A note on the roles of quantum and mechanical models in social biophysics.

Science.gov (United States)

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.

Coupling Biophysical and Socioeconomic Models for Coral Reef Systems in Quintana Roo, Mexican Caribbean

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.

Fundamental Concepts in Biophysics Volume 1

CERN Document Server

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...

Representing biophysical landscape interactions in soil models by bridging disciplines and scales.

Science.gov (United States)

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

Linking biophysical models and public preferences for ecosystem service assessments: a case study for the Southern Rocky Mountains

Science.gov (United States)

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.

Mathematical biophysics

CERN Document Server

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...

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.

Automatic Flight Controller With Model Inversion

Science.gov (United States)

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.

Statistical and Biophysical Models for Predicting Total and Outdoor Water Use in Los Angeles

Science.gov (United States)

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

Predictive biophysical modeling and understanding of the dynamics of mRNA translation and its evolution

Science.gov (United States)

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

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.

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.

Physically-based Canopy Reflectance Model Inversion of Vegetation Biophysical-Structural Information from Terra-MODIS Imagery in Boreal and Mountainous Terrain for Ecosystem, Climate and Carbon Models using the BIOPHYS-MFM Algorithm

Science.gov (United States)

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 �

Estimation efficiency of usage satellite derived and modelled biophysical products for yield forecasting

Science.gov (United States)

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.

Multiscattering inversion for low-model wavenumbers

KAUST Repository

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.

Assessment of the biophysical impacts of utility-scale photovoltaics through observations and modelling

Science.gov (United States)

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.

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

Biophysically realistic minimal model of dopamine neuron

Science.gov (United States)

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.

Similar Biophysical Abnormalities in Glomeruli and Podocytes from Two Distinct Models.

Science.gov (United States)

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.

Predicting the Presence of Scyphozoan Jellyfish in the Gulf of Mexico Using a Biophysical Model

Science.gov (United States)

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.

Theoretical Molecular Biophysics

CERN Document Server

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.

Biophysics conference 1978

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

Biophysics An Introduction

CERN Document Server

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.

Multi-scattering inversion for low model wavenumbers

KAUST Repository

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.

Chiropractic biophysics technique: a linear algebra approach to posture in chiropractic.

Science.gov (United States)

Harrison, D D; Janik, T J; Harrison, G R; Troyanovich, S; Harrison, D E; Harrison, S O

1996-10-01

This paper discusses linear algebra as applied to human posture in chiropractic, specifically chiropractic biophysics technique (CBP). Rotations, reflections and translations are geometric functions studied in vector spaces in linear algebra. These mathematical functions are termed rigid body transformations and are applied to segmental spinal movement in the literature. Review of the literature indicates that these linear algebra concepts have been used to describe vertebral motion. However, these rigid body movers are presented here as applying to the global postural movements of the head, thoracic cage and pelvis. The unique inverse functions of rotations, reflections and translations provide a theoretical basis for making postural corrections in neutral static resting posture. Chiropractic biophysics technique (CBP) uses these concepts in examination procedures, manual spinal manipulation, instrument assisted spinal manipulation, postural exercises, extension traction and clinical outcome measures.

Predicting Variation of DNA Shape Preferences in Protein-DNA Interaction in Cancer Cells with a New Biophysical Model.

Science.gov (United States)

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.

Voxel inversion of airborne electromagnetic data for improved model integration

Science.gov (United States)

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

Biophysics

CERN Document Server

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...

Benchmarking sensitivity of biophysical processes to leaf area changes in land surface models

Science.gov (United States)

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

Methods in Modern Biophysics

CERN Document Server

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...

Methods in Modern Biophysics

CERN Document Server

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...

Quantifying Vegetation Biophysical Variables from Imaging Spectroscopy Data: A Review on Retrieval Methods

Science.gov (United States)

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.

Inverse modelling of European CH4 emissions during 2006-2012 using different inverse models and reassessed atmospheric observations

Science.gov (United States)

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

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 ...

2. biophysical work meeting

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

Biophysical shunt theory for neuropsychopathology: Part I.

Science.gov (United States)

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.

On The Development of Biophysical Models for Space Radiation Risk Assessment

Science.gov (United States)

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.

New horizons in Biophysics

Science.gov (United States)

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

Inverse Modelling Problems in Linear Algebra Undergraduate Courses

Science.gov (United States)

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…

Biophysical characteristics reveal neural stem cell differentiation potential.

Directory of Open Access Journals (Sweden)

Fatima H Labeed

Full Text Available Distinguishing human neural stem/progenitor cell (huNSPC populations that will predominantly generate neurons from those that produce glia is currently hampered by a lack of sufficient cell type-specific surface markers predictive of fate potential. This limits investigation of lineage-biased progenitors and their potential use as therapeutic agents. A live-cell biophysical and label-free measure of fate potential would solve this problem by obviating the need for specific cell surface markers.We used dielectrophoresis (DEP to analyze the biophysical, specifically electrophysiological, properties of cortical human and mouse NSPCs that vary in differentiation potential. Our data demonstrate that the electrophysiological property membrane capacitance inversely correlates with the neurogenic potential of NSPCs. Furthermore, as huNSPCs are continually passaged they decrease neuron generation and increase membrane capacitance, confirming that this parameter dynamically predicts and negatively correlates with neurogenic potential. In contrast, differences in membrane conductance between NSPCs do not consistently correlate with the ability of the cells to generate neurons. DEP crossover frequency, which is a quantitative measure of cell behavior in DEP, directly correlates with neuron generation of NSPCs, indicating a potential mechanism to separate stem cells biased to particular differentiated cell fates.We show here that whole cell membrane capacitance, but not membrane conductance, reflects and predicts the neurogenic potential of human and mouse NSPCs. Stem cell biophysical characteristics therefore provide a completely novel and quantitative measure of stem cell fate potential and a label-free means to identify neuron- or glial-biased progenitors.

Encyclopedia of biophysics

CERN Document Server

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...

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

Inversion aided systems for stratigraphic models; Systemes d'aide a l'inversion des modeles stratigraphiques

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)

Multiscattering inversion for low-model wavenumbers

KAUST Repository

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

Quality Saving Mechanisms of Mitochondria during Aging in a Fully Time-Dependent Computational Biophysical Model.

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.

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....

Inverse stochastic resonance induced by synaptic background activity with unreliable synapses

Energy Technology Data Exchange (ETDEWEB)

Uzuntarla, Muhammet, E-mail: muzuntarla@yahoo.com

2013-11-15

Inverse stochastic resonance (ISR) is a recently pronounced phenomenon that is the minimum occurrence in mean firing rate of a rhythmically firing neuron as noise level varies. Here, by using a realistic modeling approach for the noise, we investigate the ISR with concrete biophysical mechanisms. It is shown that mean firing rate of a single neuron subjected to synaptic bombardment exhibits a minimum as the spike transmission probability varies. We also demonstrate that the occurrence of ISR strongly depends on the synaptic input regime, where it is most prominent in the balanced state of excitatory and inhibitory inputs.

Gradient models in molecular biophysics: progress, challenges, opportunities

Science.gov (United States)

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.

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.

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

Integrating Economic Models with Biophysical Models in the Willamette Water 2100 Project

Science.gov (United States)

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

Understanding the Day Cent model: Calibration, sensitivity, and identifiability through inverse modeling

Science.gov (United States)

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.

Hybrid Adaptive Flight Control with Model Inversion Adaptation

Science.gov (United States)

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.

Atmospheric inverse modeling via sparse reconstruction

Science.gov (United States)

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.

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.

Metamodel-based inverse method for parameter identification: elastic-plastic damage model

Science.gov (United States)

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.

Nonlinear adaptive inverse control via the unified model neural network

Science.gov (United States)

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.

Gradient Models in Molecular Biophysics: Progress, Challenges, Opportunities.

Science.gov (United States)

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

Science.gov (United States)

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

DOE/CEC [Department of Energy/Commission of the European Communities] workshop on critical evaluation of radiobiological data to biophysical modeling

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

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)

Advanced Techniques in Biophysics

CERN Document Server

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.

Preface: Special Topic on Single-Molecule Biophysics.

Science.gov (United States)

Makarov, Dmitrii E; Schuler, Benjamin

2018-03-28

Single-molecule measurements are now almost routinely used to study biological systems and processes. The scope of this special topic emphasizes the physics side of single-molecule observations, with the goal of highlighting new developments in physical techniques as well as conceptual insights that single-molecule measurements bring to biophysics. This issue also comprises recent advances in theoretical physical models of single-molecule phenomena, interpretation of single-molecule signals, and fundamental areas of statistical mechanics that are related to single-molecule observations. A particular goal is to illustrate the increasing synergy between theory, simulation, and experiment in single-molecule biophysics.

Impact of transport model errors on the global and regional methane emissions estimated by inverse modelling

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

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

Delineating Biophysical Environments of the Sunda Banda Seascape, Indonesia

Directory of Open Access Journals (Sweden)

Mingshu Wang

2015-01-01

Full Text Available The Sunda Banda Seascape (SBS, located in the center of the Coral Triangle, is a global center of marine biodiversity and a conservation priority. We proposed the first biophysical environmental delineation of the SBS using globally available satellite remote sensing and model-assimilated data to categorize this area into unique and meaningful biophysical classes. Specifically, the SBS was partitioned into eight biophysical classes characterized by similar sea surface temperature, chlorophyll a concentration, currents, and salinity patterns. Areas within each class were expected to have similar habitat types and ecosystem functions. Our work supplemented prevailing global marine management schemes by focusing in on a regional scale with finer spatial resolution. It also provided a baseline for academic research, ecological assessments and will facilitate marine spatial planning and conservation activities in the area. In addition, the framework and methods of delineating biophysical environments we presented can be expanded throughout the whole Coral Triangle to support research and conservation activities in this important region.

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

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.

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)

Multi-scattering inversion for low model wavenumbers

KAUST Repository

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

Heeding the waveform inversion nonlinearity by unwrapping the model and data

KAUST Repository

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

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

Two-dimensional probabilistic inversion of plane-wave electromagnetic data: Methodology, model constraints and joint inversion with electrical resistivity data

NARCIS (Netherlands)

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

Heeding the waveform inversion nonlinearity by unwrapping the model and data

KAUST Repository

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.

Using Biophysical Models to Understand the Effect of tDCS on Neurorehabilitation: Searching for Optimal Covariates to Enhance Poststroke Recovery.

Science.gov (United States)

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

Modeling the effects of noninvasive transcranial brain stimulation at the biophysical, network, and cognitive Level

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...

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....

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

Biotic games and cloud experimentation as novel media for biophysics education

Science.gov (United States)

Riedel-Kruse, Ingmar; Blikstein, Paulo

2014-03-01

First-hand, open-ended experimentation is key for effective formal and informal biophysics education. We developed, tested and assessed multiple new platforms that enable students and children to directly interact with and learn about microscopic biophysical processes: (1) Biotic games that enable local and online play using galvano- and photo-tactic stimulation of micro-swimmers, illustrating concepts such as biased random walks, Low Reynolds number hydrodynamics, and Brownian motion; (2) an undergraduate course where students learn optics, electronics, micro-fluidics, real time image analysis, and instrument control by building biotic games; and (3) a graduate class on the biophysics of multi-cellular systems that contains a cloud experimentation lab enabling students to execute open-ended chemotaxis experiments on slimemolds online, analyze their data, and build biophysical models. Our work aims to generate the equivalent excitement and educational impact for biophysics as robotics and video games have had for mechatronics and computer science, respectively. We also discuss how scaled-up cloud experimentation systems can support MOOCs with true lab components and life-science research in general.

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.

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 Influence of Airborne Carbon Nanomaterials on Natural Pulmonary Surfactant

OpenAIRE

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...

Biophysical impacts of climate-smart agriculture in the Midwest United States.

Science.gov (United States)

Bagley, Justin E; Miller, Jesse; Bernacchi, Carl J

2015-09-01

The potential impacts of climate change in the Midwest United States present unprecedented challenges to regional agriculture. In response to these challenges, a variety of climate-smart agricultural methodologies have been proposed to retain or improve crop yields, reduce agricultural greenhouse gas emissions, retain soil quality and increase climate resilience of agricultural systems. One component that is commonly neglected when assessing the environmental impacts of climate-smart agriculture is the biophysical impacts, where changes in ecosystem fluxes and storage of moisture and energy lead to perturbations in local climate and water availability. Using a combination of observational data and an agroecosystem model, a series of climate-smart agricultural scenarios were assessed to determine the biophysical impacts these techniques have in the Midwest United States. The first scenario extended the growing season for existing crops using future temperature and CO2 concentrations. The second scenario examined the biophysical impacts of no-till agriculture and the impacts of annually retaining crop debris. Finally, the third scenario evaluated the potential impacts that the adoption of perennial cultivars had on biophysical quantities. Each of these scenarios was found to have significant biophysical impacts. However, the timing and magnitude of the biophysical impacts differed between scenarios. © 2014 John Wiley & Sons Ltd.

Large-scale inverse model analyses employing fast randomized data reduction

Science.gov (United States)

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.

Retrieval of LAI and leaf chlorophyll content from remote sensing data by agronomy mechanism knowledge to solve the ill-posed inverse problem

Science.gov (United States)

Li, Zhenhai; Nie, Chenwei; Yang, Guijun; Xu, Xingang; Jin, Xiuliang; Gu, Xiaohe

2014-10-01

Leaf area index (LAI) and LCC, as the two most important crop growth variables, are major considerations in management decisions, agricultural planning and policy making. Estimation of canopy biophysical variables from remote sensing data was investigated using a radiative transfer model. However, the ill-posed problem is unavoidable for the unique solution of the inverse problem and the uncertainty of measurements and model assumptions. This study focused on the use of agronomy mechanism knowledge to restrict and remove the ill-posed inversion results. For this purpose, the inversion results obtained using the PROSAIL model alone (NAMK) and linked with agronomic mechanism knowledge (AMK) were compared. The results showed that AMK did not significantly improve the accuracy of LAI inversion. LAI was estimated with high accuracy, and there was no significant improvement after considering AMK. The validation results of the determination coefficient (R2) and the corresponding root mean square error (RMSE) between measured LAI and estimated LAI were 0.635 and 1.022 for NAMK, and 0.637 and 0.999 for AMK, respectively. LCC estimation was significantly improved with agronomy mechanism knowledge; the R2 and RMSE values were 0.377 and 14.495 μg cm-2 for NAMK, and 0.503 and 10.661 μg cm-2 for AMK, respectively. Results of the comparison demonstrated the need for agronomy mechanism knowledge in radiative transfer model inversion.

Modeling analysis of pulsed magnetization process of magnetic core based on inverse Jiles-Atherton model

Science.gov (United States)

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.

Institute of Biochemistry and Biophysics. Research Report 1996-1997

International Nuclear Information System (INIS)

1998-01-01

Scientific interests of the Institute of Biochemistry and Biophysics of the Polish Academy of Sciences have evolved from classical biochemistry, biophysics and physiological chemistry to up-to-date molecular biology. Research interests are focussed on replication, mutagenesis and repair of DNA; regulation of gene expression at various levels; biosynthesis and post-translational modifications of proteins; gene sequencing and functional analysis of open reading frames; structure, function and regulation of enzymes; conformation of proteins and peptides; modelling of structures and prediction of functions of proteins; mechanisms of electron transfer in polypeptides

Institute of Biochemistry and Biophysics. Research Report 1996-1997

Energy Technology Data Exchange (ETDEWEB)

NONE

1998-07-01

Scientific interests of the Institute of Biochemistry and Biophysics of the Polish Academy of Sciences have evolved from classical biochemistry, biophysics and physiological chemistry to up-to-date molecular biology. Research interests are focussed on replication, mutagenesis and repair of DNA; regulation of gene expression at various levels; biosynthesis and post-translational modifications of proteins; gene sequencing and functional analysis of open reading frames; structure, function and regulation of enzymes; conformation of proteins and peptides; modelling of structures and prediction of functions of proteins; mechanisms of electron transfer in polypeptides.

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

A quantitative overview of biophysical forces impinging on neural function

International Nuclear Information System (INIS)

Mueller, Jerel K; Tyler, William J

2014-01-01

The fundamentals of neuronal membrane excitability are globally described using the Hodgkin-Huxley (HH) model. The HH model, however, does not account for a number of biophysical phenomena associated with action potentials or propagating nerve impulses. Physical mechanisms underlying these processes, such as reversible heat transfer and axonal swelling, have been compartmentalized and separately investigated to reveal neuronal activity is not solely influenced by electrical or biochemical factors. Instead, mechanical forces and thermodynamics also govern neuronal excitability and signaling. To advance our understanding of neuronal function and dysfunction, compartmentalized analyses of electrical, chemical, and mechanical processes need to be revaluated and integrated into more comprehensive theories. The present perspective is intended to provide a broad overview of biophysical forces that can influence neural function, but which have been traditionally underappreciated in neuroscience. Further, several examples where mechanical forces have been shown to exert their actions on nervous system development, signaling, and plasticity are highlighted to underscore their importance in sculpting neural function. By considering the collective actions of biophysical forces influencing neuronal activity, our working models can be expanded and new paradigms can be applied to the investigation and characterization of brain function and dysfunction. (topical review)

LFPy: A tool for biophysical simulation of extracellular potentials generated by detailed model neurons

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.

LFPy: a tool for biophysical simulation of extracellular potentials generated by detailed model neurons.

Science.gov (United States)

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.

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.

Biophysical applications of satellite remote sensing

CERN Document Server

Hanes, Jonathan

2014-01-01

Including an introduction and historical overview of the field, this comprehensive synthesis of the major biophysical applications of satellite remote sensing includes in-depth discussion of satellite-sourced biophysical metrics such as leaf area index.

Historical and Critical Review on Biophysical Economics

Science.gov (United States)

Adigüzel, Yekbun

2016-07-01

Biophysical economics is initiated with the long history of the relation of economics with ecological basis and biophysical perspectives of the physiocrats. It inherently has social, economic, biological, environmental, natural, physical, and scientific grounds. Biological entities in economy like the resources, consumers, populations, and parts of production systems, etc. could all be dealt by biophysical economics. Considering this wide scope, current work is a “biophysical economics at a glance” rather than a comprehensive review of the full range of topics that may just be adequately covered in a book-length work. However, the sense of its wide range of applications is aimed to be provided to the reader in this work. Here, modern approaches and biophysical growth theory are presented after the long history and an overview of the concepts in biophysical economics. Examples of the recent studies are provided at the end with discussions. This review is also related to the work by Cleveland, “Biophysical Economics: From Physiocracy to Ecological Economics and Industrial Ecology” [C. J. Cleveland, in Advances in Bioeconomics and Sustainability: Essay in Honor of Nicholas Gerogescu-Roegen, eds. J. Gowdy and K. Mayumi (Edward Elgar Publishing, Cheltenham, England, 1999), pp. 125-154.]. Relevant parts include critics and comments on the presented concepts in a parallelized fashion with the Cleveland’s work.

