Bayesian aerosol retrieval algorithm for MODIS AOD retrieval over land
Lipponen, Antti; Mielonen, Tero; Pitkänen, Mikko R. A.; Levy, Robert C.; Sawyer, Virginia R.; Romakkaniemi, Sami; Kolehmainen, Ville; Arola, Antti
2018-03-01
We have developed a Bayesian aerosol retrieval (BAR) algorithm for the retrieval of aerosol optical depth (AOD) over land from the Moderate Resolution Imaging Spectroradiometer (MODIS). In the BAR algorithm, we simultaneously retrieve all dark land pixels in a granule, utilize spatial correlation models for the unknown aerosol parameters, use a statistical prior model for the surface reflectance, and take into account the uncertainties due to fixed aerosol models. The retrieved parameters are total AOD at 0.55 µm, fine-mode fraction (FMF), and surface reflectances at four different wavelengths (0.47, 0.55, 0.64, and 2.1 µm). The accuracy of the new algorithm is evaluated by comparing the AOD retrievals to Aerosol Robotic Network (AERONET) AOD. The results show that the BAR significantly improves the accuracy of AOD retrievals over the operational Dark Target (DT) algorithm. A reduction of about 29 % in the AOD root mean square error and decrease of about 80 % in the median bias of AOD were found globally when the BAR was used instead of the DT algorithm. Furthermore, the fraction of AOD retrievals inside the ±(0.05+15 %) expected error envelope increased from 55 to 76 %. In addition to retrieving the values of AOD, FMF, and surface reflectance, the BAR also gives pixel-level posterior uncertainty estimates for the retrieved parameters. The BAR algorithm always results in physical, non-negative AOD values, and the average computation time for a single granule was less than a minute on a modern personal computer.
A Bayesian Analysis of the Radioactive Releases of Fukushima
DEFF Research Database (Denmark)
Tomioka, Ryota; Mørup, Morten
2012-01-01
the types of nuclides and their levels of concentration from the recorded mixture of radiations to take necessary measures. We presently formulate a Bayesian generative model for the data available on radioactive releases from the Fukushima Daiichi disaster across Japan. From the sparsely sampled...... the Fukushima Daiichi plant we establish that the model is able to account for the data. We further demonstrate how the model extends to include all the available measurements recorded throughout Japan. The model can be considered a first attempt to apply Bayesian learning unsupervised in order to give a more......The Fukushima Daiichi disaster 11 March, 2011 is considered the largest nuclear accident since the 1986 Chernobyl disaster and has been rated at level 7 on the International Nuclear Event Scale. As different radioactive materials have different effects to human body, it is important to know...
Status of the ORNL Aerosol Release and Transport Project
International Nuclear Information System (INIS)
Adams, R.E.
1985-01-01
The behavior of aerosols assumed to be characteristic of those generated during light water reactor (LWR) accident sequences and released into containment is being studied. Recent activities in the ORNL Aerosol Release and Transport Project include studies of (1) the thermal hydraulic conditions existing during Nuclear Safety Pilot Plant (NSPP) aerosol tests in steam-air environments, (2) the thermal output and aerosol mass generation rates for plasma torch aerosol generators, and (3) the influence of humidity on the shape of agglomerated aerosols of various materials. A new Aerosol-Moisture Interaction Test (AMIT) facility was prepared at the NSPP site to accommodate the aerosol shape studies; several tests with Fe 2 O 3 aerosol have been conducted. In addition to the above activities a special study was conducted to determine the suitability of the technique of aerosol production by plasma torch under the operating conditions of future tests of the LWR Aerosol Containment Experiments (LACE) at the Hanford Engineering Development Laboratory. 3 refs., 2 figs., 7 tabs
Bayesian inference of synaptic quantal parameters from correlated vesicle release
Directory of Open Access Journals (Sweden)
Alexander D Bird
2016-11-01
Full Text Available Synaptic transmission is both history-dependent and stochastic, resulting in varying responses to presentations of the same presynaptic stimulus. This complicates attempts to infer synaptic parameters and has led to the proposal of a number of different strategies for their quantification. Recently Bayesian approaches have been applied to make more efficient use of the data collected in paired intracellular recordings. Methods have been developed that either provide a complete model of the distribution of amplitudes for isolated responses or approximate the amplitude distributions of a train of post-synaptic potentials, with correct short-term synaptic dynamics but neglecting correlations. In both cases the methods provided significantly improved inference of model parameters as compared to existing mean-variance fitting approaches. However, for synapses with high release probability, low vesicle number or relatively low restock rate and for data in which only one or few repeats of the same pattern are available, correlations between serial events can allow for the extraction of significantly more information from experiment: a more complete Bayesian approach would take this into account also. This has not been possible previously because of the technical difficulty in calculating the likelihood of amplitudes seen in correlated post-synaptic potential trains; however, recent theoretical advances have now rendered the likelihood calculation tractable for a broad class of synaptic dynamics models. Here we present a compact mathematical form for the likelihood in terms of a matrix product and demonstrate how marginals of the posterior provide information on covariance of parameter distributions. The associated computer code for Bayesian parameter inference for a variety of models of synaptic dynamics is provided in the supplementary material allowing for quantal and dynamical parameters to be readily inferred from experimental data sets.
Aerosols released from solvent fire accidents in reprocessing plants
International Nuclear Information System (INIS)
Jordan, S.; Lindner, W.
1985-01-01
Thermodynamic, aerosol characterizing and radiological data of solvent fires in reprocessing plants have been established in experiments. These are the main results: Depending on the ventilation in the containment, kerosene-TBP mixtures burn at a rate up to 120 kg/m 2 h. The aqueous phase of inorganic-organic mixtures might be released during the fire. The gaseous reaction products contain unburnable acidic compounds. Solvents with TBP-nitrate complex shows higher (up to 25%) burning rates than pure solvents (kerosene-TBP). The nitrate complex decomposes violently at about 130 0 C with a release of acid and unburnable gases. Up to 20% of the burned kerosene-TBP solvents are released during the fire in the form of soot particles, phosphoric acid and TBP decomposition products. The particles have an aerodynamic mass median diameter of about 0.5 μm and up to 1.5% of the uranium fixed in the TBP-nitrate complex is released during solvent fires. (orig.)
Large-Scale Spray Releases: Initial Aerosol Test Results
Energy Technology Data Exchange (ETDEWEB)
Schonewill, Philip P.; Gauglitz, Phillip A.; Bontha, Jagannadha R.; Daniel, Richard C.; Kurath, Dean E.; Adkins, Harold E.; Billing, Justin M.; Burns, Carolyn A.; Davis, James M.; Enderlin, Carl W.; Fischer, Christopher M.; Jenks, Jeromy WJ; Lukins, Craig D.; MacFarlan, Paul J.; Shutthanandan, Janani I.; Smith, Dennese M.
2012-12-01
One of the events postulated in the hazard analysis at the Waste Treatment and Immobilization Plant (WTP) and other U.S. Department of Energy (DOE) nuclear facilities is a breach in process piping that produces aerosols with droplet sizes in the respirable range. The current approach for predicting the size and concentration of aerosols produced in a spray leak involves extrapolating from correlations reported in the literature. These correlations are based on results obtained from small engineered spray nozzles using pure liquids with Newtonian fluid behavior. The narrow ranges of physical properties on which the correlations are based do not cover the wide range of slurries and viscous materials that will be processed in the WTP and across processing facilities in the DOE complex. Two key technical areas were identified where testing results were needed to improve the technical basis by reducing the uncertainty due to extrapolating existing literature results. The first technical need was to quantify the role of slurry particles in small breaches where the slurry particles may plug and result in substantially reduced, or even negligible, respirable fraction formed by high-pressure sprays. The second technical need was to determine the aerosol droplet size distribution and volume from prototypic breaches and fluids, specifically including sprays from larger breaches with slurries where data from the literature are scarce. To address these technical areas, small- and large-scale test stands were constructed and operated with simulants to determine aerosol release fractions and generation rates from a range of breach sizes and geometries. The properties of the simulants represented the range of properties expected in the WTP process streams and included water, sodium salt solutions, slurries containing boehmite or gibbsite, and a hazardous chemical simulant. The effect of anti-foam agents was assessed with most of the simulants. Orifices included round holes and
Small-Scale Spray Releases: Initial Aerosol Test Results
Energy Technology Data Exchange (ETDEWEB)
Mahoney, Lenna A.; Gauglitz, Phillip A.; Kimura, Marcia L.; Brown, Garrett N.; Kurath, Dean E.; Buchmiller, William C.; Smith, Dennese M.; Blanchard, Jeremy; Song, Chen; Daniel, Richard C.; Wells, Beric E.; Tran, Diana N.; Burns, Carolyn A.
2013-05-29
One of the events postulated in the hazard analysis at the Waste Treatment and Immobilization Plant (WTP) and other U.S. Department of Energy (DOE) nuclear facilities is a breach in process piping that produces aerosols with droplet sizes in the respirable range. The current approach for predicting the size and concentration of aerosols produced in a spray leak involves extrapolating from correlations reported in the literature. These correlations are based on results obtained from small engineered spray nozzles using pure liquids with Newtonian fluid behavior. The narrow ranges of physical properties on which the correlations are based do not cover the wide range of slurries and viscous materials that will be processed in the WTP and across processing facilities in the DOE complex. Two key technical areas were identified where testing results were needed to improve the technical basis by reducing the uncertainty due to extrapolating existing literature results. The first technical need was to quantify the role of slurry particles in small breaches where the slurry particles may plug and result in substantially reduced, or even negligible, respirable fraction formed by high-pressure sprays. The second technical need was to determine the aerosol droplet size distribution and volume from prototypic breaches and fluids, specifically including sprays from larger breaches with slurries where data from the literature are scarce. To address these technical areas, small- and large-scale test stands were constructed and operated with simulants to determine aerosol release fractions and net generation rates from a range of breach sizes and geometries. The properties of the simulants represented the range of properties expected in the WTP process streams and included water, sodium salt solutions, slurries containing boehmite or gibbsite, and a hazardous chemical simulant. The effect of antifoam agents was assessed with most of the simulants. Orifices included round holes and
Small-Scale Spray Releases: Initial Aerosol Test Results
Energy Technology Data Exchange (ETDEWEB)
Mahoney, Lenna A.; Gauglitz, Phillip A.; Kimura, Marcia L.; Brown, Garrett N.; Kurath, Dean E.; Buchmiller, William C.; Smith, Dennese M.; Blanchard, Jeremy; Song, Chen; Daniel, Richard C.; Wells, Beric E.; Tran, Diana N.; Burns, Carolyn A.
2012-11-01
One of the events postulated in the hazard analysis at the Waste Treatment and Immobilization Plant (WTP) and other U.S. Department of Energy (DOE) nuclear facilities is a breach in process piping that produces aerosols with droplet sizes in the respirable range. The current approach for predicting the size and concentration of aerosols produced in a spray leak involves extrapolating from correlations reported in the literature. These correlations are based on results obtained from small engineered spray nozzles using pure liquids with Newtonian fluid behavior. The narrow ranges of physical properties on which the correlations are based do not cover the wide range of slurries and viscous materials that will be processed in the WTP and across processing facilities in the DOE complex. Two key technical areas were identified where testing results were needed to improve the technical basis by reducing the uncertainty due to extrapolating existing literature results. The first technical need was to quantify the role of slurry particles in small breaches where the slurry particles may plug and result in substantially reduced, or even negligible, respirable fraction formed by high-pressure sprays. The second technical need was to determine the aerosol droplet size distribution and volume from prototypic breaches and fluids, specifically including sprays from larger breaches with slurries where data from the literature are scarce. To address these technical areas, small- and large-scale test stands were constructed and operated with simulants to determine aerosol release fractions and generation rates from a range of breach sizes and geometries. The properties of the simulants represented the range of properties expected in the WTP process streams and included water, sodium salt solutions, slurries containing boehmite or gibbsite, and a hazardous chemical simulant. The effect of anti-foam agents was assessed with most of the simulants. Orifices included round holes and
Investigation of aerosols released at high temperature from nuclear reactor core models
Energy Technology Data Exchange (ETDEWEB)
Pinter Csordas, A.; Matus, L.; Czitrovszky, A.; Jani, P.; Maroti, L.; Hozer, Z.; Windberg, P.; Hummel, R
2000-12-01
Two experiments were performed to simulate severe reactor accident with air ingress into the hot reactor core. The model bundles contained nine PWR type fuel rods. Their cladding was pre-oxidised by argon-oxygen (test 1) and steam (test 2). The released aerosol was measured continuously by laser particle counters. Morphology and elemental composition of the aerosol particles were studied on samples collected by impactors and quartz filters. The highest aerosol release was detected at the steepest rise of the bundle temperature. A second increase of the aerosol release appeared at the cooling down period. Because of the higher maximum temperature at test 2 about two orders of magnitude more uranium was released than in test 1. The highest emission was found for tin at test 1 and for zirconium and iron at test 2.
