whole-farm, stochastic and dynamic simulation model was developed to predict biophysical and economic performance of grazing dairy systems. Several whole-farm models simulate grazing dairy systems, but most of them work at a herd level. This model, named e-Dairy, differs from the few models that work at an animal level, because it allows stochastic behaviour of the genetic merit of individual cows for several traits, namely, yields of milk, fat and protein, live weight (LW) and body condition score (BCS) within a whole-farm model. This model accounts for genetic differences between cows, is sensitive to genotype × environment interactions at an animal level and allows pasture growth, milk and supplements price to behave stochastically. The model includes an energy-based animal module that predicts intake at grazing, mammary gland functioning and body lipid change. This whole-farm model simulates a 365-day period for individual cows within a herd, with cow parameters randomly generated on the basis of the mean parameter values, defined as input and variance and co-variances from experimental data sets. The main inputs of e-Dairy are farm area, use of land, type of pasture, type of crops, monthly pasture growth rate, supplements offered, nutritional quality of feeds, herd description including herd size, age structure, calving pattern, BCS and LW at calving, probabilities of pregnancy, average genetic merit and economic values for items of income and costs. The model allows to set management policies to define: dry-off cows (ceasing of lactation), target pre- and post-grazing herbage mass and feed supplementation. The main outputs are herbage dry matter intake, annual pasture utilisation, milk yield, changes in BCS and LW, economic farm profit and return on assets. The model showed satisfactory accuracy of prediction when validated against two data sets from farmlet system experiments. Relative prediction errors were <10% for all variables, and concordance
Tiampo, K. F.; Fernández, J.; Jentzsch, G.; Charco, M.; Rundle, J. B.
2004-11-01
Here we present an inversion methodology using the combination of a genetic algorithm (GA) inversion program, and an elastic-gravitational earth model to determine the parameters of a volcanic intrusion. Results from the integration of the elastic-gravitational model, a suite of FORTRAN 77 programs developed to compute the displacements due to volcanic loading, with the GA inversion code, written in the C programming language, are presented. These codes allow for the calculation of displacements (horizontal and vertical), tilt, vertical strain and potential and gravity changes on the surface of an elastic-gravitational layered Earth model due to the magmatic intrusion. We detail the appropriate methodology for examining the sensitivity of the model to variation in the constituent parameters using the GA, and present, for the first time, a Monte Carlo technique for evaluating the propagation of error through the GA inversion process. One application example is given at Mayon volcano, Philippines, for the inversion program, the sensitivity analysis, and the error evaluation. The integration of the GA with the complex elastic-gravitational model is a blueprint for an efficient nonlinear inversion methodology and its implementation into an effective tool for the evaluation of parameter sensitivity. Finally, the extension of this inversion algorithm and the error assessment methodology has important implications to the modeling and data assimilation of a number of other nonlinear applications in the field of geosciences.
Shifman, Aaron R; Longtin, André; Lewis, John E
2015-10-30
Identifying and understanding the current sources that give rise to bioelectric fields is a fundamental problem in the biological sciences. It is very difficult, for example, to attribute the time-varying features of an electroencephalogram recorded from the head surface to the neural activity of specific brain areas; model systems can provide important insight into such problems. Some species of fish actively generate an oscillating (c. 1000 Hz) quasi-dipole electric field to communicate and sense their environment in the dark. A specialized electric organ comprises neuron-like cells whose collective signal underlies this electric field. As a step towards understanding the detailed biophysics of signal generation in these fish, we use an anatomically-detailed finite-element modelling approach to reverse-engineer the electric organ signal over one oscillation cycle. We find that the spatiotemporal profile of current along the electric organ constitutes a travelling wave that is well-described by two spatial Fourier components varying in time. The conduction velocity of this wave is faster than action potential conduction in any known neuronal axon (>200 m/s), suggesting that the spatiotemporal features of high-frequency electric organ discharges are not constrained by the conduction velocities of spinal neuron pathways.
Improvement of PM10 prediction in East Asia using inverse modeling
Koo, Youn-Seo; Choi, Dae-Ryun; Kwon, Hi-Yong; Jang, Young-Kee; Han, Jin-Seok
2015-04-01
Aerosols from anthropogenic emissions in industrialized region in China as well as dust emissions from southern Mongolia and northern China that transport along prevailing northwestern wind have a large influence on the air quality in Korea. The emission inventory in the East Asia region is an important factor in chemical transport modeling (CTM) for PM10 (particulate matters less than 10 ㎛ in aerodynamic diameter) forecasts and air quality management in Korea. Most previous studies showed that predictions of PM10 mass concentration by the CTM were underestimated when comparing with observational data. In order to fill the gap in discrepancies between observations and CTM predictions, the inverse Bayesian approach with Comprehensive Air-quality Model with extension (CAMx) forward model was applied to obtain optimized a posteriori PM10 emissions in East Asia. The predicted PM10 concentrations with a priori emission were first compared with observations at monitoring sites in China and Korea for January and August 2008. The comparison showed that PM10 concentrations with a priori PM10 emissions for anthropogenic and dust sources were generally under-predicted. The result from the inverse modeling indicated that anthropogenic PM10 emissions in the industrialized and urbanized areas in China were underestimated while dust emissions from desert and barren soil in southern Mongolia and northern China were overestimated. A priori PM10 emissions from northeastern China regions including Shenyang, Changchun, and Harbin were underestimated by about 300% (i.e., the ratio of a posteriori to a priori PM10 emission was a factor of about 3). The predictions of PM10 concentrations with a posteriori emission showed better agreement with the observations, implying that the inverse modeling minimized the discrepancies in the model predictions by improving PM10 emissions in East Asia.
A stochastic approach for model reduction and memory function design in hydrogeophysical inversion
Hou, Z.; Kellogg, A.; Terry, N.
2009-12-01
Geophysical (e.g., seismic, electromagnetic, radar) techniques and statistical methods are essential for research related to subsurface characterization, including monitoring subsurface flow and transport processes, oil/gas reservoir identification, etc. For deep subsurface characterization such as reservoir petroleum exploration, seismic methods have been widely used. Recently, electromagnetic (EM) methods have drawn great attention in the area of reservoir characterization. However, considering the enormous computational demand corresponding to seismic and EM forward modeling, it is usually a big problem to have too many unknown parameters in the modeling domain. For shallow subsurface applications, the characterization can be very complicated considering the complexity and nonlinearity of flow and transport processes in the unsaturated zone. It is warranted to reduce the dimension of parameter space to a reasonable level. Another common concern is how to make the best use of time-lapse data with spatial-temporal correlations. This is even more critical when we try to monitor subsurface processes using geophysical data collected at different times. The normal practice is to get the inverse images individually. These images are not necessarily continuous or even reasonably related, because of the non-uniqueness of hydrogeophysical inversion. We propose to use a stochastic framework by integrating minimum-relative-entropy concept, quasi Monto Carlo sampling techniques, and statistical tests. The approach allows efficient and sufficient exploration of all possibilities of model parameters and evaluation of their significances to geophysical responses. The analyses enable us to reduce the parameter space significantly. The approach can be combined with Bayesian updating, allowing us to treat the updated ‘posterior’ pdf as a memory function, which stores all the information up to date about the distributions of soil/field attributes/properties, then consider the
Directory of Open Access Journals (Sweden)
Alexander eHanuschkin
2013-06-01
Full Text Available Mirror neurons are neurons whose responses to the observation of a motor act resemble responses measured during production of that act. Computationally, mirror neurons have been viewed as evidence for the existence of internal inverse models. Such models, rooted within control theory, map desired sensory targets onto the motor commands required to generate those targets. To jointly explore both the formation of mirrored responses and their functional contribution to inverse models, we develop a correlation-based theory of interactions between a sensory and a motor area. We show that a simple eligibility-weighted Hebbian learning rule, operating within a sensorimotor loop during motor explorations and stabilized by heterosynaptic competition, naturally gives rise to mirror neurons as well as control theoretic inverse models encoded in the synaptic weights from sensory to motor neurons. Crucially, we find that the correlational structure or stereotypy of the neural code underlying motor explorations determines the nature of the learned inverse model: Random motor codes lead to causal inverses that map sensory activity patterns to their motor causes; such inverses are maximally useful, they allow for imitating arbitrary sensory target sequences. By contrast, stereotyped motor codes lead to less useful predictive inverses that map sensory activity to future motor actions.Our theory generalizes previous work on inverse models by showing that such models can be learned in a simple Hebbian framework without the need for error signals or backpropagation, and it makes new conceptual connections between the causal nature of inverse models, the statistical structure of motor variability, and the time-lag between sensory and motor responses of mirror neurons. Applied to bird song learning, our theory can account for puzzling aspects of the song system, including necessity of sensorimotor gating and selectivity of auditory responses to bird’s own song
Hanuschkin, A; Ganguli, S; Hahnloser, R H R
2013-01-01
Mirror neurons are neurons whose responses to the observation of a motor act resemble responses measured during production of that act. Computationally, mirror neurons have been viewed as evidence for the existence of internal inverse models. Such models, rooted within control theory, map-desired sensory targets onto the motor commands required to generate those targets. To jointly explore both the formation of mirrored responses and their functional contribution to inverse models, we develop a correlation-based theory of interactions between a sensory and a motor area. We show that a simple eligibility-weighted Hebbian learning rule, operating within a sensorimotor loop during motor explorations and stabilized by heterosynaptic competition, naturally gives rise to mirror neurons as well as control theoretic inverse models encoded in the synaptic weights from sensory to motor neurons. Crucially, we find that the correlational structure or stereotypy of the neural code underlying motor explorations determines the nature of the learned inverse model: random motor codes lead to causal inverses that map sensory activity patterns to their motor causes; such inverses are maximally useful, by allowing the imitation of arbitrary sensory target sequences. By contrast, stereotyped motor codes lead to less useful predictive inverses that map sensory activity to future motor actions. Our theory generalizes previous work on inverse models by showing that such models can be learned in a simple Hebbian framework without the need for error signals or backpropagation, and it makes new conceptual connections between the causal nature of inverse models, the statistical structure of motor variability, and the time-lag between sensory and motor responses of mirror neurons. Applied to bird song learning, our theory can account for puzzling aspects of the song system, including necessity of sensorimotor gating and selectivity of auditory responses to bird's own song (BOS) stimuli.
Three-dimensional inverse modelling of damped elastic wave propagation in the Fourier domain
Petrov, Petr V.; Newman, Gregory A.
2014-09-01
3-D full waveform inversion (FWI) of seismic wavefields is routinely implemented with explicit time-stepping simulators. A clear advantage of explicit time stepping is the avoidance of solving large-scale implicit linear systems that arise with frequency domain formulations. However, FWI using explicit time stepping may require a very fine time step and (as a consequence) significant computational resources and run times. If the computational challenges of wavefield simulation can be effectively handled, an FWI scheme implemented within the frequency domain utilizing only a few frequencies, offers a cost effective alternative to FWI in the time domain. We have therefore implemented a 3-D FWI scheme for elastic wave propagation in the Fourier domain. To overcome the computational bottleneck in wavefield simulation, we have exploited an efficient Krylov iterative solver for the elastic wave equations approximated with second and fourth order finite differences. The solver does not exploit multilevel preconditioning for wavefield simulation, but is coupled efficiently to the inversion iteration workflow to reduce computational cost. The workflow is best described as a series of sequential inversion experiments, where in the case of seismic reflection acquisition geometries, the data has been laddered such that we first image highly damped data, followed by data where damping is systemically reduced. The key to our modelling approach is its ability to take advantage of solver efficiency when the elastic wavefields are damped. As the inversion experiment progresses, damping is significantly reduced, effectively simulating non-damped wavefields in the Fourier domain. While the cost of the forward simulation increases as damping is reduced, this is counterbalanced by the cost of the outer inversion iteration, which is reduced because of a better starting model obtained from the larger damped wavefield used in the previous inversion experiment. For cross-well data, it is
International Nuclear Information System (INIS)
Garratt, T.J.
1989-05-01
Compartment models for the transport of radionuclides in the biosphere are conventionally solved using a numerical time-stepping procedure. This report examines an alternative method based on the numerical inversion of Laplace transforms, which is potentially more efficient and accurate for some classes of problem. The central problem considered is the most efficient and robust technique for solving the Laplace-transformed rate equations. The conclusion is that Gaussian elimination is the most efficient and robust solution method. A general compartment model has been implemented on a personal computer and used to solve a realistic case including radionuclide decay chains. (author)
Hanuschkin, A.; Ganguli, S.; Hahnloser, R. H. R.
2013-01-01
Mirror neurons are neurons whose responses to the observation of a motor act resemble responses measured during production of that act. Computationally, mirror neurons have been viewed as evidence for the existence of internal inverse models. Such models, rooted within control theory, map-desired sensory targets onto the motor commands required to generate those targets. To jointly explore both the formation of mirrored responses and their functional contribution to inverse models, we develop...
The uniqueness of the solution of cone-like inversion models for halo CMEs
Zhao, X. P.
2006-12-01
Most of elliptic halo CMEs are believed to be formed by the Thompson scattering of the photospheric light by the 3-D cone-like shell of the CME plasma. To obtain the real propagation direction and angular width of the halo CMEs, such cone-like inversion models as the circular cone, the elliptic cone and the ice-cream cone models have been suggested recently. Because the number of given parameters that are used to characterize 2-D elliptic halo CMEs observed by one spacecraft are less than the number of unknown parameters that are used to characterize the 3-D elliptic cone model, the solution of the elliptic cone model is not unique. Since it is difficult to determine whether or not an observed halo CME is formed by an circular cone or elliptic cone shell, the solution of circular cone model may often be not unique too. To fix the problem of the uniqueness of the solution of various 3-D cone-like inversion models, this work tries to develop the algorithm for using the data from multi-spacecraft, such as the STEREO A and B, and the Solar Sentinels.
Colclough, Giles L; Woolrich, Mark W; Harrison, Samuel J; Rojas López, Pedro A; Valdes-Sosa, Pedro A; Smith, Stephen M
2018-05-07
A Bayesian model for sparse, hierarchical inverse covariance estimation is presented, and applied to multi-subject functional connectivity estimation in the human brain. It enables simultaneous inference of the strength of connectivity between brain regions at both subject and population level, and is applicable to fmri, meg and eeg data. Two versions of the model can encourage sparse connectivity, either using continuous priors to suppress irrelevant connections, or using an explicit description of the network structure to estimate the connection probability between each pair of regions. A large evaluation of this model, and thirteen methods that represent the state of the art of inverse covariance modelling, is conducted using both simulated and resting-state functional imaging datasets. Our novel Bayesian approach has similar performance to the best extant alternative, Ng et al.'s Sparse Group Gaussian Graphical Model algorithm, which also is based on a hierarchical structure. Using data from the Human Connectome Project, we show that these hierarchical models are able to reduce the measurement error in meg beta-band functional networks by 10%, producing concomitant increases in estimates of the genetic influence on functional connectivity. Copyright © 2018. Published by Elsevier Inc.
Katkov, Igor I
2011-06-01
In the companion paper, we discussed in details proper linearization, calculation of the inactive osmotic volume, and analysis of the results on the Boyle-vant' Hoff plots. In this Letter, we briefly address some common errors and misconceptions in osmotic modeling and propose some approaches, namely: (1) inapplicability of the Kedem-Katchalsky formalism model in regards to the cryobiophysical reality, (2) calculation of the membrane hydraulic conductivity L(p) in the presence of permeable solutes, (3) proper linearization of the Arrhenius plots for the solute membrane permeability, (4) erroneous use of the term "toxicity" for the cryoprotective agents, and (5) advantages of the relativistic permeability approach (RP) developed by us vs. traditional ("classic") 2-parameter model. Copyright © 2011 Elsevier Inc. All rights reserved.
Verrelst, Jochem; Malenovský, Zbyněk; Van der Tol, Christiaan; Camps-Valls, Gustau; Gastellu-Etchegorry, Jean-Philippe; Lewis, Philip; North, Peter; Moreno, Jose
2018-06-01
An unprecedented spectroscopic data stream will soon become available with forthcoming Earth-observing satellite missions equipped with imaging spectroradiometers. This data stream will open up a vast array of opportunities to quantify a diversity of biochemical and structural vegetation properties. The processing requirements for such large data streams require reliable retrieval techniques enabling the spatiotemporally explicit quantification of biophysical variables. With the aim of preparing for this new era of Earth observation, this review summarizes the state-of-the-art retrieval methods that have been applied in experimental imaging spectroscopy studies inferring all kinds of vegetation biophysical variables. Identified retrieval methods are categorized into: (1) parametric regression, including vegetation indices, shape indices and spectral transformations; (2) nonparametric regression, including linear and nonlinear machine learning regression algorithms; (3) physically based, including inversion of radiative transfer models (RTMs) using numerical optimization and look-up table approaches; and (4) hybrid regression methods, which combine RTM simulations with machine learning regression methods. For each of these categories, an overview of widely applied methods with application to mapping vegetation properties is given. In view of processing imaging spectroscopy data, a critical aspect involves the challenge of dealing with spectral multicollinearity. The ability to provide robust estimates, retrieval uncertainties and acceptable retrieval processing speed are other important aspects in view of operational processing. Recommendations towards new-generation spectroscopy-based processing chains for operational production of biophysical variables are given.