Core flow inversion tested with numerical dynamo models

Science.gov (United States)

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.

Biophysical model of prokaryotic diversity in geothermal hot springs.

Science.gov (United States)

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

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.

Biophysics of DNA

CERN Document Server

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.

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...

Joint leaf chlorophyll content and leaf area index retrieval from Landsat data using a regularized model inversion system (REGFLEC)

KAUST Repository

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

Science.gov (United States)

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.

A dataset mapping the potential biophysical effects of vegetation cover change

Science.gov (United States)

Duveiller, Gregory; Hooker, Josh; Cescatti, Alessandro

2018-02-01

Changing the vegetation cover of the Earth has impacts on the biophysical properties of the surface and ultimately on the local climate. Depending on the specific type of vegetation change and on the background climate, the resulting competing biophysical processes can have a net warming or cooling effect, which can further vary both spatially and seasonally. Due to uncertain climate impacts and the lack of robust observations, biophysical effects are not yet considered in land-based climate policies. Here we present a dataset based on satellite remote sensing observations that provides the potential changes i) of the full surface energy balance, ii) at global scale, and iii) for multiple vegetation transitions, as would now be required for the comprehensive evaluation of land based mitigation plans. We anticipate that this dataset will provide valuable information to benchmark Earth system models, to assess future scenarios of land cover change and to develop the monitoring, reporting and verification guidelines required for the implementation of mitigation plans that account for biophysical land processes.

Data-Driven Model Order Reduction for Bayesian Inverse Problems

KAUST Repository

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

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.

Biophysical modelling of phytoplankton communities from first principles using two-layered spheres: Equivalent Algal Populations (EAP) model.

Science.gov (United States)

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.

Biophysics an introduction

CERN Document Server

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.

Taming waveform inversion non-linearity through phase unwrapping of the model and objective functions

KAUST Repository

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.

Taming waveform inversion non-linearity through phase unwrapping of the model and objective functions

KAUST Repository

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.

Inverse Optimization: A New Perspective on the Black-Litterman Model

Science.gov (United States)

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.

Science.gov (United States)

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.

Comparative Analysis of Local Control Prediction Using Different Biophysical Models for Non-Small Cell Lung Cancer Patients Undergoing Stereotactic Body Radiotherapy

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.

Incorporating Modeling and Simulations in Undergraduate Biophysical Chemistry Course to Promote Understanding of Structure-Dynamics-Function Relationships in Proteins

Science.gov (United States)

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…

Raman spectroscopy reveals biophysical markers in skin cancer surgical margins

Science.gov (United States)

Feng, Xu; Moy, Austin J.; Nguyen, Hieu T. M.; Zhang, Yao; Fox, Matthew C.; Sebastian, Katherine R.; Reichenberg, Jason S.; Markey, Mia K.; Tunnell, James W.

2018-02-01

The recurrence rate of nonmelanoma skin cancer is highly related to the residual tumor after surgery. Although tissueconserving surgery, such as Mohs surgery, is a standard method for the treatment of nonmelanoma skin cancer, they are limited by lengthy and costly frozen-section histopathology. Raman spectroscopy (RS) is proving to be an objective, sensitive, and non-destructive tool for detecting skin cancer. Previous studies demonstrated the high sensitivity of RS in detecting tumor margins of basal cell carcinoma (BCC). However, those studies rely on statistical classification models and do not elucidate the skin biophysical composition. As a result, we aim to discover the biophysical differences between BCC and primary normal skin structures (including epidermis, dermis, hair follicle, sebaceous gland and fat). We obtained freshly resected ex vivo skin samples from fresh resection specimens from 14 patients undergoing Mohs surgery. Raman images were acquired from regions containing one or more structures using a custom built 830nm confocal Raman microscope. The spectra were grouped using K-means clustering analysis and annotated as either BCC or each of the five normal structures by comparing with the histopathology image of the serial section. The spectral data were then fit by a previously established biophysical model with eight primary skin constituents. Our results show that BCC has significant differences in the fit coefficients of nucleus, collagen, triolein, keratin and elastin compared with normal structures. Our study reveals RS has the potential to detect biophysical changes in resection margins, and supports the development of diagnostic algorithms for future intraoperative implementation of RS during Mohs surgery.

Inverse geothermal modelling applied to Danish sedimentary basins

Science.gov (United States)

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

Model Based Beamforming and Bayesian Inversion Signal Processing Methods for Seismic Localization of Underground Source

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...

Modeling and inversion Matlab algorithms for resistivity, induced polarization and seismic data

Science.gov (United States)

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

Bayesian Uncertainty Quantification for Subsurface Inversion Using a Multiscale Hierarchical Model

KAUST Repository

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 efficient strategy for the inversion of bidirectional reflectance models with satellite remote sensing data

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.

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

Science.gov (United States)

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.

Accounting for imperfect forward modeling in geophysical inverse problems — Exemplified for crosshole tomography

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...

Synergistic linkage between remote sensing and biophysical models for estimating plant ecophysiological and ecosystem processes

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

Integrated Molecular and Cellular Biophysics

CERN Document Server

Raicu, Valerica

2008-01-01

This book integrates concepts and methods from physics, biology, biochemistry and physical chemistry into a standalone, unitary text of biophysics that aims to provide a quantitative description of structures and processes occurring in living matter. The book introduces graduate physics students and physicists interested in biophysics research to 'classical' as well as emerging areas of biophysics. The advanced undergraduate physics students and the life scientists are also invited to join in, by building on their knowledge of basic physics. Essential notions of biochemistry and biology are introduced, as necessary, throughout the book, while the reader's familiarity with basic knowledge of physics is assumed. Topics covered include interactions between biological molecules, physical chemistry of phospholipids association into bilayer membranes, DNA and protein structure and folding, passive and active electrical properties of the cell membrane, classical as well as fractal aspects of reaction kinetics and di...

Neural-Based Compensation of Nonlinearities in an Airplane Longitudinal Model with Dynamic-Inversion Control

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.

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

pyGIMLi: An open-source library for modelling and inversion in geophysics

Science.gov (United States)

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

Improving evapotranspiration in a land surface model using biophysical variables derived from MSG/SEVIRI satellite

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

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

Four-dimensional variational data assimilation for inverse modelling of atmospheric methane emissions: method and comparison with synthesis inversion

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.

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.

The Nexus Land-Use model version 1.0, an approach articulating biophysical potentials and economic dynamics to model competition for land-use

Science.gov (United States)

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.

The Nexus Land-Use model version 1.0, an approach articulating biophysical potentials and economic dynamics to model competition for land-use

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.

Estimation of biological parameters of marine organisms using linear and nonlinear acoustic scattering model-based inversion methods.

Science.gov (United States)

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.

Developing a particle tracking surrogate model to improve inversion of ground water - Surface water models

Science.gov (United States)

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.

High effective inverse dynamics modelling for dual-arm robot

Science.gov (United States)

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.

Data-Driven Model Order Reduction for Bayesian Inverse Problems

KAUST Repository

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.

X-Ray structure and biophysical properties of rabbit fibroblast growth factor 1

Energy Technology Data Exchange (ETDEWEB)

Lee, Jihun; Blaber, Sachiko I.; Irsigler, Andre; Aspinwall, Eric; Blaber, Michael; (FSU)

2010-01-14

The rabbit is an important and de facto animal model in the study of ischemic disease and angiogenic therapy. Additionally, fibroblast growth factor 1 (FGF-1) is emerging as one of the most important growth factors for novel pro-angiogenic and pro-arteriogenic therapy. However, despite its significance, the fundamental biophysical properties of rabbit FGF-1, including its X-ray structure, have never been reported. Here, the cloning, crystallization, X-ray structure and determination of the biophysical properties of rabbit FGF-1 are described. The X-ray structure shows that the amino-acid differences between human and rabbit FGF-1 are solvent-exposed and therefore potentially immunogenic, while the biophysical studies identify differences in thermostability and receptor-binding affinity that distinguish rabbit FGF-1 from human FGF-1.

Global Monthly CO2 Flux Inversion Based on Results of Terrestrial Ecosystem Modeling

Science.gov (United States)

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).

Stochastic forward and inverse groundwater flow and solute transport modeling

NARCIS (Netherlands)

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

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

Science.gov (United States)

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.

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 ...

An ethnographic study: Becoming a physics expert in a biophysics research group

Science.gov (United States)

Rodriguez, Idaykis

Expertise in physics has been traditionally studied in cognitive science, where physics expertise is understood through the difference between novice and expert problem solving skills. The cognitive perspective of physics experts only create a partial model of physics expertise and does not take into account the development of physics experts in the natural context of research. This dissertation takes a social and cultural perspective of learning through apprenticeship to model the development of physics expertise of physics graduate students in a research group. I use a qualitative methodological approach of an ethnographic case study to observe and video record the common practices of graduate students in their biophysics weekly research group meetings. I recorded notes on observations and conduct interviews with all participants of the biophysics research group for a period of eight months. I apply the theoretical framework of Communities of Practice to distinguish the cultural norms of the group that cultivate physics expert practices. Results indicate that physics expertise is specific to a topic or subfield and it is established through effectively publishing research in the larger biophysics research community. The participant biophysics research group follows a learning trajectory for its students to contribute to research and learn to communicate their research in the larger biophysics community. In this learning trajectory students develop expert member competencies to learn to communicate their research and to learn the standards and trends of research in the larger research community. Findings from this dissertation expand the model of physics expertise beyond the cognitive realm and add the social and cultural nature of physics expertise development. This research also addresses ways to increase physics graduate student success towards their PhD. and decrease the 48% attrition rate of physics graduate students. Cultivating effective research

Biophysical and structural considerations for protein sequence evolution

Directory of Open Access Journals (Sweden)

Grahnen Johan A

2011-12-01

Full Text Available Abstract Background Protein sequence evolution is constrained by the biophysics of folding and function, causing interdependence between interacting sites in the sequence. However, current site-independent models of sequence evolutions do not take this into account. Recent attempts to integrate the influence of structure and biophysics into phylogenetic models via statistical/informational approaches have not resulted in expected improvements in model performance. This suggests that further innovations are needed for progress in this field. Results Here we develop a coarse-grained physics-based model of protein folding and binding function, and compare it to a popular informational model. We find that both models violate the assumption of the native sequence being close to a thermodynamic optimum, causing directional selection away from the native state. Sampling and simulation show that the physics-based model is more specific for fold-defining interactions that vary less among residue type. The informational model diffuses further in sequence space with fewer barriers and tends to provide less support for an invariant sites model, although amino acid substitutions are generally conservative. Both approaches produce sequences with natural features like dN/dS Conclusions Simple coarse-grained models of protein folding can describe some natural features of evolving proteins but are currently not accurate enough to use in evolutionary inference. This is partly due to improper packing of the hydrophobic core. We suggest possible improvements on the representation of structure, folding energy, and binding function, as regards both native and non-native conformations, and describe a large number of possible applications for such a model.

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.

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

Potential Investigation of Linking PROSAIL with the Ross-Li BRDF Model for Vegetation Characterization

Directory of Open Access Journals (Sweden)

Xiaoning Zhang

2018-03-01

Full Text Available Methods that link different models for investigating the retrieval of canopy biophysical/structural variables have been substantially adopted in the remote sensing community. To retrieve global biophysical parameters from multiangle data, the kernel-driven bidirectional reflectance distribution function (BRDF model has been widely applied to satellite multiangle observations to model (interpolate/extrapolate the bidirectional reflectance factor (BRF in an arbitrary direction of viewing and solar geometries. Such modeled BRFs, as an essential information source, are then input into an inversion procedure that is devised through a large number of simulation analyses from some widely used physical models that can generalize such an inversion relationship between the BRFs (or their simple algebraic composite and the biophysical/structural parameter. Therefore, evaluation of such a link between physical models and kernel-driven models contributes to the development of such inversion procedures to accurately retrieve vegetation properties, particularly based on the operational global BRDF parameters derived from satellite multiangle observations (e.g., MODIS. In this study, the main objective is to investigate the potential for linking a popular physical model (PROSAIL with the widely used kernel-driven Ross-Li models. To do this, the BRFs and albedo are generated by the physical PROSAIL in a forward model, and then the simulated BRFs are input into the kernel-driven BRDF model for retrieval of the BRFs and albedo in the same viewing and solar geometries. To further strengthen such an investigation, a variety of field-measured multiangle reflectances have also been used to investigate the potential for linking these two models. For simulated BRFs generated by the PROSAIL model at 659 and 865 nm, the two models are generally comparable to each other, and the resultant root mean square errors (RMSEs are 0.0092 and 0.0355, respectively, although some

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)

Structure and biophysics

CERN Document Server

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...

Hierarchy and Interactions in Environmental Interfaces Regarded as Biophysical Complex Systems

Science.gov (United States)

Mihailovic, Dragutin T.; Balaz, Igor

The field of environmental sciences is abundant with various interfaces and is the right place for the application of new fundamental approaches leading towards a better understanding of environmental phenomena. For example, following the definition of environmental interface by Mihailovic and Balaž [23], such interface can be placed between: human or animal bodies and surrounding air, aquatic species and water and air around them, and natural or artificially built surfaces (vegetation, ice, snow, barren soil, water, urban communities) and the atmosphere. Complex environmental interface systems are open and hierarchically organised, interactions between their constituent parts are nonlinear, and the interaction with the surrounding environment is noisy. These systems are therefore very sensitive to initial conditions, deterministic external perturbations and random fluctuations always present in nature. The study of noisy non-equilibrium processes is fundamental for modelling the dynamics of environmental interface systems and for understanding the mechanisms of spatio-temporal pattern formation in contemporary environmental sciences, particularly in environmental fluid mechanics. In modelling complex biophysical systems one of the main tasks is to successfully create an operative interface with the external environment. It should provide a robust and prompt translation of the vast diversity of external physical and/or chemical changes into a set of signals, which are "understandable" for an organism. Although the establishment of organisation in any system is of crucial importance for its functioning, it should not be forgotten that in biophysical systems we deal with real-life problems where a number of other conditions should be reached in order to put the system to work. One of them is the proper supply of the system by the energy. Therefore, we will investigate an aspect of dynamics of energy flow based on the energy balance equation. The energy as well as

Cerebellar supervised learning revisited: biophysical modeling and degrees-of-freedom control.

Science.gov (United States)

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.

NACP Regional: Original Observation Data and Biosphere and Inverse Model Outputs

Data.gov (United States)

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

Data.gov (United States)

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...

Incorporating Prognostic Marine Nitrogen Fixers and Related Bio-Physical Feedbacks in an Earth System Model

Science.gov (United States)

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.

A Joint Method of Envelope Inversion Combined with Hybrid-domain Full Waveform Inversion

Science.gov (United States)

CUI, C.; Hou, W.

2017-12-01

Full waveform inversion (FWI) aims to construct high-precision subsurface models by fully using the information in seismic records, including amplitude, travel time, phase and so on. However, high non-linearity and the absence of low frequency information in seismic data lead to the well-known cycle skipping problem and make inversion easily fall into local minima. In addition, those 3D inversion methods that are based on acoustic approximation ignore the elastic effects in real seismic field, and make inversion harder. As a result, the accuracy of final inversion results highly relies on the quality of initial model. In order to improve stability and quality of inversion results, multi-scale inversion that reconstructs subsurface model from low to high frequency are applied. But, the absence of very low frequencies (time domain and inversion in the frequency domain. To accelerate the inversion, we adopt CPU/GPU heterogeneous computing techniques. There were two levels of parallelism. In the first level, the inversion tasks are decomposed and assigned to each computation node by shot number. In the second level, GPU multithreaded programming is used for the computation tasks in each node, including forward modeling, envelope extraction, DFT (discrete Fourier transform) calculation and gradients calculation. Numerical tests demonstrated that the combined envelope inversion + hybrid-domain FWI could obtain much faithful and accurate result than conventional hybrid-domain FWI. The CPU/GPU heterogeneous parallel computation could improve the performance speed.

Sequential and joint hydrogeophysical inversion using a field-scale groundwater model with ERT and TDEM data

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...

Sequential and joint hydrogeophysical inversion using a field-scale groundwater model with ERT and TDEM data

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...

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.

Biophysics: for HTS hit validation, chemical lead optimization, and beyond.

Science.gov (United States)

Genick, Christine C; Wright, S Kirk

2017-09-01

There are many challenges to the drug discovery process, including the complexity of the target, its interactions, and how these factors play a role in causing the disease. Traditionally, biophysics has been used for hit validation and chemical lead optimization. With its increased throughput and sensitivity, biophysics is now being applied earlier in this process to empower target characterization and hit finding. Areas covered: In this article, the authors provide an overview of how biophysics can be utilized to assess the quality of the reagents used in screening assays, to validate potential tool compounds, to test the integrity of screening assays, and to create follow-up strategies for compound characterization. They also briefly discuss the utilization of different biophysical methods in hit validation to help avoid the resource consuming pitfalls caused by the lack of hit overlap between biophysical methods. Expert opinion: The use of biophysics early on in the drug discovery process has proven crucial to identifying and characterizing targets of complex nature. It also has enabled the identification and classification of small molecules which interact in an allosteric or covalent manner with the target. By applying biophysics in this manner and at the early stages of this process, the chances of finding chemical leads with novel mechanisms of action are increased. In the future, focused screens with biophysics as a primary readout will become increasingly common.

Embedding Term Similarity and Inverse Document Frequency into a Logical Model of Information Retrieval.

Science.gov (United States)

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…

Black Versus Gray T-Shirts: Comparison of Spectrophotometric and Other Biophysical Properties of Physical Fitness Uniforms and Modeled Heat Strain and Thermal Comfort

Science.gov (United States)

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

A nonlinear inversion for the velocity background and perturbation models

KAUST Repository

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.

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...

Numerical modeling of Harmonic Imaging and Pulse Inversion fields

Science.gov (United States)

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.

Radiation dosimetry and radiation biophysics

International Nuclear Information System (INIS)

Anon.

1981-01-01

Radiation dosimetry and radiation biophysics are two closely integrated programs whose joint purpose is to explore the connections between the primary physical events produced by radiation and their biological consequences in cellular systems. The radiation dosimetry program includes the theoretical description of primary events and their connection with the observable biological effects. This program also is concerned with the design and measurement of physical parameters used in theory or to support biological experiments. The radiation biophysics program tests and uses the theoretical developments for experimental design, and provides information for further theoretical development through experiments on cellular systems

Radiation dosimetry and radiation biophysics

International Nuclear Information System (INIS)

Anon.

1979-01-01

Radiation dosimetry and radiation biophysics are two closely integrated programs whose joint purpose is to explore the connections between the primary physical events produced by radiation and their biological consequences in cellular systems. The radiation dosimetry program includes the theoretical description of primary events and their connection with the observable biological effects. This program also is concerned with design and measurement of those physical parameters used in the theory or to support biological experiments. The radiation biophysics program tests and makes use of the theoretical developments for experimental design. Also, this program provides information for further theoretical development through experiments on cellular systems

Semi-active control of magnetorheological elastomer base isolation system utilising learning-based inverse model

Science.gov (United States)

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.