A nanomaterial release model for waste shredding using a Bayesian belief network
Shandilya, Neeraj; Ligthart, Tom; van Voorde, Imelda; Stahlmecke, Burkhard; Clavaguera, Simon; Philippot, Cecile; Ding, Yaobo; Goede, Henk
2018-02-01
The shredding of waste of electrical and electronic equipment (WEEE) and other products, incorporated with nanomaterials, can lead to a substantial release of nanomaterials. Considering the uncertainty, complexity, and scarcity of experimental data on release, we present the development of a Bayesian belief network (BBN) model. This baseline model aims to give a first prediction of the release of nanomaterials (excluding nanofibers) during their mechanical shredding. With a focus on the description of the model development methodology, we characterize nanomaterial release in terms of number, size, mass, and composition of released particles. Through a sensitivity analysis of the model, we find the material-specific parameters like affinity of nanomaterials to the matrix of the composite and their state of dispersion inside the matrix to reduce the nanomaterial release up to 50%. The shredder-specific parameters like number of shafts in a shredder and input and output size of the material for shredding could minimize it up to 98%. The comparison with two experimental test cases shows promising outcome on the prediction capacity of the model. As additional experimental data on nanomaterial release becomes available, the model is able to further adapt and update risk forecasts. When adapting the model with additional expert beliefs, experts should be selected using criteria, e.g., substantial contribution to nanomaterial and/or particulate matter release-related scientific literature, the capacity and willingness to contribute to further development of the BBN model, and openness to accepting deviating opinions. [Figure not available: see fulltext.
The part of nuclear aerosols in accidental radioactivity releases
International Nuclear Information System (INIS)
Fermandjian, J.; Manesse, D.; L'homme, A.; Renoux, A.; Madelaine, G.
1983-07-01
Studies carried out by the organizations commisionned to ensure nuclear safety, more particularly the Departement de Surete Nucleaire du Commissariat a l'Energie Atomique, principally dealt with the behaviour of aerosols inside the reactor containment building. One distinguishes the pressurized water reactors and the fast reactors. Tests and computer codes developed in this field are briefly presented. Studies are also carried out on the behaviour of radioactive aerosols in the atmosphere; the DEPSEC diffusion calculation model is presented [fr
Halogenation processes of secondary organic aerosol and implications on halogen release mechanisms
Directory of Open Access Journals (Sweden)
J. Ofner
2012-07-01
Full Text Available Reactive halogen species (RHS, such as X·, X_{2} and HOX containing X = chlorine and/or bromine, are released by various sources like photo-activated sea-salt aerosol or from salt pans, and salt lakes. Despite many studies of RHS reactions, the potential of RHS reacting with secondary organic aerosol (SOA and organic aerosol derived from biomass-burning (BBOA has been neglected. Such reactions can constitute sources of gaseous organohalogen compounds or halogenated organic matter in the tropospheric boundary layer and can influence physicochemical properties of atmospheric aerosols.
Model SOA from α-pinene, catechol, and guaiacol was used to study heterogeneous interactions with RHS. Particles were exposed to molecular chlorine and bromine in an aerosol smog-chamber in the presence of UV/VIS irradiation and to RHS, released from simulated natural halogen sources like salt pans. Subsequently, the aerosol was characterized in detail using a variety of physicochemical and spectroscopic methods. Fundamental features were correlated with heterogeneous halogenation, which results in new functional groups (FTIR spectroscopy, changes UV/VIS absorption, chemical composition (ultrahigh resolution mass spectroscopy (ICR-FT/MS, or aerosol size distribution. However, the halogen release mechanisms were also found to be affected by the presence of organic aerosol. Those interaction processes, changing chemical and physical properties of the aerosol are likely to influence e.g. the ability of the aerosol to act as cloud condensation nuclei, its potential to adsorb other gases with low-volatility, or its contribution to radiative forcing and ultimately the Earth's radiation balance.
Small-Scale Spray Releases: Additional Aerosol Test Results
Energy Technology Data Exchange (ETDEWEB)
Schonewill, Philip P.; Gauglitz, Phillip A.; Kimura, Marcia L.; Brown, G. N.; Mahoney, Lenna A.; Tran, Diana N.; Burns, Carolyn A.; Kurath, Dean E.
2013-08-01
One of the events postulated in the hazard analysis at the Waste Treatment and Immobilization Plant (WTP) and other U.S. Department of Energy (DOE) nuclear facilities is a breach in process piping that produces aerosols with droplet sizes in the respirable range. The current approach for predicting the size and concentration of aerosols produced in a spray leak involves extrapolating from correlations reported in the literature. These correlations are based on results obtained from small engineered spray nozzles using pure liquids with Newtonian fluid behavior. The narrow ranges of physical properties on which the correlations are based do not cover the wide range of slurries and viscous materials that will be processed in the WTP and across processing facilities in the DOE complex. To expand the data set upon which the WTP accident and safety analyses were based, an aerosol spray leak testing program was conducted by Pacific Northwest National Laboratory (PNNL). PNNL’s test program addressed two key technical areas to improve the WTP methodology (Larson and Allen 2010). The first technical area was to quantify the role of slurry particles in small breaches where slurry particles may plug the hole and prevent high-pressure sprays. The results from an effort to address this first technical area can be found in Mahoney et al. (2012a). The second technical area was to determine aerosol droplet size distribution and total droplet volume from prototypic breaches and fluids, including sprays from larger breaches and sprays of slurries for which literature data are largely absent. To address the second technical area, the testing program collected aerosol generation data at two scales, commonly referred to as small-scale and large-scale. The small-scale testing and resultant data are described in Mahoney et al. (2012b) and the large-scale testing and resultant data are presented in Schonewill et al. (2012). In tests at both scales, simulants were used to mimic the
Large-Scale Spray Releases: Additional Aerosol Test Results
Energy Technology Data Exchange (ETDEWEB)
Daniel, Richard C.; Gauglitz, Phillip A.; Burns, Carolyn A.; Fountain, Matthew S.; Shimskey, Rick W.; Billing, Justin M.; Bontha, Jagannadha R.; Kurath, Dean E.; Jenks, Jeromy WJ; MacFarlan, Paul J.; Mahoney, Lenna A.
2013-08-01
One of the events postulated in the hazard analysis for the Waste Treatment and Immobilization Plant (WTP) and other U.S. Department of Energy (DOE) nuclear facilities is a breach in process piping that produces aerosols with droplet sizes in the respirable range. The current approach for predicting the size and concentration of aerosols produced in a spray leak event involves extrapolating from correlations reported in the literature. These correlations are based on results obtained from small engineered spray nozzles using pure liquids that behave as a Newtonian fluid. The narrow ranges of physical properties on which the correlations are based do not cover the wide range of slurries and viscous materials that will be processed in the WTP and in processing facilities across the DOE complex. To expand the data set upon which the WTP accident and safety analyses were based, an aerosol spray leak testing program was conducted by Pacific Northwest National Laboratory (PNNL). PNNL’s test program addressed two key technical areas to improve the WTP methodology (Larson and Allen 2010). The first technical area was to quantify the role of slurry particles in small breaches where slurry particles may plug the hole and prevent high-pressure sprays. The results from an effort to address this first technical area can be found in Mahoney et al. (2012a). The second technical area was to determine aerosol droplet size distribution and total droplet volume from prototypic breaches and fluids, including sprays from larger breaches and sprays of slurries for which literature data are mostly absent. To address the second technical area, the testing program collected aerosol generation data at two scales, commonly referred to as small-scale and large-scale testing. The small-scale testing and resultant data are described in Mahoney et al. (2012b), and the large-scale testing and resultant data are presented in Schonewill et al. (2012). In tests at both scales, simulants were used
Pereira, Gabriel; Freitas, Saulo R.; Moraes, Elisabete Caria; Ferreira, Nelson Jesus; Shimabukuro, Yosio Edemir; Rao, Vadlamudi Brahmananda; Longo, Karla M.
2009-12-01
Contemporary human activities such as tropical deforestation, land clearing for agriculture, pest control and grassland management lead to biomass burning, which in turn leads to land-cover changes. However, biomass burning emissions are not correctly measured and the methods to assess these emissions form a part of current research area. The traditional methods for estimating aerosols and trace gases released into the atmosphere generally use emission factors associated with fuel loading and moisture characteristics and other parameters that are hard to estimate in near real-time applications. In this paper, fire radiative power (FRP) products were extracted from Moderate Resolution Imaging Spectroradiometer (MODIS) and from the Geostationary Operational Environmental Satellites (GOES) fire products and new South America generic biomes FRE-based smoke aerosol emission coefficients were derived and applied in 2002 South America fire season. The inventory estimated by MODIS and GOES FRP measurements were included in Coupled Aerosol-Tracer Transport model coupled to the Brazilian developments on the Regional Atmospheric Modeling System (CATT-BRAMS) and evaluated with ground truth collected in Large Scale Biosphere-Atmosphere Smoke, Aerosols, Clouds, rainfall, and Climate (SMOCC) and Radiation, Cloud, and Climate Interactions (RaCCI). Although the linear regression showed that GOES FRP overestimates MODIS FRP observations, the use of a common external parameter such as MODIS aerosol optical depth product could minimize the difference between sensors. The relationship between the PM 2.5μm (Particulate Matter with diameter less than 2.5 μm) and CO (Carbon Monoxide) model shows a good agreement with SMOCC/RaCCI data in the general pattern of temporal evolution. The results showed high correlations, with values between 0.80 and 0.95 (significant at 0.5 level by student t test), for the CATT-BRAMS simulations with PM 2.5μm and CO.
Experiment of aerosol-release time for a novel automatic metered dose inhaler
Directory of Open Access Journals (Sweden)
Mingrong Zhang
2016-05-01
Full Text Available The objective of this study was to evaluate the aerosol-release time in the development of a new automatic adapter for metered dose inhaler. With this device, regular manually operated metered dose inhalers become automatic. During the study, an inhalation simulator was designed and tested with the newly developed mechatronic system. By adjusting the volume and the pressure of the vacuum tank, most human inhalation waveforms were able to simulate. As an example, regular quick-deep and slow-deep waveforms were matched within reasonable accuracy. Finally, with the help of dynamic image processing, the aerosol-release time (Tr was carefully measured and fully discussed, including the switch-on time (Ts, the mechatronics-hysteresis (Tm and the intentional-delay (Ti. Under slow-deep inhalation condition which is suitable for metered dose inhaler medicine delivery, the switch-on flow-rate could reach as low as 10 L/min, and the corresponding switch-on time was approximately 0.20 s. While the mechatronics-hysteresis depended on the brand of metered dose inhaler, assuming there was no intentional-delay, the aerosol-release time could be as low as 0.40 and 0.60 s, respectively, for two commercially available metered dose inhalers studied in this article. Therefore, this newly developed mechatronic adapter system could ensure aerosol-release time (Tr within satisfactory range for metered dose inhalers.
Bayesian source term estimation of atmospheric releases in urban areas using LES approach.
Xue, Fei; Kikumoto, Hideki; Li, Xiaofeng; Ooka, Ryozo
2018-05-05
The estimation of source information from limited measurements of a sensor network is a challenging inverse problem, which can be viewed as an assimilation process of the observed concentration data and the predicted concentration data. When dealing with releases in built-up areas, the predicted data are generally obtained by the Reynolds-averaged Navier-Stokes (RANS) equations, which yields building-resolving results; however, RANS-based models are outperformed by large-eddy simulation (LES) in the predictions of both airflow and dispersion. Therefore, it is important to explore the possibility of improving the estimation of the source parameters by using the LES approach. In this paper, a novel source term estimation method is proposed based on LES approach using Bayesian inference. The source-receptor relationship is obtained by solving the adjoint equations constructed using the time-averaged flow field simulated by the LES approach based on the gradient diffusion hypothesis. A wind tunnel experiment with a constant point source downwind of a single building model is used to evaluate the performance of the proposed method, which is compared with that of the existing method using a RANS model. The results show that the proposed method reduces the errors of source location and releasing strength by 77% and 28%, respectively. Copyright © 2018 Elsevier B.V. All rights reserved.
Czech Academy of Sciences Publication Activity Database
Tichý, Ondřej; Šmídl, Václav; Hofman, Radek; Šindelářová, Kateřina; Hýža, M.; Stohl, A.
2017-01-01
Roč. 17, č. 20 (2017), s. 12677-12696 ISSN 1680-7316 R&D Projects: GA MŠk(CZ) 7F14287 Institutional support: RVO:67985556 Keywords : Bayesian inverse modeling * iodine-131 * consequences of the iodine release Subject RIV: BB - Applied Statistics, Operational Research OBOR OECD: Statistics and probability Impact factor: 5.318, year: 2016 http://library.utia.cas.cz/separaty/2017/AS/tichy-0480506.pdf
Aerosols generated by releases of pressurized powders and solutions in static air
Energy Technology Data Exchange (ETDEWEB)
Sutter, S.L.