Rafique, Rashad; Fienen, Michael N.; Parkin, Timothy B.; Anex, Robert P.
2013-01-01
DayCent is a biogeochemical model of intermediate complexity widely used to simulate greenhouse gases (GHG), soil organic carbon and nutrients in crop, grassland, forest and savannah ecosystems. Although this model has been applied to a wide range of ecosystems, it is still typically parameterized through a traditional “trial and error” approach and has not been calibrated using statistical inverse modelling (i.e. algorithmic parameter estimation). The aim of this study is to establish and demonstrate a procedure for calibration of DayCent to improve estimation of GHG emissions. We coupled DayCent with the parameter estimation (PEST) software for inverse modelling. The PEST software can be used for calibration through regularized inversion as well as model sensitivity and uncertainty analysis. The DayCent model was analysed and calibrated using N2O flux data collected over 2 years at the Iowa State University Agronomy and Agricultural Engineering Research Farms, Boone, IA. Crop year 2003 data were used for model calibration and 2004 data were used for validation. The optimization of DayCent model parameters using PEST significantly reduced model residuals relative to the default DayCent parameter values. Parameter estimation improved the model performance by reducing the sum of weighted squared residual difference between measured and modelled outputs by up to 67 %. For the calibration period, simulation with the default model parameter values underestimated mean daily N2O flux by 98 %. After parameter estimation, the model underestimated the mean daily fluxes by 35 %. During the validation period, the calibrated model reduced sum of weighted squared residuals by 20 % relative to the default simulation. Sensitivity analysis performed provides important insights into the model structure providing guidance for model improvement.
Inverse modeling of test SB4-VM2/216.7 at Wellenberg
International Nuclear Information System (INIS)
Finsterle, S.
1994-03-01
Pressure and flow rate data from a water sampling test, which also produced gas, at the Wellenberg site are analyzed using inverse modeling techniques. Two conceptual models are developed and used for parameter estimation. The first model assumes that the gas observed at the surface is dissolved in the pore water under natural pressure and temperature conditions and comes out of solution due to the pressure reduction during pumping. The second model considers a mobile gas phase originally present in the formation. While both models are able to explain the observed pressure response as well as the gas seen at the surface, large uncertainties in the data and in the model assumptions inhibit the determination of two-phase flow parameters. The analysis indicates, however, that the formation has a very low permeability and that formation head is far below hydrostatic
Peddle, Derek R.; Huemmrich, K. Fred; Hall, Forrest G.; Masek, Jeffrey G.; Soenen, Scott A.; Jackson, Chris D.
2011-01-01
Canopy reflectance model inversion using look-up table approaches provides powerful and flexible options for deriving improved forest biophysical structural information (BSI) compared with traditional statistical empirical methods. The BIOPHYS algorithm is an improved, physically-based inversion approach for deriving BSI for independent use and validation and for monitoring, inventory and quantifying forest disturbance as well as input to ecosystem, climate and carbon models. Based on the multiple-forward mode (MFM) inversion approach, BIOPHYS results were summarized from different studies (Minnesota/NASA COVER; Virginia/LEDAPS; Saskatchewan/BOREAS), sensors (airborne MMR; Landsat; MODIS) and models (GeoSail; GOMS). Applications output included forest density, height, crown dimension, branch and green leaf area, canopy cover, disturbance estimates based on multi-temporal chronosequences, and structural change following recovery from forest fires over the last century. Good correspondences with validation field data were obtained. Integrated analyses of multiple solar and view angle imagery further improved retrievals compared with single pass data. Quantifying ecosystem dynamics such as the area and percent of forest disturbance, early regrowth and succession provide essential inputs to process-driven models of carbon flux. BIOPHYS is well suited for large-area, multi-temporal applications involving multiple image sets and mosaics for assessing vegetation disturbance and quantifying biophysical structural dynamics and change. It is also suitable for integration with forest inventory, monitoring, updating, and other programs.
Energy Technology Data Exchange (ETDEWEB)
Xu, S.; Peddle, D.R.; Coburn, C.A.; Kienzle, S. [Univ. of Lethbridge, Dept. of Geography, Lethbridge, Alberta (Canada)
2008-06-15
Net primary productivity (NPP) is a key component of the terrestrial carbon cycle and is important in ecological, watershed, and forest management studies, and more broadly in global climate change research. Determining the relative importance and magnitude of uncertainty of NPP model inputs is important for proper carbon reporting over larger areas and time periods. This paper presents a systematic evaluation of the boreal ecosystem productivity simulator (BEPS) model in mountainous terrain using an established montane forest test site in Kananaskis, Alberta, in the Canadian Rocky Mountains. Model runs were based on forest (land cover, leaf area index (LAI), biomass) and climate-water inputs (solar radiation, temperature, precipitation, humidity, soil water holding capacity) derived from digital elevation model (DEM) derivatives, climate data, geographical information system (GIS) functions, and topographically corrected satellite imagery. Four sensitivity analyses were conducted as a controlled series of experiments involving (i) NPP individual parameter sensitivity for a full growing season, (ii) NPP independent variation tests (parameter {mu} {+-} 1{sigma}), (iii) factorial analyses to assess more complex multiple-factor interactions, and (iv) topographic correction. The results, validated against field measurements, showed that modeled NPP was sensitive to most inputs measured in the study area, with LAI and forest type the most important forest input, and solar radiation the most important climate input. Soil available water holding capacity expressed as a function of wetness index was only significant in conjunction with precipitation when both parameters represented a moisture-deficit situation. NPP uncertainty resulting from topographic influence was equivalent to 140 kg C ha{sup -1}{center_dot}year{sup -1}. This suggested that topographic correction of model inputs is important for accurate NPP estimation. The BEPS model, designed originally for flat
International Nuclear Information System (INIS)
Xu, S.; Peddle, D.R.; Coburn, C.A.; Kienzle, S.
2008-01-01
Net primary productivity (NPP) is a key component of the terrestrial carbon cycle and is important in ecological, watershed, and forest management studies, and more broadly in global climate change research. Determining the relative importance and magnitude of uncertainty of NPP model inputs is important for proper carbon reporting over larger areas and time periods. This paper presents a systematic evaluation of the boreal ecosystem productivity simulator (BEPS) model in mountainous terrain using an established montane forest test site in Kananaskis, Alberta, in the Canadian Rocky Mountains. Model runs were based on forest (land cover, leaf area index (LAI), biomass) and climate-water inputs (solar radiation, temperature, precipitation, humidity, soil water holding capacity) derived from digital elevation model (DEM) derivatives, climate data, geographical information system (GIS) functions, and topographically corrected satellite imagery. Four sensitivity analyses were conducted as a controlled series of experiments involving (i) NPP individual parameter sensitivity for a full growing season, (ii) NPP independent variation tests (parameter μ ± 1σ), (iii) factorial analyses to assess more complex multiple-factor interactions, and (iv) topographic correction. The results, validated against field measurements, showed that modeled NPP was sensitive to most inputs measured in the study area, with LAI and forest type the most important forest input, and solar radiation the most important climate input. Soil available water holding capacity expressed as a function of wetness index was only significant in conjunction with precipitation when both parameters represented a moisture-deficit situation. NPP uncertainty resulting from topographic influence was equivalent to 140 kg C ha -1 ·year -1 . This suggested that topographic correction of model inputs is important for accurate NPP estimation. The BEPS model, designed originally for flat boreal forests, was shown to be
International Nuclear Information System (INIS)
Winiarek, Victor
2014-01-01
Uncontrolled releases of pollutant in the atmosphere may be the consequence of various situations: accidents, for instance leaks or explosions in an industrial plant, or terrorist attacks such as biological bombs, especially in urban areas. In the event of such situations, authorities' objectives are various: predict the contaminated zones to apply first countermeasures such as evacuation of concerned population; determine the source location; assess the long-term polluted areas, for instance by deposition of persistent pollutants in the soil. To achieve these objectives, numerical models can be used to model the atmospheric dispersion of pollutants. We will first present the different processes that govern the transport of pollutants in the atmosphere, then the different numerical models that are commonly used in this context. The choice between these models mainly depends of the scale and the details one seeks to take into account. We will then present several inverse modeling methods to estimate the emission as well as statistical methods to estimate prior errors, to which the inversion is very sensitive. Several case studies are presented, using synthetic data as well as real data such as the estimation of source terms from the Fukushima accident in March 2011. From our results, we estimate the Cesium-137 emission to be between 12 and 19 PBq with a standard deviation between 15 and 65% and the Iodine-131 emission to be between 190 and 380 PBq with a standard deviation between 5 and 10%. Concerning the localization of an unknown source of pollutant, two strategies can be considered. On one hand parametric methods use a limited number of parameters to characterize the source term to be reconstructed. To do so, strong assumptions are made on the nature of the source. The inverse problem is hence to estimate these parameters. On the other hand nonparametric methods attempt to reconstruct a full emission field. Several parametric and nonparametric methods are
Directory of Open Access Journals (Sweden)
Xiaochao Tang
2013-03-01
Full Text Available With the movement towards the implementation of mechanistic-empirical pavement design guide (MEPDG, an accurate determination of pavement layer moduli is vital for predicting pavement critical mechanistic responses. A backcalculation procedure is commonly used to estimate the pavement layer moduli based on the non-destructive falling weight deflectometer (FWD tests. Backcalculation of flexible pavement layer properties is an inverse problem with known input and output signals based upon which unknown parameters of the pavement system are evaluated. In this study, an inverse analysis procedure that combines the finite element analysis and a population-based optimization technique, Genetic Algorithm (GA has been developed to determine the pavement layer structural properties. A lightweight deflectometer (LWD was used to infer the moduli of instrumented three-layer scaled flexible pavement models. While the common practice in backcalculating pavement layer properties still assumes a static FWD load and uses only peak values of the load and deflections, dynamic analysis was conducted to simulate the impulse LWD load. The recorded time histories of the LWD load were used as the known inputs into the pavement system while the measured time-histories of surface central deflections and subgrade deflections measured with a linear variable differential transformers (LVDT were considered as the outputs. As a result, consistent pavement layer moduli can be obtained through this inverse analysis procedure.
Estimation of semolina dough rheological parameters by inversion of a finite elements model
Directory of Open Access Journals (Sweden)
Angelo Fabbri
2015-10-01
Full Text Available The description of the rheological properties of food material plays an important role in food engineering. Particularly for the optimisation of pasta manufacturing process (extrusion is needful to know the rheological properties of semolina dough. Unfortunately characterisation of non-Newtonian fluids, such as food doughs, requires a notable time effort, especially in terms of number of tests to be carried out. The present work proposes an alternative method, based on the combination of laboratory measurement, made with a simplified tool, with the inversion of a finite elements numerical model. To determine the rheological parameters, an objective function, defined as the distance between simulation and experimental data, was considered and the well-known Levenberg-Marqard optimisation algorithm was used. In order to verify the feasibility of the method, the rheological characterisation of the dough was carried also by a traditional procedure. Results shown that the difference between measurements of rheological parameters of the semolina dough made with traditional procedure and inverse methods are very small (maximum percentage error equal to 3.6%. This agreement supports the coherence of the inverse method that, in general, may be used to characterise many non-Newtonian materials.
Evaluation of methane emissions from West Siberian wetlands based on inverse modeling
Energy Technology Data Exchange (ETDEWEB)
Kim, H-S; Inoue, G [Research Institute for Humanity and Nature, 457-4 Motoyama, Kamigamo, Kita-ku, Kyoto 603-8047 (Japan); Maksyutov, S; Machida, T [National Institute for Environmental Studies, 16-2 Onogawa, Tsukuba, Ibaraki 305-8506 (Japan); Glagolev, M V [Lomonosov Moscow State University, GSP-1, Leninskie Gory, Moscow 119991 (Russian Federation); Patra, P K [Research Institute for Global Change/JAMSTEC, 3173-25 Showa-cho, Kanazawa-ku, Yokohama, Kanagawa 236-0001 (Japan); Sudo, K, E-mail: heonsook.kim@gmail.com [Nagoya University, Furo-cho, Chikusa-ku, Nagoya 464-8601 (Japan)
2011-07-15
West Siberia contains the largest extent of wetlands in the world, including large peat deposits; the wetland area is equivalent to 27% of the total area of West Siberia. This study used inverse modeling to refine emissions estimates for West Siberia using atmospheric CH{sub 4} observations and two wetland CH{sub 4} emissions inventories: (1) the global wetland emissions dataset of the NASA Goddard Institute for Space Studies (the GISS inventory), which includes emission seasons and emission rates based on climatology of monthly surface air temperature and precipitation, and (2) the West Siberian wetland emissions data (the Bc7 inventory), based on in situ flux measurements and a detailed wetland classification. The two inversions using the GISS and Bc7 inventories estimated annual mean flux from West Siberian wetlands to be 2.9 {+-} 1.7 and 3.0 {+-} 1.4 Tg yr{sup -1}, respectively, which are lower than the 6.3 Tg yr{sup -1} predicted in the GISS inventory, but similar to those of the Bc7 inventory (3.2 Tg yr{sup -1}). The well-constrained monthly fluxes and a comparison between the predicted CH{sub 4} concentrations in the two inversions suggest that the Bc7 inventory predicts the seasonal cycle of West Siberian wetland CH{sub 4} emissions more reasonably, indicating that the GISS inventory predicts more emissions from wetlands in northern and middle taiga.
Thermal-hydraulic modeling of flow inversion in a research reactor
International Nuclear Information System (INIS)
Kazeminejad, H.
2008-01-01
The course of loss of flow accident and flow inversion in a pool type research reactor, with scram enabled under natural circulation condition is numerically investigated. The analyses were performed by a lumped parameters approach for the coupled kinetic-thermal-hydraulics, with continuous feedback due to coolant and fuel temperature effects. A modified Runge-Kutta method was adopted for a better solution to the set of stiff differential equations. Transient thermal-hydraulics during the process of flow inversion and establishment of natural circulation were considered for a 10-MW IAEA research reactor. Some important parameters such as the peak temperatures for the hot channel were obtained for both high-enriched and low enriched fuel. The model prediction is also verified through comparison with other computer code results reported in the literature for detailed simulations of loss of flow accidents (LOFA) and the agreement between the results for the peak clad temperatures and key parameters has been satisfactory. It was found that the flow inversion and subsequent establishment of natural circulation keep the peak cladding surface temperature below the saturation temperature to avoid the escalation of clad temperature to the level of onset of nucleate boiling and sub-cooled void formation to ensure the safe operation of the reactor
Al-Ma'shumah, Fathimah; Permana, Dony; Sidarto, Kuntjoro Adji
2015-12-01
Customer Lifetime Value is an important and useful concept in marketing. One of its benefits is to help a company for budgeting marketing expenditure for customer acquisition and customer retention. Many mathematical models have been introduced to calculate CLV considering the customer retention/migration classification scheme. A fairly new class of these models which will be described in this paper uses Markov Chain Models (MCM). This class of models has the major advantage for its flexibility to be modified to several different cases/classification schemes. In this model, the probabilities of customer retention and acquisition play an important role. From Pfeifer and Carraway, 2000, the final formula of CLV obtained from MCM usually contains nonlinear form of the transition probability matrix. This nonlinearity makes the inverse problem of CLV difficult to solve. This paper aims to solve this inverse problem, yielding the approximate transition probabilities for the customers, by applying metaheuristic optimization algorithm developed by Yang, 2013, Flower Pollination Algorithm. The major interpretation of obtaining the transition probabilities are to set goals for marketing teams in keeping the relative frequencies of customer acquisition and customer retention.
Multi-year Estimates of Methane Fluxes in Alaska from an Atmospheric Inverse Model
Miller, S. M.; Commane, R.; Chang, R. Y. W.; Miller, C. E.; Michalak, A. M.; Dinardo, S. J.; Dlugokencky, E. J.; Hartery, S.; Karion, A.; Lindaas, J.; Sweeney, C.; Wofsy, S. C.
2015-12-01
We estimate methane fluxes across Alaska over a multi-year period using observations from a three-year aircraft campaign, the Carbon Arctic Reservoirs Vulnerability Experiment (CARVE). Existing estimates of methane from Alaska and other Arctic regions disagree in both magnitude and distribution, and before the CARVE campaign, atmospheric observations in the region were sparse. We combine these observations with an atmospheric particle trajectory model and a geostatistical inversion to estimate surface fluxes at the model grid scale. We first use this framework to estimate the spatial distribution of methane fluxes across the state. We find the largest fluxes in the south-east and North Slope regions of Alaska. This distribution is consistent with several estimates of wetland extent but contrasts with the distribution in most existing flux models. These flux models concentrate methane in warmer or more southerly regions of Alaska compared to the estimate presented here. This result suggests a discrepancy in how existing bottom-up models translate wetland area into methane fluxes across the state. We next use the inversion framework to explore inter-annual variability in regional-scale methane fluxes for 2012-2014. We examine the extent to which this variability correlates with weather or other environmental conditions. These results indicate the possible sensitivity of wetland fluxes to near-term variability in climate.