A Direct inverse model to determine permeability fields from pressure and flow rate measurements

NARCIS (Netherlands)

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

Lebedev acceleration and comparison of different photometric models in the inversion of lightcurves for asteroids

Science.gov (United States)

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.

Regime transitions in near-surface temperature inversions : a conceptual model

NARCIS (Netherlands)

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

Inverse modeling of geochemical and mechanical compaction in sedimentary basins

Science.gov (United States)

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

Inverse modeling of the Chernobyl source term using atmospheric concentration and deposition measurements

Science.gov (United States)

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

Inverse modeling of the Chernobyl source term using atmospheric concentration and deposition measurements

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

Radiation fields, dosimetry, biokinetics and biophysical models for cancer induction by ionising radiation 1996-1999. Executive summary

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.)

Joint Inversion Modelling of Geophysical Data From Lough Neagh Basin

Science.gov (United States)

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

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

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.

Sonographic biophysical profile in detection of foetal hypoxia in 100 cases of suspected high risk pregnancy

International Nuclear Information System (INIS)

Ullah, N.; Khan, A.R.; Usman, M.

2010-01-01

Background: The foetus has become increasingly accessible and visible as a patient over the last two decades. Ultrasound imaging has broadened the scope of foetal assessment. Dynamic real time B-Mode ultrasound is used to monitor cluster of biophysical variables, both dynamic and static collectively termed as biophysical profile. The purpose of this study was to determine the effect of sonographic biophysical profile score on perinatal outcome in terms of mortality and morbidity. Methods: This descriptive study was carried on 100 randomly select ed high risk pregnant patients in Radiology Department PGMI, Government Lady Reading Hospital, Peshawar from December 2007 to June 2008. Manning biophysical profile including non-stress was employed for foetal screening, using Toshiba ultrasound machine model Nemio SSA-550A and 7.5 MHZ probe. Results: Out of 100 cases 79 (79%) had a normal biophysical profile in the last scan of 10/10 and had a normal perinatal outcome with 5 minutes Apgar score >7/10. In 13 (13%) cases Apgar score at 5 minute was < 7/10 and babies were shifted to nursery. There were 2 (2%) false positive cases that showed abnormal biophysical profile scores of 6/10 but babies were born with an Apgar score of 8/10 at 5 minutes. There were 2 (2%) neonatal deaths in this study group. The sensitivity of biophysical profile was 79.1%, specificity 92.9%. Predictive value for a positive test was 98.55%; predictive value for a negative test was 41.93%. Conclusion: Biophysical profile is highly accurate and reliable test of diagnosing foetal hypoxia. (author)

Biophysical processes in the Indian Ocean

Digital Repository Service at National Institute of Oceanography (India)

Mc; Murtugudde, R.; Vialard, J.; Vinayachandran, P.N.; Wiggert, J.D.; Hood, R.R.; Shankar, D.; Shetye, S.R.

Ocean Biogeochemical Processes and Ecological Variability Geophysical Monograph Series 185 Copyright 200� by the American Geophysical Union. 10.102�/2008GM000768 Biophysical Processes in the Indian Ocean J. P. McCreary, 1 R. Murtugudde, 2 J. Vialard, 3...) also plots the upper-layer thickness, h 1 , from the model of McCreary et al. [1��3] (hereinafter referred to as MKM); h 1 simulates the structure of the top of the actual thermocline reasonably well, except that it is somewhat too thin from 5...

The biophysics of neuronal growth

International Nuclear Information System (INIS)

Franze, Kristian; Guck, Jochen

2010-01-01

For a long time, neuroscience has focused on biochemical, molecular biological and electrophysiological aspects of neuronal physiology and pathology. However, there is a growing body of evidence indicating the importance of physical stimuli for neuronal growth and development. In this review we briefly summarize the historical background of neurobiophysics and give an overview over the current understanding of neuronal growth from a physics perspective. We show how biophysics has so far contributed to a better understanding of neuronal growth and discuss current inconsistencies. Finally, we speculate how biophysics may contribute to the successful treatment of lesions to the central nervous system, which have been considered incurable until very recently.

Biophysical regulation of stem cell differentiation.

Science.gov (United States)

Govey, Peter M; Loiselle, Alayna E; Donahue, Henry J

2013-06-01

Bone adaptation to its mechanical environment, from embryonic through adult life, is thought to be the product of increased osteoblastic differentiation from mesenchymal stem cells. In parallel with tissue-scale loading, these heterogeneous populations of multipotent stem cells are subject to a variety of biophysical cues within their native microenvironments. Bone marrow-derived mesenchymal stem cells-the most broadly studied source of osteoblastic progenitors-undergo osteoblastic differentiation in vitro in response to biophysical signals, including hydrostatic pressure, fluid flow and accompanying shear stress, substrate strain and stiffness, substrate topography, and electromagnetic fields. Furthermore, stem cells may be subject to indirect regulation by mechano-sensing osteocytes positioned to more readily detect these same loading-induced signals within the bone matrix. Such paracrine and juxtacrine regulation of differentiation by osteocytes occurs in vitro. Further studies are needed to confirm both direct and indirect mechanisms of biophysical regulation within the in vivo stem cell niche.

The relationship between fetal biophysical profile and cord blood PH

Directory of Open Access Journals (Sweden)

Valadan M

2009-02-01

Full Text Available "nBackground: The Biophysical Profile (BPP is a noninvasive test that predicts the presence or absence of fetal asphyxia and, ultimately, the risk of fetal death in the antenatal period. Intervention on the basis of an abnormal biophysical profile result has been reported to yield a significant reduction in prenatal mortality, and an association exists between biophysical profile scoring and a decreased cerebral palsy rate in a given population. The BPP evaluates five characteristics: fetal movement, tone, breathing, heart reactivity, and amniotic fluid (AF volume estimation. The purpose of study was to determine whether there are different degree of acidosis at which the biophysical activity (acute marker are affected. "nMethods: In a prospective study of 140 patients undergoing cesarean section before onset of labor, the fetal biophysical profile was performed 24h before the time of cesarean and was matched with cord arterial PH that was obtained from a cord segment (10-20cm that was double clamped after delivery of newborn. (using cord arterial PH less than 7.20 for the diagnosis of acidosis. "nResults: The fetal biophysical profile was found to have a significant relationship with umbilical blood PH. The sensitivity, specificity, positive predictive value, negative predictive value of fetal biophysical profile score were: 88.9%, 88.6%, 50%, 98.1%. "nConclusion: The first manifestations of fetal acidosis are nonreactive nonstress testing and fetal breathing loss; in advanced acidemia fetal movements and fetal tone are compromised. A protocol of antepartum fetal evaluation is suggested based upon the individual biophysical components rather than the score alone.

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)

A Hebbian learning rule gives rise to mirror neurons and links them to control theoretic inverse models

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

A Hebbian learning rule gives rise to mirror neurons and links them to control theoretic inverse models.

Science.gov (United States)

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.

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.

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

Application of a regularized model inversion system (REGFLEC) to multi-temporal RapidEye imagery for retrieving vegetation characteristics

KAUST Repository

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).

Biophysical Evaluation of SonoSteam®:

DEFF Research Database (Denmark)

Andersen, Ann Zahle; Duelund, Lars; Brewer, Jonathan R.

and safety evaluations. Our results show that there are no contradictions between data obtained by either approach. However, the biophysical methods draw a much more nuanced picture of the effects and efficiency of the investigated decontamination method, revealing e.g. an exponential dose/response...... relationship between SonoSteam treatment time and changes in collagen I, and a depth dependency in bacterial reduction, which points toward CFU counts overestimating total bacterial reduction. In conclusion the biophysical methods provide a less biased, reproducible and highly detailed system description...

Polynomial model inversion control: numerical tests and applications

OpenAIRE

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...

Theoretical molecular biophysics

CERN Document Server

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...

Application of a two-and-a-half dimensional model-based algorithm to crosswell electromagnetic data inversion

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

Winnowing and Flocculation in Bio-physical Cohesive Substrate: A Flume Experimental and Estuarine Study

Science.gov (United States)

Ye, L.; Parsons, D. R.; Manning, A. J.

2016-12-01

Cohesive sediment, or mud, is ubiquitously found in most aqueous environments, such as coasts and estuaries. The study of cohesive sediment behaviors requires the synchronous description of mutual interactions of grains (e.g., winnowing and flocculation), their physical properties (e.g., grain size) and also the ambient water. Herein, a series of flume experiments (14 runs) with different substrate mixtures of sand-clay-EPS (Extracellular Polymeric Substrates: secreted by aquatic microorganisms) are combined with an estuarine field survey (Dee estuary, NW England) to investigate the behavior of suspensions over bio-physical cohesive substrates. The experimental results indicate that winnowing and flocculation occur pervasively in bio-physical cohesive flow systems. Importantly however, the evolution of the bed and bedform dynamics and hence turbulence production can be lower when cohesivity is high. The estuarine survey also revealed that the bio-physical cohesion provided by both the clay and microorganism fractions in the bed, that pervasively exists in many natural estuarine systems, plays a significant role in controlling the interactions between bed substrate and sediment suspension and deposition, including controlling processes such as sediment winnowing, flocculation and re-deposition. Full understanding of these processes are essential in advancing sediment transport modelling and prediction studies across natural estuarine systems and the work will report on an improved conceptual model for sediment sorting deposition in bio-physical cohesive substrates.

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

Accounting for model error in Bayesian solutions to hydrogeophysical inverse problems using a local basis approach

Science.gov (United States)

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

Polarimetric SAR Interferometry based modeling for tree height and aboveground biomass retrieval in a tropical deciduous forest

Science.gov (United States)

Kumar, Shashi; Khati, Unmesh G.; Chandola, Shreya; Agrawal, Shefali; Kushwaha, Satya P. S.

2017-08-01

The regulation of the carbon cycle is a critical ecosystem service provided by forests globally. It is, therefore, necessary to have robust techniques for speedy assessment of forest biophysical parameters at the landscape level. It is arduous and time taking to monitor the status of vast forest landscapes using traditional field methods. Remote sensing and GIS techniques are efficient tools that can monitor the health of forests regularly. Biomass estimation is a key parameter in the assessment of forest health. Polarimetric SAR (PolSAR) remote sensing has already shown its potential for forest biophysical parameter retrieval. The current research work focuses on the retrieval of forest biophysical parameters of tropical deciduous forest, using fully polarimetric spaceborne C-band data with Polarimetric SAR Interferometry (PolInSAR) techniques. PolSAR based Interferometric Water Cloud Model (IWCM) has been used to estimate aboveground biomass (AGB). Input parameters to the IWCM have been extracted from the decomposition modeling of SAR data as well as PolInSAR coherence estimation. The technique of forest tree height retrieval utilized PolInSAR coherence based modeling approach. Two techniques - Coherence Amplitude Inversion (CAI) and Three Stage Inversion (TSI) - for forest height estimation are discussed, compared and validated. These techniques allow estimation of forest stand height and true ground topography. The accuracy of the forest height estimated is assessed using ground-based measurements. PolInSAR based forest height models showed enervation in the identification of forest vegetation and as a result height values were obtained in river channels and plain areas. Overestimation in forest height was also noticed at several patches of the forest. To overcome this problem, coherence and backscatter based threshold technique is introduced for forest area identification and accurate height estimation in non-forested regions. IWCM based modeling for forest

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

Inverse modeling with RZWQM2 to predict water quality

Science.gov (United States)

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

Computational study on full-wave inversion based on the acoustic wave-equation; Onkyoha hado hoteishiki full wave inversion no model keisan ni yoru kento

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.

Volcanic source inversion using a genetic algorithm and an elastic-gravitational layered earth model for magmatic intrusions

Science.gov (United States)

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.

Forward and Inverse Modeling of Self-potential. A Tomography of Groundwater Flow and Comparison Between Deterministic and Stochastic Inversion Methods

Science.gov (United States)

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.

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 (CHClF₂, HCFC-141b (CH₃CCl₂F and HCFC-142b (CH₃CClF₂ and three hydrofluorocarbons, HFC-23 (CHF₃, HFC-134a (CH₂FCF₃ and HFC-152a (CH₃CHF₂ 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

Inverse modeling of hydrologic parameters using surface flux and runoff observations in the Community Land Model

Science.gov (United States)

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.

Establishment of a biophysical model to optimize endoscopic targeting of magnetic nanoparticles for cancer treatment.

Science.gov (United States)

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.

Satellite mapping of surface biophysical parameters at the biome scale over the North American grasslands: A case study

Science.gov (United States)

Wylie, B.K.; Meyer, D.J.; Tieszen, L.L.; Mannel, S.

2002-01-01

Quantification of biophysical parameters is needed by terrestrial process modeling and other applications. A study testing the role of multispectral data for monitoring biophysical parameters was conducted over a network of grassland field sites in the Great Plains of North America. Grassland biophysical parameters [leaf area index (LAI), fraction of absorbed photosynthetically active radiation (fPAR), and biomass] and their relationships with ground radiometer normalized difference vegetation index (NDVI) were established in this study (r2=.66–.85) from data collected across the central and northern Great Plains in 1995. These spectral/biophysical relationships were compared to 1996 field data from the Tallgrass Prairie Preserve in northeastern Oklahoma and showed no consistent biases, with most regression estimates falling within the respective 95% confidence intervals. Biophysical parameters were estimated for 21 “ground pixels” (grids) at the Tallgrass Prairie Preserve in 1996, representing three grazing/burning treatments. Each grid was 30×30 m in size and was systematically sampled with ground radiometer readings. The radiometric measurements were then converted to biophysical parameters and spatially interpolated using geostatistical kriging. Grid-based biophysical parameters were monitored through the growing season and regressed against Landsat Thematic Mapper (TM) NDVI (r2=.92–.94). These regression equations were used to estimate biophysical parameters for grassland TM pixels over the Tallgrass Prairie Preserve in 1996. This method maintained consistent regression development and prediction scales and attempted to minimize scaling problems associated with mixed land cover pixels. A method for scaling Landsat biophysical parameters to coarser resolution satellite data sets (1 km2) was also investigated.

The physical basis of biochemistry the foundations of molecular biophysics

CERN Document Server

Bergethon, Peter R

1998-01-01

The objective of this book is to provide a unifying approach to the study of biophysical chemistry for the advanced undergraduate who has had a year of physics, organic chem­ istry, calculus, and biology. This book began as a revised edition of Biophysical Chemistry: Molecules to Membranes, which Elizabeth Simons and I coauthored. That short volume was written in an attempt to provide a concise text for a one-semester course in biophysical chemistry at the graduate level. The experience of teaching biophysical chemistry to bi­ ologically oriented students over the last decade has made it clear that the subject requires a more fundamental text that unifies the many threads of modem science: physics, chem­ istry, biology, mathematics, and statistics. This book represents that effort. This volume is not a treatment of modem biophysical chemistry with its rich history and many contro­ versies, although a book on that topic is also needed. The Physical Basis of Biochemistry is an introduction to the philosophy...

Inverting reflections using full-waveform inversion with inaccurate starting models

KAUST Repository

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

Modeling and forecasting foreign exchange daily closing prices with normal inverse Gaussian

Science.gov (United States)

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.

Social and Biophysical Predictors of Public Perceptions of Extreme Fires

Science.gov (United States)

Hall, T. E.; Kooistra, C. M.; Paveglio, T.; Gress, S.; Smith, A. M.

2013-12-01

To date, what constitutes an 'extreme' fire has been approached separately by biophysical and social scientists. Research on the biophysical characteristics of fires has identified potential dimensions of extremity, including fire size and vegetation mortality. On the social side, factors such as the degree of immediate impact to one's life and property or the extent of social disruption in the community contribute to a perception of extremity. However, some biophysical characteristics may also contribute to perceptions of extremity, including number of simultaneous ignitions, rapidity of fire spread, atypical fire behavior, and intensity of smoke. Perceptions of these impacts can vary within and across communities, but no studies to date have investigated such perceptions in a comprehensive way. In this study, we address the question, to what extent is the magnitude of impact of fires on WUI residents' well-being explained by measurable biophysical characteristics of the fire and subjective evaluations of the personal and community-level impacts of the fire? We bring together diverse strands of psychological theory, including landscape perception, mental models, risk perception, and community studies. The majority of social science research on fires has been in the form of qualitative case studies, and our study is methodologically unique by using a nested design (hierarchical modeling) to enable generalizable conclusions across a wide range of fires and human communities. We identified fires that burned in 2011 or 2012 in the northern Rocky Mountain region that were at least 1,000 acres and that intersected (within 15 km) urban clusters or identified Census places. For fires where an adequately large number of households was located in proximity to the fire, we drew random samples of approximately 150 individuals for each fire. We used a hybrid internet (Qualtrics) and mail survey, following the Dillman method, to measure individual perceptions. We developed two

Biophysical Neural Spiking, Bursting, and Excitability Dynamics in Reconfigurable Analog VLSI.

Science.gov (United States)

Yu, T; Sejnowski, T J; Cauwenberghs, G

2011-10-01

We study a range of neural dynamics under variations in biophysical parameters underlying extended Morris-Lecar and Hodgkin-Huxley models in three gating variables. The extended models are implemented in NeuroDyn, a four neuron, twelve synapse continuous-time analog VLSI programmable neural emulation platform with generalized channel kinetics and biophysical membrane dynamics. The dynamics exhibit a wide range of time scales extending beyond 100 ms neglected in typical silicon models of tonic spiking neurons. Circuit simulations and measurements show transition from tonic spiking to tonic bursting dynamics through variation of a single conductance parameter governing calcium recovery. We similarly demonstrate transition from graded to all-or-none neural excitability in the onset of spiking dynamics through the variation of channel kinetic parameters governing the speed of potassium activation. Other combinations of variations in conductance and channel kinetic parameters give rise to phasic spiking and spike frequency adaptation dynamics. The NeuroDyn chip consumes 1.29 mW and occupies 3 mm × 3 mm in 0.5 μm CMOS, supporting emerging developments in neuromorphic silicon-neuron interfaces.

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

On the biophysics and kinetics of toehold-mediated DNA strand displacement.