1983-08-01
Safety assessments and environmental impact statements for nuclear fuel cycle facilities require an estimate of potential airborne releases caused by accidents. Aerosols generated by accidents are being investigated by Pacific Northwest Laboratory to develop the source terms for these releases. An upper boundary accidental release event would be a pressurized release of powder or liquid in static air. Experiments were run using various source sizes and pressures and measuring the mass airborne and the particle size distribution of aerosols produced by these pressurized releases. Two powder and two liquid sources were used: TiO/sub 2/ and depleted uranium dioxide (DUO); and aqueous uranine (sodium fluorescein) and uranyl nitrate solutions. Results of the experiments showed that pressurization level and source size were significant variables for the airborne powder releases. For this experimental configuration, the liquid releases were a function of pressure, but volume did not appear to be a significant variable. During the experiments 100 g and 350 g of DUO (1 ..mu..m dia) and TiO/sub 2/ (1.7 ..mu..m dia) powders and 100 cm/sup 3/ and 350 cm/sup 3/ of uranine and uranyl nitrate solutions were released at pressures ranging from 50 to 500 psig. The average of the largest fractions of powder airborne was about 24%. The maximum amount of liquid source airborne was significantly less, about 0.15%. The median aerodynamic equivalent diameters (AED) for collected airborne powders ranged from 5 to 19 ..mu..m; liquids ranged from 2 to 29 ..mu..m. All of the releases produced a significant fraction of respirable particles of 10 ..mu..m and less. 12 references, 10 figures, 23 tables.
Aerosol material release rates from zircaloy-4 at temperatures from 2000 to 22000C
International Nuclear Information System (INIS)
Mulpuru, S.R.; Wren, D.J.; Rondeau, R.K.
1987-01-01
During some postulated severe accidents involving loss of coolant and loss of emergency coolant injection, the temperatures in a CANDU reactor fuel channel become high enough to cause failure and melting of the Zircaloy fuel cladding. At such high temperatures, vapors of fission products and structural (fuel and cladding) materials will be released into the coolant steam and hydrogen mixture. These vapors will condense as cooler conditions are encountered downstream. The vapors from structural materials are relatively involatile; therefore, they will condense readily into aerosol particles. These particles, in turn, will provide sites for the condensation of the more volatile fission products. The aerosol transport of fission products in the primary heat transport system (PHTS) will thus be influenced by the structural material release rates. As part of an ongoing program to develop predictive tools for aerosol and associated fission product transport through the PHTS, experiments have been conducted to measure the vapor mass release rates of the alloying elements from Zircaloy-4 at high temperatures. The paper presents the results and analysis of these experiments
Aerosol release from a hot sodium pool and behaviour in inert gas atmosphere
International Nuclear Information System (INIS)
Sauter, H.; Schuetz, W.
1986-01-01
In the KfK-NALA program, experiments were carried out on the subject of aerosol release from a contaminated sodium pool into inert gas atmosphere under various conditions. Besides the determination of retention factors for fuel and fission products, the sodium aerosol system was investigated and characterized, concerning aerosol generation (evaporation rate), particle size, mass concentration, and deposition behaviour. Pool temperatures were varied between 700 and 1000 K at different geometrical and convective conditions. Technical scale experiments with a 531-cm 2 pool surface area were performed at natural convection in a 2.2-m 3 heated vessel, as well as additional small scale experiments at forced convection and 38.5-cm 2 pool surface area. A best-fit formula is given for the specific evaporation rate into a 400 K argon atmosphere. Approximately, the very convenient relation (dm/dt) (kg/m 2 /h) = 0.1 p (mm Hg) was found. The sodium aerosol diameter lay between 0.6 μm, less than 1 sec after production, and 2.5 μm at maximum concentration. The deposition behaviour was characterized by very small quantities ( 80%) on the bottom cover of the vessel. In the model theoretic studies with the PARDISEKO code, calculations were performed of the mass concentration, particle diameter and deposition behaviour. Agreement with the experimental values could not be achieved until a modulus was introduced to allow for turbulent deposition. (author)
Park, Chun-Woong; Li, Xiaojian; Vogt, Frederick G; Hayes, Don; Zwischenberger, Joseph B; Park, Eun-Seok; Mansour, Heidi M
2013-10-15
Respirable microparticles/nanoparticles of the antibiotics vancomycin (VCM) and clarithromycin (CLM) were successfully designed and developed by novel organic solution advanced spray drying from methanol solution. Formulation optimization was achieved through statistical experimental design of pump feeding rates of 25% (Low P), 50% (Medium P) and 75% (High P). Systematic and comprehensive physicochemical characterization and imaging were carried out using scanning electron microscopy (SEM), hot-stage microscopy (HSM), differential scanning calorimetry (DSC), X-ray powder diffraction (XRPD), Karl Fischer titration (KFT), laser size diffraction (LSD), gravimetric vapor sorption (GVS), confocal Raman microscopy (CRM) and spectroscopy for chemical imaging mapping. These novel spray-dried (SD) microparticulate/nanoparticulate dry powders displayed excellent aerosol dispersion performance as dry powder inhalers (DPIs) with high values in emitted dose (ED), respirable fraction (RF), and fine particle fraction (FPF). VCM DPIs displayed better aerosol dispersion performance compared to CLM DPIs which was related to differences in the physicochemical and particle properties of VCM and CLM. In addition, organic solution advanced co-spray drying particle engineering design was employed to successfully produce co-spray-dried (co-SD) multifunctional microparticulate/nanoparticulate aerosol powder formulations of VCM and CLM with the essential lung surfactant phospholipid, dipalmitoylphosphatidylcholine (DPPC), for controlled release pulmonary nanomedicine delivery as inhalable dry powder aerosols. Formulation optimization was achieved through statistical experimental design of molar ratios of co-SD VCM:DPPC and co-SD CLM:DPPC. XRPD and DSC confirmed that the phospholipid bilayer structure in the solid-state was preserved following spray drying. Co-SD VCM:DPPC and co-SD CLM:DPPC dry powder aerosols demonstrated controlled release of antibiotic drug that was fitted to various
Results of aerosol code comparisons with releases from ACE MCCI tests
International Nuclear Information System (INIS)
Fink, J.K.; Corradini, M.; Hidaka, A.; Hontanon, E.; Mignanelli, M.A.; Schroedl, E.; Strizhov, V.
1992-01-01
Results of aerosol release calculations by six groups from six countries are compared with the releases from ACE MCCI Test L6. The codes used for these calculations included: SOLGASMIX-PV, SOLGASMIX Reactor 1986, CORCON.UW, VANESA 1.01, and CORCON mod2.04/VANESA 1.01. Calculations were performed with the standard VANESA 1.01 code and with modifications to the VANESA code such as the inclusion of various zirconium-silica chemical reactions. Comparisons of results from these calculations were made with Test L6 release fractions for U, Zr, Si, the fission-product elements Te, Ba, Sr, Ce, La, Mo and control materials Ag, In, and Ru. Reasonable agreement was obtained between calculations and Test L6 results for the volatile elements Ag, In and Te. Calculated releases of the low volatility fission products ranged from within an order of magnitude to five orders of magnitude of Test L6 values. Releases were over and underestimated by calculations. Poorest agreements were obtained for Mo and Si
Large methane releases lead to strong aerosol forcing and reduced cloudiness
Directory of Open Access Journals (Sweden)
T. Kurtén
2011-07-01
Full Text Available The release of vast quantities of methane into the atmosphere as a result of clathrate destabilization is a potential mechanism for rapid amplification of global warming. Previous studies have calculated the enhanced warming based mainly on the radiative effect of the methane itself, with smaller contributions from the associated carbon dioxide or ozone increases. Here, we study the effect of strongly elevated methane (CH_{4} levels on oxidant and aerosol particle concentrations using a combination of chemistry-transport and general circulation models. A 10-fold increase in methane concentrations is predicted to significantly decrease hydroxyl radical (OH concentrations, while moderately increasing ozone (O_{3}. These changes lead to a 70 % increase in the atmospheric lifetime of methane, and an 18 % decrease in global mean cloud droplet number concentrations (CDNC. The CDNC change causes a radiative forcing that is comparable in magnitude to the longwave radiative forcing ("enhanced greenhouse effect" of the added methane. Together, the indirect CH_{4}-O_{3} and CH_{4}-OH-aerosol forcings could more than double the warming effect of large methane increases. Our findings may help explain the anomalously large temperature changes associated with historic methane releases.
Release the BEESTS: Bayesian Estimation of Ex-Gaussian STop-Signal Reaction Time Distributions
Directory of Open Access Journals (Sweden)
Dora eMatzke
2013-12-01
Full Text Available The stop-signal paradigm is frequently used to study response inhibition. Inthis paradigm, participants perform a two-choice response time task wherethe primary task is occasionally interrupted by a stop-signal that promptsparticipants to withhold their response. The primary goal is to estimatethe latency of the unobservable stop response (stop signal reaction timeor SSRT. Recently, Matzke, Dolan, Logan, Brown, and Wagenmakers (inpress have developed a Bayesian parametric approach that allows for theestimation of the entire distribution of SSRTs. The Bayesian parametricapproach assumes that SSRTs are ex-Gaussian distributed and uses Markovchain Monte Carlo sampling to estimate the parameters of the SSRT distri-bution. Here we present an efficient and user-friendly software implementa-tion of the Bayesian parametric approach —BEESTS— that can be appliedto individual as well as hierarchical stop-signal data. BEESTS comes withan easy-to-use graphical user interface and provides users with summarystatistics of the posterior distribution of the parameters as well various diag-nostic tools to assess the quality of the parameter estimates. The softwareis open source and runs on Windows and OS X operating systems. In sum,BEESTS allows experimental and clinical psychologists to estimate entiredistributions of SSRTs and hence facilitates the more rigorous analysis ofstop-signal data.
Investigation of molten corium-concrete interaction phenomena and aerosol release
International Nuclear Information System (INIS)
Spencer, B.W.; Thompson, D.H.; Armstrong, D.R.; Fink, J.K.; Gunther, W.H.; Kilsdonk, D.J.; Sehgal, B.R.
1987-01-01
The Electric Power Research Institute is sponsoring a program of laboratory investigations at Argonne National Laboratory to study the interaction between molten core materials and reactor concrete basemats during postulated severe reactor accidents, with particular emphasis on measurements of the magnitude and chemical species present in the aerosol releases. The approach in this program is to sustain internal heat generation in reactor-material corium using direct electrical heating and to develop test operating and diagnostics capabilities with a series of small- and intermediate-scale scoping tests followed by fully instrumented large-scale testing. Real reactor materials (UO 2 , ZrO 2 , oxides of stainless steel, plus metallics) are used, with small amounts of La 2 O 3 , BaO, and SrO added to simulate nonvolatile fission products. In intermediate-scale scoping tests completed to date, corium inventories of up to 29 kg have been heated with power inputs in excess of 1 kW/kg melt. The measured concrete ablation rates have ranged from 0.9 to 3.9 mm/minute. Aerosol samples have been examined using a scanning electron microscope and show submicron particles, 2-6 micrometer spheres, and agglomerates that range from a few micrometers to string 13 micrometers in length
Tichý, Ondřej; Šmídl, Václav; Hofman, Radek; Šindelářová, Kateřina; Hýža, Miroslav; Stohl, Andreas
2017-10-01
In the fall of 2011, iodine-131 (131I) was detected at several radionuclide monitoring stations in central Europe. After investigation, the International Atomic Energy Agency (IAEA) was informed by Hungarian authorities that 131I was released from the Institute of Isotopes Ltd. in Budapest, Hungary. It was reported that a total activity of 342 GBq of 131I was emitted between 8 September and 16 November 2011. In this study, we use the ambient concentration measurements of 131I to determine the location of the release as well as its magnitude and temporal variation. As the location of the release and an estimate of the source strength became eventually known, this accident represents a realistic test case for inversion models. For our source reconstruction, we use no prior knowledge. Instead, we estimate the source location and emission variation using only the available 131I measurements. Subsequently, we use the partial information about the source term available from the Hungarian authorities for validation of our results. For the source determination, we first perform backward runs of atmospheric transport models and obtain source-receptor sensitivity (SRS) matrices for each grid cell of our study domain. We use two dispersion models, FLEXPART and Hysplit, driven with meteorological analysis data from the global forecast system (GFS) and from European Centre for Medium-range Weather Forecasts (ECMWF) weather forecast models. Second, we use a recently developed inverse method, least-squares with adaptive prior covariance (LS-APC), to determine the 131I emissions and their temporal variation from the measurements and computed SRS matrices. For each grid cell of our simulation domain, we evaluate the probability that the release was generated in that cell using Bayesian model selection. The model selection procedure also provides information about the most suitable dispersion model for the source term reconstruction. Third, we select the most probable location of
Directory of Open Access Journals (Sweden)
O. Tichý
2017-10-01
Full Text Available In the fall of 2011, iodine-131 (131I was detected at several radionuclide monitoring stations in central Europe. After investigation, the International Atomic Energy Agency (IAEA was informed by Hungarian authorities that 131I was released from the Institute of Isotopes Ltd. in Budapest, Hungary. It was reported that a total activity of 342 GBq of 131I was emitted between 8 September and 16 November 2011. In this study, we use the ambient concentration measurements of 131I to determine the location of the release as well as its magnitude and temporal variation. As the location of the release and an estimate of the source strength became eventually known, this accident represents a realistic test case for inversion models. For our source reconstruction, we use no prior knowledge. Instead, we estimate the source location and emission variation using only the available 131I measurements. Subsequently, we use the partial information about the source term available from the Hungarian authorities for validation of our results. For the source determination, we first perform backward runs of atmospheric transport models and obtain source-receptor sensitivity (SRS matrices for each grid cell of our study domain. We use two dispersion models, FLEXPART and Hysplit, driven with meteorological analysis data from the global forecast system (GFS and from European Centre for Medium-range Weather Forecasts (ECMWF weather forecast models. Second, we use a recently developed inverse method, least-squares with adaptive prior covariance (LS-APC, to determine the 131I emissions and their temporal variation from the measurements and computed SRS matrices. For each grid cell of our simulation domain, we evaluate the probability that the release was generated in that cell using Bayesian model selection. The model selection procedure also provides information about the most suitable dispersion model for the source term reconstruction. Third, we select the most
A nanomaterial release model for waste shredding using a Bayesian belief network
Shandilya, N.; Ligthart, T.; Voorde, I. van; Stahlmecke, B.; Clavaguera, S.; Philippot, C.; Goede, H.