Large-scale inverse and forward modeling of adaptive resonance in the tinnitus decompensation.
Low, Yin Fen; Trenado, Carlos; Delb, Wolfgang; D'Amelio, Roberto; Falkai, Peter; Strauss, Daniel J
2006-01-01
Neural correlates of psychophysiological tinnitus models in humans may be used for their neurophysiological validation as well as for their refinement and improvement to better understand the pathogenesis of the tinnitus decompensation and to develop new therapeutic approaches. In this paper we make use of neural correlates of top-down projections, particularly, a recently introduced synchronization stability measure, together with a multiscale evoked response potential (ERP) model in order to study and evaluate the tinnitus decompensation by using a hybrid inverse-forward mathematical methodology. The neural synchronization stability, which according to the underlying model is linked to the focus of attention on the tinnitus signal, follows the experimental and inverse way and allows to discriminate between a group of compensated and decompensated tinnitus patients. The multiscale ERP model, which works in the forward direction, is used to consolidate hypotheses which are derived from the experiments for a known neural source dynamics related to attention. It is concluded that both methodologies agree and support each other in the description of the discriminatory character of the neural correlate proposed, but also help to fill the gap between the top-down adaptive resonance theory and the Jastreboff model of tinnitus.
Finite-Source Inversion for the 2004 Parkfield Earthquake using 3D Velocity Model Green's Functions
Kim, A.; Dreger, D.; Larsen, S.
2008-12-01
We determine finite fault models of the 2004 Parkfield earthquake using 3D Green's functions. Because of the dense station coverage and detailed 3D velocity structure model in this region, this earthquake provides an excellent opportunity to examine how the 3D velocity structure affects the finite fault inverse solutions. Various studies (e.g. Michaels and Eberhart-Phillips, 1991; Thurber et al., 2006) indicate that there is a pronounced velocity contrast across the San Andreas Fault along the Parkfield segment. Also the fault zone at Parkfield is wide as evidenced by mapped surface faults and where surface slip and creep occurred in the 1966 and the 2004 Parkfield earthquakes. For high resolution images of the rupture process"Ait is necessary to include the accurate 3D velocity structure for the finite source inversion. Liu and Aurchuleta (2004) performed finite fault inversions using both 1D and 3D Green's functions for 1989 Loma Prieta earthquake using the same source paramerization and data but different Green's functions and found that the models were quite different. This indicates that the choice of the velocity model significantly affects the waveform modeling at near-fault stations. In this study, we used the P-wave velocity model developed by Thurber et al (2006) to construct the 3D Green's functions. P-wave speeds are converted to S-wave speeds and density using by the empirical relationships of Brocher (2005). Using a finite difference method, E3D (Larsen and Schultz, 1995), we computed the 3D Green's functions numerically by inserting body forces at each station. Using reciprocity, these Green's functions are recombined to represent the ground motion at each station due to the slip on the fault plane. First we modeled the waveforms of small earthquakes to validate the 3D velocity model and the reciprocity of the Green"fs function. In the numerical tests we found that the 3D velocity model predicted the individual phases well at frequencies lower than 0
Dewaele, Hélène; Munier, Simon; Albergel, Clément; Planque, Carole; Laanaia, Nabil; Carrer, Dominique; Calvet, Jean-Christophe
2017-09-01
Soil maximum available water content (MaxAWC) is a key parameter in land surface models (LSMs). However, being difficult to measure, this parameter is usually uncertain. This study assesses the feasibility of using a 15-year (1999-2013) time series of satellite-derived low-resolution observations of leaf area index (LAI) to estimate MaxAWC for rainfed croplands over France. LAI interannual variability is simulated using the CO2-responsive version of the Interactions between Soil, Biosphere and Atmosphere (ISBA) LSM for various values of MaxAWC. Optimal value is then selected by using (1) a simple inverse modelling technique, comparing simulated and observed LAI and (2) a more complex method consisting in integrating observed LAI in ISBA through a land data assimilation system (LDAS) and minimising LAI analysis increments. The evaluation of the MaxAWC estimates from both methods is done using simulated annual maximum above-ground biomass (Bag) and straw cereal grain yield (GY) values from the Agreste French agricultural statistics portal, for 45 administrative units presenting a high proportion of straw cereals. Significant correlations (p value Bag and GY are found for up to 36 and 53 % of the administrative units for the inverse modelling and LDAS tuning methods, respectively. It is found that the LDAS tuning experiment gives more realistic values of MaxAWC and maximum Bag than the inverse modelling experiment. Using undisaggregated LAI observations leads to an underestimation of MaxAWC and maximum Bag in both experiments. Median annual maximum values of disaggregated LAI observations are found to correlate very well with MaxAWC.
Inverse grey-box model-based control of a dielectric elastomer actuator
DEFF Research Database (Denmark)
Jones, Richard William; Sarban, Rahimullah
2012-01-01
control performance across the operating range of the DE actuator, a gain scheduling term, which linearizes the operating characteristics of the tubular dielectric elastomer actuator, is developed and implemented in series with the IMC controller. The IMC-based approach is investigated for servo control......An accurate physical-based electromechanical model of a commercially available tubular dielectric elastomer (DE) actuator has been developed and validated. In this contribution, the use of the physical-based electromechanical model to formulate a model-based controller is examined. The choice...... of control scheme was dictated by the desire for transparency in both controller design and operation. The internal model control (IMC) approach was chosen. In this particular application, the inverse of the linearized form of the grey-box model is used to formulate the IMC controller. To ensure consistent...
Alp, Murat; Cucinotta, Francis A
2018-03-01
Exposure to heavy-ion radiation during cancer treatment or space travel may cause cognitive detriments that have been associated with changes in neuron morphology and plasticity. Observations in mice of reduced neuronal dendritic complexity have revealed a dependence on radiation quality and absorbed dose, suggesting that microscopic energy deposition plays an important role. In this work we used morphological data for mouse dentate granular cell layer (GCL) neurons and a stochastic model of particle track structure and microscopic energy deposition (ED) to develop a predictive model of high-charge and energy (HZE) particle-induced morphological changes to the complex structures of dendritic arbors. We represented dendrites as cylindrical segments of varying diameter with unit aspect ratios, and developed a fast sampling method to consider the stochastic distribution of ED by δ rays (secondary electrons) around the path of heavy ions, to reduce computational times. We introduce probabilistic models with a small number of parameters to describe the induction of precursor lesions that precede dendritic snipping, denoted as snip sites. Predictions for oxygen ( 16 O, 600 MeV/n) and titanium ( 48 Ti, 600 MeV/n) particles with LET of 16.3 and 129 keV/μm, respectively, are considered. Morphometric parameters to quantify changes in neuron morphology are described, including reduction in total dendritic length, number of branch points and branch numbers. Sholl analysis is applied for single neurons to elucidate dose-dependent reductions in dendritic complexity. We predict important differences in measurements from imaging of tissues from brain slices with single neuron cell observations due to the role of neuron death through both soma apoptosis and excessive dendritic length reduction. To further elucidate the role of track structure, random segment excision (snips) models are introduced and a sensitivity study of the effects of the modes of neuron death in predictions
The Biophysics Microgravity Initiative
Gorti, S.
2016-01-01
Biophysical microgravity research on the International Space Station using biological materials has been ongoing for several decades. The well-documented substantive effects of long duration microgravity include the facilitation of the assembly of biological macromolecules into large structures, e.g., formation of large protein crystals under micro-gravity. NASA is invested not only in understanding the possible physical mechanisms of crystal growth, but also promoting two flight investigations to determine the influence of µ-gravity on protein crystal quality. In addition to crystal growth, flight investigations to determine the effects of shear on nucleation and subsequent formation of complex structures (e.g., crystals, fibrils, etc.) are also supported. It is now considered that long duration microgravity research aboard the ISS could also make possible the formation of large complex biological and biomimetic materials. Investigations of various materials undergoing complex structure formation in microgravity will not only strengthen NASA science programs, but may also provide invaluable insight towards the construction of large complex tissues, organs, or biomimetic materials on Earth.
DEFF Research Database (Denmark)
Andresen, Elisa; Kappel, Sophie; Stärk, Hans-Joachim
2016-01-01
Cadmium (Cd) is an important environmental pollutant and is poisonous to most organisms. We aimed to unravel the mechanisms of Cd toxicity in the model water plant Ceratophyllum demersum exposed to low (nM) concentrations of Cd as are present in nature. Experiments were conducted under environmen......Cadmium (Cd) is an important environmental pollutant and is poisonous to most organisms. We aimed to unravel the mechanisms of Cd toxicity in the model water plant Ceratophyllum demersum exposed to low (nM) concentrations of Cd as are present in nature. Experiments were conducted under...... environmentally relevant conditions, including nature-like light and temperature cycles, and a low biomass to water ratio. We measured chlorophyll (Chl) fluorescence kinetics, oxygen exchange, the concentrations of reactive oxygen species and pigments, metal binding to proteins, and the accumulation of starch...... and metals. The inhibition threshold concentration for most parameters was 20 nM. Below this concentration, hardly any stress symptoms were observed. The first site of inhibition was photosynthetic light reactions (the maximal quantum yield of photosystem II (PSII) reaction centre measured as Fv /Fm , light...
International Nuclear Information System (INIS)
De-Marzi, Ludovic
2016-01-01
The main objective of this thesis is to develop and optimize algorithms for intensity modulated proton therapy, taking into account the physical and biological pencil beam properties. A model based on the summation and fluence weighted division of the pencil beams has been used. A new parameterization of the lateral dose distribution has been developed using a combination of three Gaussian functions. The algorithms have been implemented into a treatment planning system, then experimentally validated and compared with Monte Carlo simulations. Some approximations have been made and validated in order to achieve reasonable calculation times for clinical purposes. In a second phase, a collaboration with Institut Curie radiobiological teams has been started in order to implement radiobiological parameters and results into the optimization loop of the treatment planning process. Indeed, scanned pencil beams are pulsed and delivered at high dose rates (from 10 to 100 Gy/s), and the relative biological efficiency of protons is still relatively unknown given the wide diversity of use of these beams: the different models available and their dependence with linear energy transfers have been studied. A good agreement between dose calculations and measurements (deviations lower than 3 % and 2 mm) has been obtained. An experimental protocol has been set in order to qualify pulsed high dose rate effects and preliminary results obtained on one cell line suggested variations of the biological efficiency up to 10 %, though with large uncertainties. (author) [fr
International Nuclear Information System (INIS)
Li, Maokun; Abubakar, Aria; Habashy, Tarek M
2010-01-01
In this paper, we apply a model-based inversion scheme for the interpretation of the crosswell electromagnetic data. In this approach, we use open and closed polygons to parameterize the unknown configuration. The parameters that define these polygons are then inverted for by minimizing the data misfit cost function. Compared with the pixel-based inversion approach, the model-based inversion uses only a few number of parameters; hence, it is more efficient. Furthermore, with sufficient sensitivity in the data, the model-based approach can provide quantitative estimates of the inverted parameters such as the conductivity. The model-based inversion also provides a convenient way to incorporate a priori information from other independent measurements such as seismic, gravity and well logs
International Nuclear Information System (INIS)
Desesquelles, P.
1997-01-01
Computer Monte Carlo simulations occupy an increasingly important place between theory and experiment. This paper introduces a global protocol for the comparison of model simulations with experimental results. The correlated distributions of the model parameters are determined using an original recursive inversion procedure. Multivariate analysis techniques are used in order to optimally synthesize the experimental information with a minimum number of variables. This protocol is relevant in all fields if physics dealing with event generators and multi-parametric experiments. (authors)
Source modeling and inversion with near real-time GPS: a GITEWS perspective for Indonesia
Babeyko, A. Y.; Hoechner, A.; Sobolev, S. V.
2010-07-01
We present the GITEWS approach to source modeling for the tsunami early warning in Indonesia. Near-field tsunami implies special requirements to both warning time and details of source characterization. To meet these requirements, we employ geophysical and geological information to predefine a maximum number of rupture parameters. We discretize the tsunamigenic Sunda plate interface into an ordered grid of patches (150×25) and employ the concept of Green's functions for forward and inverse rupture modeling. Rupture Generator, a forward modeling tool, additionally employs different scaling laws and slip shape functions to construct physically reasonable source models using basic seismic information only (magnitude and epicenter location). GITEWS runs a library of semi- and fully-synthetic scenarios to be extensively employed by system testing as well as by warning center personnel teaching and training. Near real-time GPS observations are a very valuable complement to the local tsunami warning system. Their inversion provides quick (within a few minutes on an event) estimation of the earthquake magnitude, rupture position and, in case of sufficient station coverage, details of slip distribution.
Oguz, Ozgur S; Zhou, Zhehua; Glasauer, Stefan; Wollherr, Dirk
2018-04-03
Human motor control is highly efficient in generating accurate and appropriate motor behavior for a multitude of tasks. This paper examines how kinematic and dynamic properties of the musculoskeletal system are controlled to achieve such efficiency. Even though recent studies have shown that the human motor control relies on multiple models, how the central nervous system (CNS) controls this combination is not fully addressed. In this study, we utilize an Inverse Optimal Control (IOC) framework in order to find the combination of those internal models and how this combination changes for different reaching tasks. We conducted an experiment where participants executed a comprehensive set of free-space reaching motions. The results show that there is a trade-off between kinematics and dynamics based controllers depending on the reaching task. In addition, this trade-off depends on the initial and final arm configurations, which in turn affect the musculoskeletal load to be controlled. Given this insight, we further provide a discomfort metric to demonstrate its influence on the contribution of different inverse internal models. This formulation together with our analysis not only support the multiple internal models (MIMs) hypothesis but also suggest a hierarchical framework for the control of human reaching motions by the CNS.
Inverse modeling of the biodegradation of emerging organic contaminants in the soil-plant system.
Hurtado, Carlos; Trapp, Stefan; Bayona, Josep M
2016-08-01
Understanding the processes involved in the uptake and accumulation of organic contaminants into plants is very important to assess the possible human risk associated with. Biodegradation of emerging contaminants in plants has been observed, but kinetical studies are rare. In this study, we analyse experimental data on the uptake of emerging organic contaminants into lettuce derived in a greenhouse experiment. Measured soil, root and leaf concentrations from four contaminants were selected within the applicability domain of a steady-state two-compartment standard plant uptake model: bisphenol A (BPA), carbamazepine (CBZ), triclosan (TCS) and caffeine (CAF). The model overestimated concentrations in most cases, when no degradation rates in plants were entered. Subsequently, biodegradation rates were fitted so that the measured concentrations were met. Obtained degradation kinetics are in the order, BPA < CAF ≈ TCS < CBZ in roots, and BPA ≈ TCS < CBZ < CAF in leaves. Kinetics determined by inverse modeling are, despite the inherent uncertainty, indicative of the dissipation rates. The advantage of the procedure that is additional knowledge can be gained from existing experimental data. Dissipation kinetics found via inverse modeling is not a conclusive proof for biodegradation and confirmation by experimental studies is needed. Copyright © 2016. Published by Elsevier Ltd.
Inversion of the Jacobi-Porstendorfer room model for the radon progeny
International Nuclear Information System (INIS)
Thomas, J.; Jilek, K.; Brabec, M.
2010-01-01
The Jacobi-Porstendoerfer (J-P) room model describes the behaviour of radon progeny in the atmosphere of a room. It distinguishes between free and attached radon progeny in air. It has been successfully used without substantial changes for nearly 40 years. There have been several attempts to invert the model approximately to determine the parameters describing the physical processes. Here, an exact solution is aimed at as an algebraic inversion of the system of six linear equations for the five unknown physical parameters k, X, R, q f , q a of the room model. Two strong linear dependencies in this system, unfortunately do not allow to obtain a general solution (especially not for the ventilation coefficient k), but only a parameterized one or for reduced sets of unknown parameters. More, the impossibility to eliminate one of the two linear dependencies and the departures of the measured concentrations forces to solve a set of allowed combinations of equations of the algebraic system and to accept its mean values (therefore with variances) as a result of the algebraic inversion. These results are in agreement with results of the least squares method as well as of a sophisticated modern statistical approach. The algebraic approach provides, of course, a lot of analytical relations to study the mutual dependencies between the model parameters and the measurable quantities. (authors)
Hamim, Salah Uddin Ahmed
Nanoindentation involves probing a hard diamond tip into a material, where the load and the displacement experienced by the tip is recorded continuously. This load-displacement data is a direct function of material's innate stress-strain behavior. Thus, theoretically it is possible to extract mechanical properties of a material through nanoindentation. However, due to various nonlinearities associated with nanoindentation the process of interpreting load-displacement data into material properties is difficult. Although, simple elastic behavior can be characterized easily, a method to characterize complicated material behavior such as nonlinear viscoelasticity is still lacking. In this study, a nanoindentation-based material characterization technique is developed to characterize soft materials exhibiting nonlinear viscoelasticity. Nanoindentation experiment was modeled in finite element analysis software (ABAQUS), where a nonlinear viscoelastic behavior was incorporated using user-defined subroutine (UMAT). The model parameters were calibrated using a process called inverse analysis. In this study, a surrogate model-based approach was used for the inverse analysis. The different factors affecting the surrogate model performance are analyzed in order to optimize the performance with respect to the computational cost.