Science.gov (United States)

Srinivas, Niranjan; Ouldridge, Thomas E; Sulc, Petr; Schaeffer, Joseph M; Yurke, Bernard; Louis, Ard A; Doye, Jonathan P K; Winfree, Erik

2013-12-01

Dynamic DNA nanotechnology often uses toehold-mediated strand displacement for controlling reaction kinetics. Although the dependence of strand displacement kinetics on toehold length has been experimentally characterized and phenomenologically modeled, detailed biophysical understanding has remained elusive. Here, we study strand displacement at multiple levels of detail, using an intuitive model of a random walk on a 1D energy landscape, a secondary structure kinetics model with single base-pair steps and a coarse-grained molecular model that incorporates 3D geometric and steric effects. Further, we experimentally investigate the thermodynamics of three-way branch migration. Two factors explain the dependence of strand displacement kinetics on toehold length: (i) the physical process by which a single step of branch migration occurs is significantly slower than the fraying of a single base pair and (ii) initiating branch migration incurs a thermodynamic penalty, not captured by state-of-the-art nearest neighbor models of DNA, due to the additional overhang it engenders at the junction. Our findings are consistent with previously measured or inferred rates for hybridization, fraying and branch migration, and they provide a biophysical explanation of strand displacement kinetics. Our work paves the way for accurate modeling of strand displacement cascades, which would facilitate the simulation and construction of more complex molecular systems.

Diffusion-weighted MRI and quantitative biophysical modeling of hippocampal neurite loss in chronic stress.

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.

Effects of induced stress on seismic forward modelling and inversion

Science.gov (United States)

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.

Biophysical Regulation of Vascular Differentiation and Assembly

CERN Document Server

Gerecht, Sharon

2011-01-01

The ability to grow stem cells in the laboratory and to guide their maturation to functional cells allows us to study the underlying mechanisms that govern vasculature differentiation and assembly in health and disease. Accumulating evidence suggests that early stages of vascular growth are exquisitely tuned by biophysical cues from the microenvironment, yet the scientific understanding of such cellular environments is still in its infancy. Comprehending these processes sufficiently to manipulate them would pave the way to controlling blood vessel growth in therapeutic applications. This book assembles the works and views of experts from various disciplines to provide a unique perspective on how different aspects of its microenvironment regulate the differentiation and assembly of the vasculature. In particular, it describes recent efforts to exploit modern engineering techniques to study and manipulate various biophysical cues. Biophysical Regulation of Vascular Differentiation and Assembly provides an inter...

A biophysical approach to the optimisation of dendritic-tumour cell electrofusion

International Nuclear Information System (INIS)

Sukhorukov, Vladimir L.; Reuss, Randolph; Endter, Joerg M.; Fehrmann, Steffen; Katsen-Globa, Alisa; Gessner, Petra; Steinbach, Andrea; Mueller, Kilian J.; Karpas, Abraham; Zimmermann, Ulrich; Zimmermann, Heiko

2006-01-01

Electrofusion of tumour and dendritic cells (DCs) is a promising approach for production of DC-based anti-tumour vaccines. Although human DCs are well characterised immunologically, little is known about their biophysical properties, including dielectric and osmotic parameters, both of which are essential for the development of efficient electrofusion protocols. In the present study, human DCs from the peripheral blood along with a tumour cell line used as a model fusion partner were examined by means of time-resolved cell volumetry and electrorotation. Based on the biophysical cell data, the electrofusion protocol could be rapidly optimised with respect to the sugar composition of the fusion medium, duration of hypotonic treatment, frequency range for stable cell alignment, and field strengths of breakdown pulses triggering membrane fusion. The hypotonic electrofusion consistently gave a tumour-DC hybrid rate of up to 19%, as determined by counting dually labelled fluorescent hybrids in a microscope. This fusion rate is nearly twice as high as that usually reported in the literature for isotonic media. The experimental findings and biophysical approach presented here are generally useful for the development of efficient electrofusion protocols, especially for rare and valuable human cells

Biophysics and systems biology.

Science.gov (United States)

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.

Large-scale biophysical evaluation of protein PEGylation effects

DEFF Research Database (Denmark)

Vernet, Erik; Popa, Gina; Pozdnyakova, Irina

2016-01-01

PEGylation is the most widely used method to chemically modify protein biopharmaceuticals, but surprisingly limited public data is available on the biophysical effects of protein PEGylation. Here we report the first large-scale study, with site-specific mono-PEGylation of 15 different proteins...... of PEGylation on the thermal stability of a protein based on data generated by circular dichroism (CD), differential scanning calorimetry (DSC), or differential scanning fluorimetry (DSF). In addition, DSF was validated as a fast and inexpensive screening method for thermal unfolding studies of PEGylated...... proteins. Multivariate data analysis revealed clear trends in biophysical properties upon PEGylation for a subset of proteins, although no universal trends were found. Taken together, these findings are important in the consideration of biophysical methods and evaluation of second...

Biophysical and biomathematical adventures in radiobiology

International Nuclear Information System (INIS)

Scott, B.R.

1991-01-01

Highlights of my biophysical and biomathematical adventures in radiobiology is presented. Early adventures involved developing ''state-vector models'' for specific harmful effects (cell killing, life shortening) of exposure to radiation. More recent adventures led to developing ''hazard-function models'' for predicting biological effects (e.g., cell killing, mutations, tumor induction) of combined exposure to different toxicants. Hazard-function models were also developed for predicting harm to man from exposure to large radiation doses. Major conclusions derived from the modeling adventures are as follows: (1) synergistic effects of different genotoxic agents should not occur at low doses; (2) for exposure of the lung or bone marrow to large doses of photon radiation, low rates of exposure should be better tolerated than high rates; and (3) for some types of radiation (e.g., alpha particles and fission neutrons), moderate doses delivered at a low rate may be more harmful than the same dose given at a high rate. 53 refs., 7 figs

Sharp spatially constrained inversion

DEFF Research Database (Denmark)

Vignoli, Giulio G.; Fiandaca, Gianluca G.; Christiansen, Anders Vest C A.V.C.

2013-01-01

We present sharp reconstruction of multi-layer models using a spatially constrained inversion with minimum gradient support regularization. In particular, its application to airborne electromagnetic data is discussed. Airborne surveys produce extremely large datasets, traditionally inverted...... by using smoothly varying 1D models. Smoothness is a result of the regularization constraints applied to address the inversion ill-posedness. The standard Occam-type regularized multi-layer inversion produces results where boundaries between layers are smeared. The sharp regularization overcomes...... inversions are compared against classical smooth results and available boreholes. With the focusing approach, the obtained blocky results agree with the underlying geology and allow for easier interpretation by the end-user....

Impact of petrophysical uncertainty on Bayesian hydrogeophysical inversion and model selection

Science.gov (United States)

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.

Trans-dimensional matched-field geoacoustic inversion with hierarchical error models and interacting Markov chains.

Science.gov (United States)

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.

Structural biophysics

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

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

Approaches to highly parameterized inversion-A guide to using PEST for groundwater-model calibration

Science.gov (United States)

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.

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

Spin model for nontrivial types of magnetic order in inverse-perovskite antiferromagnets

Science.gov (United States)

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.

Recent progress in Biophysics

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

Inverse modeling of the terrestrial carbon flux in China with flux covariance among inverted regions

Science.gov (United States)

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.

Assimilation of Biophysical Neuronal Dynamics in Neuromorphic VLSI.

Science.gov (United States)

Wang, Jun; Breen, Daniel; Akinin, Abraham; Broccard, Frederic; Abarbanel, Henry D I; Cauwenberghs, Gert

2017-12-01

Representing the biophysics of neuronal dynamics and behavior offers a principled analysis-by-synthesis approach toward understanding mechanisms of nervous system functions. We report on a set of procedures assimilating and emulating neurobiological data on a neuromorphic very large scale integrated (VLSI) circuit. The analog VLSI chip, NeuroDyn, features 384 digitally programmable parameters specifying for 4 generalized Hodgkin-Huxley neurons coupled through 12 conductance-based chemical synapses. The parameters also describe reversal potentials, maximal conductances, and spline regressed kinetic functions for ion channel gating variables. In one set of experiments, we assimilated membrane potential recorded from one of the neurons on the chip to the model structure upon which NeuroDyn was designed using the known current input sequence. We arrived at the programmed parameters except for model errors due to analog imperfections in the chip fabrication. In a related set of experiments, we replicated songbird individual neuron dynamics on NeuroDyn by estimating and configuring parameters extracted using data assimilation from intracellular neural recordings. Faithful emulation of detailed biophysical neural dynamics will enable the use of NeuroDyn as a tool to probe electrical and molecular properties of functional neural circuits. Neuroscience applications include studying the relationship between molecular properties of neurons and the emergence of different spike patterns or different brain behaviors. Clinical applications include studying and predicting effects of neuromodulators or neurodegenerative diseases on ion channel kinetics.

The Source Inversion Validation (SIV) Initiative: A Collaborative Study on Uncertainty Quantification in Earthquake Source Inversions

Science.gov (United States)

Mai, P. M.; Schorlemmer, D.; Page, M.

2012-04-01

Earthquake source inversions image the spatio-temporal rupture evolution on one or more fault planes using seismic and/or geodetic data. Such studies are critically important for earthquake seismology in general, and for advancing seismic hazard analysis in particular, as they reveal earthquake source complexity and help (i) to investigate earthquake mechanics; (ii) to develop spontaneous dynamic rupture models; (iii) to build models for generating rupture realizations for ground-motion simulations. In applications (i - iii), the underlying finite-fault source models are regarded as "data" (input information), but their uncertainties are essentially unknown. After all, source models are obtained from solving an inherently ill-posed inverse problem to which many a priori assumptions and uncertain observations are applied. The Source Inversion Validation (SIV) project is a collaborative effort to better understand the variability between rupture models for a single earthquake (as manifested in the finite-source rupture model database) and to develop robust uncertainty quantification for earthquake source inversions. The SIV project highlights the need to develop a long-standing and rigorous testing platform to examine the current state-of-the-art in earthquake source inversion, and to develop and test novel source inversion approaches. We will review the current status of the SIV project, and report the findings and conclusions of the recent workshops. We will briefly discuss several source-inversion methods, how they treat uncertainties in data, and assess the posterior model uncertainty. Case studies include initial forward-modeling tests on Green's function calculations, and inversion results for synthetic data from spontaneous dynamic crack-like strike-slip earthquake on steeply dipping fault, embedded in a layered crustal velocity-density structure.

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.

A Comparison between Model Base Hardconstrain, Bandlimited, and Sparse-Spike Seismic Inversion: New Insights for CBM Reservoir Modelling on Muara Enim Formation, South Sumatra

Science.gov (United States)

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.

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.

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

Climate Change Effects on Agriculture: Economic Responses to Biophysical Shocks

Science.gov (United States)

Nelson, Gerald C.; Valin, Hugo; Sands, Ronald D.; Havlik, Petr; Ahammad, Helal; Deryng, Delphine; Elliott, Joshua; Fujimori, Shinichiro; Hasegawa, Tomoko; Heyhoe, Edwina

2014-01-01

Agricultural production is sensitive to weather and thus directly affected by climate change. Plausible estimates of these climate change impacts require combined use of climate, crop, and economic models. Results from previous studies vary substantially due to differences in models, scenarios, and data. This paper is part of a collective effort to systematically integrate these three types of models. We focus on the economic component of the assessment, investigating how nine global economic models of agriculture represent endogenous responses to seven standardized climate change scenarios produced by two climate and five crop models. These responses include adjustments in yields, area, consumption, and international trade. We apply biophysical shocks derived from the Intergovernmental Panel on Climate Change's representative concentration pathway with end-of-century radiative forcing of 8.5 W/m(sup 2). The mean biophysical yield effect with no incremental CO2 fertilization is a 17% reduction globally by 2050 relative to a scenario with unchanging climate. Endogenous economic responses reduce yield loss to 11%, increase area of major crops by 11%, and reduce consumption by 3%. Agricultural production, cropland area, trade, and prices show the greatest degree of variability in response to climate change, and consumption the lowest. The sources of these differences include model structure and specification; in particular, model assumptions about ease of land use conversion, intensification, and trade. This study identifies where models disagree on the relative responses to climate shocks and highlights research activities needed to improve the representation of agricultural adaptation responses to climate change.

PyFolding: Open-Source Graphing, Simulation, and Analysis of the Biophysical Properties of Proteins.

Science.gov (United States)

Lowe, Alan R; Perez-Riba, Albert; Itzhaki, Laura S; Main, Ewan R G

2018-02-06

For many years, curve-fitting software has been heavily utilized to fit simple models to various types of biophysical data. Although such software packages are easy to use for simple functions, they are often expensive and present substantial impediments to applying more complex models or for the analysis of large data sets. One field that is reliant on such data analysis is the thermodynamics and kinetics of protein folding. Over the past decade, increasingly sophisticated analytical models have been generated, but without simple tools to enable routine analysis. Consequently, users have needed to generate their own tools or otherwise find willing collaborators. Here we present PyFolding, a free, open-source, and extensible Python framework for graphing, analysis, and simulation of the biophysical properties of proteins. To demonstrate the utility of PyFolding, we have used it to analyze and model experimental protein folding and thermodynamic data. Examples include: 1) multiphase kinetic folding fitted to linked equations, 2) global fitting of multiple data sets, and 3) analysis of repeat protein thermodynamics with Ising model variants. Moreover, we demonstrate how PyFolding is easily extensible to novel functionality beyond applications in protein folding via the addition of new models. Example scripts to perform these and other operations are supplied with the software, and we encourage users to contribute notebooks and models to create a community resource. Finally, we show that PyFolding can be used in conjunction with Jupyter notebooks as an easy way to share methods and analysis for publication and among research teams. Copyright © 2017 Biophysical Society. 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

Continuous time random walk model with asymptotical probability density of waiting times via inverse Mittag-Leffler function

Science.gov (United States)

Liang, Yingjie; Chen, Wen

2018-04-01

The mean squared displacement (MSD) of the traditional ultraslow diffusion is a logarithmic function of time. Recently, the continuous time random walk model is employed to characterize this ultraslow diffusion dynamics by connecting the heavy-tailed logarithmic function and its variation as the asymptotical waiting time density. In this study we investigate the limiting waiting time density of a general ultraslow diffusion model via the inverse Mittag-Leffler function, whose special case includes the traditional logarithmic ultraslow diffusion model. The MSD of the general ultraslow diffusion model is analytically derived as an inverse Mittag-Leffler function, and is observed to increase even more slowly than that of the logarithmic function model. The occurrence of very long waiting time in the case of the inverse Mittag-Leffler function has the largest probability compared with the power law model and the logarithmic function model. The Monte Carlo simulations of one dimensional sample path of a single particle are also performed. The results show that the inverse Mittag-Leffler waiting time density is effective in depicting the general ultraslow random motion.

Handbook of Single-Molecule Biophysics

CERN Document Server

Hinterdorfer, Peter

2009-01-01

The last decade has seen the development of a number of novel biophysical methods that allow the manipulation and study of individual biomolecules. The ability to monitor biological processes at this fundamental level of sensitivity has given rise to an improved understanding of the underlying molecular mechanisms. Through the removal of ensemble averaging, distributions and fluctuations of molecular properties can be characterized, transient intermediates identified, and catalytic mechanisms elucidated. By applying forces on biomolecules while monitoring their activity, important information can be obtained on how proteins couple function to structure. The Handbook of Single-Molecule Biophysics provides an introduction to these techniques and presents an extensive discussion of the new biological insights obtained from them. Coverage includes: Experimental techniques to monitor and manipulate individual biomolecules The use of single-molecule techniques in super-resolution and functional imaging Single-molec...

Improvement of PM10 prediction in East Asia using inverse modeling

Science.gov (United States)

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.

Nitrous oxide emissions from cropland: a procedure for calibrating the DayCent biogeochemical model using inverse modelling

Science.gov (United States)

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.

Numerical modeling of axi-symmetrical cold forging process by ``Pseudo Inverse Approach''

Science.gov (United States)

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.

Optimal Inversion Parameters for Full Waveform Inversion using OBS Data Set

Science.gov (United States)

Kim, S.; Chung, W.; Shin, S.; Kim, D.; Lee, D.

2017-12-01

In recent years, full Waveform Inversion (FWI) has been the most researched technique in seismic data processing. It uses the residuals between observed and modeled data as an objective function; thereafter, the final subsurface velocity model is generated through a series of iterations meant to minimize the residuals.Research on FWI has expanded from acoustic media to elastic media. In acoustic media, the subsurface property is defined by P-velocity; however, in elastic media, properties are defined by multiple parameters, such as P-velocity, S-velocity, and density. Further, the elastic media can also be defined by Lamé constants, density or impedance PI, SI; consequently, research is being carried out to ascertain the optimal parameters.From results of advanced exploration equipment and Ocean Bottom Seismic (OBS) survey, it is now possible to obtain multi-component seismic data. However, to perform FWI on these data and generate an accurate subsurface model, it is important to determine optimal inversion parameters among (Vp, Vs, ρ), (λ, μ, ρ), and (PI, SI) in elastic media. In this study, staggered grid finite difference method was applied to simulate OBS survey. As in inversion, l2-norm was set as objective function. Further, the accurate computation of gradient direction was performed using the back-propagation technique and its scaling was done using the Pseudo-hessian matrix.In acoustic media, only Vp is used as the inversion parameter. In contrast, various sets of parameters, such as (Vp, Vs, ρ) and (λ, μ, ρ) can be used to define inversion in elastic media. Therefore, it is important to ascertain the parameter that gives the most accurate result for inversion with OBS data set.In this study, we generated Vp and Vs subsurface models by using (λ, μ, ρ) and (Vp, Vs, ρ) as inversion parameters in every iteration, and compared the final two FWI results.This research was supported by the Basic Research Project(17-3312) of the Korea Institute of

Contributions of computational chemistry and biophysical techniques to fragment-based drug discovery.

Science.gov (United States)

Gozalbes, Rafael; Carbajo, Rodrigo J; Pineda-Lucena, Antonio

2010-01-01

In the last decade, fragment-based drug discovery (FBDD) has evolved from a novel approach in the search of new hits to a valuable alternative to the high-throughput screening (HTS) campaigns of many pharmaceutical companies. The increasing relevance of FBDD in the drug discovery universe has been concomitant with an implementation of the biophysical techniques used for the detection of weak inhibitors, e.g. NMR, X-ray crystallography or surface plasmon resonance (SPR). At the same time, computational approaches have also been progressively incorporated into the FBDD process and nowadays several computational tools are available. These stretch from the filtering of huge chemical databases in order to build fragment-focused libraries comprising compounds with adequate physicochemical properties, to more evolved models based on different in silico methods such as docking, pharmacophore modelling, QSAR and virtual screening. In this paper we will review the parallel evolution and complementarities of biophysical techniques and computational methods, providing some representative examples of drug discovery success stories by using FBDD.

Role of Membrane Biophysics in Alzheimer's - related cell pathways

Directory of Open Access Journals (Sweden)

Donghui eZhu

2015-05-01

Full Text Available Cellular membrane alterations are commonly observed in many diseases, including Alzheimer’s disease (AD. Membrane biophysical properties, such as membrane molecular order, membrane fluidity, organization of lipid rafts, and adhesion between membrane and cytoskeleton, play an important role in various cellular activities and functions. While membrane biophysics impacts a broad range of cellular pathways, this review addresses the role of membrane biophysics in amyloid-β peptide aggregation, Aβ-induced oxidative pathways, amyloid precursor protein processing, and cerebral endothelial functions in AD. Understanding the mechanism(s underlying the effects of cell membrane properties on cellular processes should shed light on the development of new preventive and therapeutic strategies for this devastating disease.