2018-01-01
The shredding of waste of electrical and electronic equipment (WEEE) and other products, incorporated with nanomaterials, can lead to a substantial release of nanomaterials. Considering the uncertainty, complexity, and scarcity of experimental data on release, we present the development of a
Energy Technology Data Exchange (ETDEWEB)
Marzouk, Youssef; Fast P. (Lawrence Livermore National Laboratory, Livermore, CA); Kraus, M. (Peterson AFB, CO); Ray, J. P.
2006-01-01
Terrorist attacks using an aerosolized pathogen preparation have gained credibility as a national security concern after the anthrax attacks of 2001. The ability to characterize such attacks, i.e., to estimate the number of people infected, the time of infection, and the average dose received, is important when planning a medical response. We address this question of characterization by formulating a Bayesian inverse problem predicated on a short time-series of diagnosed patients exhibiting symptoms. To be of relevance to response planning, we limit ourselves to 3-5 days of data. In tests performed with anthrax as the pathogen, we find that these data are usually sufficient, especially if the model of the outbreak used in the inverse problem is an accurate one. In some cases the scarcity of data may initially support outbreak characterizations at odds with the true one, but with sufficient data the correct inferences are recovered; in other words, the inverse problem posed and its solution methodology are consistent. We also explore the effect of model error-situations for which the model used in the inverse problem is only a partially accurate representation of the outbreak; here, the model predictions and the observations differ by more than a random noise. We find that while there is a consistent discrepancy between the inferred and the true characterizations, they are also close enough to be of relevance when planning a response.
Drop of canistered spent fuel segments into a deep borehole and subsequent aerosol release
International Nuclear Information System (INIS)
Bantle, S.; Herbe, H.; Miu, J.
1991-09-01
The source term of the released aerosols is estimated. First, the number of failing canisters is calculated for the case of an axial symmetric canister (POLLUX) pile, and then, for the case of a 'zig-zag' pile, as found in reality. The weight-specific energy acting on the fuel - a measure for the degree of fuel fractioning - is determined from the acceleration acting on the pin segments. In the borehole prevails a steady-state flow pattern which is stimulated by the heat of the disposed waste canister, and is also influenced by the ventilation of the drift above the borehole. Based on this stationary flow pattern flow velocities are calculated by means of fluid mechanical methods. Further investigations deal with the unsteady case which occurs during and immediately after the canister drop as well as with the wake behind the canister. The most relevant result is that under the considered boundary conditions no release form the borehole into the repository is to be expected. (orig./HP) [de
Multiple batch extraction test to estimate contaminant release parameters using a Bayesian approach
Iden, S. C.; Durner, W.
2008-01-01
Industrial activities produce vast amounts of weakly contaminated materials which are commonly reused as filling materials on natural ground. There is a strong demand to define guidelines for the application of these materials, to estimate the leaching potential of contaminants from the materials, and to assess the potential hazard for groundwater pollution. We present a multiple batch experiment, where measurements of liquid-phase concentrations at varying liquid/solid ratios are used to estimate the total mass of contaminant that can be extracted from a contaminated material with a mild extractant like water. Furthermore, the experiment yields estimates of the isotherm describing the partitioning of the contaminant between the solid and liquid phases, and a concentration that might be expected under soil hydraulic conditions representative for the field situation. Model parameters are estimated from liquid-phase concentrations within a Bayesian framework by applying the Shuffled Complex Evolution Metropolis Algorithm (SCEM-UA), an efficient Markov Chain Monte Carlo sampler. A sensitivity analysis and inversions of synthetically generated data corrupted with noise show the general suitability of the proposed method. An uncertainty analysis for model parameters and model predictions shows the expected accuracy of the estimates. An application to concentration measurements obtained from a multiple batch extraction test illustrates the applicability of the approach for a real situation.
International Nuclear Information System (INIS)
Lee, M.; Davis, R.E.; Khatib-Rahbar, M.
1987-01-01
During a core meltdown accident in a light water reactor, molten core materials (corium) could leave the reactor vessel and interact with concrete. In this paper, the impact of the zirconium content of the corium pool and the coking reaction on the release of fission products during Molten Core Concrete Interactions (MCCI) are quantified using CORCON/MOD2 and VANESA computer codes. Detailed calculations show that the total aerosol generation is proportional to the zirconium content of the corium pool. Among the twelve fission product groups treated by the VANESA code, CsI, CsO 2 and Nb 2 O 5 are completely released over the course of the core/concrete interaction, while an insignificant quantity of Mo, Ru and ZrO 2 are predicted to be released. The release of BaO, SrO and CeO 2 increase with increased Zr content, while the releases of Te and La 2 O 3 are relatively unaffected by the Zr content of the corium pool. The impact of the coking reaction on the radiological releases is estimated to be significant; while the impact of the coking reaction on the aerosol production is insignificant
Energy Technology Data Exchange (ETDEWEB)
Kleppe, John; Norris, William; Etezadi, Mehdi
2006-07-19
This contract was awarded in response to a proposal in which a deployable plume and aerosol release prediction and tracking system would be designed, fabricated, and tested. The system would gather real time atmospheric data and input it into a real time atmospheric model that could be used for plume predition and tracking. The system would be able to be quickly deployed by aircraft to points of interest or positioned for deployment by vehicles. The system would provide three dimensional (u, v, and w) wind vector data, inversion height measurements, surface wind information, classical weather station data, and solar radiation. The on-board real time computer model would provide the prediction of the behavior of plumes and released aerosols.
Fission product partitioning in aerosol release from simulated spent nuclear fuel
Di Lemma, F.G.; Colle, J.Y.; Rasmussen, G.; Konings, R.J.M.
2015-01-01
Aerosols created by the vaporization of simulated spent nuclear fuel (simfuel) were produced by laser heating techniques and characterised by a wide range of post-analyses. In particular attention has been focused on determining the fission product behaviour in the aerosols, in order to improve the
International Nuclear Information System (INIS)
Weber, G.; Huber, J.
1989-01-01
The event 'earthquake with subsequent solvent fire' in the low level active waste management area of the Wackersdorf reprocessing plant has been modelled by two different computer codes FIPLOC-M (GRS) and FIRAC (Los Alamos). The results have been compared and are described. The fire itself with its gas and aerosol generation rates has been simulated by the FIRIN code which is integrated in FIRAC. The results were fed identically into FIPLOC and FIRAC. FIRAC underestimated the quantity of aerosols released to the environment by about 20%. The reasons for this result have been determined and analysed. Other differences between the results and models are described too. For the simulation of transportation pathways which comprise one or more larger volumes and which are therefore not typical for the application of FIRAC the FIPLOC code gives better results concerning aerosol transport and deposition effects. Also aerosol modelling is more precise in FIPLOC (MAEROS module) than in FIRAC. On the other hand ventilation components such as filters or blowers are simulated much better in the FIRAC code. (orig./HP) [de
Lefèvre, Jérôme; Menkes, Christophe; Bani, Philipson; Marchesiello, Patrick; Curci, Gabriele; Grell, Georg A.; Frouin, Robert
2016-08-01
The Melanesian Volcanic Arc (MVA) emits about 12 kT d- 1 of sulfur dioxide (SO2) to the atmosphere from continuous passive (non-explosive) volcanic degassing, which contributes 20% of the global SO2 emission from volcanoes. Here we assess, from up-to-date and long-term observations, the SO2 emission of the Ambrym volcano, one of the dominant volcanoes in the MVA, and we investigate its role as sulfate precursor on the regional distribution of aerosols, using both satellite observations and model results at 1° × 1° spatial resolution from WRF-Chem/GOCART. Without considering aerosol forcing on clouds, our model parameterizations for convection, vertical mixing and cloud properties provide a reliable chemical weather representation, making possible a cross-examination of model solution and observations. This preliminary work enables the identification of biases and limitations affecting both the model (missing sources) and satellite sensors and algorithms (for aerosol detection and classification) and leads to the implementation of improved transport and aerosol processes in the modeling system. On the one hand, the model confirms a 50% underestimation of SO2 emissions due to satellite swath sampling of the Ozone Monitoring Instrument (OMI), consistent with field studies. The OMI irregular sampling also produces a level of noise that impairs its monitoring capacity during short-term volcanic events. On the other hand, the model reveals a large sensitivity on aerosol composition and Aerosol Optical Depth (AOD) due to choices of both the source function in WRF-Chem and size parameters for sea-salt in FlexAOD, the post-processor used to compute offline the simulated AOD. We then proceed to diagnosing the role of SO2 volcanic emission in the regional aerosol composition. The model shows that both dynamics and cloud properties associated with the South Pacific Convergence Zone (SPCZ) have a large influence on the oxidation of SO2 and on the transport pathways of
Use of a novel modified TSI for the evaluation of controlled-release aerosol formulations. I.
McConville, J T; Patel, N; Ditchburn, N; Tobyn, M J; Staniforth, J N; Woodcock, P
2000-11-01
When considering the development of potential controlled-release pulmonary drug delivery systems, there is at present no standard method available for the assessment of in vitro drug release profiles necessary to understand how the drug might release following deposition in the lungs. For this purpose, the twin-stage impinger (TSI), apparatus A of the BP, has been redesigned and tested. This modified TSI was found capable of discriminating between drug release rates from conventional and different dry powder formulations consisting of model controlled-release excipients, providing information related to (a) drug diffusion properties of controlled-release dry powder blends with different excipient components and (b) the effect of varying drug concentration within a given formulation.
Directory of Open Access Journals (Sweden)
Hong Lei
2016-05-01
Full Text Available Microencapsulation is highly attractive for oral drug delivery. Microparticles are a common form of drug carrier for this purpose. There is still a high demand on efficient methods to fabricate microparticles with uniform sizes and well-controlled particle properties. In this paper, uniform hydroxypropyl methylcellulose phthalate (HPMCP-based pharmaceutical microparticles loaded with either hydrophobic or hydrophilic model drugs have been directly formulated by using a unique aerosol technique, i.e., the microfluidic spray drying technology. A series of microparticles of controllable particle sizes, shapes, and structures are fabricated by tuning the solvent composition and drying temperature. It is found that a more volatile solvent and a higher drying temperature can result in fast evaporation rates to form microparticles of larger lateral size, more irregular shape, and denser matrix. The nature of the model drugs also plays an important role in determining particle properties. The drug release behaviors of the pharmaceutical microparticles are dependent on their structural properties and the nature of a specific drug, as well as sensitive to the pH value of the release medium. Most importantly, drugs in the microparticles obtained by using a more volatile solvent or a higher drying temperature can be well protected from degradation in harsh simulated gastric fluids due to the dense structures of the microparticles, while they can be fast-released in simulated intestinal fluids through particle dissolution. These pharmaceutical microparticles are potentially useful for site-specific (enteric delivery of orally-administered drugs.
Detection of Carbonaceous Aerosols Released in CNT Workplaces Using an Aethalometer.
Kim, Jong Bum; Kim, Kyung Hwan; Yun, Seong-Taek; Bae, Gwi-Nam
2016-07-01
Black carbon (BC) originating from various combustion sources has been extensively surveyed to characterize the effects of BC on global warming and human health, and many online monitors are available. In this study, BC was considered as a surrogate for carbon-based nanomaterials in an occupational health study. Specifically, BC concentrations were monitored continuously with an aethalometer for 24h at four carbon nanotube (CNT) workplaces located in rural, urban, and industrial areas, which had different background air pollution levels. Average BC concentrations for both nonworking (background) and working periods were compared with the recommended exposure limit (REL) of 1 μg m(-3) for elemental carbon that was suggested by the National Institute for Occupational Safety and Health (NIOSH). Diurnal variation of BC concentrations indicated that BC measurements corresponded well with carbonaceous aerosols such as vehicle exhaust particles and CNT aerosols. In the rural CNT workplace, the average background BC concentration (0.36 μg m(-3)) was lower than the REL, but the BC concentration without background correction was higher than the REL during manufacturing hours. In this case, BC measurement is useful to estimate CNT exposure for comparison with the REL. Conversely, in the urban and industrial CNT workplaces, average background BC concentrations (2.05, 1.82, and 2.64 μg m(-3)) were well above the REL, and during working hours, BC concentrations were substantially higher than the background level at workplace C; however, BC concentrations showed no difference from the background levels at workplaces B and D. In these cases (B and D), it is hard to determine CNT exposure because of the substantial environmental exposures. Most of the urban ambient BC concentrations were above the REL. Therefore, further analysis and test methods for carbonaceous aerosols need to be developed so that the exposure assessment can be easily carried out at CNT workplaces with high
Large methane releases lead to strong aerosol forcing and reduced cloudiness
DEFF Research Database (Denmark)
Kurten, T.; Zhou, L.; Makkonen, R.