DEFF Research Database (Denmark)
Khan, Ahmad Raza; Chuhutin, Andrey; Wiborg, Ove
2016-01-01
anhedonia is considered to be a realistic model of depression in studies of animal subjects. Stereological and neuronal tracing techniques have demonstrated persistent remodeling of microstructure in hippocampus, prefrontal cortex and amygdala of CMS brains. Recent developments in diffusion MRI (d...... microstructure in the hippocampus, prefrontal cortex, caudate putamen and amygdala regions of CMS rat brains by comparison to brains from normal controls. To validate findings of CMS induced microstructural alteration, histology was performed to determine neurite, nuclear and astrocyte density. d-MRI based...... neurite density and tensor-based mean kurtosis (MKT) were significantly higher, while mean diffusivity (MD), extracellular diffusivity (Deff) and intra-neurite diffusivity(DL) were significantly lower in the amygdala of CMS rat brains. Deff was also significantly lower in the hippocampus and caudate...
International Nuclear Information System (INIS)
Gabrielle, Benoit; Gagnaire, Nathalie
2008-01-01
Cereal straw, a by-product in the production of agricultural crops, is considered as a potentially large source of energy supply with an estimated value of 47 x 10 18 J worldwide. However, there is some debate regarding the actual amounts of straw which could be removed from arable soils without jeopardizing their quality, as well as the potential trade-offs in the overall straw-to-energy chain compared to the use of fossil energy sources. Here, we used a deterministic model of C and N dynamics in soil-crop systems to simulate the effect of straw removal under various sets of soil, climate and crop management conditions in northeastern France. Model results in terms of nitrate leaching, soil C variations, nitrous oxide and ammonia emissions were subsequently inputted into the life-cycle assessment (LCA) of a particular bio-energy chain in which straw was used to generate heat and power in a plant producing bio-ethanol from wheat grains. Straw removal had little influence on simulated environmental emissions in the field, and straw incorporation in soil resulted in a sequestration of only 5-10% of its C in the long term (30 years). The LCA concluded to significant benefits of straw use for energy in terms of global warming and use of non-renewable energy. Only the eutrophication and atmospheric acidification impact categories were slightly unfavourable to straw use in some cases, with a difference of 8% at most relative to straw incorporation. These results based on a novel methodology thereby confirm the environmental benefits of substituting fossil energy with straw. (author)
Brain functional BOLD perturbation modelling for forward fMRI and inverse mapping
Robinson, Jennifer; Calhoun, Vince
2018-01-01
Purpose To computationally separate dynamic brain functional BOLD responses from static background in a brain functional activity for forward fMRI signal analysis and inverse mapping. Methods A brain functional activity is represented in terms of magnetic source by a perturbation model: χ = χ0 +δχ, with δχ for BOLD magnetic perturbations and χ0 for background. A brain fMRI experiment produces a timeseries of complex-valued images (T2* images), whereby we extract the BOLD phase signals (denoted by δP) by a complex division. By solving an inverse problem, we reconstruct the BOLD δχ dataset from the δP dataset, and the brain χ distribution from a (unwrapped) T2* phase image. Given a 4D dataset of task BOLD fMRI, we implement brain functional mapping by temporal correlation analysis. Results Through a high-field (7T) and high-resolution (0.5mm in plane) task fMRI experiment, we demonstrated in detail the BOLD perturbation model for fMRI phase signal separation (P + δP) and reconstructing intrinsic brain magnetic source (χ and δχ). We also provided to a low-field (3T) and low-resolution (2mm) task fMRI experiment in support of single-subject fMRI study. Our experiments show that the δχ-depicted functional map reveals bidirectional BOLD χ perturbations during the task performance. Conclusions The BOLD perturbation model allows us to separate fMRI phase signal (by complex division) and to perform inverse mapping for pure BOLD δχ reconstruction for intrinsic functional χ mapping. The full brain χ reconstruction (from unwrapped fMRI phase) provides a new brain tissue image that allows to scrutinize the brain tissue idiosyncrasy for the pure BOLD δχ response through an automatic function/structure co-localization. PMID:29351339
Brain functional BOLD perturbation modelling for forward fMRI and inverse mapping.
Chen, Zikuan; Robinson, Jennifer; Calhoun, Vince
2018-01-01
To computationally separate dynamic brain functional BOLD responses from static background in a brain functional activity for forward fMRI signal analysis and inverse mapping. A brain functional activity is represented in terms of magnetic source by a perturbation model: χ = χ0 +δχ, with δχ for BOLD magnetic perturbations and χ0 for background. A brain fMRI experiment produces a timeseries of complex-valued images (T2* images), whereby we extract the BOLD phase signals (denoted by δP) by a complex division. By solving an inverse problem, we reconstruct the BOLD δχ dataset from the δP dataset, and the brain χ distribution from a (unwrapped) T2* phase image. Given a 4D dataset of task BOLD fMRI, we implement brain functional mapping by temporal correlation analysis. Through a high-field (7T) and high-resolution (0.5mm in plane) task fMRI experiment, we demonstrated in detail the BOLD perturbation model for fMRI phase signal separation (P + δP) and reconstructing intrinsic brain magnetic source (χ and δχ). We also provided to a low-field (3T) and low-resolution (2mm) task fMRI experiment in support of single-subject fMRI study. Our experiments show that the δχ-depicted functional map reveals bidirectional BOLD χ perturbations during the task performance. The BOLD perturbation model allows us to separate fMRI phase signal (by complex division) and to perform inverse mapping for pure BOLD δχ reconstruction for intrinsic functional χ mapping. The full brain χ reconstruction (from unwrapped fMRI phase) provides a new brain tissue image that allows to scrutinize the brain tissue idiosyncrasy for the pure BOLD δχ response through an automatic function/structure co-localization.
Salvus: A scalable software suite for full-waveform modelling & inversion
Afanasiev, M.; Boehm, C.; van Driel, M.; Krischer, L.; Fichtner, A.
2017-12-01
Full-waveform inversion (FWI), whether at the lab, exploration, or planetary scale, requires the cooperation of five principal components. (1) The geometry of the domain needs to be properly discretized and an initial guess of the model parameters must be projected onto it; (2) Large volumes of recorded waveform data must be collected, organized, and processed; (3) Synthetic waveform data must be efficiently and accurately computed through complex domains; (4) Suitable misfit functions and optimization techniques must be used to relate discrepancies in data space to perturbations in the model; and (5) Some form of workflow management must be employed to schedule and run (1) - (4) in the correct order. Each one of these components can represent a formidable technical challenge which redirects energy from the true task at hand: using FWI to extract new information about some underlying continuum.In this presentation we give an overview of the current status of the Salvus software suite, which was introduced to address the challenges listed above. Specifically, we touch on (1) salvus_mesher, which eases the discretization of complex Earth models into hexahedral meshes; (2) salvus_seismo, which integrates with LASIF and ObsPy to streamline the processing and preparation of seismic data; (3) salvus_wave, a high-performance and scalable spectral-element solver capable of simulating waveforms through general unstructured 2- and 3-D domains, and (4) salvus_opt, an optimization toolbox specifically designed for full-waveform inverse problems. Tying everything together, we also discuss (5) salvus_flow: a workflow package designed to orchestrate and manage the rest of the suite. It is our hope that these developments represent a step towards the automation of large-scale seismic waveform inversion, while also lowering the barrier of entry for new applications. We include several examples of Salvus' use in (extra-) planetary seismology, non-destructive testing, and medical
Energy Technology Data Exchange (ETDEWEB)
Ait-gougam, Y.; Ibtiouen, R.; Touhami, O. [Laboratoire de Recherche en Electrotechnique, Ecole Nationale Polytechnique, BP 182, El-Harrach 16200 (Algeria); Louis, J.-P.; Gabsi, M. [Systemes et Applications des Technologies de l' Information et de l' Energie (SATIE), CNRS UMR 8029, Ecole Normale Superieure de Cachan, 61 Avenue du President Wilson, 94235 Cachan Cedex (France)
2008-01-15
Sinusoidal motor's mathematical models are usually obtained using classical d-q transformation in the case of salient pole synchronous motors having sinusoidal field distribution. In this paper, a new inverse modelling for synchronous motors is presented. This modelling is derived from the properties of constant torque curves in the Concordia's reference frame. It takes into account the non-sinusoidal field distribution; EMF, self and mutual inductances having non-sinusoidal variations with respect to the angular rotor position. Both copper losses and torque ripples are minimized by adapted currents waveforms calculated from this model. Experimental evaluation was carried out on a DSP-controlled PMSM drive platform. Test results obtained demonstrate the effectiveness of the proposed method in reducing torque ripple. (author)
A model for the inverse 1-median problem on trees under uncertain costs
Directory of Open Access Journals (Sweden)
Kien Trung Nguyen
2016-01-01
Full Text Available We consider the problem of justifying vertex weights of a tree under uncertain costs so that a prespecified vertex become optimal and the total cost should be optimal in the uncertainty scenario. We propose a model which delivers the information about the optimal cost which respect to each confidence level \\(\\alpha \\in [0,1]\\. To obtain this goal, we first define an uncertain variable with respect to the minimum cost in each confidence level. If all costs are independently linear distributed, we present the inverse distribution function of this uncertain variable in \\(O(n^{2}\\log n\\ time, where \\(n\\ is the number of vertices in the tree.
Inverse stochastic-dynamic models for high-resolution Greenland ice core records
DEFF Research Database (Denmark)
Boers, Niklas; Chekroun, Mickael D.; Liu, Honghu
2017-01-01
as statistical properties such as probability density functions, waiting times and power spectra, with no need for any external forcing. The crucial ingredients for capturing these properties are (i) high-resolution training data, (ii) cubic drift terms, (iii) nonlinear coupling terms between the 18O and dust......Proxy records from Greenland ice cores have been studied for several decades, yet many open questions remain regarding the climate variability encoded therein. Here, we use a Bayesian framework for inferring inverse, stochastic-dynamic models from 18O and dust records of unprecedented, subdecadal...
DEFF Research Database (Denmark)
Sonnenborg, Torben Obel; Engesgaard, Peter Knudegaard; Rosbjerg, Dan
1996-01-01
An application of an inverse flow and transport model to a contaminated aquifer is presented. The objective of the study is to identify physical and nonreactive flow and transport parameters through an optimization approach. The approach can be classified as a statistical procedure, where a flow...... to steady state versus transient flow conditions and to the amount of hydraulic and solute data used is investigated. The flow parameters, transmissivity and leakage factor, are estimated simultaneously with the transport parameters: source strength, porosity, and longitudinal dispersivity. This paper...
Bayesian and Classical Estimation of Stress-Strength Reliability for Inverse Weibull Lifetime Models
Directory of Open Access Journals (Sweden)
Qixuan Bi
2017-06-01
Full Text Available In this paper, we consider the problem of estimating stress-strength reliability for inverse Weibull lifetime models having the same shape parameters but different scale parameters. We obtain the maximum likelihood estimator and its asymptotic distribution. Since the classical estimator doesn’t hold explicit forms, we propose an approximate maximum likelihood estimator. The asymptotic confidence interval and two bootstrap intervals are obtained. Using the Gibbs sampling technique, Bayesian estimator and the corresponding credible interval are obtained. The Metropolis-Hastings algorithm is used to generate random variates. Monte Carlo simulations are conducted to compare the proposed methods. Analysis of a real dataset is performed.
Inverse Dynamics Model for the Ankle Joint with Applications in Tibia Malleolus Fracture
Budescu, E.; Merticaru, E.; Chirazi, M.
The paper presents a biomechanical model of the ankle joint, in order to determine the force and the torque of reaction into the articulation, through inverse dynamic analysis, in various stages of the gait. Thus, knowing the acceleration of the foot and the reaction force between foot and ground during the gait, determined by experimental measurement, there was calculated, for five different positions of the foot, the joint reaction forces, on the basis of dynamic balance equations. The values numerically determined were compared with the admissible forces appearing in the technical systems of osteosynthesis of tibia malleolus fracture, in order to emphasize the motion restrictions during bone healing.
Regional-scale geostatistical inverse modeling of North American CO2 fluxes: a synthetic data study
Directory of Open Access Journals (Sweden)
A. M. Michalak
2010-07-01
Full Text Available A series of synthetic data experiments is performed to investigate the ability of a regional atmospheric inversion to estimate grid-scale CO2 fluxes during the growing season over North America. The inversions are performed within a geostatistical framework without the use of any prior flux estimates or auxiliary variables, in order to focus on the atmospheric constraint provided by the nine towers collecting continuous, calibrated CO2 measurements in 2004. Using synthetic measurements and their associated concentration footprints, flux and model-data mismatch covariance parameters are first optimized, and then fluxes and their uncertainties are estimated at three different temporal resolutions. These temporal resolutions, which include a four-day average, a four-day-average diurnal cycle with 3-hourly increments, and 3-hourly fluxes, are chosen to help assess the impact of temporal aggregation errors on the estimated fluxes and covariance parameters. Estimating fluxes at a temporal resolution that can adjust the diurnal variability is found to be critical both for recovering covariance parameters directly from the atmospheric data, and for inferring accurate ecoregion-scale fluxes. Accounting for both spatial and temporal a priori covariance in the flux distribution is also found to be necessary for recovering accurate a posteriori uncertainty bounds on the estimated fluxes. Overall, the results suggest that even a fairly sparse network of 9 towers collecting continuous CO2 measurements across the continent, used with no auxiliary information or prior estimates of the flux distribution in time or space, can be used to infer relatively accurate monthly ecoregion scale CO2 surface fluxes over North America within estimated uncertainty bounds. Simulated random transport error is shown to decrease the quality of flux estimates in under-constrained areas at the ecoregion scale, although the uncertainty bounds remain realistic. While these synthetic
Castaldo, R.; Tizzani, P.; Lollino, P.; Calò, F.; Ardizzone, F.; Lanari, R.; Guzzetti, F.; Manunta, M.
2015-11-01
The aim of this paper is to propose a methodology to perform inverse numerical modelling of slow landslides that combines the potentialities of both numerical approaches and well-known remote-sensing satellite techniques. In particular, through an optimization procedure based on a genetic algorithm, we minimize, with respect to a proper penalty function, the difference between the modelled displacement field and differential synthetic aperture radar interferometry (DInSAR) deformation time series. The proposed methodology allows us to automatically search for the physical parameters that characterize the landslide behaviour. To validate the presented approach, we focus our analysis on the slow Ivancich landslide (Assisi, central Italy). The kinematical evolution of the unstable slope is investigated via long-term DInSAR analysis, by exploiting about 20 years of ERS-1/2 and ENVISAT satellite acquisitions. The landslide is driven by the presence of a shear band, whose behaviour is simulated through a two-dimensional time-dependent finite element model, in two different physical scenarios, i.e. Newtonian viscous flow and a deviatoric creep model. Comparison between the model results and DInSAR measurements reveals that the deviatoric creep model is more suitable to describe the kinematical evolution of the landslide. This finding is also confirmed by comparing the model results with the available independent inclinometer measurements. Our analysis emphasizes that integration of different data, within inverse numerical models, allows deep investigation of the kinematical behaviour of slow active landslides and discrimination of the driving forces that govern their deformation processes.
Surface roughness retrieval by inversion of the Hapke model: A multiscale approach
Labarre, S.; Ferrari, C.; Jacquemoud, S.
2017-07-01
Surface roughness is a key property of soils that controls many surface processes and influences the scattering of incident electromagnetic waves at a wide range of scales. Hapke (2012b) designed a photometric model providing an approximate analytical solution of the Bidirectional Reflectance Distribution Function (BRDF) of a particulate medium: he introduced the effect of surface roughness as a correction factor of the BRDF of a smooth surface. This photometric roughness is defined as the mean slope angle of the facets composing the surface, integrated over all scales from the grain size to the local topography. Yet its physical meaning is still a question at issue, as the scale at which it occurs is not clearly defined. This work aims at better understanding the relative influence of roughness scales on soil BRDF and to test the ability of the Hapke model to retrieve a roughness that depicts effectively the ground truth. We apply a wavelet transform on millimeter digital terrain models (DTM) acquired over volcanic terrains. This method allows splitting the frequency band of a signal in several sub-bands, each corresponding to a spatial scale. We demonstrate that sub-centimeter surface features dominate both the integrated roughness and the BRDF shape. We investigate the suitability of the Hapke model for surface roughness retrieval by inversion on optical data. A global sensitivity analysis of the model shows that soil BRDF is very sensitive to surface roughness, nearly as much as the single scattering albedo according to the phase angle, but also that these two parameters are strongly correlated. Based on these results, a simplified two-parameter model depending on surface albedo and roughness is proposed. Inversion of this model on BRDF data simulated by a ray-tracing code over natural targets shows a good estimation of surface roughness when the assumptions of the model are verified, with a priori knowledge on surface albedo.