Three-dimensional magnetotelluric axial anisotropic forward modeling and inversion

Science.gov (United States)

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.

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 ...

Voxel inversion of airborne EM data

DEFF Research Database (Denmark)

Fiandaca, Gianluca G.; Auken, Esben; Christiansen, Anders Vest C A.V.C.

2013-01-01

We present a geophysical inversion algorithm working directly in a voxel grid disconnected from the actual measuring points, which allows for straightforward integration of different data types in joint inversion, for informing geological/hydrogeological models directly and for easier incorporation...... of prior information. Inversion of geophysical data usually refers to a model space being linked to the actual observation points. For airborne surveys the spatial discretization of the model space reflects the flight lines. Often airborne surveys are carried out in areas where other ground......-based geophysical data are available. The model space of geophysical inversions is usually referred to the positions of the measurements, and ground-based model positions do not generally coincide with the airborne model positions. Consequently, a model space based on the measuring points is not well suited...

Inverse problem theory methods for data fitting and model parameter estimation

CERN Document Server

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

Mixed linear-nonlinear fault slip inversion: Bayesian inference of model, weighting, and smoothing parameters

Science.gov (United States)

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

Definition and solution of a stochastic inverse problem for the Manning's n parameter field in hydrodynamic models

Science.gov (United States)

Butler, T.; Graham, L.; Estep, D.; Dawson, C.; Westerink, J. J.

2015-04-01

The uncertainty in spatially heterogeneous Manning's n fields is quantified using a novel formulation and numerical solution of stochastic inverse problems for physics-based models. The uncertainty is quantified in terms of a probability measure and the physics-based model considered here is the state-of-the-art ADCIRC model although the presented methodology applies to other hydrodynamic models. An accessible overview of the formulation and solution of the stochastic inverse problem in a mathematically rigorous framework based on measure theory is presented. Technical details that arise in practice by applying the framework to determine the Manning's n parameter field in a shallow water equation model used for coastal hydrodynamics are presented and an efficient computational algorithm and open source software package are developed. A new notion of "condition" for the stochastic inverse problem is defined and analyzed as it relates to the computation of probabilities. This notion of condition is investigated to determine effective output quantities of interest of maximum water elevations to use for the inverse problem for the Manning's n parameter and the effect on model predictions is analyzed.

Biophysics of molecular gastronomy.

Science.gov (United States)

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.

Canopy-scale biophysical controls on transpiration and evaporation in the Amazon Basin

DEFF Research Database (Denmark)

Mallick, Kaniska; Trebs, Ivonne; Bøgh, Eva

2016-01-01

to directly quantify the canopy-scale biophysical controls on λET and λEE over multiple plant functional types (PFTs) in the Amazon Basin. Combining data from six LBA (Large-scale Biosphere-Atmosphere Experiment in Amazonia) eddy covariance tower sites and a TR-driven physically based modeling approach, we...

Bayesian seismic AVO inversion

Energy Technology Data Exchange (ETDEWEB)

Buland, Arild

2002-07-01

A new linearized AVO inversion technique is developed in a Bayesian framework. The objective is to obtain posterior distributions for P-wave velocity, S-wave velocity and density. Distributions for other elastic parameters can also be assessed, for example acoustic impedance, shear impedance and P-wave to S-wave velocity ratio. The inversion algorithm is based on the convolutional model and a linearized weak contrast approximation of the Zoeppritz equation. The solution is represented by a Gaussian posterior distribution with explicit expressions for the posterior expectation and covariance, hence exact prediction intervals for the inverted parameters can be computed under the specified model. The explicit analytical form of the posterior distribution provides a computationally fast inversion method. Tests on synthetic data show that all inverted parameters were almost perfectly retrieved when the noise approached zero. With realistic noise levels, acoustic impedance was the best determined parameter, while the inversion provided practically no information about the density. The inversion algorithm has also been tested on a real 3-D dataset from the Sleipner Field. The results show good agreement with well logs but the uncertainty is high. The stochastic model includes uncertainties of both the elastic parameters, the wavelet and the seismic and well log data. The posterior distribution is explored by Markov chain Monte Carlo simulation using the Gibbs sampler algorithm. The inversion algorithm has been tested on a seismic line from the Heidrun Field with two wells located on the line. The uncertainty of the estimated wavelet is low. In the Heidrun examples the effect of including uncertainty of the wavelet and the noise level was marginal with respect to the AVO inversion results. We have developed a 3-D linearized AVO inversion method with spatially coupled model parameters where the objective is to obtain posterior distributions for P-wave velocity, S

Multi-dimensional Inversion Modeling of Surface Nuclear Magnetic Resonance (SNMR Data for Groundwater Exploration

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.

Workflow for near-surface velocity automatic estimation: Source-domain full-traveltime inversion followed by waveform inversion

KAUST Repository

Liu, Lu

2017-08-17

This paper presents a workflow for near-surface velocity automatic estimation using the early arrivals of seismic data. This workflow comprises two methods, source-domain full traveltime inversion (FTI) and early-arrival waveform inversion. Source-domain FTI is capable of automatically generating a background velocity that can kinematically match the reconstructed plane-wave sources of early arrivals with true plane-wave sources. This method does not require picking first arrivals for inversion, which is one of the most challenging aspects of ray-based first-arrival tomographic inversion. Moreover, compared with conventional Born-based methods, source-domain FTI can distinguish between slower or faster initial model errors via providing the correct sign of the model gradient. In addition, this method does not need estimation of the source wavelet, which is a requirement for receiver-domain wave-equation velocity inversion. The model derived from source-domain FTI is then used as input to early-arrival waveform inversion to obtain the short-wavelength velocity components. We have tested the workflow on synthetic and field seismic data sets. The results show source-domain FTI can generate reasonable background velocities for early-arrival waveform inversion even when subsurface velocity reversals are present and the workflow can produce a high-resolution near-surface velocity model.

Forward and Inverse Predictive Model for the Trajectory Tracking Control of a Lower Limb Exoskeleton for Gait Rehabilitation: Simulation modelling analysis

Science.gov (United States)

Zakaria, M. A.; Majeed, A. P. P. A.; Taha, Z.; Alim, M. M.; Baarath, K.

2018-03-01

The movement of a lower limb exoskeleton requires a reasonably accurate control method to allow for an effective gait therapy session to transpire. Trajectory tracking is a nontrivial means of passive rehabilitation technique to correct the motion of the patients’ impaired limb. This paper proposes an inverse predictive model that is coupled together with the forward kinematics of the exoskeleton to estimate the behaviour of the system. A conventional PID control system is used to converge the required joint angles based on the desired input from the inverse predictive model. It was demonstrated through the present study, that the inverse predictive model is capable of meeting the trajectory demand with acceptable error tolerance. The findings further suggest the ability of the predictive model of the exoskeleton to predict a correct joint angle command to the system.

2. biophysical work meeting. Papers; 2. Biophysikalische Arbeitstagung; Vortraege

Energy Technology Data Exchange (ETDEWEB)

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.) [Deutsch] Der Report enthaelt 18 Vortraege, die auf der 2. Biophysikalischen Arbeitstagung in Schlema vom 11. bis 13. September 1991 gehalten wurden. Es werden die Geschichte der Biophysik in Deutschland, speziell der Strahlenbiophysik und Radonforschung, Messungen von Radon und seinen Folgeprodukten, Epidemiologie und Strahlenbiologie zur Bestimmung der Strahlenwirkung des Radons und die Ableitung entsprechender Grenzwerte, Radon-Balneotherapie und Folgen des Uranerzbergbaus behandelt. (orig.)

Inverse modeling and animation of growing single-stemmed trees at interactive rates

Science.gov (United States)

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...

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

Mass spectrometry in structural biology and biophysics architecture, dynamics, and interaction of biomolecules

CERN Document Server

Kaltashov, Igor A; Desiderio, Dominic M; Nibbering, Nico M

2012-01-01

The definitive guide to mass spectrometry techniques in biology and biophysics The use of mass spectrometry (MS) to study the architecture and dynamics of proteins is increasingly common within the biophysical community, and Mass Spectrometry in Structural Biology and Biophysics: Architecture, Dynamics, and Interaction of Biomolecules, Second Edition provides readers with detailed, systematic coverage of the current state of the art. Offering an unrivalled overview of modern MS-based armamentarium that can be used to solve the most challenging problems in biophysics, structural biol

Comparison of biophysical factors influencing on emphysema quantification with low-dose CT

Science.gov (United States)

Heo, Chang Yong; Kim, Jong Hyo

2014-03-01

Emphysema Index(EI) measurements in MDCT is known to be influenced by various biophysical factors such as total lung volume, and body size. We investigated the association of the four biophysical factors with emphysema index in low-dose MDCT. In particular, we attempted to identify a potentially stronger biophysical factor than total lung volume. A total of 400 low-dose MDCT volumes taken at 120kVp, 40mAs, 1mm thickness, and B30f reconstruction kernel were used. The lungs, airways, and pulmonary vessels were automatically segmented, and two Emphysema Indices, relative area below -950HU(RA950) and 15th percentile(Perc15), were extracted from the segmented lungs. The biophysical factors such as total lung volume(TLV), mode of lung attenuation(ModLA), effective body diameter(EBD), and the water equivalent body diameter(WBD) were estimated from the segmented lung and body area. The association of biophysical factors with emphysema indices were evaluated by correlation coefficients. The mean emphysema indices were 8.3±5.5(%) in RA950, and -930±18(HU) in Perc15. The estimates of biophysical factors were 4.7±1.0(L) in TLV, -901±21(HU) in ModLA, 26.9±2.2(cm) in EBD, and 25.9±2.6(cm) in WBD. The correlation coefficients of biophysical factors with RA950 were 0.73 in TLV, 0.94 in ModLA, 0.31 in EBD, and 0.18 WBD, the ones with Perc15 were 0.74 in TLV, 0.98 in ModLA, 0.29 in EBD, and 0.15 WBD. Study results revealed that two biophysical factors, TLV and ModLA, mostly affects the emphysema indices. In particular, the ModLA exhibited strongest correlation of 0.98 with Perc15, which indicating the ModLA is the most significant confounding biophysical factor in emphysema indices measurement.

Aircraft automatic-flight-control system with inversion of the model in the feed-forward path using a Newton-Raphson technique for the inversion

Science.gov (United States)

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.

Computational electromagnetics and model-based inversion a modern paradigm for eddy-current nondestructive evaluation

CERN Document Server

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,...

Radiation biophysics

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

Angle-domain inverse scattering migration/inversion in isotropic media

Science.gov (United States)

Li, Wuqun; Mao, Weijian; Li, Xuelei; Ouyang, Wei; Liang, Quan

2018-07-01

The classical seismic asymptotic inversion can be transformed into a problem of inversion of generalized Radon transform (GRT). In such methods, the combined parameters are linearly attached to the scattered wave-field by Born approximation and recovered by applying an inverse GRT operator to the scattered wave-field data. Typical GRT-style true-amplitude inversion procedure contains an amplitude compensation process after the weighted migration via dividing an illumination associated matrix whose elements are integrals of scattering angles. It is intuitional to some extent that performs the generalized linear inversion and the inversion of GRT together by this process for direct inversion. However, it is imprecise to carry out such operation when the illumination at the image point is limited, which easily leads to the inaccuracy and instability of the matrix. This paper formulates the GRT true-amplitude inversion framework in an angle-domain version, which naturally degrades the external integral term related to the illumination in the conventional case. We solve the linearized integral equation for combined parameters of different fixed scattering angle values. With this step, we obtain high-quality angle-domain common-image gathers (CIGs) in the migration loop which provide correct amplitude-versus-angle (AVA) behavior and reasonable illumination range for subsurface image points. Then we deal with the over-determined problem to solve each parameter in the combination by a standard optimization operation. The angle-domain GRT inversion method keeps away from calculating the inaccurate and unstable illumination matrix. Compared with the conventional method, the angle-domain method can obtain more accurate amplitude information and wider amplitude-preserved range. Several model tests demonstrate the effectiveness and practicability.

Neuro-fuzzy inverse model control structure of robotic manipulators utilized for physiotherapy applications

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.

Cerebellum as a forward but not inverse model in visuomotor adaptation task: a tDCS-based and modeling study.

Science.gov (United States)

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.

Salvus: A scalable software suite for full-waveform modelling & inversion

Science.gov (United States)

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

Inverse Compton gamma-rays from pulsars

International Nuclear Information System (INIS)

Morini, M.

1983-01-01

A model is proposed for pulsar optical and gamma-ray emission where relativistic electrons beams: (i) scatter the blackbody photons from the polar cap surface giving inverse Compton gamma-rays and (ii) produce synchrotron optical photons in the light cylinder region which are then inverse Compton scattered giving other gamma-rays. The model is applied to the Vela pulsar, explaining the first gamma-ray pulse by inverse Compton scattering of synchrotron photons near the light cylinder and the second gamma-ray pulse partly by inverse Compton scattering of synchrotron photons and partly by inverse Compton scattering of the thermal blackbody photons near the star surface. (author)

Electron dose map inversion based on several algorithms

International Nuclear Information System (INIS)

Li Gui; Zheng Huaqing; Wu Yican; Fds Team

2010-01-01

The reconstruction to the electron dose map in radiation therapy was investigated by constructing the inversion model of electron dose map with different algorithms. The inversion model of electron dose map based on nonlinear programming was used, and this model was applied the penetration dose map to invert the total space one. The realization of this inversion model was by several inversion algorithms. The test results with seven samples show that except the NMinimize algorithm, which worked for just one sample, with great error,though,all the inversion algorithms could be realized to our inversion model rapidly and accurately. The Levenberg-Marquardt algorithm, having the greatest accuracy and speed, could be considered as the first choice in electron dose map inversion.Further tests show that more error would be created when the data close to the electron range was used (tail error). The tail error might be caused by the approximation of mean energy spectra, and this should be considered to improve the method. The time-saving and accurate algorithms could be used to achieve real-time dose map inversion. By selecting the best inversion algorithm, the clinical need in real-time dose verification can be satisfied. (authors)

Effects of LiDAR point density, sampling size and height threshold on estimation accuracy of crop biophysical parameters.

Science.gov (United States)

Luo, Shezhou; Chen, Jing M; Wang, Cheng; Xi, Xiaohuan; Zeng, Hongcheng; Peng, Dailiang; Li, Dong

2016-05-30

Vegetation leaf area index (LAI), height, and aboveground biomass are key biophysical parameters. Corn is an important and globally distributed crop, and reliable estimations of these parameters are essential for corn yield forecasting, health monitoring and ecosystem modeling. Light Detection and Ranging (LiDAR) is considered an effective technology for estimating vegetation biophysical parameters. However, the estimation accuracies of these parameters are affected by multiple factors. In this study, we first estimated corn LAI, height and biomass (R2 = 0.80, 0.874 and 0.838, respectively) using the original LiDAR data (7.32 points/m2), and the results showed that LiDAR data could accurately estimate these biophysical parameters. Second, comprehensive research was conducted on the effects of LiDAR point density, sampling size and height threshold on the estimation accuracy of LAI, height and biomass. Our findings indicated that LiDAR point density had an important effect on the estimation accuracy for vegetation biophysical parameters, however, high point density did not always produce highly accurate estimates, and reduced point density could deliver reasonable estimation results. Furthermore, the results showed that sampling size and height threshold were additional key factors that affect the estimation accuracy of biophysical parameters. Therefore, the optimal sampling size and the height threshold should be determined to improve the estimation accuracy of biophysical parameters. Our results also implied that a higher LiDAR point density, larger sampling size and height threshold were required to obtain accurate corn LAI estimation when compared with height and biomass estimations. In general, our results provide valuable guidance for LiDAR data acquisition and estimation of vegetation biophysical parameters using LiDAR data.

Centered Differential Waveform Inversion with Minimum Support Regularization

KAUST Repository

Kazei, Vladimir

2017-05-26

Time-lapse full-waveform inversion has two major challenges. The first one is the reconstruction of a reference model (baseline model for most of approaches). The second is inversion for the time-lapse changes in the parameters. Common model approach is utilizing the information contained in all available data sets to build a better reference model for time lapse inversion. Differential (Double-difference) waveform inversion allows to reduce the artifacts introduced into estimates of time-lapse parameter changes by imperfect inversion for the baseline-reference model. We propose centered differential waveform inversion (CDWI) which combines these two approaches in order to benefit from both of their features. We apply minimum support regularization commonly used with electromagnetic methods of geophysical exploration. We test the CDWI method on synthetic dataset with random noise and show that, with Minimum support regularization, it provides better resolution of velocity changes than with total variation and Tikhonov regularizations in time-lapse full-waveform inversion.

Full-model wavenumber inversion: An emphasis on the appropriate wavenumber continuation

KAUST Repository

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

KAUST Repository

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.

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...

Joint inversion of seismic refraction and resistivity data using layered models - applications to hydrogeology

Science.gov (United States)

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.

Thermodynamic Modeling for Open Combined Regenerative Brayton and Inverse Brayton Cycles with Regeneration before the Inverse Cycle

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.

Biophysics of Human Hair Structural, Nanomechanical, and Nanotribological Studies

CERN Document Server

Bhushan, Bharat

2010-01-01

This book presents the biophysics of hair. It deals with the structure of hair, its mechanical properties, the nanomechanical characterization, tensile deformation, tribological characterization, the thickness distribution and binding interactions on hair surface. Another important topic of the book is the health of hair, human hair and skin, hair care, cleaning and conditioning treatments and damaging processes. It is the first book on the biophysical properties of hair.

FIACH: A biophysical model for automatic retrospective noise control in fMRI.

Science.gov (United States)

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.

Multi-subject hierarchical inverse covariance modelling improves estimation of functional brain networks.

Science.gov (United States)

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.

An inverse problem strategy based on forward model evaluations: Gradient-based optimization without adjoint solves

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.

Applications of synchrotron radiation in Biophysics

International Nuclear Information System (INIS)

Bemski, G.

1983-01-01

A short introduction to the generation of the synchrotron radiation is made. Following, the applications of such a radiation in biophysics with emphasis to the study of the hemoglobin molecule are presented. (L.C.) [pt

Flow modelling in fractured aquifers, development of multi-continua model (direct and inverse problems) and application to the CEA/Cadarache site

International Nuclear Information System (INIS)

Cartalade, Alain

2002-01-01

This research thesis concerns the modelling of aquifer flows under the CEA/Cadarache site. The author reports the implementation of a numerical simulation tool adapted to large scale flows in fractured media, and its application to the Cadarache nuclear site. After a description of the site geological and hydrogeological characteristics, the author presents the conceptual model on which the modelling is based, presents the inverse model which allows a better definition of parameters, reports the validation of the inverse approach by means of synthetic and semi-synthetic cases. Then, he reports experiments and simulation of the Cadarache site

Three-dimensional inverse modelling of damped elastic wave propagation in the Fourier domain

Science.gov (United States)

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

Interplay of mycolic acids, antimycobacterial compounds and pulmonary surfactant membrane: a biophysical approach to disease.