2011-01-01
The release of vast quantities of methane into the atmosphere as a result of clathrate destabilization is a potential mechanism for rapid amplification of global warming. Previous studies have calculated the enhanced warming based mainly on the radiative effect of the methane itself, with smaller...... is predicted to significantly decrease hydroxyl radical (OH) concentrations, while moderately increasing ozone (O-3). These changes lead to a 70% increase in the atmospheric lifetime of methane, and an 18% decrease in global mean cloud droplet number concentrations (CDNC). The CDNC change causes a radiative...... forcing that is comparable in magnitude to the long-wave radiative forcing ("enhanced greenhouse effect") of the added methane. Together, the indirect CH4-O-3 and CH4-OHaerosol forcings could more than double the warming effect of large methane increases. Our findings may help explain the anomalously...
Maynard, Andrew D; Baron, Paul A; Foley, Michael; Shvedova, Anna A; Kisin, Elena R; Castranova, Vincent
2004-01-09
Carbon nanotubes represent a relatively recently discovered allotrope of carbon that exhibits unique properties. While commercial interest in the material is leading to the development of mass production and handling facilities, little is known of the risk associated with exposure. In a two-part study, preliminary investigations have been carried out into the potential exposure routes and toxicity of single-walled carbon nanotube material (SWCNT)--a specific form of the allotrope. The material is characterized by bundles of fibrous carbon molecules that may be a few nanometers in diameter, but micrometers in length. The two production processes investigated use-transition metal catalysts, leading to the inclusion of nanometer-scale metallic particles within unrefined SWCNT material. A laboratory-based study was undertaken to evaluate the physical nature of the aerosol formed from SWCNT during mechanical agitation. This was complemented by a field study in which airborne and dermal exposure to SWCNT was investigated while handling unrefined material. Although laboratory studies indicated that with sufficient agitation, unrefined SWCNT material can release fine particles into the air, concentrations generated while handling material in the field were very low. Estimates of the airborne concentration of nanotube material generated during handling suggest that concentrations were lower than 53 microg/m(3) in all cases. Glove deposits of SWCNT during handling were estimated at between 0.2 mg and 6 mg per hand.
Li, Qingrui; Zhan, Shuyao; Liu, Qing; Su, Hao; Dai, Xi; Wang, Hai; Beng, Huimin; Tan, Wen
2018-01-01
An aerosolized liposome formulation for the pulmonary delivery of an anti-asthmatic medication was developed. Asthma treatment usually requires frequent administration of medication for a sustained bronchodilator response. Liposomes are known for their sustained drug release capability and thus would be a suitable delivery system for prolonging the therapeutic effect of anti-asthmatic medication. Liposomes prepared by thin film hydration were loaded with a model drug, R-terbutaline hydrochloride(R-TBH), using an ammonium sulfate-induced transmembrane electrochemical gradient. This technique provided an encapsulation efficiency of up to 71.35% and yielded R-TBH liposomes with a particle size of approximately 145 ± 20 nm. According to stability studies, these R-TBH liposomes should be stored at 4°C before usage. Compared to R-TBH solution, which showed 90.84% release within 8 h, liposomal R-TBH had a cumulative release of 73.53% at 37°C over 192 h. A next generation impactor (NGI) was used to analyze the particle size distribution in the lungs of R-TBH liposome aerosol in vitro at 5°C. The therapeutic efficacy of the nebulized aerosol of the R-TBH liposomes was assessed via pulmonary delivery in guinea pigs. The results showed that, compared to the R-TBH solution group, the R-TBH liposome group had a prolonged anti-asthma effect.
International Nuclear Information System (INIS)
Bourham, Mohamed A.; Gilligan, John G.
1999-01-01
Safety considerations in large future fusion reactors like ITER are important before licensing the reactor. Several scenarios are considered hazardous, which include safety of plasma-facing components during hard disruptions, high heat fluxes and thermal stresses during normal operation, accidental energy release, and aerosol formation and transport. Disruption events, in large tokamaks like ITER, are expected to produce local heat fluxes on plasma-facing components, which may exceed 100 GW/m 2 over a period of about 0.1 ms. As a result, the surface temperature dramatically increases, which results in surface melting and vaporization, and produces thermal stresses and surface erosion. Plasma-facing components safety issues extends to cover a wide range of possible scenarios, including disruption severity and the impact of plasma-facing components on disruption parameters, accidental energy release and short/long term LOCA's, and formation of airborne particles by convective current transport during a LOVA (water/air ingress disruption) accident scenario. Study, and evaluation of, disruption-induced aerosol generation and mobilization is essential to characterize database on particulate formation and distribution for large future fusion tokamak reactor like ITER. In order to provide database relevant to ITER, the SIRENS electrothermal plasma facility at NCSU has been modified to closely simulate heat fluxes expected in ITER
Lesaffre, Emmanuel
2012-01-01
The growth of biostatistics has been phenomenal in recent years and has been marked by considerable technical innovation in both methodology and computational practicality. One area that has experienced significant growth is Bayesian methods. The growing use of Bayesian methodology has taken place partly due to an increasing number of practitioners valuing the Bayesian paradigm as matching that of scientific discovery. In addition, computational advances have allowed for more complex models to be fitted routinely to realistic data sets. Through examples, exercises and a combination of introd
International Nuclear Information System (INIS)
Ginsberg, T.
1983-02-01
A model for calculation of the aerosol generation rate resulting from surface bubble rupture during molten core-concrete interactions is discussed. One aspect of the model, based upon previous work in the literature, considers that film rupture occurs due to growth of film oscillation disturbances in the surface liquid film. Calculations are presented for molten pools with liquid properties in the range of prototypic interest
International Nuclear Information System (INIS)
Camus, H.; Delmas, J.; Grauby, A.; Disdier, R.
1977-01-01
Industrial development of sodium cooled breeder reactors have lead to the study of consequences of accidents occuring in the cooling circuit. The effects on crops when aerosols are released in the atmosphere can be determined after a certain amount of molten sodium (stable element 23 Na) is brought into contact with the open air. Experiments are performed in a 'Phytotron' equipment on lettuces and tomatoes in dry atmosphere at two different moments during the growing process. The aerosol studied has a high content of sodium peroxide and the ground deposit varied between 17 and 450 mg/m 2 sodium equivalent. (Concentration at distances of 800 m and 1500 m calculated for a theoretical fire involving 10 tons of sodium). Necrosis, visible only with a microscope, were reported when deposits in sodium equivalent were under 300 mg/m 2 . Leaves were destroyed if necrosis were numerous (deposit above 300 mg/m 2 ). Tomatoes, and fruits in particular, were found to be more resistant than lettuces. Sodium embedded in dead cells does not migrate in the plant and does not disturb the plants physiological equilibrium. New sprouts have normal sodium percentage. The consequences are essentially burns of which the extent is more or less high depending on the deposits and the kind of species involved [fr
International Nuclear Information System (INIS)
Bowsher, B.R.; Nichols, A.L.
1989-12-01
A comprehensive review has been undertaken of appropriate analytical techniques to monitor and measure the chemical effects that occur in large-scale tests designed to study severe reactor accidents. Various methods have been developed to determine the chemical forms of the vapours, aerosols and deposits generated during and after such integral experiments. Other specific techniques have the long-term potential to provide some of the desired data in greater detail, although considerable efforts are still required to apply these techniques to the study of radioactive debris. Such in-situ and post-test methods of analysis have been also assessed in terms of their applicability to the analysis of samples from the Phebus-FP tests. The recommended in-situ methods of analysis are gamma-ray spectroscopy, potentiometry, mass spectrometry, and Raman/UV-visible absorption spectroscopy. Vapour/aerosol and deposition samples should also be obtained at well-defined time intervals during each experiment for subsequent post-test analysis. No single technique can provide all the necessary chemical data from these samples, and the most appropriate method of analysis involves a complementary combination of autoradiography, AES, IR, MRS, SEMS/EDS, SIMS/LMIS, XPS and XRD
DEFF Research Database (Denmark)
Jappe Frandsen, Flemming
2017-01-01
is that there are still in 2017, a number of big gaps in our current understanding of these phenomena, and that we need focus on these points, in order to be able to describe, understand, and, quantify the processes of ash and deposit formation completely [Frandsen, 2009].This paper provide a brief outline of the current......) shedding of deposits. Some of the steps may be repetitive, as the process is partly cyclic [Frandsen, 2011]. The inorganic fraction of solid fuels, may cause several problems during combustion, most importantly formation of particulate matter (aerosols and fly ashes). These may subsequently induce deposit...... of combustion units.Through several years, high quality research has been conducted on characterization of fuels, ashes and deposit formation in utility boilers fired with coal, biomass and waste fractions. Huge amounts of experimental data have been reported, from such work, but the fact...
International Nuclear Information System (INIS)
Agethen, K.; Koch, M.K.
2016-04-01
The present report is the 3 rd Technical Report within the research project ''ASMO'' founded by the German Federal Ministry for Economic Affairs and Energy (BMWi 1501433) and projected at the Chair of Energy Systems and Energy Economics (LEE) within the workgroup Reactor Simulation and Safety at the Ruhr-Universitaet Bochum (RUB). The focus in this report is set on the release of fission products and the contribution to the source term, which is formed in the late phase after failure of the reactor pressure vessel during MCCI. By comparing the RUB simulation results including the fission product release rates with further simulations of GRS and VEIKI it can be indicated that the simulations have a high sensitivity in respect to the melting point temperature. It can be noted that the release rates are underestimated for most fission product species with the current model. Especially semi-volatile fission products and the lanthanum release is underestimated by several orders of magnitude. Based on the ACE experiment L2, advanced considerations are presented concerning the melt temperature, the gas temperature, the segregation and a varied melt configuration. Furthermore, the influence of the gas velocity is investigated. This variation of the gas velocity causes an underestimation of the release rates compared to the RUB base calculation. A model extension to oxidic species for lanthanum and ruthenium shows a significant improvement of the simulation results. In addition, the MEDICIS module has been enhanced to document the currently existing species, are displayed in a *.ist-file. This expansion shows inconsistencies between the melt composition and the fission product composition. Based on these results, there are still some difficulties regarding the release of fission products in the MEDICIS module and the interaction with the material data base (MOB) which needs further investigation.
International Nuclear Information System (INIS)
Manixay, S; Bencsik, A; Delaby, S; Gaie-Levrel, F; Wiart, M; Motzkus, C
2017-01-01
Engineered Nanomaterials (ENM) provide technical and specific benefits due to their physical-chemical properties at the nanometer scale. For instance, many ENM are used to improve products in the building industry. Nanoscaled titanium dioxide (TiO 2 ) is one of the most used ENM in this industry. Incorporated in different matrix, cement, glass, paints… TiO 2 nanoparticles (NPs) provide the final product with anti-UV, air purification and self-cleaning properties, thanks to their photocatalytic activity. However, ageing processes of such products, as photocatalytic paints, during a mechanical stress have been shown to release TiO 2 NPs from this matrix associated with sanding dust. Thus, workers who sand painted walls could be exposed to TiO 2 NPs through inhalation. As inhalation may lead to a translocation of particulate matter to the brain via olfactory or trigeminal nerves, there is an urgent need for evaluating a potential neurotoxicity. In order to provide new knowledge on this topic, we developed a dedicated experimental set-up using a rodent model exposed via inhalation. The aerosol released from a mechanical stress of photocatalytic paints containing TiO 2 NPs was characterized and coupled to an exposition chamber containing group of mice free to move and chronically exposed (2 hours per day for 5 days a week during 8 weeks). (paper)
Manixay, S.; Delaby, S.; Gaie-Levrel, F.; Wiart, M.; Motzkus, C.; Bencsik, A.
2017-06-01
Engineered Nanomaterials (ENM) provide technical and specific benefits due to their physical-chemical properties at the nanometer scale. For instance, many ENM are used to improve products in the building industry. Nanoscaled titanium dioxide (TiO2) is one of the most used ENM in this industry. Incorporated in different matrix, cement, glass, paints… TiO2 nanoparticles (NPs) provide the final product with anti-UV, air purification and self-cleaning properties, thanks to their photocatalytic activity. However, ageing processes of such products, as photocatalytic paints, during a mechanical stress have been shown to release TiO2 NPs from this matrix associated with sanding dust. Thus, workers who sand painted walls could be exposed to TiO2 NPs through inhalation. As inhalation may lead to a translocation of particulate matter to the brain via olfactory or trigeminal nerves, there is an urgent need for evaluating a potential neurotoxicity. In order to provide new knowledge on this topic, we developed a dedicated experimental set-up using a rodent model exposed via inhalation. The aerosol released from a mechanical stress of photocatalytic paints containing TiO2 NPs was characterized and coupled to an exposition chamber containing group of mice free to move and chronically exposed (2 hours per day for 5 days a week during 8 weeks).