Inverse Force Determination on a Small Scale Launch Vehicle Model Using a Dynamic Balance
Ngo, Christina L.; Powell, Jessica M.; Ross, James C.
2017-01-01
A launch vehicle can experience large unsteady aerodynamic forces in the transonic regime that, while usually only lasting for tens of seconds during launch, could be devastating if structural components and electronic hardware are not designed to account for them. These aerodynamic loads are difficult to experimentally measure and even harder to computationally estimate. The current method for estimating buffet loads is through the use of a few hundred unsteady pressure transducers and wind tunnel test. Even with a large number of point measurements, the computed integrated load is not an accurate enough representation of the total load caused by buffeting. This paper discusses an attempt at using a dynamic balance to experimentally determine buffet loads on a generic scale hammer head launch vehicle model tested at NASA Ames Research Center's 11' x 11' transonic wind tunnel. To use a dynamic balance, the structural characteristics of the model needed to be identified so that the natural modal response could be and removed from the aerodynamic forces. A finite element model was created on a simplified version of the model to evaluate the natural modes of the balance flexures, assist in model design, and to compare to experimental data. Several modal tests were conducted on the model in two different configurations to check for non-linearity, and to estimate the dynamic characteristics of the model. The experimental results were used in an inverse force determination technique with a psuedo inverse frequency response function. Due to the non linearity, the model not being axisymmetric, and inconsistent data between the two shake tests from different mounting configuration, it was difficult to create a frequency response matrix that satisfied all input and output conditions for wind tunnel configuration to accurately predict unsteady aerodynamic loads.
Smoothing of, and parameter estimation from, noisy biophysical recordings.
Directory of Open Access Journals (Sweden)
Quentin J M Huys
2009-05-01
Full Text Available Biophysically detailed models of single cells are difficult to fit to real data. Recent advances in imaging techniques allow simultaneous access to various intracellular variables, and these data can be used to significantly facilitate the modelling task. These data, however, are noisy, and current approaches to building biophysically detailed models are not designed to deal with this. We extend previous techniques to take the noisy nature of the measurements into account. Sequential Monte Carlo ("particle filtering" methods, in combination with a detailed biophysical description of a cell, are used for principled, model-based smoothing of noisy recording data. We also provide an alternative formulation of smoothing where the neural nonlinearities are estimated in a non-parametric manner. Biophysically important parameters of detailed models (such as channel densities, intercompartmental conductances, input resistances, and observation noise are inferred automatically from noisy data via expectation-maximization. Overall, we find that model-based smoothing is a powerful, robust technique for smoothing of noisy biophysical data and for inference of biophysical parameters in the face of recording noise.
Cortex Inspired Model for Inverse Kinematics Computation for a Humanoid Robotic Finger
Gentili, Rodolphe J.; Oh, Hyuk; Molina, Javier; Reggia, James A.; Contreras-Vidal, José L.
2013-01-01
In order to approach human hand performance levels, artificial anthropomorphic hands/fingers have increasingly incorporated human biomechanical features. However, the performance of finger reaching movements to visual targets involving the complex kinematics of multi-jointed, anthropomorphic actuators is a difficult problem. This is because the relationship between sensory and motor coordinates is highly nonlinear, and also often includes mechanical coupling of the two last joints. Recently, we developed a cortical model that learns the inverse kinematics of a simulated anthropomorphic finger. Here, we expand this previous work by assessing if this cortical model is able to learn the inverse kinematics for an actual anthropomorphic humanoid finger having its two last joints coupled and controlled by pneumatic muscles. The findings revealed that single 3D reaching movements, as well as more complex patterns of motion of the humanoid finger, were accurately and robustly performed by this cortical model while producing kinematics comparable to those of humans. This work contributes to the development of a bioinspired controller providing adaptive, robust and flexible control of dexterous robotic and prosthetic hands. PMID:23366569
A Low-Cost Maximum Power Point Tracking System Based on Neural Network Inverse Model Controller
Directory of Open Access Journals (Sweden)
Carlos Robles Algarín
2018-01-01
Full Text Available This work presents the design, modeling, and implementation of a neural network inverse model controller for tracking the maximum power point of a photovoltaic (PV module. A nonlinear autoregressive network with exogenous inputs (NARX was implemented in a serial-parallel architecture. The PV module mathematical modeling was developed, a buck converter was designed to operate in the continuous conduction mode with a switching frequency of 20 KHz, and the dynamic neural controller was designed using the Neural Network Toolbox from Matlab/Simulink (MathWorks, Natick, MA, USA, and it was implemented on an open-hardware Arduino Mega board. To obtain the reference signals for the NARX and determine the 65 W PV module behavior, a system made of a 0.8 W PV cell, a temperature sensor, a voltage sensor and a static neural network, was used. To evaluate performance a comparison with the P&O traditional algorithm was done in terms of response time and oscillations around the operating point. Simulation results demonstrated the superiority of neural controller over the P&O. Implementation results showed that approximately the same power is obtained with both controllers, but the P&O controller presents oscillations between 7 W and 10 W, in contrast to the inverse controller, which had oscillations between 1 W and 2 W.
Directory of Open Access Journals (Sweden)
Congrong Li
2015-08-01
Full Text Available Forest canopy leaf area index (LAI inversion based on remote sensing data is an important method to obtain LAI. Currently, the most widely-used model to achieve forest canopy structure parameters is the Li-Strahler geometric-optical bidirectional reflectance model, by considering the effect of crown shape and mutual shadowing, which is referred to as the GOMS model. However, it is difficult to retrieve LAI through the GOMS model directly because LAI is not a fundamental parameter of the model. In this study, a gap probability model was used to obtain the relationship between the canopy structure parameter nR2 and LAI. Thus, LAI was introduced into the GOMS model as an independent variable by replacing nR2 The modified GOMS (MGOMS model was validated by application to Dayekou in the Heihe River Basin of China. The LAI retrieved using the MGOMS model with optical multi-angle remote sensing data, high spatial resolution images and field-measured data was in good agreement with the field-measured LAI, with an R-square (R2 of 0.64, and an RMSE of 0.67. The results demonstrate that the MGOMS model obtained by replacing the canopy structure parameter nR2 of the GOMS model with LAI can be used to invert LAI directly and precisely.
Moraru, I; Tai, A; Erickson, B; Li, X
2012-06-01
We have analyzed recent clinical data obtained from chemoradiation of unresectable, locally advanced pancreatic cancer in order to examine possible benefits from radiotherapy (RT) dose escalation as well as to propose possible dose escalated fractionation schemes. A modified linear quadratic (LQ) model was used to fit clinical tumor response data from chemoradiation treatments using different fractionations. Biophysical radiosensitivy parameters, a and α/β, tumor potential doubling time, Td, and delay time for tumor doubling during treatment, Tk, were extracted from the fits and were used to calculate feasible fractionation schemes for dose escalations. Examination of published data from 20 institutions showed no clear indication of improved survival with raised radiation dose. However, an enhancement in tumor response was observed for higher irradiation doses, an important and promising clinical Result with respect to palliation and quality of life. The radiobiological parameter estimates obtained from the analysis are: α/β = 10 ± 3 Gy, a = 0.010 ± 0.003 Gŷ-1, Td = 56 ± 5 days and Tk = 7 ± 2 days. Possible dose escalation schemes are proposed based on the calculation of the biologically equivalent dose (BED) required for a 50% tumor response rate. From the point of view of tumor response, escalation of the administered radiation dose leads to a potential clinical benefit, which when combined with normal tissue complication analyses may Result in improved treatments for certain patients with advanced pancreatic cancer. Based on this analysis, a dose escalation trial with 2.25 Gy/fraction up to 69.75 Gy is being initiated for unresectable pancreatic cancer at our institution. Partially supported by MCW Cancer Center Meinerz Foundation. © 2012 American Association of Physicists in Medicine.
Kumar, B Shiva; Venkateswarlu, Ch
2014-08-01
The complex nature of biological reactions in biofilm reactors often poses difficulties in analyzing such reactors experimentally. Mathematical models could be very useful for their design and analysis. However, application of biofilm reactor models to practical problems proves somewhat ineffective due to the lack of knowledge of accurate kinetic models and uncertainty in model parameters. In this work, we propose an inverse modeling approach based on tabu search (TS) to estimate the parameters of kinetic and film thickness models. TS is used to estimate these parameters as a consequence of the validation of the mathematical models of the process with the aid of measured data obtained from an experimental fixed-bed anaerobic biofilm reactor involving the treatment of pharmaceutical industry wastewater. The results evaluated for different modeling configurations of varying degrees of complexity illustrate the effectiveness of TS for accurate estimation of kinetic and film thickness model parameters of the biofilm process. The results show that the two-dimensional mathematical model with Edward kinetics (with its optimum parameters as mu(max)rho(s)/Y = 24.57, Ks = 1.352 and Ki = 102.36) and three-parameter film thickness expression (with its estimated parameters as a = 0.289 x 10(-5), b = 1.55 x 10(-4) and c = 15.2 x 10(-6)) better describes the biofilm reactor treating the industry wastewater.
International Nuclear Information System (INIS)
Otero, F A; Frontini, G L; Elicabe, G E
2011-01-01
An analytic model for the scattering of a spherical particle with spherical inclusions has been proposed under the RG approximation. The model can be used without limitations to describe an X-ray scattering experiment. However, for light scattering several conditions must be fulfilled. Based on this model an inverse methodology is proposed to estimate the radii of host particle and inclusions, the number of inclusions and the Distance Distribution Functions (DDF's) of the distances between inclusions and the distances between inclusions and the origin of coordinates. The methodology is numerically tested in a light scattering example in which the host particle is eliminated by matching the refractive indices of host particle and medium. The results obtained for this cluster particle are very satisfactory.
International Nuclear Information System (INIS)
Kim, Joo Yeon; Ryu, Hyung Joon; Jung, Gyu Hwan; Lee, Jai Ki
2011-01-01
The dependency within the sequential realizations in the generated Markov chains and their reliabilities are monitored by introducing the autocorrelation and the potential scale reduction factor (PSRF) by model parameters in the atmospheric dispersion. These two diagnostics have been applied for the posterior quantities of the release point and the release rate inferred through the inverse tracking of unknown model parameters for the Yonggwang atmospheric tracer experiment in Korea. The autocorrelations of model parameters are decreasing to low values approaching to zero with increase of lag, resulted in decrease of the dependencies within the two sequential realizations. Their PSRFs are reduced to within 1.2 and the adequate simulation number recognized from these results. From these two convergence diagnostics, the validation of Markov chains generated have been ensured and PSRF then is especially suggested as the efficient tool for convergence monitoring for the source reconstruction in atmospheric dispersion. (author)
Thin-Sheet Inversion Modeling of Geomagnetic Deep Sounding Data Using MCMC Algorithm
Directory of Open Access Journals (Sweden)
Hendra Grandis
2013-01-01
Full Text Available The geomagnetic deep sounding (GDS method is one of electromagnetic (EM methods in geophysics that allows the estimation of the subsurface electrical conductivity distribution. This paper presents the inversion modeling of GDS data employing Markov Chain Monte Carlo (MCMC algorithm to evaluate the marginal posterior probability of the model parameters. We used thin-sheet model to represent quasi-3D conductivity variations in the heterogeneous subsurface. The algorithm was applied to invert field GDS data from the zone covering an area that spans from eastern margin of the Bohemian Massif to the West Carpathians in Europe. Conductivity anomalies obtained from this study confirm the well-known large-scale tectonic setting of the area.
Muon anomalous magnetic moment in SUSY B−L model with inverse seesaw
Directory of Open Access Journals (Sweden)
Shaaban Khalil
2016-12-01
Full Text Available Motivated by the tension between the Higgs mass and muon g−2 in minimal supersymmetric standard model (MSSM, we analyze the muon g−2 in supersymmetric B−L extension of the standard model (BLSSM with inverse seesaw mechanism. In this model, the Higgs mass receives extra important radiative corrections proportional to large neutrino Yukawa coupling. We point out that muon g−2 also gets significant contribution, due to the constructive interferences of light neutralino effects. The light neutralinos are typically the MSSM Bino like and the supersymmetric partner of U(1B−L gauge boson (B˜′-ino. We show that with universal soft supersymmetry breaking terms, the muon g−2 resides within 2σ of the measured value, namely ∼20×10−10, with Higgs mass equal to 125 GeV.
Mohammadi, Jahangir; Shataee, Shaban; Namiranian, Manochehr; Næsset, Erik
2017-09-01
Inventories of mixed broad-leaved forests of Iran mainly rely on terrestrial measurements. Due to rapid changes and disturbances and great complexity of the silvicultural systems of these multilayer forests, frequent repetition of conventional ground-based plot surveys is often cost prohibitive. Airborne laser scanning (ALS) and multispectral data offer an alternative or supplement to conventional inventories in the Hyrcanian forests of Iran. In this study, the capability of a combination of ALS and UltraCam-D data to model stand volume, tree density, and basal area using random forest (RF) algorithm was evaluated. Systematic sampling was applied to collect field plot data on a 150 m × 200 m sampling grid within a 1100 ha study area located at 36°38‧- 36°42‧N and 54°24‧-54°25‧E. A total of 308 circular plots (0.1 ha) were measured for calculation of stand volume, tree density, and basal area per hectare. For each plot, a set of variables was extracted from both ALS and multispectral data. The RF algorithm was used for modeling of the biophysical properties using ALS and UltraCam-D data separately and combined. The results showed that combining the ALS data and UltraCam-D images provided a slight increase in prediction accuracy compared to separate modeling. The RMSE as percentage of the mean, the mean difference between observed and predicted values, and standard deviation of the differences using a combination of ALS data and UltraCam-D images in an independent validation at 0.1-ha plot level were 31.7%, 1.1%, and 84 m3 ha-1 for stand volume; 27.2%, 0.86%, and 6.5 m2 ha-1 for basal area, and 35.8%, -4.6%, and 77.9 n ha-1 for tree density, respectively. Based on the results, we conclude that fusion of ALS and UltraCam-D data may be useful for modeling of stand volume, basal area, and tree density and thus gain insights into structural characteristics in the complex Hyrcanian forests.
Directory of Open Access Journals (Sweden)
Mac Sisson
2016-11-01
Full Text Available Poquoson River is a tidal coastal embayment located along the Western Shore of the Chesapeake Bay about 4 km south of the York River mouth in the City of Poquoson and in York County, Virginia. Its drainage area has diversified land uses, including high densities of residence, agricultural, salt marsh land uses, as well as a National Wildlife Refuge. This embayment experiences elevated bacterial concentration due to excess bacterial inputs from storm water runoff, nonpoint sources, and wash off from marshes due to tide and wind-induced set-up and set-down. Bacteria can also grow in the marsh and small tributaries. It is difficult to use a traditional watershed model to simulate bacterial loading, especially in this low-lying marsh area with abundant wildlife, while runoff is not solely driven by precipitation. An inverse approach is introduced to estimate loading from unknown sources based on observations in the embayment. The estimated loadings were combined with loadings estimated from different sources (human, wildlife, agriculture, pets, etc. and input to the watershed model. The watershed model simulated long-term flow and bacterial loading and discharged to a three-dimensional transport model driven by tide, wind, and freshwater discharge. The transport model efficiently simulates the transport and fate of the bacterial concentration in the embayment and is capable of determining the loading reduction needed to improve the water quality condition of the embayment. Combining inverse, watershed, and transport models is a sound approach for simulating bacterial transport correctly in the coastal embayment with complex unknown bacterial sources, which are not solely driven by precipitation.
Understanding the Yellowstone magmatic system using 3D geodynamic inverse models
Kaus, B. J. P.; Reuber, G. S.; Popov, A.; Baumann, T.
2017-12-01
The Yellowstone magmatic system is one of the largest magmatic systems on Earth. Recent seismic tomography suggest that two distinct magma chambers exist: a shallow, presumably felsic chamber and a deeper much larger, partially molten, chamber above the Moho. Why melt stalls at different depth levels above the Yellowstone plume, whereas dikes cross-cut the whole lithosphere in the nearby Snake River Plane is unclear. Partly this is caused by our incomplete understanding of lithospheric scale melt ascent processes from the upper mantle to the shallow crust, which requires better constraints on the mechanics and material properties of the lithosphere.Here, we employ lithospheric-scale 2D and 3D geodynamic models adapted to Yellowstone to better understand magmatic processes in active arcs. The models have a number of (uncertain) input parameters such as the temperature and viscosity structure of the lithosphere, geometry and melt fraction of the magmatic system, while the melt content and rock densities are obtained by consistent thermodynamic modelling of whole rock data of the Yellowstone stratigraphy. As all of these parameters affect the dynamics of the lithosphere, we use the simulations to derive testable model predictions such as gravity anomalies, surface deformation rates and lithospheric stresses and compare them with observations. We incorporated it within an inversion method and perform 3D geodynamic inverse models of the Yellowstone magmatic system. An adjoint based method is used to derive the key model parameters and the factors that affect the stress field around the Yellowstone plume, locations of enhanced diking and melt accumulations. Results suggest that the plume and the magma chambers are connected with each other and that magma chamber overpressure is required to explain the surface displacement in phases of high activity above the Yellowstone magmatic system.