Science.gov (United States)

Pinheiro, Marina; Giner-Casares, Juan J; Lúcio, Marlene; Caio, João M; Moiteiro, Cristina; Lima, José L F C; Reis, Salette; Camacho, Luis

2013-02-01

This work focuses on the interaction of mycolic acids (MAs) and two antimycobacterial compounds (Rifabutin and N'-acetyl-Rifabutin) at the pulmonary membrane level to convey a biophysical perspective of their role in disease. For this purpose, accurate biophysical techniques (Langmuir isotherms, Brewster angle microscopy, and polarization-modulation infrared reflection spectroscopy) and lipid model systems were used to mimic biomembranes: MAs mimic bacterial lipids of the Mycobacterium tuberculosis (MTb) membrane, whereas Curosurf® was used as the human pulmonary surfactant (PS) membrane model. The results obtained show that high quantities of MAs are responsible for significant changes on PS biophysical properties. At the dynamic inspiratory surface tension, high amounts of MAs decrease the order of the lipid monolayer, which appears to be a concentration dependent effect. These results suggest that the amount of MAs might play a critical role in the initial access of the bacteria to their targets. Both molecules also interact with the PS monolayer at the dynamic inspiratory surface. However, in the presence of higher amounts of MAs, both compounds improve the phospholipid packing and, therefore, the order of the lipid surfactant monolayer. In summary, this work discloses the putative protective effects of antimycobacterial compounds against the MAs induced biophysical impairment of PS lipid monolayers. These protective effects are most of the times overlooked, but can constitute an additional therapeutic value in the treatment of pulmonary tuberculosis (Tb) and may provide significant insights for the design of new and more efficient anti-Tb drugs based on their behavior as membrane ordering agents. Copyright © 2012 Elsevier B.V. All rights reserved.

A stochastic approach for model reduction and memory function design in hydrogeophysical inversion

Science.gov (United States)

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

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....

Appropriate Objective Functions for Quantifying Iris Mechanical Properties Using Inverse Finite Element Modeling.

Science.gov (United States)

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.

Establishment of a biophysical model to optimize endoscopic targeting of magnetic nanoparticles for cancer treatment

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

Inverse uncertainty quantification of reactor simulations under the Bayesian framework using surrogate models constructed by polynomial chaos expansion

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.

A Hebbian learning rule gives rise to mirror neurons and links them to control theoretic inverse models

OpenAIRE

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...

Inverse modelling of radionuclide release rates using gamma dose rate observations

Science.gov (United States)

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

An allosteric gating model recapitulates the biophysical properties of IK,L expressed in mouse vestibular type I hair cells.

Science.gov (United States)

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

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

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

Double point source W-phase inversion: Real-time implementation and automated model selection

Science.gov (United States)

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.

Biophysical aspects of photodynamic therapy.

Science.gov (United States)

Juzeniene, Asta; Nielsen, Kristian Pagh; Moan, Johan

2006-01-01

Over the last three decades photodynamic therapy (PDT) has been developed to a useful clinical tool, a viable alternative in the treatment of cancer and other diseases. Several disciplines have contributed to this development: chemistry in the development of new photosensitizing agents, biology in the elucidation of cellular processes involved in PDT, pharmacology and physiology in identifying the mechanisms of distribution of photosensitizers in an organism, and, last but not least, physics in the development of better light sources, dosimetric concepts and construction of imaging devices, optical sensors and spectroscopic methods for determining sensitizer concentrations in different tissues. Physics and biophysics have also helped to focus on the role of pH for sensitizer accumulation, dose rate effects, oxygen depletion, temperature, and optical penetration of light of different wavelengths into various types of tissue. These are all important parameters for optimally effective PDT. The present review will give a brief, physically based, overview of PDT and then discuss some of the main biophysical aspects of this therapeutic modality.

Simulation of atmospheric temperature inversions over greater cairo using the MM5 Meso-Scale atmospheric model

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

Meaningful questions: The acquisition of auxiliary inversion in a connectionist model of sentence production.

Science.gov (United States)

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.

Inverse modeling of cloud-aerosol interactions -- Part 1: Detailed response surface analysis

NARCIS (Netherlands)

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

A multivariate decision tree analysis of biophysical factors in tropical forest fire occurrence

Science.gov (United States)

Rey S. Ofren; Edward Harvey

2000-01-01

A multivariate decision tree model was used to quantify the relative importance of complex hierarchical relationships between biophysical variables and the occurrence of tropical forest fires. The study site is the Huai Kha Kbaeng wildlife sanctuary, a World Heritage Site in northwestern Thailand where annual fires are common and particularly destructive. Thematic...

Testing models of basin inversion in the eastern North Sea using exceptionally accurate thermal and maturity data

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...

Transient Inverse Calibration of Site-Wide Groundwater Model to Hanford Operational Impacts from 1943 to 1996-Alternative Conceptual Model Considering Interaction with Uppermost Basalt Confined Aquifer; FINAL

International Nuclear Information System (INIS)

Vermeul, Vince R; Cole, Charles R; Bergeron, Marcel P; Thorne, Paul D; Wurstner, Signe K

2001-01-01

The baseline three-dimensional transient inverse model for the estimation of site-wide scale flow parameters, including their uncertainties, using data on the transient behavior of the unconfined aquifer system over the entire historical period of Hanford operations, has been modified to account for the effects of basalt intercommunication between the Hanford unconfined aquifer and the underlying upper basalt confined aquifer. Both the baseline and alternative conceptual models (ACM-1) considered only the groundwater flow component and corresponding observational data in the 3-Dl transient inverse calibration efforts. Subsequent efforts will examine both groundwater flow and transport. Comparisons of goodness of fit measures and parameter estimation results for the ACM-1 transient inverse calibrated model with those from previous site-wide groundwater modeling efforts illustrate that the new 3-D transient inverse model approach will strengthen the technical defensibility of the final model(s) and provide the ability to incorporate uncertainty in predictions related to both conceptual model and parameter uncertainty

Building biophysics in mid-century China: the University of Science and Technology of China.

Science.gov (United States)

Luk, Yi Lai Christine

2015-01-01

Biophysics has been either an independent discipline or an element of another discipline in the United States, but it has always been recognized as a stand-alone discipline in the People's Republic of China (PRC) since 1949. To inquire into this apparent divergence, this paper investigates the formational history of biophysics in China by examining the early institutional history of one of the best-known and prestigious science and technology universities in the PRC, the University of Science and Technology of China (USTC). By showing how the university and its biophysics program co-evolved with national priorities from the school's founding in 1958 to the eve of the Cultural Revolution in 1966, the purpose of this paper is to assess the development of a scientific discipline in the context of national demands and institutional politics. Specific materials for analysis include the school's admission policies, curricula, students' dissertations, and research program. To further contextualize the institutional setting of Chinese biophysics, this paper begins with a general history of proto-biophysical institutions in China during the Nationalist-Communist transitional years. This paper could be of interest to historians wanting to know more about the origin of the biophysics profession in China, and in particular how research areas that constitute biophysics changed in tandem with socio-political contingencies.

Bayesian inversion of refraction seismic traveltime data

Science.gov (United States)

Ryberg, T.; Haberland, Ch

2018-03-01

We apply a Bayesian Markov chain Monte Carlo (McMC) formalism to the inversion of refraction seismic, traveltime data sets to derive 2-D velocity models below linear arrays (i.e. profiles) of sources and seismic receivers. Typical refraction data sets, especially when using the far-offset observations, are known as having experimental geometries which are very poor, highly ill-posed and far from being ideal. As a consequence, the structural resolution quickly degrades with depth. Conventional inversion techniques, based on regularization, potentially suffer from the choice of appropriate inversion parameters (i.e. number and distribution of cells, starting velocity models, damping and smoothing constraints, data noise level, etc.) and only local model space exploration. McMC techniques are used for exhaustive sampling of the model space without the need of prior knowledge (or assumptions) of inversion parameters, resulting in a large number of models fitting the observations. Statistical analysis of these models allows to derive an average (reference) solution and its standard deviation, thus providing uncertainty estimates of the inversion result. The highly non-linear character of the inversion problem, mainly caused by the experiment geometry, does not allow to derive a reference solution and error map by a simply averaging procedure. We present a modified averaging technique, which excludes parts of the prior distribution in the posterior values due to poor ray coverage, thus providing reliable estimates of inversion model properties even in those parts of the models. The model is discretized by a set of Voronoi polygons (with constant slowness cells) or a triangulated mesh (with interpolation within the triangles). Forward traveltime calculations are performed by a fast, finite-difference-based eikonal solver. The method is applied to a data set from a refraction seismic survey from Northern Namibia and compared to conventional tomography. An inversion test

Landslide Kinematical Analysis through Inverse Numerical Modelling and Differential SAR Interferometry

Science.gov (United States)

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.

Laterally constrained inversion for CSAMT data interpretation

Science.gov (United States)

Wang, Ruo; Yin, Changchun; Wang, Miaoyue; Di, Qingyun

2015-10-01

Laterally constrained inversion (LCI) has been successfully applied to the inversion of dc resistivity, TEM and airborne EM data. However, it hasn't been yet applied to the interpretation of controlled-source audio-frequency magnetotelluric (CSAMT) data. In this paper, we apply the LCI method for CSAMT data inversion by preconditioning the Jacobian matrix. We apply a weighting matrix to Jacobian to balance the sensitivity of model parameters, so that the resolution with respect to different model parameters becomes more uniform. Numerical experiments confirm that this can improve the convergence of the inversion. We first invert a synthetic dataset with and without noise to investigate the effect of LCI applications to CSAMT data, for the noise free data, the results show that the LCI method can recover the true model better compared to the traditional single-station inversion; and for the noisy data, the true model is recovered even with a noise level of 8%, indicating that LCI inversions are to some extent noise insensitive. Then, we re-invert two CSAMT datasets collected respectively in a watershed and a coal mine area in Northern China and compare our results with those from previous inversions. The comparison with the previous inversion in a coal mine shows that LCI method delivers smoother layer interfaces that well correlate to seismic data, while comparison with a global searching algorithm of simulated annealing (SA) in a watershed shows that though both methods deliver very similar good results, however, LCI algorithm presented in this paper runs much faster. The inversion results for the coal mine CSAMT survey show that a conductive water-bearing zone that was not revealed by the previous inversions has been identified by the LCI. This further demonstrates that the method presented in this paper works for CSAMT data inversion.

Anti-pulmonary fibrotic activity of salvianolic acid B was screened by a novel method based on the cyto-biophysical properties

International Nuclear Information System (INIS)

Liu, Miao; Zheng, Mingjing; Xu, Hanying; Liu, Lianqing; Li, Yanchun; Xiao, Wei; Li, Jianchun; Ma, Enlong

2015-01-01

Various methods have been used to evaluate anti-fibrotic activity of drugs. However, most of them are complicated, labor-intensive and lack of efficiency. This study was intended to develop a rapid method for anti-fibrotic drugs screening based on biophysical properties. A549 cells in vitro were stimulated with transforming growth factor-β1 (TGF-β1), and fibrogenesis was confirmed by conventional immunological assays. Meanwhile, the alterations of cyto-biophysical properties including morphology, roughness and stiffness were measured utilizing atomic force microscopy (AFM). It was found that fibrogenesis was accompanied with changes of cellular biophysical properties. TGF-β1-stimulated A549 cells became remarkably longer, rougher and stiffer than the control. Then, the effect of N-acetyl-L-cysteine (NAC) as a positive drug on ameliorating fibrogenesis in TGF-β1-stimulated A549 cells was verified respectively by immunological and biophysical markers. The result of Principal Component Analysis showed that stiffness was a leading index among all biophysical markers during fibrogenesis. Salvianolic acid B (SalB), a natural anti-oxidant, was detected by AFM to protect TGF-β1-stimulated A549 cells against stiffening. Then, SalB treatment was provided in preventive mode on a rat model of bleomycin (BLM) -induced pulmonary fibrosis. The results showed that SalB treatment significantly ameliorated BLM-induced histological alterations, blocked collagen accumulations and reduced α-SMA expression in lung tissues. All these results revealed the anti-pulmonary fibrotic activity of SalB. Detection of cyto-biophysical properties were therefore recommended as a rapid method for anti-pulmonary fibrotic drugs screening. - Highlights: • Fibrogenesis was accompanied with the changes of cyto-biophysical properties. • Cyto-biophysical properties could be markers for anti-fibrotic drugs screening. • Stiffness is a leading index among all biophysical markers. • SalB was

Anti-pulmonary fibrotic activity of salvianolic acid B was screened by a novel method based on the cyto-biophysical properties

Energy Technology Data Exchange (ETDEWEB)

Liu, Miao; Zheng, Mingjing; Xu, Hanying [Department of Pharmacology, Shenyang Pharmaceutical University, Shenyang, 110016 (China); Liu, Lianqing [Shenyang Institute of Automation China Academy of Sciences, Shenyang, 110016 (China); Li, Yanchun [Department of Pharmacology, Shenyang Pharmaceutical University, Shenyang, 110016 (China); Xiao, Wei [Jiangsu Kanion Pharmaceutical Co., Ltd., Nanjing, 222001 (China); Li, Jianchun, E-mail: lijianchun0317@sina.com.cn [Department of Pharmacology, Shenyang Pharmaceutical University, Shenyang, 110016 (China); Ma, Enlong, E-mail: enlong_ma2014@hotmail.com [Department of Pharmacology, Shenyang Pharmaceutical University, Shenyang, 110016 (China); Jiangsu Kanion Pharmaceutical Co., Ltd., Nanjing, 222001 (China)

2015-12-04

Various methods have been used to evaluate anti-fibrotic activity of drugs. However, most of them are complicated, labor-intensive and lack of efficiency. This study was intended to develop a rapid method for anti-fibrotic drugs screening based on biophysical properties. A549 cells in vitro were stimulated with transforming growth factor-β1 (TGF-β1), and fibrogenesis was confirmed by conventional immunological assays. Meanwhile, the alterations of cyto-biophysical properties including morphology, roughness and stiffness were measured utilizing atomic force microscopy (AFM). It was found that fibrogenesis was accompanied with changes of cellular biophysical properties. TGF-β1-stimulated A549 cells became remarkably longer, rougher and stiffer than the control. Then, the effect of N-acetyl-L-cysteine (NAC) as a positive drug on ameliorating fibrogenesis in TGF-β1-stimulated A549 cells was verified respectively by immunological and biophysical markers. The result of Principal Component Analysis showed that stiffness was a leading index among all biophysical markers during fibrogenesis. Salvianolic acid B (SalB), a natural anti-oxidant, was detected by AFM to protect TGF-β1-stimulated A549 cells against stiffening. Then, SalB treatment was provided in preventive mode on a rat model of bleomycin (BLM) -induced pulmonary fibrosis. The results showed that SalB treatment significantly ameliorated BLM-induced histological alterations, blocked collagen accumulations and reduced α-SMA expression in lung tissues. All these results revealed the anti-pulmonary fibrotic activity of SalB. Detection of cyto-biophysical properties were therefore recommended as a rapid method for anti-pulmonary fibrotic drugs screening. - Highlights: • Fibrogenesis was accompanied with the changes of cyto-biophysical properties. • Cyto-biophysical properties could be markers for anti-fibrotic drugs screening. • Stiffness is a leading index among all biophysical markers. • SalB was

Biophysical aspects of cancer - Electromagnetic mechanism

Czech Academy of Sciences Publication Activity Database

Pokorný, Jiří; Hašek, Jiří; Vaniš, Jan; Jelínek, František

2008-01-01

Roč. 46, č. 5 (2008), s. 310-321 ISSN 0019-5189 Institutional research plan: CEZ:AV0Z20670512; CEZ:AV0Z50200510 Keywords : Electromagnetic Fields * Biophysics * Cancer Subject RIV: JA - Electronics ; Optoelectronics, Electrical Engineering Impact factor: 0.599, year: 2008

Research Note: Full-waveform inversion of the unwrapped phase of a model

KAUST Repository

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.

The inverse Gamma process: A family of continuous stochastic models for describing state-dependent deterioration phenomena

International Nuclear Information System (INIS)

Guida, M.; Pulcini, G.

2013-01-01

This paper proposes the family of non-stationary inverse Gamma processes for modeling state-dependent deterioration processes with nonlinear trend. The proposed family of processes, which is based on the assumption that the “inverse” time process is Gamma, is mathematically more tractable than previously proposed state-dependent processes, because, unlike the previous models, the inverse Gamma process is a time-continuous and state-continuous model and does not require discretization of time and state. The conditional distribution of the deterioration growth over a generic time interval, the conditional distribution of the residual life and the residual reliability of the unit, given the current state, are provided. Point and interval estimation of the parameters which index the proposed process, as well as of several quantities of interest, are also discussed. Finally, the proposed model is applied to the wear process of the liners of some Diesel engines which was previously analyzed and proved to be a purely state-dependent process. The comparison of the inferential results obtained under the competitor models shows the ability of the Inverse Gamma process to adequately model the observed state-dependent wear process

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

Nonlinear inversion of resistivity sounding data for 1-D earth models using the Neighbourhood Algorithm

Science.gov (United States)

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.

A Parameterized Inversion Model for Soil Moisture and Biomass from Polarimetric Backscattering Coefficients

Science.gov (United States)

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

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...

Modeling and Inversion of Magnetic Anomalies Caused by Sediment–Basement Interface Using Three-Dimensional Cauchy-Type Integrals

DEFF Research Database (Denmark)

Cai, Hongzhu; Zhdanov, Michael

2014-01-01

This letter introduces a new method for the modeling and inversion of magnetic anomalies caused by crystalline basements. The method is based on the 3-D Cauchy-type integral representation of the magnetic field. Traditional methods use volume integrals over the domains occupied by anomalous...... is particularly significant in solving problems of the modeling and inversion of magnetic data for the depth to the basement. In this letter, a novel method is proposed, which only requires discretizing the magnetic contrast surface for modeling and inversion. We demonstrate the method using several synthetic...... susceptibility and on the prismatic representation of the volumes with an anomalous susceptibility distribution. Such discretization is computationally expensive, particularly in 3-D cases. The technique of Cauchy-type integrals makes it possible to represent the magnetic field as surface integrals, which...

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

Cortex Inspired Model for Inverse Kinematics Computation for a Humanoid Robotic Finger

Science.gov (United States)

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

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.