Bessiere, Pierre; Ahuactzin, Juan Manuel; Mekhnacha, Kamel
2013-01-01
Probability as an Alternative to Boolean LogicWhile logic is the mathematical foundation of rational reasoning and the fundamental principle of computing, it is restricted to problems where information is both complete and certain. However, many real-world problems, from financial investments to email filtering, are incomplete or uncertain in nature. Probability theory and Bayesian computing together provide an alternative framework to deal with incomplete and uncertain data. Decision-Making Tools and Methods for Incomplete and Uncertain DataEmphasizing probability as an alternative to Boolean
Energy Technology Data Exchange (ETDEWEB)
Journeau, Ch.; Piluso, P.; Correggio, P.; Godin-Jacqmin, L
2007-07-01
In a hypothetical case of severe accident in a PWR type VVER-440, a complex corium pool could be formed and fission products could be released. In order to study aerosols release in terms of mechanisms, kinetics, nature or quantity, and to better precise the source term of VVER-440, a series of experiments have been performed in the Colima facility and the test Colima CA-U3 has been successfully performed thanks to technological modifications to melt a prototypical corium at 2760 C degrees. Specific instrumentation has allowed us to follow the evolution of the corium melt and the release, transport and deposition of the fission products. The main conclusions are: -) there is a large release of Cr, Te, Sr, Pr and Rh (>95%w), -) there is a significant release of Fe (50%w), -) there is a small release of Ba, Ce, La, Nb, Nd and Y (<90%w), -) there is a very small release of U in proportion (<5%w) but it is one of the major released species in mass, and -) there is no release of Zr. The Colima experimental results are consistent with previous experiments on irradiated fuels except for Ba, Fe and U releases. (A.C.)
International Nuclear Information System (INIS)
Lanza, S.; Mariotti, P.
1986-01-01
The US program LACE (LWR Aerosol Containment Experiments), in which Italy participates together with several European countries, Canada and Japan, aims at evaluating by means of a large scale experimental activity at HEDL the retention in the pipings and primary container of the radioactive aerosol released following severe accidents in light water reactors. At the same time these experiences will make available data through which the codes used to analyse the behaviour of the aerosol in the containment and to verify whether by means of the codes of thermohydraulic computation it is possible to evaluate with sufficient accuracy variable influencing the aerosol behaviour, can be validated. This report shows and compares the results obtained by the participants in the LACE program with the aerosol containment codes NAVA 5 and CONTAIN for the pre-test computations of the test LA 1, in which an accident called containment by pass is simulated
CATS Aerosol Typing and Future Directions
McGill, Matt; Yorks, John; Scott, Stan; Palm, Stephen; Hlavka, Dennis; Hart, William; Nowottnick, Ed; Selmer, Patrick; Kupchock, Andrew; Midzak, Natalie;
2016-01-01
The Cloud Aerosol Transport System (CATS), launched in January of 2015, is a lidar remote sensing instrument that will provide range-resolved profile measurements of atmospheric aerosols and clouds from the International Space Station (ISS). CATS is intended to operate on-orbit for at least six months, and up to three years. Status of CATS Level 2 and Plans for the Future:Version. 1. Aerosol Typing (ongoing): Mode 1: L1B data released later this summer; L2 data released shortly after; Identify algorithm biases (ex. striping, FOV (field of view) biases). Mode 2: Processed Released Currently working on correcting algorithm issues. Version 2 Aerosol Typing (Fall, 2016): Implementation of version 1 modifications Integrate GEOS-5 aerosols for typing guidance for non spherical aerosols. Version 3 Aerosol Typing (2017): Implementation of 1-D Var Assimilation into GEOS-5 Dynamic lidar ratio that will evolve in conjunction with simulated aerosol mixtures.
Introduction to Bayesian statistics
Bolstad, William M
2017-01-01
There is a strong upsurge in the use of Bayesian methods in applied statistical analysis, yet most introductory statistics texts only present frequentist methods. Bayesian statistics has many important advantages that students should learn about if they are going into fields where statistics will be used. In this Third Edition, four newly-added chapters address topics that reflect the rapid advances in the field of Bayesian staistics. The author continues to provide a Bayesian treatment of introductory statistical topics, such as scientific data gathering, discrete random variables, robust Bayesian methods, and Bayesian approaches to inferenfe cfor discrete random variables, bionomial proprotion, Poisson, normal mean, and simple linear regression. In addition, newly-developing topics in the field are presented in four new chapters: Bayesian inference with unknown mean and variance; Bayesian inference for Multivariate Normal mean vector; Bayesian inference for Multiple Linear RegressionModel; and Computati...
Bayesian artificial intelligence
Korb, Kevin B
2003-01-01
As the power of Bayesian techniques has become more fully realized, the field of artificial intelligence has embraced Bayesian methodology and integrated it to the point where an introduction to Bayesian techniques is now a core course in many computer science programs. Unlike other books on the subject, Bayesian Artificial Intelligence keeps mathematical detail to a minimum and covers a broad range of topics. The authors integrate all of Bayesian net technology and learning Bayesian net technology and apply them both to knowledge engineering. They emphasize understanding and intuition but also provide the algorithms and technical background needed for applications. Software, exercises, and solutions are available on the authors' website.
Bayesian artificial intelligence
Korb, Kevin B
2010-01-01
Updated and expanded, Bayesian Artificial Intelligence, Second Edition provides a practical and accessible introduction to the main concepts, foundation, and applications of Bayesian networks. It focuses on both the causal discovery of networks and Bayesian inference procedures. Adopting a causal interpretation of Bayesian networks, the authors discuss the use of Bayesian networks for causal modeling. They also draw on their own applied research to illustrate various applications of the technology.New to the Second EditionNew chapter on Bayesian network classifiersNew section on object-oriente
International Nuclear Information System (INIS)
Penner, J.E.
1994-01-01
Organic aerosols scatter solar radiation. They may also either enhance or decrease concentrations of cloud condensation nuclei. This paper summarizes observed concentrations of aerosols in remote continental and marine locations and provides estimates for the sources of organic aerosol matter. The anthropogenic sources of organic aerosols may be as large as the anthropogenic sources of sulfate aerosols, implying a similar magnitude of direct forcing of climate. The source estimates are highly uncertain and subject to revision in the future. A slow secondary source of organic aerosols of unknown origin may contribute to the observed oceanic concentrations. The role of organic aerosols acting as cloud condensation nuclei (CCN) is described and it is concluded that they may either enhance or decrease the ability of anthropogenic sulfate aerosols to act as CCN
International Nuclear Information System (INIS)
Chamberlain, A.C.
1991-01-01
Radon. Fission product aerosols. Radioiodine. Tritium. Plutonium. Mass transfer of radioactive vapours and aerosols. Studies with radioactive particles and human subjects. Index. This paper explores the environmental and health aspects of radioactive aerosols. Covers radioactive nuclides of potential concern to public health and applications to the study of boundary layer transport. Contains bibliographic references. Suitable for environmental chemistry collections in academic and research libraries
Understanding Computational Bayesian Statistics
Bolstad, William M
2011-01-01
A hands-on introduction to computational statistics from a Bayesian point of view Providing a solid grounding in statistics while uniquely covering the topics from a Bayesian perspective, Understanding Computational Bayesian Statistics successfully guides readers through this new, cutting-edge approach. With its hands-on treatment of the topic, the book shows how samples can be drawn from the posterior distribution when the formula giving its shape is all that is known, and how Bayesian inferences can be based on these samples from the posterior. These ideas are illustrated on common statistic
Bayesian statistics an introduction
Lee, Peter M
2012-01-01
Bayesian Statistics is the school of thought that combines prior beliefs with the likelihood of a hypothesis to arrive at posterior beliefs. The first edition of Peter Lee’s book appeared in 1989, but the subject has moved ever onwards, with increasing emphasis on Monte Carlo based techniques. This new fourth edition looks at recent techniques such as variational methods, Bayesian importance sampling, approximate Bayesian computation and Reversible Jump Markov Chain Monte Carlo (RJMCMC), providing a concise account of the way in which the Bayesian approach to statistics develops as wel
DEFF Research Database (Denmark)
Jensen, Finn Verner; Nielsen, Thomas Dyhre
2016-01-01
is largely due to the availability of efficient inference algorithms for answering probabilistic queries about the states of the variables in the network. Furthermore, to support the construction of Bayesian network models, learning algorithms are also available. We give an overview of the Bayesian network...
Kleibergen, F.R.; Kleijn, R.; Paap, R.
2000-01-01
We propose a novel Bayesian test under a (noninformative) Jeffreys'priorspecification. We check whether the fixed scalar value of the so-calledBayesian Score Statistic (BSS) under the null hypothesis is aplausiblerealization from its known and standardized distribution under thealternative. Unlike
Yuan, Ying; MacKinnon, David P.
2009-01-01
In this article, we propose Bayesian analysis of mediation effects. Compared with conventional frequentist mediation analysis, the Bayesian approach has several advantages. First, it allows researchers to incorporate prior information into the mediation analysis, thus potentially improving the efficiency of estimates. Second, under the Bayesian…
DSMC multicomponent aerosol dynamics: Sampling algorithms and aerosol processes
Palaniswaamy, Geethpriya
The post-accident nuclear reactor primary and containment environments can be characterized by high temperatures and pressures, and fission products and nuclear aerosols. These aerosols evolve via natural transport processes as well as under the influence of engineered safety features. These aerosols can be hazardous and may pose risk to the public if released into the environment. Computations of their evolution, movement and distribution involve the study of various processes such as coagulation, deposition, condensation, etc., and are influenced by factors such as particle shape, charge, radioactivity and spatial inhomogeneity. These many factors make the numerical study of nuclear aerosol evolution computationally very complicated. The focus of this research is on the use of the Direct Simulation Monte Carlo (DSMC) technique to elucidate the role of various phenomena that influence the nuclear aerosol evolution. In this research, several aerosol processes such as coagulation, deposition, condensation, and source reinforcement are explored for a multi-component, aerosol dynamics problem in a spatially homogeneous medium. Among the various sampling algorithms explored the Metropolis sampling algorithm was found to be effective and fast. Several test problems and test cases are simulated using the DSMC technique. The DSMC results obtained are verified against the analytical and sectional results for appropriate test problems. Results show that the assumption of a single mean density is not appropriate due to the complicated effect of component densities on the aerosol processes. The methods developed and the insights gained will also be helpful in future research on the challenges associated with the description of fission product and aerosol releases.
International Nuclear Information System (INIS)
Rosen, J.; Ivanov, V.A.
1993-01-01
Stratospheric aerosol measurements can provide both spatial and temporal data of sufficient resolution to be of use in climate models. Relatively recent results from a wide range of instrument techniques for measuring stratospheric aerosol parameters are described. Such techniques include impactor sampling, lidar system sensing, filter sampling, photoelectric particle counting, satellite extinction-sensing using the sun as a source, and optical depth probing, at sites mainly removed from tropospheric aerosol sources. Some of these techniques have also had correlative and intercomparison studies. The main methods for determining the vertical profiles of stratospheric aerosols are outlined: lidar extinction measurements from satellites; impactor measurements from balloons and aircraft; and photoelectric particle counter measurements from balloons, aircraft, and rockets. The conversion of the lidar backscatter to stratospheric aerosol mass loading is referred to. Absolute measurements of total solar extinction from satellite orbits can be used to extract the aerosol extinction, and several examples of vertical profiles of extinction obtained with the SAGE satellite are given. Stratospheric mass loading can be inferred from extinction using approximate linear relationships but under restrictive conditions. Impactor sampling is essentially the only method in which the physical nature of the stratospheric aerosol is observed visually. Vertical profiles of stratospheric aerosol number concentration using impactor data are presented. Typical profiles using a dual-size-range photoelectric dustsonde particle counter are given for volcanically disturbed and inactive periods. Some measurements of the global distribution of stratospheric aerosols are also presented. Volatility measurements are described, indicating that stratospheric aerosols are composed primarily of about 75% sulfuric acid and 25% water
van Maris, V. R.; Naji, M.; Di Lemma, F.G.; Colle, J-Y; Bykov, D.; Konings, R.J.M.
2017-01-01
During rapid high-temperature events, like a terrorist attack with radiological dispersal device, radiological material will be released into the environment. In these scenarios, the ratio between parent and daughter nuclides can be used for nuclear forensic investigations to determine the age of
Bayesian data analysis for newcomers.
Kruschke, John K; Liddell, Torrin M
2018-02-01
This article explains the foundational concepts of Bayesian data analysis using virtually no mathematical notation. Bayesian ideas already match your intuitions from everyday reasoning and from traditional data analysis. Simple examples of Bayesian data analysis are presented that illustrate how the information delivered by a Bayesian analysis can be directly interpreted. Bayesian approaches to null-value assessment are discussed. The article clarifies misconceptions about Bayesian methods that newcomers might have acquired elsewhere. We discuss prior distributions and explain how they are not a liability but an important asset. We discuss the relation of Bayesian data analysis to Bayesian models of mind, and we briefly discuss what methodological problems Bayesian data analysis is not meant to solve. After you have read this article, you should have a clear sense of how Bayesian data analysis works and the sort of information it delivers, and why that information is so intuitive and useful for drawing conclusions from data.