Singing with yourself: evidence for an inverse modeling account of poor-pitch singing.
Pfordresher, Peter Q; Mantell, James T
2014-05-01
Singing is a ubiquitous and culturally significant activity that humans engage in from an early age. Nevertheless, some individuals - termed poor-pitch singers - are unable to match target pitches within a musical semitone while singing. In the experiments reported here, we tested whether poor-pitch singing deficits would be reduced when individuals imitate recordings of themselves as opposed to recordings of other individuals. This prediction was based on the hypothesis that poor-pitch singers have not developed an abstract "inverse model" of the auditory-vocal system and instead must rely on sensorimotor associations that they have experienced directly, which is true for sequences an individual has already produced. In three experiments, participants, both accurate and poor-pitch singers, were better able to imitate sung recordings of themselves than sung recordings of other singers. However, this self-advantage was enhanced for poor-pitch singers. These effects were not a byproduct of self-recognition (Experiment 1), vocal timbre (Experiment 2), or the absolute pitch of target recordings (i.e., the advantage remains when recordings are transposed, Experiment 3). Results support the conceptualization of poor-pitch singing as an imitative deficit resulting from a deficient inverse model of the auditory-vocal system with respect to pitch. Copyright © 2014 Elsevier Inc. All rights reserved.
International Nuclear Information System (INIS)
Kandil, H.A.; Elhadidi, B.M.; Kader, A. A.; Moaty, A.A.; Sherif, A.O.
2006-01-01
Air pollution episodes have been recorded in Cairo, during the fall season, since 1999, as a result of specific meteorological conditions combined with large quantity of pollutants created by several ground-based sources. The main reason for the smog-like episodes (black clouds) is adverse weather conditions with low and variable winds, high humidity and strong temperature inversions in the few-hundred meters above the ground. The two important types of temperature inversion affecting the air pollution are surface or ground (radiation) inversion and subsidence (elevated) inversion. The surface temperature inversion is associated with a rapid decrease in the ground surface temperature with the simultaneous existence of warm air in the lower troposphere. The inversion develops at dusk and continues until the surface warms again the following day. Pollutants emitted during the night are caught under this i nversion lid. S ubsidence inversion forms when warm air masses move over colder air masses. The inversion develops with a stagnating high-pressure system (generally associated with fair weather). Under these conditions, the pressure gradient becomes progressively weaker so that winds become light. These light winds greatly reduce the horizontal transport and dispersion of pollutants. At the same time, the subsidence inversion acts as a barrier to the vertical dispersion of the pollutants. In this study, the Penn State/NCAR meso -scale model (MM5) is used to simulate the temperature inversion phenomenon over Greater Cairo region during the fall season of 2004. Accurate computations of the heat transfer at the surface are needed to capture this phenomenon. This can only be achieved by high-resolution simulations in both horizontal and vertical directions. Hence, for accurate simulation of the temperature inversion over Greater Cairo, four nested domains of resolutions of 27 km, 9 km, 3 km and 1 km, respectively, were used in the horizontal planes. Furthermore, 42
Ghattas, O.; Petra, N.; Cui, T.; Marzouk, Y.; Benjamin, P.; Willcox, K.
2016-12-01
Model-based projections of the dynamics of the polar ice sheets play a central role in anticipating future sea level rise. However, a number of mathematical and computational challenges place significant barriers on improving predictability of these models. One such challenge is caused by the unknown model parameters (e.g., in the basal boundary conditions) that must be inferred from heterogeneous observational data, leading to an ill-posed inverse problem and the need to quantify uncertainties in its solution. In this talk we discuss the problem of estimating the uncertainty in the solution of (large-scale) ice sheet inverse problems within the framework of Bayesian inference. Computing the general solution of the inverse problem--i.e., the posterior probability density--is intractable with current methods on today's computers, due to the expense of solving the forward model (3D full Stokes flow with nonlinear rheology) and the high dimensionality of the uncertain parameters (which are discretizations of the basal sliding coefficient field). To overcome these twin computational challenges, it is essential to exploit problem structure (e.g., sensitivity of the data to parameters, the smoothing property of the forward model, and correlations in the prior). To this end, we present a data-informed approach that identifies low-dimensional structure in both parameter space and the forward model state space. This approach exploits the fact that the observations inform only a low-dimensional parameter space and allows us to construct a parameter-reduced posterior. Sampling this parameter-reduced posterior still requires multiple evaluations of the forward problem, therefore we also aim to identify a low dimensional state space to reduce the computational cost. To this end, we apply a proper orthogonal decomposition (POD) approach to approximate the state using a low-dimensional manifold constructed using ``snapshots'' from the parameter reduced posterior, and the discrete
Directory of Open Access Journals (Sweden)
Patrick Piprek
2018-02-01
Full Text Available This paper presents an approach to model a ski jumper as a multi-body system for an optimal control application. The modeling is based on the constrained Newton-Euler-Equations. Within this paper the complete multi-body modeling methodology as well as the musculoskeletal modeling is considered. For the musculoskeletal modeling and its incorporation in the optimization model, we choose a nonlinear dynamic inversion control approach. This approach uses the muscle models as nonlinear reference models and links them to the ski jumper movement by a control law. This strategy yields a linearized input-output behavior, which makes the optimal control problem easier to solve. The resulting model of the ski jumper can then be used for trajectory optimization whose results are compared to literature jumps. Ultimately, this enables the jumper to get a very detailed feedback of the flight. To achieve the maximal jump length, exact positioning of his body with respect to the air can be displayed.
DEFF Research Database (Denmark)
Nielsen, S.B.; Clausen, O.R.; Gallagher, Kerry
2011-01-01
the thermal history information contained in high quality thermal maturity data comprising temperature profiles, vitrinite reflectance and apatite fission track data. Having remained open for experimental purposes, the data of two of the deep wells (Aars-1 and Farsoe-1) are of exceptionally high quality. Here...... about the magnitude of deposition and erosion during this hiatus. We use Markov Chain Monte Carlo with a transient one-dimensional thermal model to explore the parameter space of potential thermal history solutions, using the different available data as constraints. The variable parameters comprise...... inversion of the STZ. This is in agreement with numerical rheological models of inversion zone dynamics, which explain how marginal trough subsidence occurred as a consequence of late Cretaceous compressional inversion and erosion along the inversion axis (Nielsen et al. 2005, 2007). Following this, the in-plane...
Uncertainty estimates of a GRACE inversion modelling technique over Greenland using a simulation
Bonin, Jennifer; Chambers, Don
2013-07-01
The low spatial resolution of GRACE causes leakage, where signals in one location spread out into nearby regions. Because of this leakage, using simple techniques such as basin averages may result in an incorrect estimate of the true mass change in a region. A fairly simple least squares inversion technique can be used to more specifically localize mass changes into a pre-determined set of basins of uniform internal mass distribution. However, the accuracy of these higher resolution basin mass amplitudes has not been determined, nor is it known how the distribution of the chosen basins affects the results. We use a simple `truth' model over Greenland as an example case, to estimate the uncertainties of this inversion method and expose those design parameters which may result in an incorrect high-resolution mass distribution. We determine that an appropriate level of smoothing (300-400 km) and process noise (0.30 cm2 of water) gets the best results. The trends of the Greenland internal basins and Iceland can be reasonably estimated with this method, with average systematic errors of 3.5 cm yr-1 per basin. The largest mass losses found from GRACE RL04 occur in the coastal northwest (-19.9 and -33.0 cm yr-1) and southeast (-24.2 and -27.9 cm yr-1), with small mass gains (+1.4 to +7.7 cm yr-1) found across the northern interior. Acceleration of mass change is measurable at the 95 per cent confidence level in four northwestern basins, but not elsewhere in Greenland. Due to an insufficiently detailed distribution of basins across internal Canada, the trend estimates of Baffin and Ellesmere Islands are expected to be incorrect due to systematic errors caused by the inversion technique.
Moisan, John R.; Moisan, Tiffany A. H.; Linkswiler, Matthew A.
2011-01-01
Phytoplankton absorption spectra and High-Performance Liquid Chromatography (HPLC) pigment observations from the Eastern U.S. and global observations from NASA's SeaBASS archive are used in a linear inverse calculation to extract pigment-specific absorption spectra. Using these pigment-specific absorption spectra to reconstruct the phytoplankton absorption spectra results in high correlations at all visible wavelengths (r(sup 2) from 0.83 to 0.98), and linear regressions (slopes ranging from 0.8 to 1.1). Higher correlations (r(sup 2) from 0.75 to 1.00) are obtained in the visible portion of the spectra when the total phytoplankton absorption spectra are unpackaged by multiplying the entire spectra by a factor that sets the total absorption at 675 nm to that expected from absorption spectra reconstruction using measured pigment concentrations and laboratory-derived pigment-specific absorption spectra. The derived pigment-specific absorption spectra were further used with the total phytoplankton absorption spectra in a second linear inverse calculation to estimate the various phytoplankton HPLC pigments. A comparison between the estimated and measured pigment concentrations for the 18 pigment fields showed good correlations (r(sup 2) greater than 0.5) for 7 pigments and very good correlations (r(sup 2) greater than 0.7) for chlorophyll a and fucoxanthin. Higher correlations result when the analysis is carried out at more local geographic scales. The ability to estimate phytoplankton pigments using pigment-specific absorption spectra is critical for using hyperspectral inverse models to retrieve phytoplankton pigment concentrations and other Inherent Optical Properties (IOPs) from passive remote sensing observations.
Directory of Open Access Journals (Sweden)
Thomas Heiberg
2016-05-01
Full Text Available Despite its prominent placement between the retina and primary visual cortex in the early visual pathway, the role of the dorsal lateral geniculate nucleus (dLGN in molding and regulating the visual signals entering the brain is still poorly understood. A striking feature of the dLGN circuit is that relay cells (RCs and interneurons (INs form so-called triadic synapses, where an IN dendritic terminal can be simultaneously postsynaptic to a retinal ganglion cell (GC input and presynaptic to an RC dendrite, allowing for so-called triadic inhibition. Taking advantage of a recently developed biophysically detailed multicompartmental model for an IN, we here investigate putative effects of these different inhibitory actions of INs, i.e., triadic inhibition and standard axonal inhibition, on the response properties of RCs. We compute and investigate so-called area-response curves, that is, trial-averaged visual spike responses vs. spot size, for circular flashing spots in a network of RCs and INs. The model parameters are grossly tuned to give results in qualitative accordance with previous in vivo data of responses to such stimuli for cat GCs and RCs. We particularly investigate how the model ingredients affect salient response properties such as the receptive-field center size of RCs and INs, maximal responses and center-surround antagonisms. For example, while triadic inhibition not involving firing of IN action potentials was found to provide only a non-linear gain control of the conversion of input spikes to output spikes by RCs, axonal inhibition was in contrast found to substantially affect the receptive-field center size: the larger the inhibition, the more the RC center size shrinks compared to the GC providing the feedforward excitation. Thus, a possible role of the different inhibitory actions from INs to RCs in the dLGN circuit is to provide separate mechanisms for overall gain control (direct triadic inhibition and regulation of spatial
Heiberg, Thomas; Hagen, Espen; Halnes, Geir; Einevoll, Gaute T
2016-05-01
Despite its prominent placement between the retina and primary visual cortex in the early visual pathway, the role of the dorsal lateral geniculate nucleus (dLGN) in molding and regulating the visual signals entering the brain is still poorly understood. A striking feature of the dLGN circuit is that relay cells (RCs) and interneurons (INs) form so-called triadic synapses, where an IN dendritic terminal can be simultaneously postsynaptic to a retinal ganglion cell (GC) input and presynaptic to an RC dendrite, allowing for so-called triadic inhibition. Taking advantage of a recently developed biophysically detailed multicompartmental model for an IN, we here investigate putative effects of these different inhibitory actions of INs, i.e., triadic inhibition and standard axonal inhibition, on the response properties of RCs. We compute and investigate so-called area-response curves, that is, trial-averaged visual spike responses vs. spot size, for circular flashing spots in a network of RCs and INs. The model parameters are grossly tuned to give results in qualitative accordance with previous in vivo data of responses to such stimuli for cat GCs and RCs. We particularly investigate how the model ingredients affect salient response properties such as the receptive-field center size of RCs and INs, maximal responses and center-surround antagonisms. For example, while triadic inhibition not involving firing of IN action potentials was found to provide only a non-linear gain control of the conversion of input spikes to output spikes by RCs, axonal inhibition was in contrast found to substantially affect the receptive-field center size: the larger the inhibition, the more the RC center size shrinks compared to the GC providing the feedforward excitation. Thus, a possible role of the different inhibitory actions from INs to RCs in the dLGN circuit is to provide separate mechanisms for overall gain control (direct triadic inhibition) and regulation of spatial resolution
Melton, J. R.; Kaplan, J. O.; Matthews, R.; Sydneysmith, R.; Tesluk, J.; Piggot, G.; Robinson, D. C.; Brinkman, D.; Marmorek, D.; Cohen, S.; McPherson, K.
2011-12-01
The Skeena region of British Columbia, Canada is among the world's most important commercial forest production areas, a key transportation corridor, and provides critical habitat for salmon and other wildlife. Climate change compounds threats to the region from other local environmental and social challenges. To aid local communities in adaptive planning for future climate change impacts, our project combined biophysical modelling, social science, and community engagement in a participatory approach to build regional capacity to prepare and respond to climate change. The sociological aspect of our study interviewed local leaders and resource managers (both First Nations and settlers groups in three communities) to examine how perceptions of environmental and socioeconomic issues have changed in the recent past, and the values placed on diverse natural resources at the present. The three communities differed in their perception of the relative value and condition of community resources, such as small business, natural resource trade, education and local government. However, all three communities regarded salmon as their most important and threatened resource. The most important future drivers of change in the study region were perceived to be: "aboriginal rights, title and treaty settlements", "availability of natural resources", "natural resource policies", and the "global economy". Climate change, as a potential driver of change in the region, was perceived as less important than other socio-economic factors; even though climate records for the region already demonstrate warmer winters, decreased snowfall, and decreased spring precipitation over the last half century. The natural science component of our project applies a regional-scale dynamic vegetation model (LPJ-GUESS) to simulate the potential future of forest ecosystems, with a focus on how climate change and management strategy interact to influence forest productivity, disturbance frequency, species
Inverse modeling of multicomponent reactive transport through single and dual porosity media
Samper, Javier; Zheng, Liange; Fernández, Ana María; Montenegro, Luis
2008-06-01
Compacted bentonite is foreseen as buffer material for high-level radioactive waste in deep geological repositories because it provides hydraulic isolation, chemical stability, and radionuclide sorption. A wide range of laboratory tests were performed within the framework of FEBEX ( Full-scale Engineered Barrier EXperiment) project to characterize buffer properties and develop numerical models for FEBEX bentonite. Here we present inverse single and dual-continuum multicomponent reactive transport models of a long-term permeation test performed on a 2.5 cm long sample of FEBEX bentonite. Initial saline bentonite porewater was flushed with 5.5 pore volumes of fresh granitic water. Water flux and chemical composition of effluent waters were monitored during almost 4 years. The model accounts for solute advection and diffusion and geochemical reactions such as aqueous complexation, acid-base, cation exchange, protonation/deprotonation by surface complexation and dissolution/precipitation of calcite, chalcedony and gypsum. All of these processes are assumed at local equilibrium. Similar to previous studies of bentonite porewater chemistry on batch systems which attest the relevance of protonation/deprotonation on buffering pH, our results confirm that protonation/deprotonation is a key process in maintaining a stable pH under dynamic transport conditions. Breakthrough curves of reactive species are more sensitive to initial porewater concentration than to effective diffusion coefficient. Optimum estimates of initial porewater chemistry of saturated compacted FEBEX bentonite are obtained by solving the inverse problem of multicomponent reactive transport. While the single-continuum model reproduces the trends of measured data for most chemical species, it fails to match properly the long tails of most breakthrough curves. Such limitation is overcome by resorting to a dual-continuum reactive transport model.