Probabilistic inversion for chicken processing lines

International Nuclear Information System (INIS)

Cooke, Roger M.; Nauta, Maarten; Havelaar, Arie H.; Fels, Ine van der

2006-01-01

We discuss an application of probabilistic inversion techniques to a model of campylobacter transmission in chicken processing lines. Such techniques are indicated when we wish to quantify a model which is new and perhaps unfamiliar to the expert community. In this case there are no measurements for estimating model parameters, and experts are typically unable to give a considered judgment. In such cases, experts are asked to quantify their uncertainty regarding variables which can be predicted by the model. The experts' distributions (after combination) are then pulled back onto the parameter space of the model, a process termed 'probabilistic inversion'. This study illustrates two such techniques, iterative proportional fitting (IPF) and PARmeter fitting for uncertain models (PARFUM). In addition, we illustrate how expert judgement on predicted observable quantities in combination with probabilistic inversion may be used for model validation and/or model criticism

Time-reversal and Bayesian inversion

Science.gov (United States)

Debski, Wojciech

2017-04-01

Probabilistic inversion technique is superior to the classical optimization-based approach in all but one aspects. It requires quite exhaustive computations which prohibit its use in huge size inverse problems like global seismic tomography or waveform inversion to name a few. The advantages of the approach are, however, so appealing that there is an ongoing continuous afford to make the large inverse task as mentioned above manageable with the probabilistic inverse approach. One of the perspective possibility to achieve this goal relays on exploring the internal symmetry of the seismological modeling problems in hand - a time reversal and reciprocity invariance. This two basic properties of the elastic wave equation when incorporating into the probabilistic inversion schemata open a new horizons for Bayesian inversion. In this presentation we discuss the time reversal symmetry property, its mathematical aspects and propose how to combine it with the probabilistic inverse theory into a compact, fast inversion algorithm. We illustrate the proposed idea with the newly developed location algorithm TRMLOC and discuss its efficiency when applied to mining induced seismic data.

The Earthquake‐Source Inversion Validation (SIV) Project

KAUST Repository

Mai, Paul Martin

2016-04-27

Finite-fault earthquake source inversions infer the (time-dependent) displacement on the rupture surface from geophysical data. The resulting earthquake source models document the complexity of the rupture process. However, multiple source models for the same earthquake, obtained by different research teams, often exhibit remarkable dissimilarities. To address the uncertainties in earthquake-source inversion methods and to understand strengths and weaknesses of the various approaches used, the Source Inversion Validation (SIV) project conducts a set of forward-modeling exercises and inversion benchmarks. In this article, we describe the SIV strategy, the initial benchmarks, and current SIV results. Furthermore, we apply statistical tools for quantitative waveform comparison and for investigating source-model (dis)similarities that enable us to rank the solutions, and to identify particularly promising source inversion approaches. All SIV exercises (with related data and descriptions) and statistical comparison tools are available via an online collaboration platform, and we encourage source modelers to use the SIV benchmarks for developing and testing new methods. We envision that the SIV efforts will lead to new developments for tackling the earthquake-source imaging problem.

The Earthquake‐Source Inversion Validation (SIV) Project

KAUST Repository

Mai, Paul Martin; Schorlemmer, Danijel; Page, Morgan; Ampuero, Jean‐Paul; Asano, Kimiyuki; Causse, Mathieu; Custodio, Susana; Fan, Wenyuan; Festa, Gaetano; Galis, Martin; Gallovic, Frantisek; Imperatori, Walter; Kä ser, Martin; Malytskyy, Dmytro; Okuwaki, Ryo; Pollitz, Fred; Passone, Luca; Razafindrakoto, Hoby; Sekiguchi, Haruko; Song, Seok Goo; Somala, Surendra N.; Thingbaijam, Kiran Kumar; Twardzik, Cedric; van Driel, Martin; Vyas, Jagdish Chandra; Wang, Rongjiang; Yagi, Yuji; Zielke, Olaf

2016-01-01

Finite-fault earthquake source inversions infer the (time-dependent) displacement on the rupture surface from geophysical data. The resulting earthquake source models document the complexity of the rupture process. However, multiple source models for the same earthquake, obtained by different research teams, often exhibit remarkable dissimilarities. To address the uncertainties in earthquake-source inversion methods and to understand strengths and weaknesses of the various approaches used, the Source Inversion Validation (SIV) project conducts a set of forward-modeling exercises and inversion benchmarks. In this article, we describe the SIV strategy, the initial benchmarks, and current SIV results. Furthermore, we apply statistical tools for quantitative waveform comparison and for investigating source-model (dis)similarities that enable us to rank the solutions, and to identify particularly promising source inversion approaches. All SIV exercises (with related data and descriptions) and statistical comparison tools are available via an online collaboration platform, and we encourage source modelers to use the SIV benchmarks for developing and testing new methods. We envision that the SIV efforts will lead to new developments for tackling the earthquake-source imaging problem.

The Earthquake‐Source Inversion Validation (SIV) Project

Science.gov (United States)

Mai, P. Martin; Schorlemmer, Danijel; Page, Morgan T.; Ampuero, Jean-Paul; Asano, Kimiyuki; Causse, Mathieu; Custodio, Susana; Fan, Wenyuan; Festa, Gaetano; Galis, Martin; Gallovic, Frantisek; Imperatori, Walter; Käser, Martin; Malytskyy, Dmytro; Okuwaki, Ryo; Pollitz, Fred; Passone, Luca; Razafindrakoto, Hoby N. T.; Sekiguchi, Haruko; Song, Seok Goo; Somala, Surendra N.; Thingbaijam, Kiran K. S.; Twardzik, Cedric; van Driel, Martin; Vyas, Jagdish C.; Wang, Rongjiang; Yagi, Yuji; Zielke, Olaf

2016-01-01

Finite‐fault earthquake source inversions infer the (time‐dependent) displacement on the rupture surface from geophysical data. The resulting earthquake source models document the complexity of the rupture process. However, multiple source models for the same earthquake, obtained by different research teams, often exhibit remarkable dissimilarities. To address the uncertainties in earthquake‐source inversion methods and to understand strengths and weaknesses of the various approaches used, the Source Inversion Validation (SIV) project conducts a set of forward‐modeling exercises and inversion benchmarks. In this article, we describe the SIV strategy, the initial benchmarks, and current SIV results. Furthermore, we apply statistical tools for quantitative waveform comparison and for investigating source‐model (dis)similarities that enable us to rank the solutions, and to identify particularly promising source inversion approaches. All SIV exercises (with related data and descriptions) and statistical comparison tools are available via an online collaboration platform, and we encourage source modelers to use the SIV benchmarks for developing and testing new methods. We envision that the SIV efforts will lead to new developments for tackling the earthquake‐source imaging problem.

Biophysical basis for the geometry of conical stromatolites.

Science.gov (United States)

Petroff, Alexander P; Sim, Min Sub; Maslov, Andrey; Krupenin, Mikhail; Rothman, Daniel H; Bosak, Tanja

2010-06-01

Stromatolites may be Earth's oldest macroscopic fossils; however, it remains controversial what, if any, biological processes are recorded in their morphology. Although the biological interpretation of many stromatolite morphologies is confounded by the influence of sedimentation, conical stromatolites form in the absence of sedimentation and are, therefore, considered to be the most robust records of biophysical processes. A qualitative similarity between conical stromatolites and some modern microbial mats suggests a photosynthetic origin for ancient stromatolites. To better understand and interpret ancient fossils, we seek a quantitative relationship between the geometry of conical stromatolites and the biophysical processes that control their growth. We note that all modern conical stromatolites and many that formed in the last 2.8 billion years display a characteristic centimeter-scale spacing between neighboring structures. To understand this prominent-but hitherto uninterpreted-organization, we consider the role of diffusion in mediating competition between stromatolites. Having confirmed this model through laboratory experiments and field observation, we find that organization of a field of stromatolites is set by a diffusive time scale over which individual structures compete for nutrients, thus linking form to physiology. The centimeter-scale spacing between modern and ancient stromatolites corresponds to a rhythmically fluctuating metabolism with a period of approximately 20 hr. The correspondence between the observed spacing and the day length provides quantitative support for the photosynthetic origin of conical stromatolites throughout geologic time.

Biophysics of filament length regulation by molecular motors

International Nuclear Information System (INIS)

Kuan, Hui-Shun; Betterton, M D

2013-01-01

Regulating physical size is an essential problem that biological organisms must solve from the subcellular to the organismal scales, but it is not well understood what physical principles and mechanisms organisms use to sense and regulate their size. Any biophysical size-regulation scheme operates in a noisy environment and must be robust to other cellular dynamics and fluctuations. This work develops theory of filament length regulation inspired by recent experiments on kinesin-8 motor proteins, which move with directional bias on microtubule filaments and alter microtubule dynamics. Purified kinesin-8 motors can depolymerize chemically-stabilized microtubules. In the length-dependent depolymerization model, the rate of depolymerization tends to increase with filament length, because long filaments accumulate more motors at their tips and therefore shorten more quickly. When balanced with a constant filament growth rate, this mechanism can lead to a fixed polymer length. However, the mechanism by which kinesin-8 motors affect the length of dynamic microtubules in cells is less clear. We study the more biologically realistic problem of microtubule dynamic instability modulated by a motor-dependent increase in the filament catastrophe frequency. This leads to a significant decrease in the mean filament length and a narrowing of the filament length distribution. The results improve our understanding of the biophysics of length regulation in cells. (paper)

Evaluation of inverse modeling techniques for pinpointing water leakages at building constructions

NARCIS (Netherlands)

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 method for climate and vegetation reconstruction through the inversion of a dynamic vegetation model

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.)

The uniqueness of the solution of cone-like inversion models for halo CMEs

Science.gov (United States)

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.

Eigenvalue based inverse model of beam for structural modification and diagnostics: examples of using

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.

Thermal measurements and inverse techniques

CERN Document Server

Orlande, Helcio RB; Maillet, Denis; Cotta, Renato M

2011-01-01

With its uncommon presentation of instructional material regarding mathematical modeling, measurements, and solution of inverse problems, Thermal Measurements and Inverse Techniques is a one-stop reference for those dealing with various aspects of heat transfer. Progress in mathematical modeling of complex industrial and environmental systems has enabled numerical simulations of most physical phenomena. In addition, recent advances in thermal instrumentation and heat transfer modeling have improved experimental procedures and indirect measurements for heat transfer research of both natural phe

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)

Inversion modeling of the natural state and production history of Mutnovsky geothermal field in 1986-2006

Directory of Open Access Journals (Sweden)

А. В. Кирюхин

2017-04-01

Full Text Available Numerical 3D model of Mutnovsky geothermal field (Dachny springs, which consist of 517 elements and partially takes into account double porosity, was developed in 1992-1993 using computer program TOUGH2. Calibration of the model was based on data from test yield of the wells and initial distribution of temperature and pressure in the reservoir. This model was used for techno-economic justification of power plant construction (Mutnovskaya GeoES, 2002. The model was recreated in the program PetraSim v.5.2, the calibration was carried out using additional data on production history before year 2006 and inversion iTOUGH2-EOS1 modeling. Comparison of reservoir parameters, estimated using inversion modeling, with previous parameter estimations (given in brackets showed the following: upflow rate of heat-transfer agent in natural conditions 80.5 (54.1 kg/s, heat flux enthalpy 1430 (1390 kJ/kg, reservoir permeability 27∙10–15-616∙10–15 (3∙10–15-90∙10–15 m2. Inversion modeling was also used to estimate reinjection rates, inflow of meteoric water in the central part of geothermal field and compressibility of reservoir rocks.

Atmospheric dispersion and inverse modelling for the reconstruction of accidental sources of pollutants

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

The continental source of glyoxal estimated by the synergistic use of spaceborne measurements and inverse modelling

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

Sensitivity of Global Methane Bayesian Inversion to Surface Observation Data Sets and Chemical-Transport Model Resolution

Science.gov (United States)

Lew, E. J.; Butenhoff, C. L.; Karmakar, S.; Rice, A. L.; Khalil, A. K.

2017-12-01

Methane is the second most important greenhouse gas after carbon dioxide. In efforts to control emissions, a careful examination of the methane budget and source strengths is required. To determine methane surface fluxes, Bayesian methods are often used to provide top-down constraints. Inverse modeling derives unknown fluxes using observed methane concentrations, a chemical transport model (CTM) and prior information. The Bayesian inversion reduces prior flux uncertainties by exploiting information content in the data. While the Bayesian formalism produces internal error estimates of source fluxes, systematic or external errors that arise from user choices in the inversion scheme are often much larger. Here we examine model sensitivity and uncertainty of our inversion under different observation data sets and CTM grid resolution. We compare posterior surface fluxes using the data product GLOBALVIEW-CH4 against the event-level molar mixing ratio data available from NOAA. GLOBALVIEW-CH4 is a collection of CH4 concentration estimates from 221 sites, collected by 12 laboratories, that have been interpolated and extracted to provide weekly records from 1984-2008. Differently, the event-level NOAA data records methane mixing ratios field measurements from 102 sites, containing sampling frequency irregularities and gaps in time. Furthermore, the sampling platform types used by the data sets may influence the posterior flux estimates, namely fixed surface, tower, ship and aircraft sites. To explore the sensitivity of the posterior surface fluxes to the observation network geometry, inversions composed of all sites, only aircraft, only ship, only tower and only fixed surface sites, are performed and compared. Also, we investigate the sensitivity of the error reduction associated with the resolution of the GEOS-Chem simulation (4°×5° vs 2°×2.5°) used to calculate the response matrix. Using a higher resolution grid decreased the model-data error at most sites, thereby

Dynamic Inversion for Hydrological Process Monitoring with Electrical Resistance Tomography Under Model Uncertainty

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.

Stochastic modeling of the Earth's magnetic field: Inversion for covariances over the observatory era

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....

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.

A mathematical approach to protein biophysics

CERN Document Server

Scott, L Ridgway

2017-01-01

This book explores quantitative aspects of protein biophysics and attempts to delineate certain rules of molecular behavior that make atomic scale objects behave in a digital way.  This book will help readers to understand how certain biological systems involving proteins function as digital information systems despite the fact that underlying processes are analog in nature. The in-depth explanation of proteins from a quantitative point of view and the variety of level of exercises (including physical experiments) at the end of each chapter will appeal to graduate and senior undergraduate students in mathematics, computer science, mechanical engineering, and physics, wanting to learn about the biophysics of proteins.  L. Ridgway Scott has been Professor of Computer Science and of Mathematics at the University of Chicago since 1998, and the Louis Block Professor since 2001.  He obtained a B.S. degree (Magna Cum Laude) from Tulane University in 1969 and a PhD degree in Mathematics from the Massachusetts Ins...

Research Institute for Medical Biophysics

International Nuclear Information System (INIS)

Wynchank, S.

1989-01-01

The effects of ionising and non-ionising radiation on rodent tumours and normal tissue were studied in terms of cellular repair and the relevant biochemical and biophysical changes following radiation. Rodent tumours investigated in vivo were the CaNT adenocarcinoma and a chemically induced transplantable rhabdomyosarcoma. Radiations used were 100KVp of X-Rays, neutron beams, various magnetic fields, and microwave radiation of 2450MHz. The biochemical parameters measured were, inter alia, levels of adenosine-5'-triphoshate (ATP) and the specific activity of hexokinase (HK). Metabolic changes in ATP levels and the activity of HK were observed in tumour and normal tissues following ionising and non-ionising radiation in normoxia and hypoxia. The observation that the effect of radiation and chemotherapeutic treatment of some tumours may be size dependent can possibly now be explained by the variation of ATP content with tumour size. The enhanced tumour HK specific activity implies increased metabolism, possibly a consequence of cellular requirements to maintain homeostasis during repair processes. Other research projects of the Research Institute for Medical Biophysics involved, inter alia, gastroesophageal scintigraphies to evaluate the results of new forms of therapy. 1 ill

A hybrid finite-difference and integral-equation method for modeling and inversion of marine controlled-source electromagnetic data

DEFF Research Database (Denmark)

Yoon, Daeung; Zhdanov, Michael; Mattsson, Johan

2016-01-01

One of the major problems in the modeling and inversion of marine controlled-source electromagnetic (CSEM) 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...... should be powerful and fast enough to be suitable for repeated use in hundreds of iterations of the inversion and for multiple transmitter/receiver positions. To this end, we have developed a novel 3D modeling and inversion approach, which combines the advantages of the finite-difference (FD......) and integral-equation (IE) methods. In the framework of this approach, we have solved Maxwell’s equations for anomalous electric fields using the FD approximation on a staggered grid. Once the unknown electric fields in the computation domain of the FD method are computed, the electric and magnetic fields...

Optimization for nonlinear inverse problem

International Nuclear Information System (INIS)

Boyadzhiev, G.; Brandmayr, E.; Pinat, T.; Panza, G.F.

2007-06-01

The nonlinear inversion of geophysical data in general does not yield a unique solution, but a single model, representing the investigated field, is preferred for an easy geological interpretation of the observations. The analyzed region is constituted by a number of sub-regions where the multi-valued nonlinear inversion is applied, which leads to a multi-valued solution. Therefore, combining the values of the solution in each sub-region, many acceptable models are obtained for the entire region and this complicates the geological interpretation of geophysical investigations. In this paper are presented new methodologies, capable to select one model, among all acceptable ones, that satisfies different criteria of smoothness in the explored space of solutions. In this work we focus on the non-linear inversion of surface waves dispersion curves, which gives structural models of shear-wave velocity versus depth, but the basic concepts have a general validity. (author)

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

Biophysics of NASA radiation quality factors

International Nuclear Information System (INIS)

Cucinotta, Francis A.

2015-01-01

NASA has implemented new radiation quality factors (QFs) for projecting cancer risks from space radiation exposures to astronauts. The NASA QFs are based on particle track structure concepts with parameters derived from available radiobiology data, and NASA introduces distinct QFs for solid cancer and leukaemia risk estimates. The NASA model was reviewed by the US National Research Council and approved for use by NASA for risk assessment for International Space Station missions and trade studies of future exploration missions to Mars and other destinations. A key feature of the NASA QFs is to represent the uncertainty in the QF assessments and evaluate the importance of the QF uncertainty to overall uncertainties in cancer risk projections. In this article, the biophysical basis for the probability distribution functions representing QF uncertainties was reviewed, and approaches needed to reduce uncertainties were discussed. (author)

Biophysics of Hair Cell Sensory Systems

NARCIS (Netherlands)

Duifhuis, Hendrikus; Horst, Johannes; van Dijk, Pim; van Netten, Sietse

1993-01-01

The last decade revealed to auditory researchers that hair cells can not only detect and process mechanical energy, but are also able to produce it. Thanks to the active hair cell, ears can produce otoacoustic emissions. This book gives the newest insights into the biophysics and physiology of

Decomposing Large Inverse Problems with an Augmented Lagrangian Approach: Application to Joint Inversion of Body-Wave Travel Times and Surface-Wave Dispersion Measurements

Science.gov (United States)

Reiter, D. T.; Rodi, W. L.