Bayesian methods for data analysis
Carlin, Bradley P.
2009-01-01
Approaches for statistical inference Introduction Motivating Vignettes Defining the Approaches The Bayes-Frequentist Controversy Some Basic Bayesian Models The Bayes approach Introduction Prior Distributions Bayesian Inference Hierarchical Modeling Model Assessment Nonparametric Methods Bayesian computation Introduction Asymptotic Methods Noniterative Monte Carlo Methods Markov Chain Monte Carlo Methods Model criticism and selection Bayesian Modeling Bayesian Robustness Model Assessment Bayes Factors via Marginal Density Estimation Bayes Factors
Statistics: a Bayesian perspective
National Research Council Canada - National Science Library
Berry, Donald A
1996-01-01
...: it is the only introductory textbook based on Bayesian ideas, it combines concepts and methods, it presents statistics as a means of integrating data into the significant process, it develops ideas...
Noncausal Bayesian Vector Autoregression
DEFF Research Database (Denmark)
Lanne, Markku; Luoto, Jani
We propose a Bayesian inferential procedure for the noncausal vector autoregressive (VAR) model that is capable of capturing nonlinearities and incorporating effects of missing variables. In particular, we devise a fast and reliable posterior simulator that yields the predictive distribution...
Granade, Christopher; Combes, Joshua; Cory, D. G.
2016-03-01
In recent years, Bayesian methods have been proposed as a solution to a wide range of issues in quantum state and process tomography. State-of-the-art Bayesian tomography solutions suffer from three problems: numerical intractability, a lack of informative prior distributions, and an inability to track time-dependent processes. Here, we address all three problems. First, we use modern statistical methods, as pioneered by Huszár and Houlsby (2012 Phys. Rev. A 85 052120) and by Ferrie (2014 New J. Phys. 16 093035), to make Bayesian tomography numerically tractable. Our approach allows for practical computation of Bayesian point and region estimators for quantum states and channels. Second, we propose the first priors on quantum states and channels that allow for including useful experimental insight. Finally, we develop a method that allows tracking of time-dependent states and estimates the drift and diffusion processes affecting a state. We provide source code and animated visual examples for our methods.
Variational Bayesian Filtering
Czech Academy of Sciences Publication Activity Database
Šmídl, Václav; Quinn, A.
2008-01-01
Roč. 56, č. 10 (2008), s. 5020-5030 ISSN 1053-587X R&D Projects: GA MŠk 1M0572 Institutional research plan: CEZ:AV0Z10750506 Keywords : Bayesian filtering * particle filtering * Variational Bayes Subject RIV: BC - Control Systems Theory Impact factor: 2.335, year: 2008 http://library.utia.cas.cz/separaty/2008/AS/smidl-variational bayesian filtering.pdf
Bayesian Networks An Introduction
Koski, Timo
2009-01-01
Bayesian Networks: An Introduction provides a self-contained introduction to the theory and applications of Bayesian networks, a topic of interest and importance for statisticians, computer scientists and those involved in modelling complex data sets. The material has been extensively tested in classroom teaching and assumes a basic knowledge of probability, statistics and mathematics. All notions are carefully explained and feature exercises throughout. Features include:.: An introduction to Dirichlet Distribution, Exponential Families and their applications.; A detailed description of learni
Buseck, P. R.; Schwartz, S. E.
2003-12-01
It is widely believed that "On a clear day you can see forever," as proclaimed in the 1965 Broadway musical of the same name. While an admittedly beautiful thought, we all know that this concept is only figurative. Aside from Earth's curvature and Rayleigh scattering by air molecules, aerosols - colloidal suspensions of solid or liquid particles in a gas - limit our vision. Even on the clearest day, there are billions of aerosol particles per cubic meter of air.Atmospheric aerosols are commonly referred to as smoke, dust, haze, and smog, terms that are loosely reflective of their origin and composition. Aerosol particles have arisen naturally for eons from sea spray, volcanic emissions, wind entrainment of mineral dust, wildfires, and gas-to-particle conversion of hydrocarbons from plants and dimethylsulfide from the oceans. However, over the industrial period, the natural background aerosol has been greatly augmented by anthropogenic contributions, i.e., those produced by human activities. One manifestation of this impact is reduced visibility (Figure 1). Thus, perhaps more than in other realms of geochemistry, when considering the composition of the troposphere one must consider the effects of these activities. The atmosphere has become a reservoir for vast quantities of anthropogenic emissions that exert important perturbations on it and on the planetary ecosystem in general. Consequently, much recent research focuses on the effects of human activities on the atmosphere and, through them, on the environment and Earth's climate. For these reasons consideration of the geochemistry of the atmosphere, and of atmospheric aerosols in particular, must include the effects of human activities. (201K)Figure 1. Impairment of visibility by aerosols. Photographs at Yosemite National Park, California, USA. (a) Low aerosol concentration (particulate matter of aerodynamic diameter less than 2.5 μm, PM2.5=0.3 μg m-3; particulate matter of aerodynamic diameter less than 10
Toxicity of atmospheric aerosols on marine phytoplankton
Paytan, A.; Mackey, K.R.M.; Chen, Y.; Lima, I.D.; Doney, S.C.; Mahowald, N.; Labiosa, R.; Post, A.F.
2009-01-01
Atmospheric aerosol deposition is an important source of nutrients and trace metals to the open ocean that can enhance ocean productivity and carbon sequestration and thus influence atmospheric carbon dioxide concentrations and climate. Using aerosol samples from different back trajectories in incubation experiments with natural communities, we demonstrate that the response of phytoplankton growth to aerosol additions depends on specific components in aerosols and differs across phytoplankton species. Aerosol additions enhanced growth by releasing nitrogen and phosphorus, but not all aerosols stimulated growth. Toxic effects were observed with some aerosols, where the toxicity affected picoeukaryotes and Synechococcus but not Prochlorococcus.We suggest that the toxicity could be due to high copper concentrations in these aerosols and support this by laboratory copper toxicity tests preformed with Synechococcus cultures. However, it is possible that other elements present in the aerosols or unknown synergistic effects between these elements could have also contributed to the toxic effect. Anthropogenic emissions are increasing atmospheric copper deposition sharply, and based on coupled atmosphere-ocean calculations, we show that this deposition can potentially alter patterns of marine primary production and community structure in high aerosol, low chlorophyll areas, particularly in the Bay of Bengal and downwind of South and East Asia.
Bayesian Exploratory Factor Analysis
DEFF Research Database (Denmark)
Conti, Gabriella; Frühwirth-Schnatter, Sylvia; Heckman, James J.
2014-01-01
This paper develops and applies a Bayesian approach to Exploratory Factor Analysis that improves on ad hoc classical approaches. Our framework relies on dedicated factor models and simultaneously determines the number of factors, the allocation of each measurement to a unique factor, and the corr......This paper develops and applies a Bayesian approach to Exploratory Factor Analysis that improves on ad hoc classical approaches. Our framework relies on dedicated factor models and simultaneously determines the number of factors, the allocation of each measurement to a unique factor......, and the corresponding factor loadings. Classical identification criteria are applied and integrated into our Bayesian procedure to generate models that are stable and clearly interpretable. A Monte Carlo study confirms the validity of the approach. The method is used to produce interpretable low dimensional aggregates...
Analysis of aerosol agglomeration and removal mechanisms relevant to a reactor containment
International Nuclear Information System (INIS)
Chiang, H.W.; Mulpuru, S.R.; Lindquist, E.D.
1995-01-01
During some Postulated accidents in a nuclear reactor, radioactive aerosols may be formed and could be released from a rupture of the primary heat transport system into the containment. The released aerosols can agglomerate and form larger aerosol particles. The airborne aerosols can be removed from containment atmosphere by deposition onto the walls and other surfaces in contact with the gas-aerosol mixture. The rate of removal of aerosols depends on the aerosol size, which, in turn, is related to the amount of agglomeration of the aerosol particles. The extent of the removal of the aerosol mass from the containment atmosphere is important in determining the potential radioactive releases to the outside atmosphere. In this paper, selected conditions have been assessed to illustrate the significance of agglomeration for situations potentially of interest in containment safety studies
Berliner, M.
2017-12-01
Bayesian statistical decision theory offers a natural framework for decision-policy making in the presence of uncertainty. Key advantages of the approach include efficient incorporation of information and observations. However, in complicated settings it is very difficult, perhaps essentially impossible, to formalize the mathematical inputs needed in the approach. Nevertheless, using the approach as a template is useful for decision support; that is, organizing and communicating our analyses. Bayesian hierarchical modeling is valuable in quantifying and managing uncertainty such cases. I review some aspects of the idea emphasizing statistical model development and use in the context of sea-level rise.
Bayesian Exploratory Factor Analysis
Conti, Gabriella; Frühwirth-Schnatter, Sylvia; Heckman, James J.; Piatek, Rémi
2014-01-01
This paper develops and applies a Bayesian approach to Exploratory Factor Analysis that improves on ad hoc classical approaches. Our framework relies on dedicated factor models and simultaneously determines the number of factors, the allocation of each measurement to a unique factor, and the corresponding factor loadings. Classical identification criteria are applied and integrated into our Bayesian procedure to generate models that are stable and clearly interpretable. A Monte Carlo study confirms the validity of the approach. The method is used to produce interpretable low dimensional aggregates from a high dimensional set of psychological measurements. PMID:25431517
2016-03-01
comparison of these Bayesian- inferred IDF curves under stationary and nonstationary conditions. BACKGROUND: Probability concepts and related relevant...second edition, texts in statistical science. United Kingdom: Chapman & Hall/CRC. Gelman, A., and D. B. Rubin. 1992. Inference from iterative...ERDC/CHL CHETN-X-2 March 2016 Approved for public release; distribution is unlimited. Bayesian Inference of Nonstationary Precipitation Intensity
Bayesian methods for hackers probabilistic programming and Bayesian inference
Davidson-Pilon, Cameron
2016-01-01
Bayesian methods of inference are deeply natural and extremely powerful. However, most discussions of Bayesian inference rely on intensely complex mathematical analyses and artificial examples, making it inaccessible to anyone without a strong mathematical background. Now, though, Cameron Davidson-Pilon introduces Bayesian inference from a computational perspective, bridging theory to practice–freeing you to get results using computing power. Bayesian Methods for Hackers illuminates Bayesian inference through probabilistic programming with the powerful PyMC language and the closely related Python tools NumPy, SciPy, and Matplotlib. Using this approach, you can reach effective solutions in small increments, without extensive mathematical intervention. Davidson-Pilon begins by introducing the concepts underlying Bayesian inference, comparing it with other techniques and guiding you through building and training your first Bayesian model. Next, he introduces PyMC through a series of detailed examples a...
Attachment behavior of fission products to solution aerosol
Energy Technology Data Exchange (ETDEWEB)
Takamiya, Koichi; Tanaka, Toru; Nitta, Shinnosuke; Itosu, Satoshi; Sekimoto, Shun; Oki, Yuichi; Ohtsuki, Tsutomu [Research Reactor Institute, Kyoto University, Osaka (Japan)
2016-12-15
Various characteristics such as size distribution, chemical component and radioactivity have been analyzed for radioactive aerosols released from Fukushima Daiichi Nuclear Power Plant. Measured results for radioactive aerosols suggest that the potential transport medium for radioactive cesium was non-sea-salt sulfate. This result indicates that cesium isotopes would preferentially attach with sulfate compounds. In the present work the attachment behavior of fission products to aqueous solution aerosols of sodium salts has been studied using a generation system of solution aerosols and spontaneous fission source of {sup 248}Cm. Attachment ratios of fission products to the solution aerosols were compared among the aerosols generated by different solutions of sodium salt. A significant difference according as a solute of solution aerosols was found in the attachment behavior. The present results suggest the existence of chemical effects in the attachment behavior of fission products to solution aerosols.
Bayesian logistic regression analysis
Van Erp, H.R.N.; Van Gelder, P.H.A.J.M.
2012-01-01
In this paper we present a Bayesian logistic regression analysis. It is found that if one wishes to derive the posterior distribution of the probability of some event, then, together with the traditional Bayes Theorem and the integrating out of nuissance parameters, the Jacobian transformation is an
Bayesian statistical inference
Directory of Open Access Journals (Sweden)
Bruno De Finetti
2017-04-01
Full Text Available This work was translated into English and published in the volume: Bruno De Finetti, Induction and Probability, Biblioteca di Statistica, eds. P. Monari, D. Cocchi, Clueb, Bologna, 1993.Bayesian statistical Inference is one of the last fundamental philosophical papers in which we can find the essential De Finetti's approach to the statistical inference.
Bayesian optimization for materials science
Packwood, Daniel
2017-01-01
This book provides a short and concise introduction to Bayesian optimization specifically for experimental and computational materials scientists. After explaining the basic idea behind Bayesian optimization and some applications to materials science in Chapter 1, the mathematical theory of Bayesian optimization is outlined in Chapter 2. Finally, Chapter 3 discusses an application of Bayesian optimization to a complicated structure optimization problem in computational surface science. Bayesian optimization is a promising global optimization technique that originates in the field of machine learning and is starting to gain attention in materials science. For the purpose of materials design, Bayesian optimization can be used to predict new materials with novel properties without extensive screening of candidate materials. For the purpose of computational materials science, Bayesian optimization can be incorporated into first-principles calculations to perform efficient, global structure optimizations. While re...