De Matteo, Ada; Massa, Bruno; D'Auria, Luca; Castaldo, Raffaele
2017-04-01
Geological processes are generally very complex and too slow to be directly observed in their completeness; modelling procedures overcome this limit. The state of stress in the upper lithosphere is the main responsible for driving geodynamical processes; in order to retrieve the active stress field in a rock volume, stress inversion techniques can be applied on both seismological and structural datasets. This approach has been successfully applied to active tectonics as well as volcanic areas. In this context the best approach in managing heterogeneous datasets in volcanic environments consists in the analysis of spatial variations of the stress field by applying robust techniques of inversion. The study of volcanic seismicity is an efficient tool to retrieve spatial and temporal pattern of the pre-, syn- and inter-eruptive stress field: magma migration as well as dynamics of magma chamber and hydrothermal system are directly connected to the volcanic seismicity. Additionally, analysis of the temporal variations of stress field pattern in volcanoes could be a useful monitoring tool. Recently the stress field acting on several active volcanoes has been investigated by using stress inversion techniques on seismological datasets (Massa et al., 2016). The Bayesian Right Trihedra Method (BRTM; D'Auria and Massa, 2015) is able to successfully manage heterogeneous datasets allowing the identification of regional fields locally overcame by the stress field due to volcano specific dynamics. In particular, the analysis of seismicity and stress field inversion at the Somma-Vesuvius highlighted the presence of two superposed volumes characterized by different behaviour and stress field pattern: a top volume dominated by an extensional stress field, in accordance with a gravitational spreading-style of deformation, and a bottom volume related to a regional extensional stress field. In addition, in order to evaluate the dynamics of deformation, both analogue and numerical
Biophysical Influence of Airborne Carbon Nanomaterials on Natural Pulmonary Surfactant
Valle, Russell P.; Wu, Tony; Zuo, Yi Y.
2015-01-01
Inhalation of nanoparticles (NP), including lightweight airborne carbonaceous nanomaterials (CNM), poses a direct and systemic health threat to those who handle them. Inhaled NP penetrate deep pulmonary structures in which they first interact with the pulmonary surfactant (PS) lining at the alveolar air–water interface. In spite of many research efforts, there is a gap of knowledge between in vitro biophysical study and in vivo inhalation toxicology since all existing biophysical models handl...
A Variance Distribution Model of Surface EMG Signals Based on Inverse Gamma Distribution.
Hayashi, Hideaki; Furui, Akira; Kurita, Yuichi; Tsuji, Toshio
2017-11-01
Objective: This paper describes the formulation of a surface electromyogram (EMG) model capable of representing the variance distribution of EMG signals. Methods: In the model, EMG signals are handled based on a Gaussian white noise process with a mean of zero for each variance value. EMG signal variance is taken as a random variable that follows inverse gamma distribution, allowing the representation of noise superimposed onto this variance. Variance distribution estimation based on marginal likelihood maximization is also outlined in this paper. The procedure can be approximated using rectified and smoothed EMG signals, thereby allowing the determination of distribution parameters in real time at low computational cost. Results: A simulation experiment was performed to evaluate the accuracy of distribution estimation using artificially generated EMG signals, with results demonstrating that the proposed model's accuracy is higher than that of maximum-likelihood-based estimation. Analysis of variance distribution using real EMG data also suggested a relationship between variance distribution and signal-dependent noise. Conclusion: The study reported here was conducted to examine the performance of a proposed surface EMG model capable of representing variance distribution and a related distribution parameter estimation method. Experiments using artificial and real EMG data demonstrated the validity of the model. Significance: Variance distribution estimated using the proposed model exhibits potential in the estimation of muscle force. Objective: This paper describes the formulation of a surface electromyogram (EMG) model capable of representing the variance distribution of EMG signals. Methods: In the model, EMG signals are handled based on a Gaussian white noise process with a mean of zero for each variance value. EMG signal variance is taken as a random variable that follows inverse gamma distribution, allowing the representation of noise superimposed onto this
Mohamad Noor, Faris; Adipta, Agra
2018-03-01
Coal Bed Methane (CBM) as a newly developed resource in Indonesia is one of the alternatives to relieve Indonesia’s dependencies on conventional energies. Coal resource of Muara Enim Formation is known as one of the prolific reservoirs in South Sumatra Basin. Seismic inversion and well analysis are done to determine the coal seam characteristics of Muara Enim Formation. This research uses three inversion methods, which are: model base hard- constrain, bandlimited, and sparse-spike inversion. Each type of seismic inversion has its own advantages to display the coal seam and its characteristic. Interpretation result from the analysis data shows that the Muara Enim coal seam has 20 (API) gamma ray value, 1 (gr/cc) – 1.4 (gr/cc) from density log, and low AI cutoff value range between 5000-6400 (m/s)*(g/cc). The distribution of coal seam is laterally thinning northwest to southeast. Coal seam is seen biasedly on model base hard constraint inversion and discontinued on band-limited inversion which isn’t similar to the geological model. The appropriate AI inversion is sparse spike inversion which has 0.884757 value from cross plot inversion as the best correlation value among the chosen inversion methods. Sparse Spike inversion its self-has high amplitude as a proper tool to identify coal seam continuity which commonly appears as a thin layer. Cross-sectional sparse spike inversion shows that there are possible new boreholes in CDP 3662-3722, CDP 3586-3622, and CDP 4004-4148 which is seen in seismic data as a thick coal seam.
Inversion of CO and NOx emissions using the adjoint of the IMAGES model
Directory of Open Access Journals (Sweden)
J.-F. Müller
2005-01-01
Full Text Available We use ground-based observations of CO mixing ratios and vertical column abundances together with tropospheric NO2 columns from the GOME satellite instrument as constraints for improving the global annual emission estimates of CO and NOx for the year 1997. The agreement between concentrations calculated by the global 3-dimensional CTM IMAGES and the observations is optimized using the adjoint modelling technique, which allows to invert for CO and NOx fluxes simultaneously, taking their chemical interactions into account. Our analysis quantifies a total of 39 flux parameters, comprising anthropogenic and biomass burning sources over large continental regions, soil and lightning emissions of NOx, biogenic emissions of CO and non-methane hydrocarbons, as well as the deposition velocities of both CO and NOx. Comparison between observed, prior and optimized CO mixing ratios at NOAA/CMDL sites shows that the inversion performs well at the northern mid- and high latitudes, and that it is less efficient in the Southern Hemisphere, as expected due to the scarsity of measurements over this part of the globe. The inversion, moreover, brings the model much closer to the measured NO2 columns over all regions. Sensitivity tests show that anthropogenic sources exhibit weak sensitivity to changes of the a priori errors associated to the bottom-up inventory, whereas biomass burning sources are subject to a strong variability. Our best estimate for the 1997 global top-down CO source amounts to 2760 Tg CO. Anthropogenic emissions increase by 28%, in agreement with previous inverse modelling studies, suggesting that the present bottom-up inventories underestimate the anthropogenic CO emissions in the Northern Hemisphere. The magnitude of the optimized NOx global source decreases by 14% with respect to the prior, and amounts to 42.1 Tg N, out of which 22.8 Tg N are due to anthropogenic sources. The NOx emissions increase over Tropical regions, whereas they decrease
Fréour , Sylvain; Gloaguen , David; François , Marc; Guillén , Ronald
2006-01-01
International audience; The scope of this work is the determination of the coefficients of thermal expansion of the Ti-17 beta-phase. A rigorous inverse thermo-elastic self-consistent scale transition inicro-mechanical model extended to multi-phase materials was used. The experimental data required for the application of the inverse method were obtained from both the available literature and especially dedicated X-ray diffraction lattice strain measurements performed on the studied (alpha + b...
Energy Technology Data Exchange (ETDEWEB)
Freour, S. [GeM, Institut de Recherche en Genie Civil et Mecanique (UMR CNRS 6183), Universite de Nantes, Ecole Centrale de Nantes, 37 Boulevard de l' Universite, BP 406, 44 602 Saint-Nazaire cedex (France)]. E-mail: freour@crttsn.univ-nantes.fr; Gloaguen, D. [GeM, Institut de Recherche en Genie Civil et Mecanique (UMR CNRS 6183), Universite de Nantes, Ecole Centrale de Nantes, 37 Boulevard de l' Universite, BP 406, 44 602 Saint-Nazaire cedex (France); Francois, M. [Laboratoire des Systemes Mecaniques et d' Ingenierie Simultanee (LASMIS FRE CNRS 2719), Universite de Technologie de Troyes, 12 Rue Marie Curie, BP 2060, 10010 Troyes (France); Guillen, R. [GeM, Institut de Recherche en Genie Civil et Mecanique (UMR CNRS 6183), Universite de Nantes, Ecole Centrale de Nantes, 37 Boulevard de l' Universite, BP 406, 44 602 Saint-Nazaire cedex (France)
2006-04-15
scope of this work is the determination of the coefficients of thermal expansion of the Ti-17 {beta}-phase. A rigorous inverse thermo-elastic self-consistent scale transition micro-mechanical model extended to multi-phase materials was used. The experimental data required for the application of the inverse method were obtained from both the available literature and especially dedicated X-ray diffraction lattice strain measurements performed on the studied ({alpha} + {beta}) two-phase titanium alloy.
Cui, Y.; Falk, M.; Chen, Y.; Herner, J.; Croes, B. E.; Vijayan, A.
2017-12-01
Methane (CH4) is an important short-lived climate pollutant (SLCP), and the second most important greenhouse gas (GHG) in California which accounts for 9% of the statewide GHG emissions inventory. Over the years, California has enacted several ambitious climate change mitigation goals, including the California Global Warming Solutions Act of 2006 which requires ARB to reduce statewide GHG emissions to 1990 emission level by 2020, as well as Assembly Bill 1383 which requires implementation of a climate mitigation program to reduce statewide methane emissions by 40% below the 2013 levels. In order to meet these requirements, ARB has proposed a comprehensive SLCP Strategy with goals to reduce oil and gas related emissions and capture methane emissions from dairy operations and organic waste. Achieving these goals will require accurate understanding of the sources of CH4 emissions. Since direct monitoring of CH4 emission sources in large spatial and temporal scales is challenging and resource intensive, we developed a complex inverse technique combined with atmospheric three-dimensional (3D) transport model and atmospheric observations of CH4 concentrations from a regional tower network and aircraft measurements, to gain insights into emission sources in California. In this study, develop a comprehensive inversion estimate using available aircraft measurements from CalNex airborne campaigns (May-June 2010) and three years of hourly continuous measurements from the ARB Statewide GHG Monitoring Network (2014-2016). The inversion analysis is conducted using two independent 3D Lagrangian models (WRF-STILT and WRF-FLEXPART), with a variety of bottom-up prior inputs from national and regional inventories, as well as two different probability density functions (Gaussian and Lognormal). Altogether, our analysis provides a detailed picture of the spatially resolved CH4 emission sources and their temporal variation over a multi-year period.
Energy Technology Data Exchange (ETDEWEB)
Wu, Xu, E-mail: xuwu2@illinois.edu; Kozlowski, Tomasz
2017-03-15
Modeling and simulations are naturally augmented by extensive Uncertainty Quantification (UQ) and sensitivity analysis requirements in the nuclear reactor system design, in which uncertainties must be quantified in order to prove that the investigated design stays within acceptance criteria. Historically, expert judgment has been used to specify the nominal values, probability density functions and upper and lower bounds of the simulation code random input parameters for the forward UQ process. The purpose of this paper is to replace such ad-hoc expert judgment of the statistical properties of input model parameters with inverse UQ process. Inverse UQ seeks statistical descriptions of the model random input parameters that are consistent with the experimental data. Bayesian analysis is used to establish the inverse UQ problems based on experimental data, with systematic and rigorously derived surrogate models based on Polynomial Chaos Expansion (PCE). The methods developed here are demonstrated with the Point Reactor Kinetics Equation (PRKE) coupled with lumped parameter thermal-hydraulics feedback model. Three input parameters, external reactivity, Doppler reactivity coefficient and coolant temperature coefficient are modeled as uncertain input parameters. Their uncertainties are inversely quantified based on synthetic experimental data. Compared with the direct numerical simulation, surrogate model by PC expansion shows high efficiency and accuracy. In addition, inverse UQ with Bayesian analysis can calibrate the random input parameters such that the simulation results are in a better agreement with the experimental data.
Naturalness and lepton number/flavor violation in inverse seesaw models
Energy Technology Data Exchange (ETDEWEB)
Haba, Naoyuki [Graduate School of Science and Engineering, Shimane University,1060, Nishikawatsu, Matsue, Shimane (Japan); Ishida, Hiroyuki [Graduate School of Science and Engineering, Shimane University,1060, Nishikawatsu, Matsue, Shimane (Japan); Physics Division, National Center for Theoretical Sciences,101, Section 2 Kuang Fu Road, Hsinchu, 300 Taiwan (China); Yamaguchi, Yuya [Graduate School of Science and Engineering, Shimane University,1060, Nishikawatsu, Matsue, Shimane (Japan); Department of Physics, Faculty of Science, Hokkaido University,Kita 9 Nishi 8, Kita-ku, Sapporo, Hokkaido (Japan)
2016-11-02
We introduce three right-handed neutrinos and three sterile neutrinos, and consider an inverse seesaw mechanism for neutrino mass generation. From naturalness point of view, their Majorana masses should be small, while it induces a large neutrino Yukawa coupling. Then, a neutrinoless double beta decay rate can be enhanced, and a sizable Higgs mass correction is inevitable. We find that the enhancement rate can be more than ten times compared with a standard prediction from light neutrino contribution alone, and an analytic form of heavy neutrino contributions to the Higgs mass correction. In addition, we numerically analyze the model, and find almost all parameter space of the model can be complementarily searched by future experiments of neutrinoless double beta decay and μ→e conversion.
On the critical behavior of the inverse susceptibility of a model of structural phase transitions
International Nuclear Information System (INIS)
Pisanova, E.S.; Ivanov, S.I.
2013-01-01
An exactly solvable lattice model describing structural phase transitions in an anharmonic crystal with long-range interaction is considered in the neighborhoods of the quantum and classical critical points at the corresponding upper critical dimensions. In a broader neighborhood of the critical region the inverse susceptibility of the model is exactly calculated in terms of the Lambert W-function and graphically presented as a function of the deviation from the critical point and the upper critical dimension. For quantum and classical systems with real physical dimensions (chains, thin layers and three-dimensional systems) the exact results are compared with the asymptotic ones on the basis of some numerical data for their ratio. Relative errors are also provided
Modeling of inverse Cherenkov laser acceleration with axicon laser-beam focusing
International Nuclear Information System (INIS)
Romea, R.D.; Kimura, W.D.
1990-01-01
Acceleration of free electrons by the inverse Cherenkov effect using radially polarized laser light focused through an axicon [J. P. Fontana and R. H. Pantell, J. Appl. Phys. 54, 4285 (1983)] has been studied utilizing a Monte Carlo computer simulation and further theoretical analysis. The model includes effects, such as scattering of the electrons by the gas, and diffraction and interference effects of the axicon laser beam, that were not included in the original analysis of Fontana and Pantell. Its accuracy is validated using available experimental data. The model results show that effective acceleration is possible even with the effects of scattering. Sample results are given. The analysis includes examining the issues of axicon focusing, phase errors, energy gain, phase slippage, focusing of the e beam, and emittance growth
Smith, James A.
1992-01-01
The inversion of the leaf area index (LAI) canopy parameter from optical spectral reflectance measurements is obtained using a backpropagation artificial neural network trained using input-output pairs generated by a multiple scattering reflectance model. The problem of LAI estimation over sparse canopies (LAI 1000 percent for low LAI. Minimization methods applied to merit functions constructed from differences between measured reflectances and predicted reflectances using multiple-scattering models are unacceptably sensitive to a good initial guess for the desired parameter. In contrast, the neural network reported generally yields absolute percentage errors of <30 percent when weighting coefficients trained on one soil type were applied to predicted canopy reflectance at a different soil background.