2015-12-01

Constructing 3D Earth models through the joint inversion of large geophysical data sets presents numerous theoretical and practical challenges, especially when diverse types of data and model parameters are involved. Among the challenges are the computational complexity associated with large data and model vectors and the need to unify differing model parameterizations, forward modeling methods and regularization schemes within a common inversion framework. The challenges can be addressed in part by decomposing the inverse problem into smaller, simpler inverse problems that can be solved separately, providing one knows how to merge the separate inversion results into an optimal solution of the full problem. We have formulated an approach to the decomposition of large inverse problems based on the augmented Lagrangian technique from optimization theory. As commonly done, we define a solution to the full inverse problem as the Earth model minimizing an objective function motivated, for example, by a Bayesian inference formulation. Our decomposition approach recasts the minimization problem equivalently as the minimization of component objective functions, corresponding to specified data subsets, subject to the constraints that the minimizing models be equal. A standard optimization algorithm solves the resulting constrained minimization problems by alternating between the separate solution of the component problems and the updating of Lagrange multipliers that serve to steer the individual solution models toward a common model solving the full problem. We are applying our inversion method to the reconstruction of the·crust and upper-mantle seismic velocity structure across Eurasia.· Data for the inversion comprise a large set of P and S body-wave travel times·and fundamental and first-higher mode Rayleigh-wave group velocities.

Efficient scattering-angle enrichment for a nonlinear inversion of the background and perturbations components of a velocity model

KAUST Repository

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

Approximate 2D inversion of airborne TEM data

DEFF Research Database (Denmark)

Christensen, N.B.; Wolfgram, Peter

2006-01-01

We propose an approximate two-dimensional inversion procedure for transient electromagnetic data. The method is a two-stage procedure, where data are first inverted with 1D multi-layer models. The 1D model section is then considered as data for the next inversion stage that produces the 2D model...... section. For moving platform data there is translational invariance and the second part of the inversion becomes a deconvolution. The convolution kernels are computed by perturbing one model element in an otherwise homogeneous 2D section and calculating full nonlinear responses. These responses...... are then inverted with 1D models to produce a 1D model section. This section is the convolution kernel for the deconvolution. Within its limitations, the approximate 2D inversion performs well. Theoretical modeling shows that it delivers model sections that are a definite improvement over 1D model sections...

Using biophysical models to manage nitrogen pollution from agricultural sources: Utopic or realistic approach for non-scientist users? Case study of a drinking water catchment area in Lorraine, France.

Science.gov (United States)

Bernard, Pierre-Yves; Benoît, Marc; Roger-Estrade, Jean; Plantureux, Sylvain

2016-12-01

The objectives of this comparison of two biophysical models of nitrogen losses were to evaluate first whether results were similar and second whether both were equally practical for use by non-scientist users. Results were obtained with the crop model STICS and the environmental model AGRIFLUX based on nitrogen loss simulations across a small groundwater catchment area (<1 km(2)) located in the Lorraine region in France. Both models simulate the influences of leaching and cropping systems on nitrogen losses in a relevant manner. The authors conclude that limiting the simulations to areas where soils with a greater risk of leaching cover a significant spatial extent would likely yield acceptable results because those soils have more predictable leaching of nitrogen. In addition, the choice of an environmental model such as AGRIFLUX which requires fewer parameters and input variables seems more user-friendly for agro-environmental assessment. The authors then discuss additional challenges for non-scientists such as lack of parameter optimization, which is essential to accurately assessing nitrogen fluxes and indirectly not to limit the diversity of uses of simulated results. Despite current restrictions, with some improvement, biophysical models could become useful environmental assessment tools for non-scientists. Copyright © 2016 Elsevier Ltd. All rights reserved.

Large-scale inverse and forward modeling of adaptive resonance in the tinnitus decompensation.

Science.gov (United States)

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.

Unwrapped phase inversion for near surface seismic data

KAUST Repository

Choi, Yun Seok

2012-11-04

The Phase-wrapping is one of the main obstacles of waveform inversion. We use an inversion algorithm based on the instantaneous-traveltime that overcomes the phase-wrapping problem. With a high damping factor, the frequency-dependent instantaneous-traveltime inversion provides the stability of refraction tomography, with higher resolution results, and no arrival picking involved. We apply the instantaneous-traveltime inversion to the synthetic data generated by the elastic time-domain modeling. The synthetic data is a representative of the near surface seismic data. Although the inversion algorithm is based on the acoustic wave equation, the numerical examples show that the instantaneous-traveltime inversion generates a convergent velocity model, very similar to what we see from traveltime tomography.

Frequency domain, waveform inversion of laboratory crosswell radar data

Science.gov (United States)

Ellefsen, Karl J.; Mazzella, Aldo T.; Horton, Robert J.; McKenna, Jason R.

2010-01-01

A new waveform inversion for crosswell radar is formulated in the frequency-domain for a 2.5D model. The inversion simulates radar waves using the vector Helmholtz equation for electromagnetic waves. The objective function is minimized using a backpropagation method suitable for a 2.5D model. The inversion is tested by processing crosswell radar data collected in a laboratory tank. The estimated model is consistent with the known electromagnetic properties of the tank. The formulation for the 2.5D model can be extended to inversions of acoustic and elastic data.

Testing earthquake source inversion methodologies

KAUST Repository

Page, Morgan T.

2011-01-01

Source Inversion Validation Workshop; Palm Springs, California, 11-12 September 2010; Nowadays earthquake source inversions are routinely performed after large earthquakes and represent a key connection between recorded seismic and geodetic data and the complex rupture process at depth. The resulting earthquake source models quantify the spatiotemporal evolution of ruptures. They are also used to provide a rapid assessment of the severity of an earthquake and to estimate losses. However, because of uncertainties in the data, assumed fault geometry and velocity structure, and chosen rupture parameterization, it is not clear which features of these source models are robust. Improved understanding of the uncertainty and reliability of earthquake source inversions will allow the scientific community to use the robust features of kinematic inversions to more thoroughly investigate the complexity of the rupture process and to better constrain other earthquakerelated computations, such as ground motion simulations and static stress change calculations.

Replacing natural wetlands with stormwater management facilities: Biophysical and perceived social values.

Science.gov (United States)

Rooney, R C; Foote, L; Krogman, N; Pattison, J K; Wilson, M J; Bayley, S E

2015-04-15

Urban expansion replaces wetlands of natural origin with artificial stormwater management facilities. The literature suggests that efforts to mimic natural wetlands in the design of stormwater facilities can expand the provision of ecosystem services. Policy developments seek to capitalize on these improvements, encouraging developers to build stormwater wetlands in place of stormwater ponds; however, few have compared the biophysical values and social perceptions of these created wetlands to those of the natural wetlands they are replacing. We compared four types of wetlands: natural references sites, natural wetlands impacted by agriculture, created stormwater wetlands, and created stormwater ponds. We anticipated that they would exhibit a gradient in biodiversity, ecological integrity, chemical and hydrologic stress. We further anticipated that perceived values would mirror measured biophysical values. We found higher biophysical values associated with wetlands of natural origin (both reference and agriculturally impacted). The biophysical values of stormwater wetlands and stormwater ponds were lower and indistinguishable from one another. The perceived wetland values assessed by the public differed from the observed biophysical values. This has important policy implications, as the public are not likely to perceive the loss of values associated with the replacement of natural wetlands with created stormwater management facilities. We conclude that 1) agriculturally impacted wetlands provide biophysical values equivalent to those of natural wetlands, meaning that land use alone is not a great predictor of wetland value; 2) stormwater wetlands are not a substantive improvement over stormwater ponds, relative to wetlands of natural origin; 3) stormwater wetlands are poor mimics of natural wetlands, likely due to fundamental distinctions in terms of basin morphology, temporal variation in hydrology, ground water connectivity, and landscape position; 4) these

Inverse analyses of effective diffusion parameters relevant for a two-phase moisture model of cementitious materials

DEFF Research Database (Denmark)

Addassi, Mouadh; Johannesson, Björn; Wadsö, Lars

2018-01-01

Here we present an inverse analyses approach to determining the two-phase moisture transport properties relevant to concrete durability modeling. The purposed moisture transport model was based on a continuum approach with two truly separate equations for the liquid and gas phase being connected...... test, and, (iv) capillary suction test. Mass change over time, as obtained from the drying test, the two different cup test intervals and the capillary suction test, was used to obtain the effective diffusion parameters using the proposed inverse analyses approach. The moisture properties obtained...

A Nonlinear Dynamic Inversion Predictor-Based Model Reference Adaptive Controller for a Generic Transport Model

Science.gov (United States)

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).

Conjugate heat transfer analysis of an energy conversion device with an updated numerical model obtained through inverse identification

International Nuclear Information System (INIS)

Hey, Jonathan; Malloy, Adam C.; Martinez-Botas, Ricardo; Lamperth, Michael

2015-01-01

Highlights: • Conjugate heat transfer analysis of an electric machine. • Inverse identification method for estimating the model parameters. • Experimentally determined thermal properties and electromagnetic losses. • Coupling of inverse identification method with a numerical model. • Improved modeling accuracy through introduction of interface material. - Abstract: Energy conversion devices undergo thermal loading during their operation as a result of inefficiencies in the energy conversion process. This will eventually lead to degradation and possible failure of the device if the heat generated is not properly managed. The ability to accurately predict the thermal behavior of such a device during the initial developmental stage is an important requirement. However, accurate predictions of critical temperature is challenging due to the variation of heat transfer parameters from one device to another. The ability to determine the model parameters is key to accurately representing the heat transfer in such a device. This paper presents the use of an inverse identification technique to estimate the model parameters of an energy conversion device designed for vehicular applications. To simulate the imperfect contact and the presence of insulating materials in the permanent magnet electric machine, thin material are introduced at the component interface of the numerical model. The proposed inverse identification method is used to estimate the equivalent thermal conductance of the thin material. In addition, the electromagnetic losses generated in the permanent magnet is also derived indirectly from the temperature measurement using the same method. With the thermal properties and input parameters of the numerical model obtained from the inverse identification method, the critical temperature of the device can be predicted more accurately. The deviation between the maximum measured and predicted winding temperature is less than 2.4%

Polish Academy of Sciences Institute of Biochemistry and Biophysics research report 1994-1995

Energy Technology Data Exchange (ETDEWEB)

NONE

1997-12-31

Scientific interests of Institute of Biochemistry and Biophysics Polish Academy of Sciences are focused on DNA replication and repair, gene expression, gene sequencing and molecular biophysics. The work reviews research projects of the Institute in 1994-1995.

Polish Academy of Sciences Institute of Biochemistry and Biophysics research report 1994-1995

International Nuclear Information System (INIS)

1996-01-01

Scientific interests of Institute of Biochemistry and Biophysics Polish Academy of Sciences are focused on DNA replication and repair, gene expression, gene sequencing and molecular biophysics. The work reviews research projects of the Institute in 1994-1995

Polish Academy of Sciences Institute of Biochemistry and Biophysics research report 1994-1995

Energy Technology Data Exchange (ETDEWEB)

NONE

1996-12-31

Scientific interests of Institute of Biochemistry and Biophysics Polish Academy of Sciences are focused on DNA replication and repair, gene expression, gene sequencing and molecular biophysics. The work reviews research projects of the Institute in 1994-1995.

Surface-enhanced Raman scattering: a new optical probe in molecular biophysics and biomedicine

DEFF Research Database (Denmark)

Kneipp, J.; Wittig, B.; Bohr, Henrik

2010-01-01

Sensitive and detailed molecular structural information plays an increasing role in molecular biophysics and molecular medicine. Therefore, vibrational spectroscopic techniques, such as Raman scattering, which provide high structural information content are of growing interest in biophysical and ...

The role of bio-physical cohesive substrates on sediment winnowing and bedform development

Science.gov (United States)

Ye, Leiping; Parsons, Daniel; Manning, Andrew

2017-04-01

Existing sediment transport and bedform size predictions for natural open-channel flows in many environments are seriously impeded by a lack of process-based knowledge concerning the dynamics of complex bed sediment mixtures comprising cohesionless sand and biologically-active cohesive muds. A series of flume experiments (14 experimental runs) with different substrate mixtures of sand-clay-EPS (Extracellular Polymeric Substance) are combined with a detailed estuarine field survey (Dee estuary, NW England) to investigate the development of bedform morphologies and characteristics of suspended sediment over bio-physical cohesive substrates. The experimental results indicate that winnowing and sediment sorting can occur pervasively in bio-physical cohesive sediment - flow systems. Importantly however, the evolution of the bed and bedform dynamics, and hence turbulence production, is significantly reduced as bed substrate cohesivity increases. The estuarine subtidal zone survey also revealed that the bio-physical cohesion provided by both the clay and microorganism fractions in the bed plays a significant role in controlling the interactions between bed substrate and sediment suspension, deposition and bedform generation. The work will be presented here concludes by outlining the need to extend and revisit the effects of cohesivity in morphodynamic systems and the sets of parameters presently used in numerical modelling, particularly in the context of the impact of climate change on estuarine and coastal systems.

Unraveling biophysical interactions of radiation pneumonitis in non-small-cell lung cancer via Bayesian network analysis.

Science.gov (United States)

Luo, Yi; El Naqa, Issam; McShan, Daniel L; Ray, Dipankar; Lohse, Ines; Matuszak, Martha M; Owen, Dawn; Jolly, Shruti; Lawrence, Theodore S; Kong, Feng-Ming Spring; Ten Haken, Randall K

2017-04-01

In non-small-cell lung cancer radiotherapy, radiation pneumonitis≥grade 2 (RP2) depends on patients' dosimetric, clinical, biological and genomic characteristics. We developed a Bayesian network (BN) approach to explore its potential for interpreting biophysical signaling pathways influencing RP2 from a heterogeneous dataset including single nucleotide polymorphisms, micro RNAs, cytokines, clinical data, and radiation treatment plans before and during the course of radiotherapy. Model building utilized 79 patients (21 with RP2) with complete data, and model testing used 50 additional patients with incomplete data. A developed large-scale Markov blanket approach selected relevant predictors. Resampling by k-fold cross-validation determined the optimal BN structure. Area under the receiver-operating characteristics curve (AUC) measured performance. Pre- and during-treatment BNs identified biophysical signaling pathways from the patients' relevant variables to RP2 risk. Internal cross-validation for the pre-BN yielded an AUC=0.82 which improved to 0.87 by incorporating during treatment changes. In the testing dataset, the pre- and during AUCs were 0.78 and 0.82, respectively. Our developed BN approach successfully handled a high number of heterogeneous variables in a small dataset, demonstrating potential for unraveling relevant biophysical features that could enhance prediction of RP2, although the current observations would require further independent validation. Copyright © 2017 Elsevier B.V. All rights reserved.

Anisotropic wave-equation traveltime and waveform inversion

KAUST Repository

Feng, Shihang

2016-09-06

The wave-equation traveltime and waveform inversion (WTW) methodology is developed to invert for anisotropic parameters in a vertical transverse isotropic (VTI) meidum. The simultaneous inversion of anisotropic parameters v0, ε and δ is initially performed using the wave-equation traveltime inversion (WT) method. The WT tomograms are then used as starting background models for VTI full waveform inversion. Preliminary numerical tests on synthetic data demonstrate the feasibility of this method for multi-parameter inversion.

Sensitivity in reflectance attributed to phytoplankton cell size: forward and inverse modelling approaches

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...

TransCom N2O model inter-comparison, Part II : Atmospheric inversion estimates of N2O emissions

NARCIS (Netherlands)

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

Centered Differential Waveform Inversion with Minimum Support Regularization

KAUST Repository

Kazei, Vladimir; Alkhalifah, Tariq Ali

2017-01-01

Time-lapse full-waveform inversion has two major challenges. The first one is the reconstruction of a reference model (baseline model for most of approaches). The second is inversion for the time-lapse changes in the parameters. Common model

[Biophysical methods in assessment of the skin microcirculation system].

Science.gov (United States)

Dynnik, O B; Mostovoĭ, S E; Berezovskiĭ, V A

2008-01-01

In this work has been analyzed the potential of biophysics methods in estimations of the microcirculatory system. Capillaroresistometry, Computer capillaroscopy and Laser Doppler Flowmetry can to detect of the endothelial dysfunction in the patients with chronic hepatic diseases. This instrumentals biophysics methods may be used in clinical investigations for screening early pathological conditions with dysfunction of the microcirculatory system. The methods Laser Doppler Flowmetry is important for investigations the patients with others diseases and for dynamical monitoring by quality of the treatment. The purpose of these methods an objective estimation of disorders in the microcirculatory system.

Applying a probabilistic seismic-petrophysical inversion and two different rock-physics models for reservoir characterization in offshore Nile Delta

Science.gov (United States)

Aleardi, Mattia

2018-01-01

We apply a two-step probabilistic seismic-petrophysical inversion for the characterization of a clastic, gas-saturated, reservoir located in offshore Nile Delta. In particular, we discuss and compare the results obtained when two different rock-physics models (RPMs) are employed in the inversion. The first RPM is an empirical, linear model directly derived from the available well log data by means of an optimization procedure. The second RPM is a theoretical, non-linear model based on the Hertz-Mindlin contact theory. The first step of the inversion procedure is a Bayesian linearized amplitude versus angle (AVA) inversion in which the elastic properties, and the associated uncertainties, are inferred from pre-stack seismic data. The estimated elastic properties constitute the input to the second step that is a probabilistic petrophysical inversion in which we account for the noise contaminating the recorded seismic data and the uncertainties affecting both the derived rock-physics models and the estimated elastic parameters. In particular, a Gaussian mixture a-priori distribution is used to properly take into account the facies-dependent behavior of petrophysical properties, related to the different fluid and rock properties of the different litho-fluid classes. In the synthetic and in the field data tests, the very minor differences between the results obtained by employing the two RPMs, and the good match between the estimated properties and well log information, confirm the applicability of the inversion approach and the suitability of the two different RPMs for reservoir characterization in the investigated area.

Exploring the Subtleties of Inverse Probability Weighting and Marginal Structural Models.

Science.gov (United States)

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.

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...

IRaPPA: information retrieval based integration of biophysical models for protein assembly selection.

Science.gov (United States)

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

Wave-equation dispersion inversion

KAUST Repository

Li, Jing

2016-12-08

We present the theory for wave-equation inversion of dispersion curves, where the misfit function is the sum of the squared differences between the wavenumbers along the predicted and observed dispersion curves. The dispersion curves are obtained from Rayleigh waves recorded by vertical-component geophones. Similar to wave-equation traveltime tomography, the complicated surface wave arrivals in traces are skeletonized as simpler data, namely the picked dispersion curves in the phase-velocity and frequency domains. Solutions to the elastic wave equation and an iterative optimization method are then used to invert these curves for 2-D or 3-D S-wave velocity models. This procedure, denoted as wave-equation dispersion inversion (WD), does not require the assumption of a layered model and is significantly less prone to the cycle-skipping problems of full waveform inversion. The synthetic and field data examples demonstrate that WD can approximately reconstruct the S-wave velocity distributions in laterally heterogeneous media if the dispersion curves can be identified and picked. The WD method is easily extended to anisotropic data and the inversion of dispersion curves associated with Love waves.