Development of CO2 inversion system based on the adjoint of the global coupled transport model
Belikov, Dmitry; Maksyutov, Shamil; Chevallier, Frederic; Kaminski, Thomas; Ganshin, Alexander; Blessing, Simon
2014-05-01
We present the development of an inverse modeling system employing an adjoint of the global coupled transport model consisting of the National Institute for Environmental Studies (NIES) Eulerian transport model (TM) and the Lagrangian plume diffusion model (LPDM) FLEXPART. NIES TM is a three-dimensional atmospheric transport model, which solves the continuity equation for a number of atmospheric tracers on a grid spanning the entire globe. Spatial discretization is based on a reduced latitude-longitude grid and a hybrid sigma-isentropic coordinate in the vertical. NIES TM uses a horizontal resolution of 2.5°×2.5°. However, to resolve synoptic-scale tracer distributions and to have the ability to optimize fluxes at resolutions of 0.5° and higher we coupled NIES TM with the Lagrangian model FLEXPART. The Lagrangian component of the forward and adjoint models uses precalculated responses of the observed concentration to the surface fluxes and 3-D concentrations field simulated with the FLEXPART model. NIES TM and FLEXPART are driven by JRA-25/JCDAS reanalysis dataset. Construction of the adjoint of the Lagrangian part is less complicated, as LPDMs calculate the sensitivity of measurements to the surrounding emissions field by tracking a large number of "particles" backwards in time. Developing of the adjoint to Eulerian part was performed with automatic differentiation tool the Transformation of Algorithms in Fortran (TAF) software (http://www.FastOpt.com). This method leads to the discrete adjoint of NIES TM. The main advantage of the discrete adjoint is that the resulting gradients of the numerical cost function are exact, even for nonlinear algorithms. The overall advantages of our method are that: 1. No code modification of Lagrangian model is required, making it applicable to combination of global NIES TM and any Lagrangian model; 2. Once run, the Lagrangian output can be applied to any chemically neutral gas; 3. High-resolution results can be obtained over
Optimization-Based Inverse Identification of the Parameters of a Concrete Cap Material Model
Král, Petr; Hokeš, Filip; Hušek, Martin; Kala, Jiří; Hradil, Petr
2017-10-01
Issues concerning the advanced numerical analysis of concrete building structures in sophisticated computing systems currently require the involvement of nonlinear mechanics tools. The efforts to design safer, more durable and mainly more economically efficient concrete structures are supported via the use of advanced nonlinear concrete material models and the geometrically nonlinear approach. The application of nonlinear mechanics tools undoubtedly presents another step towards the approximation of the real behaviour of concrete building structures within the framework of computer numerical simulations. However, the success rate of this application depends on having a perfect understanding of the behaviour of the concrete material models used and having a perfect understanding of the used material model parameters meaning. The effective application of nonlinear concrete material models within computer simulations often becomes very problematic because these material models very often contain parameters (material constants) whose values are difficult to obtain. However, getting of the correct values of material parameters is very important to ensure proper function of a concrete material model used. Today, one possibility, which permits successful solution of the mentioned problem, is the use of optimization algorithms for the purpose of the optimization-based inverse material parameter identification. Parameter identification goes hand in hand with experimental investigation while it trying to find parameter values of the used material model so that the resulting data obtained from the computer simulation will best approximate the experimental data. This paper is focused on the optimization-based inverse identification of the parameters of a concrete cap material model which is known under the name the Continuous Surface Cap Model. Within this paper, material parameters of the model are identified on the basis of interaction between nonlinear computer simulations
An adaptive ANOVA-based PCKF for high-dimensional nonlinear inverse modeling
Li, Weixuan; Lin, Guang; Zhang, Dongxiao
2014-02-01
The probabilistic collocation-based Kalman filter (PCKF) is a recently developed approach for solving inverse problems. It resembles the ensemble Kalman filter (EnKF) in every aspect-except that it represents and propagates model uncertainty by polynomial chaos expansion (PCE) instead of an ensemble of model realizations. Previous studies have shown PCKF is a more efficient alternative to EnKF for many data assimilation problems. However, the accuracy and efficiency of PCKF depends on an appropriate truncation of the PCE series. Having more polynomial chaos basis functions in the expansion helps to capture uncertainty more accurately but increases computational cost. Selection of basis functions is particularly important for high-dimensional stochastic problems because the number of polynomial chaos basis functions required to represent model uncertainty grows dramatically as the number of input parameters (random dimensions) increases. In classic PCKF algorithms, the PCE basis functions are pre-set based on users' experience. Also, for sequential data assimilation problems, the basis functions kept in PCE expression remain unchanged in different Kalman filter loops, which could limit the accuracy and computational efficiency of classic PCKF algorithms. To address this issue, we present a new algorithm that adaptively selects PCE basis functions for different problems and automatically adjusts the number of basis functions in different Kalman filter loops. The algorithm is based on adaptive functional ANOVA (analysis of variance) decomposition, which approximates a high-dimensional function with the summation of a set of low-dimensional functions. Thus, instead of expanding the original model into PCE, we implement the PCE expansion on these low-dimensional functions, which is much less costly. We also propose a new adaptive criterion for ANOVA that is more suited for solving inverse problems. The new algorithm was tested with different examples and demonstrated
Adaptive forward-inverse modeling of reservoir fluids away from wellbores; TOPICAL
International Nuclear Information System (INIS)
Ziagos, J P; Gelinas, R J; Doss, S K; Nelson, R G
1999-01-01
This Final Report contains the deliverables of the DeepLook Phase I project entitled, ''Adaptive Forward-Inverse Modeling of Reservoir Fluids Away from Wellbores''. The deliverables are: (i) a description of 2-D test problem results, analyses, and technical descriptions of the techniques used, (ii) a listing of program setup commands that construct and execute the codes for selected test problems (these commands are in mathematical terminology, which reinforces technical descriptions in the text), and (iii) an evaluation and recommendation regarding continuance of this project, including considerations of possible extensions to 3-D codes, additional technical scope, and budget for the out-years. The far-market objective in this project is to develop advanced technologies that can help locate and enhance the recovery of oil from heterogeneous rock formations. The specific technical objective in Phase I was to develop proof-of-concept of new forward and inverse (F-I) modeling techniques[Gelinas et al, 1998] that seek to enhance estimates (images) of formation permeability distributions and fluid motion away from wellbore volumes. This goes to the heart of improving industry's ability to jointly image reservoir permeability and flow predictions of trapped and recovered oil versus time. The estimation of formation permeability away from borehole measurements is an ''inverse'' problem. It is an inseparable part of modeling fluid flows throughout the reservoir in efforts to increase the efficiency of oil recovery at minimum cost. Classic issues of non-uniqueness, mathematical instability, noise effects, and inadequate numerical solution techniques have historically impeded progress in reservoir parameter estimations. Because information pertaining to fluid and rock properties is always sampled sparsely by wellbore measurements, a successful method for interpolating permeability and fluid data between the measurements must be: (i) physics-based, (ii) conditioned by signal
Pietro Carante, Mario; Aimè, Chiara; Tello Cajiao, John James; Ballarini, Francesca
2018-04-01
An upgraded version of the BIANCA II biophysical model, which describes more realistically interphase chromosome organization and the link between chromosome aberrations and cell death, was applied to V79 and AG01522 cells exposed to protons, C-ions and He-ions over a wide LET interval (0.6–502 keV µm‑1), as well as proton-irradiated U87 cells. The model assumes that (i) ionizing radiation induces DNA ‘cluster lesions’ (CLs), where by definition each CL produces two independent chromosome fragments; (ii) fragment (distance-dependent) mis-rejoining, or un-rejoining, produces chromosome aberrations; (iii) some aberrations lead to cell death. The CL yield, which mainly depends on radiation quality but is also modulated by the target cell, is an adjustable parameter. The fragment un-rejoining probability, f, is the second, and last, parameter. The value of f, which is assumed to depend on the cell type but not on radiation quality, was taken from previous studies, and only the CL yield was adjusted in the present work. Good agreement between simulations and experimental data was obtained, suggesting that BIANCA II is suitable for calculating the biological effectiveness of hadrontherapy beams. For both V79 and AG01522 cells, the mean number of CLs per micrometer was found to increase with LET in a linear-quadratic fashion before the over-killing region, where a less rapid increase, with a tendency to saturation, was observed. Although the over-killing region deserves further investigation, the possibility of fitting the CL yields is an important feature for hadrontherapy, because it allows performing predictions also at LET values where experimental data are not available. Finally, an approach was proposed to predict the ion-response of the cell line(s) of interest from the ion-response of a reference cell line and the photon response of both. A pilot study on proton-irradiated AG01522 and U87 cells, taking V79 cells as a reference, showed encouraging
Blondeau-Patissier, D.; Tilstone, G. H.; Martinez-Vicente, V.; Moore, G. F.
2004-09-01
In this paper, we compare bio-physical marine products from SeaWiFS, MODIS and a novel bio-optical absorption model with in situ measurements of chlorophyll-a (Chla) concentrations, total suspended material (TSM) concentrations, normalized water-leaving radiances (nLw) and absorption coefficients of coloured dissolved organic matter (aCDOM), total particulate (atotal) and phytoplankton (aphy) for 26 satellite match-ups in three Northern European seas. Cruises were undertaken in 2002 and 2003 in phytoplankton dominated open ocean waters of the Celtic Sea and optically complex waters of the Western English Channel (WEC) and North Sea. For all environments, Chla concentrations varied from 0.4 to 7.8 mg m-3, TSM from 0.2 to 6.0 mg l-1 and aCDOM at 440 nm from 0.02 to 0.30 m-1. SeaWiFS OC4v4, with the Remote Sensing Data Analysis Service (RSDAS) atmospheric correction for turbid waters, showed the most accurate retrieval of in situ Chla (RMS = 0.24; n = 26), followed by MODIS chlor_a_3 (RMS = 0.40; n = 26). This suggested that improving the atmospheric correction over optically complex waters results in more accurate Chla concentrations compared to those obtained using more complicated Chla algorithms. We found that the SeaWiFS OC4v4 and the MODIS chlor_a_2 switching band ratio algorithms, which mainly use longer wavebands than 443 nm, were less affected by CDOM. They were both more accurate than chlor_MODIS in the higher CDOM waters of the North Sea. Compared to MODIS the absorption model was better at retrieving atotal (RMS = 0.39; n = 78) and aCDOM (RMS = 0.79; n = 12) in all study areas and TSM in the WEC (RMS = 0.04; n = 10) but it underestimated Chla concentrations (RMS = 0.45; n = 26). The results are discussed in terms of atmospheric correction, sensor characteristics and the functioning and performance of Chla algorithms. This paper was presented at the Institute of Physics Meeting on Underwater Optics held during Photonex 03 at Warwick, UK, in October 2003
DEFF Research Database (Denmark)
Viezzoli, Andrea; Christiansen, Anders Vest; Auken, Esben
This paper aims at providing more insight into the parameters that need to be modelled during inversion of Helicopter TEM data for accurate modelling, both for hydrogeophysical and exploration applications. We use synthetic data to show in details the effect, both in data and in model space...
Inverse modelling of radionuclide release rates using gamma dose rate observations
Hamburger, Thomas; Evangeliou, Nikolaos; Stohl, Andreas; von Haustein, Christoph; Thummerer, Severin; Wallner, Christian
2015-04-01
Severe accidents in nuclear power plants such as the historical accident in Chernobyl 1986 or the more recent disaster in the Fukushima Dai-ichi nuclear power plant in 2011 have drastic impacts on the population and environment. Observations and dispersion modelling of the released radionuclides help to assess the regional impact of such nuclear accidents. Modelling the increase of regional radionuclide activity concentrations, which results from nuclear accidents, underlies a multiplicity of uncertainties. One of the most significant uncertainties is the estimation of the source term. That is, the time dependent quantification of the released spectrum of radionuclides during the course of the nuclear accident. The quantification of the source term may either remain uncertain (e.g. Chernobyl, Devell et al., 1995) or rely on estimates given by the operators of the nuclear power plant. Precise measurements are mostly missing due to practical limitations during the accident. The release rates of radionuclides at the accident site can be estimated using inverse modelling (Davoine and Bocquet, 2007). The accuracy of the method depends amongst others on the availability, reliability and the resolution in time and space of the used observations. Radionuclide activity concentrations are observed on a relatively sparse grid and the temporal resolution of available data may be low within the order of hours or a day. Gamma dose rates, on the other hand, are observed routinely on a much denser grid and higher temporal resolution and provide therefore a wider basis for inverse modelling (Saunier et al., 2013). We present a new inversion approach, which combines an atmospheric dispersion model and observations of radionuclide activity concentrations and gamma dose rates to obtain the source term of radionuclides. We use the Lagrangian particle dispersion model FLEXPART (Stohl et al., 1998; Stohl et al., 2005) to model the atmospheric transport of the released radionuclides. The
[Bare Soil Moisture Inversion Model Based on Visible-Shortwave Infrared Reflectance].
Zheng, Xiao-po; Sun, Yue-jun; Qin, Qi-ming; Ren, Hua-zhong; Gao, Zhong-ling; Wu, Ling; Meng, Qing-ye; Wang, Jin-liang; Wang, Jian-hua
2015-08-01
Soil is the loose solum of land surface that can support plants. It consists of minerals, organics, atmosphere, moisture, microbes, et al. Among its complex compositions, soil moisture varies greatly. Therefore, the fast and accurate inversion of soil moisture by using remote sensing is very crucial. In order to reduce the influence of soil type on the retrieval of soil moisture, this paper proposed a normalized spectral slope and absorption index named NSSAI to estimate soil moisture. The modeling of the new index contains several key steps: Firstly, soil samples with different moisture level were artificially prepared, and soil reflectance spectra was consequently measured using spectroradiometer produced by ASD Company. Secondly, the moisture absorption spectral feature located at shortwave wavelengths and the spectral slope of visible wavelengths were calculated after analyzing the regular spectral feature change patterns of different soil at different moisture conditions. Then advantages of the two features at reducing soil types' effects was synthesized to build the NSSAI. Thirdly, a linear relationship between NSSAI and soil moisture was established. The result showed that NSSAI worked better (correlation coefficient is 0.93) than most of other traditional methods in soil moisture extraction. It can weaken the influences caused by soil types at different moisture levels and improve the bare soil moisture inversion accuracy.
A forward model and conjugate gradient inversion technique for low-frequency ultrasonic imaging.
van Dongen, Koen W A; Wright, William M D
2006-10-01
Emerging methods of hyperthermia cancer treatment require noninvasive temperature monitoring, and ultrasonic techniques show promise in this regard. Various tomographic algorithms are available that reconstruct sound speed or contrast profiles, which can be related to temperature distribution. The requirement of a high enough frequency for adequate spatial resolution and a low enough frequency for adequate tissue penetration is a difficult compromise. In this study, the feasibility of using low frequency ultrasound for imaging and temperature monitoring was investigated. The transient probing wave field had a bandwidth spanning the frequency range 2.5-320.5 kHz. The results from a forward model which computed the propagation and scattering of low-frequency acoustic pressure and velocity wave fields were used to compare three imaging methods formulated within the Born approximation, representing two main types of reconstruction. The first uses Fourier techniques to reconstruct sound-speed profiles from projection or Radon data based on optical ray theory, seen as an asymptotical limit for comparison. The second uses backpropagation and conjugate gradient inversion methods based on acoustical wave theory. The results show that the accuracy in localization was 2.5 mm or better when using low frequencies and the conjugate gradient inversion scheme, which could be used for temperature monitoring.
Invariant models in the inversion of gravity and magnetic fields and their derivatives
Ialongo, Simone; Fedi, Maurizio; Florio, Giovanni
2014-11-01
In potential field inversion problems we usually solve underdetermined systems and realistic solutions may be obtained by introducing a depth-weighting function in the objective function. The choice of the exponent of such power-law is crucial. It was suggested to determine it from the field-decay due to a single source-block; alternatively it has been defined as the structural index of the investigated source distribution. In both cases, when k-order derivatives of the potential field are considered, the depth-weighting exponent has to be increased by k with respect that of the potential field itself, in order to obtain consistent source model distributions. We show instead that invariant and realistic source-distribution models are obtained using the same depth-weighting exponent for the magnetic field and for its k-order derivatives. A similar behavior also occurs in the gravity case. In practice we found that the depth weighting-exponent is invariant for a given source-model and equal to that of the corresponding magnetic field, in the magnetic case, and of the 1st derivative of the gravity field, in the gravity case. In the case of the regularized inverse problem, with depth-weighting and general constraints, the mathematical demonstration of such invariance is difficult, because of its non-linearity, and of its variable form, due to the different constraints used. However, tests performed on a variety of synthetic cases seem to confirm the invariance of the depth-weighting exponent. A final consideration regards the role of the regularization parameter; we show that the regularization can severely affect the depth to the source because the estimated depth tends to increase proportionally with the size of the regularization parameter. Hence, some care is needed in handling the combined effect of the regularization parameter and depth weighting.
Charco, María; González, Pablo J.; Galán del Sastre, Pedro
2017-04-01
The Kilauea volcano (Hawaii, USA) is one of the most active volcanoes world-wide and therefore one of the better monitored volcanoes around the world. Its complex system provides a unique opportunity to investigate the dynamics of magma transport and supply. Geodetic techniques, as Interferometric Synthetic Aperture Radar (InSAR) are being extensively used to monitor ground deformation at volcanic areas. The quantitative interpretation of such surface ground deformation measurements using geodetic data requires both, physical modelling to simulate the observed signals and inversion approaches to estimate the magmatic source parameters. Here, we use synthetic aperture radar data from Sentinel-1 radar interferometry satellite mission to image volcano deformation sources during the inflation along Kilauea's Southwest Rift Zone in April-May 2015. We propose a Finite Element Model (FEM) for the calculation of Green functions in a mechanically heterogeneous domain. The key aspect of the methodology lies in applying the reciprocity relationship of the Green functions between the station and the source for efficient numerical inversions. The search for the best-fitting magmatic (point) source(s) is generally conducted for an array of 3-D locations extending below a predefined volume region. However, our approach allows to reduce the total number of Green functions to the number of the observation points by using the, above mentioned, reciprocity relationship. This new methodology is able to accurately represent magmatic processes using physical models capable of simulating volcano deformation in non-uniform material properties distribution domains, which eventually will lead to better description of the status of the volcano.
Loos, Martin; Krauss, Martin; Fenner, Kathrin
2012-09-18
Formation of soil