The intercomparison of aerosol codes
International Nuclear Information System (INIS)
Dunbar, I.H.; Fermandjian, J.; Gauvain, J.
1988-01-01
The behavior of aerosols in a reactor containment vessel following a severe accident could be an important determinant of the accident source term to the environment. Various processes result in the deposition of the aerosol onto surfaces within the containment, from where they are much less likely to be released. Some of these processes are very sensitive to particle size, so it is important to model the aerosol growth processes: agglomeration and condensation. A number of computer codes have been written to model growth and deposition processes. They have been tested against each other in a series of code comparison exercises. These exercises have investigated sensitivities to physical and numerical assumptions and have also proved a useful means of quality control for the codes. Various exercises in which code predictions are compared with experimental results are now under way
International Nuclear Information System (INIS)
Hayakawa, Hironobu; Oonishi, Masaki; Matsuura, Hiroyuki
1990-01-01
We have constructed the system to monitor the artificial beta aerosol activity around the nuclear power plants continuously in real time. The smaller releases of artificial radionuclides from the nuclear power plants can be lost in the fluctuations of the natural background of the beta aerosol activity, when only the beta activity of the aerosol is measured. This method to discriminate the artificial and the natural beta activity of the aerosol is based on the fact that the ratio of the natural alpha and beta activities of the aerosol is almost constant. The detection limit of this system is below 3 Bq/m 3 . (author)
Washington University St Louis — TOMS_AI_G is an aerosol related dataset derived from the Total Ozone Monitoring Satellite (TOMS) Sensor. The TOMS aerosol index arises from absorbing aerosols such...
Electrically Driven Technologies for Radioactive Aerosol Abatement
Energy Technology Data Exchange (ETDEWEB)
David W. DePaoli; Ofodike A. Ezekoye; Costas Tsouris; Valmor F. de Almeida
2003-01-28
The purpose of this research project was to develop an improved understanding of how electriexecy driven processes, including electrocoalescence, acoustic agglomeration, and electric filtration, may be employed to efficiently treat problems caused by the formation of aerosols during DOE waste treatment operations. The production of aerosols during treatment and retrieval operations in radioactive waste tanks and during thermal treatment operations such as calcination presents a significant problem of cost, worker exposure, potential for release, and increased waste volume.
Bayesian Independent Component Analysis
DEFF Research Database (Denmark)
Winther, Ole; Petersen, Kaare Brandt
2007-01-01
In this paper we present an empirical Bayesian framework for independent component analysis. The framework provides estimates of the sources, the mixing matrix and the noise parameters, and is flexible with respect to choice of source prior and the number of sources and sensors. Inside the engine...... in a Matlab toolbox, is demonstrated for non-negative decompositions and compared with non-negative matrix factorization.......In this paper we present an empirical Bayesian framework for independent component analysis. The framework provides estimates of the sources, the mixing matrix and the noise parameters, and is flexible with respect to choice of source prior and the number of sources and sensors. Inside the engine...
Arregui, Iñigo
2018-01-01
In contrast to the situation in a laboratory, the study of the solar atmosphere has to be pursued without direct access to the physical conditions of interest. Information is therefore incomplete and uncertain and inference methods need to be employed to diagnose the physical conditions and processes. One of such methods, solar atmospheric seismology, makes use of observed and theoretically predicted properties of waves to infer plasma and magnetic field properties. A recent development in solar atmospheric seismology consists in the use of inversion and model comparison methods based on Bayesian analysis. In this paper, the philosophy and methodology of Bayesian analysis are first explained. Then, we provide an account of what has been achieved so far from the application of these techniques to solar atmospheric seismology and a prospect of possible future extensions.
Mørup, Morten; Schmidt, Mikkel N
2012-09-01
Many networks of scientific interest naturally decompose into clusters or communities with comparatively fewer external than internal links; however, current Bayesian models of network communities do not exert this intuitive notion of communities. We formulate a nonparametric Bayesian model for community detection consistent with an intuitive definition of communities and present a Markov chain Monte Carlo procedure for inferring the community structure. A Matlab toolbox with the proposed inference procedure is available for download. On synthetic and real networks, our model detects communities consistent with ground truth, and on real networks, it outperforms existing approaches in predicting missing links. This suggests that community structure is an important structural property of networks that should be explicitly modeled.
Probability and Bayesian statistics
1987-01-01
This book contains selected and refereed contributions to the "Inter national Symposium on Probability and Bayesian Statistics" which was orga nized to celebrate the 80th birthday of Professor Bruno de Finetti at his birthplace Innsbruck in Austria. Since Professor de Finetti died in 1985 the symposium was dedicated to the memory of Bruno de Finetti and took place at Igls near Innsbruck from 23 to 26 September 1986. Some of the pa pers are published especially by the relationship to Bruno de Finetti's scientific work. The evolution of stochastics shows growing importance of probability as coherent assessment of numerical values as degrees of believe in certain events. This is the basis for Bayesian inference in the sense of modern statistics. The contributions in this volume cover a broad spectrum ranging from foundations of probability across psychological aspects of formulating sub jective probability statements, abstract measure theoretical considerations, contributions to theoretical statistics an...
Energy Technology Data Exchange (ETDEWEB)
Andrews, Stephen A. [Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Sigeti, David E. [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
2017-11-15
These are a set of slides about Bayesian hypothesis testing, where many hypotheses are tested. The conclusions are the following: The value of the Bayes factor obtained when using the median of the posterior marginal is almost the minimum value of the Bayes factor. The value of τ^{2} which minimizes the Bayes factor is a reasonable choice for this parameter. This allows a likelihood ratio to be computed with is the least favorable to H_{0}.
Bayesian networks in reliability
Energy Technology Data Exchange (ETDEWEB)
Langseth, Helge [Department of Mathematical Sciences, Norwegian University of Science and Technology, N-7491 Trondheim (Norway)]. E-mail: helgel@math.ntnu.no; Portinale, Luigi [Department of Computer Science, University of Eastern Piedmont ' Amedeo Avogadro' , 15100 Alessandria (Italy)]. E-mail: portinal@di.unipmn.it
2007-01-15
Over the last decade, Bayesian networks (BNs) have become a popular tool for modelling many kinds of statistical problems. We have also seen a growing interest for using BNs in the reliability analysis community. In this paper we will discuss the properties of the modelling framework that make BNs particularly well suited for reliability applications, and point to ongoing research that is relevant for practitioners in reliability.
DEFF Research Database (Denmark)
Antoniou, Constantinos; Harrison, Glenn W.; Lau, Morten I.
2015-01-01
A large literature suggests that many individuals do not apply Bayes’ Rule when making decisions that depend on them correctly pooling prior information and sample data. We replicate and extend a classic experimental study of Bayesian updating from psychology, employing the methods of experimental...... economics, with careful controls for the confounding effects of risk aversion. Our results show that risk aversion significantly alters inferences on deviations from Bayes’ Rule....
Approximate Bayesian recursive estimation
Czech Academy of Sciences Publication Activity Database
Kárný, Miroslav
2014-01-01
Roč. 285, č. 1 (2014), s. 100-111 ISSN 0020-0255 R&D Projects: GA ČR GA13-13502S Institutional support: RVO:67985556 Keywords : Approximate parameter estimation * Bayesian recursive estimation * Kullback–Leibler divergence * Forgetting Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 4.038, year: 2014 http://library.utia.cas.cz/separaty/2014/AS/karny-0425539.pdf
Bayesian theory and applications
Dellaportas, Petros; Polson, Nicholas G; Stephens, David A
2013-01-01
The development of hierarchical models and Markov chain Monte Carlo (MCMC) techniques forms one of the most profound advances in Bayesian analysis since the 1970s and provides the basis for advances in virtually all areas of applied and theoretical Bayesian statistics. This volume guides the reader along a statistical journey that begins with the basic structure of Bayesian theory, and then provides details on most of the past and present advances in this field. The book has a unique format. There is an explanatory chapter devoted to each conceptual advance followed by journal-style chapters that provide applications or further advances on the concept. Thus, the volume is both a textbook and a compendium of papers covering a vast range of topics. It is appropriate for a well-informed novice interested in understanding the basic approach, methods and recent applications. Because of its advanced chapters and recent work, it is also appropriate for a more mature reader interested in recent applications and devel...
International Nuclear Information System (INIS)
Sheely, W.F.
1986-01-01
The Submerged Gravel Scrubber is an air cleaning system developed by the Department of Energy's Liquid Metal Reactor Program. The Scrubber System has been patented by the Department of Energy. This technology is being transferred to industry by the DOE. Its basic principles can be adapted for individual applications and the commercialized version can be used to perform a variety of tasks. The gas to be cleaned is percolated through a continuously washed gravel bed. The passage of the gas through the gravel breaks the stream into many small bubbles rising in a turbulent body of water. These conditions allow very highly efficient removal of aerosols from the gas
Calibration of aerosol radiometers. Special aerosol sources
International Nuclear Information System (INIS)
Belkina, S.K.; Zalmanzon, Yu.E.; Kuznetsov, Yu.V.; Fertman, D.E.
1988-01-01
Problems of calibration of artificial aerosol radiometry and information-measurement systems of radiometer radiation control, in particular, are considered. Special aerosol source is suggested, which permits to perform certification and testing of aerosol channels of the systems in situ without the dismantling
Aerosol behavior in the reactor containment building during severe accident
International Nuclear Information System (INIS)
Berthion, Y.; Lhiaubet, G.; Gauvain, J.
1984-07-01
Thermohydraulic behavior inside a PWR containment during severe accident depends on decay heat transferred to the sump water by aerosol gravitational settling and deposition. Conversely, aerosol behavior depends on thermal hydraulic conditions, especially atmosphere moisture for soluble aerosol GsI, and CsOH. Therefore, a small iterative procedure between thermo-hydraulic and aerosol calculations has been performed in order to evaluate the importance of this coupling between the two phenomena. In this paper, it is shown that with this procedure and using our codes JERICHO, RICOCHET and AEROSOLS/B1, the steam condensation on aerosols is an important phenomenon for a correct estimation of the attenuation factor of the suspended mass of aerosols in the airborne of the containment. Then, we have a more realistic assessment of the source term released by the containment
Bayesian analysis in plant pathology.
Mila, A L; Carriquiry, A L
2004-09-01
ABSTRACT Bayesian methods are currently much discussed and applied in several disciplines from molecular biology to engineering. Bayesian inference is the process of fitting a probability model to a set of data and summarizing the results via probability distributions on the parameters of the model and unobserved quantities such as predictions for new observations. In this paper, after a short introduction of Bayesian inference, we present the basic features of Bayesian methodology using examples from sequencing genomic fragments and analyzing microarray gene-expressing levels, reconstructing disease maps, and designing experiments.
Energy Technology Data Exchange (ETDEWEB)
Moore, Murray E. [Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Tao, Yong [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
2017-02-16
Cerium oxide (CeO2) dust is recommended as a surrogate for plutonium oxide (PuO2) in airborne release fraction experiments. The total range of applicable particle sizes for PuO2 extends from 0.0032 μm (the diameter of a single PuO2 molecule) to 10 μm (the defined upper boundary for respirable particles). For particulates with a physical particle diameter of 1.0 μm, the corresponding aerodynamic diameters for CeO2 and PuO2 are 2.7 μm and 3.4 μm, respectively. Cascade impactor air samplers are capable of measuring the size distributions of CeO2 or PuO2 particulates. In this document, the aerodynamic diameters for CeO2 and PuO2 were calculated for seven different physical diameters (0.0032, 0.02, 0.11, 0.27, 1.0, 3.2, and 10 μm). For cascade impactor measurements, CeO2 and PuO2 particulates with the same physical diameter would be collected onto the same or adjacent collection substrates. The difference between the aerodynamic diameter of CeO2 and PuO2 particles (that have the same physical diameter) is 39% of the resolution of a twelve-stage MSP Inc. 125 cascade impactor, and 34% for an eight-stage Andersen impactor. An approach is given to calculate the committed effective dose (CED) coefficient for PuO2 aerosol particles, compared to a corresponding aerodynamic diameter of CeO2 particles. With this approach, use of CeO2 as a surrogate for PuO2 material would follow a direct conversion based on a molar equivalent. In addition to the analytical information developed for this document, several US national labs have published articles about the use of CeO2 as a PuO2 surrogate. Different physical and chemical aspects were considered by these investigators, including thermal properties, ceramic formulations, cold pressing, sintering, molecular reactions, and mass loss in high temperature gas flows. All of those US national lab studies recommended the use of CeO2 as a surrogate material for PuO2.
Congdon, Peter
2014-01-01
This book provides an accessible approach to Bayesian computing and data analysis, with an emphasis on the interpretation of real data sets. Following in the tradition of the successful first edition, this book aims to make a wide range of statistical modeling applications accessible using tested code that can be readily adapted to the reader's own applications. The second edition has been thoroughly reworked and updated to take account of advances in the field. A new set of worked examples is included. The novel aspect of the first edition was the coverage of statistical modeling using WinBU
Bayesian nonparametric data analysis
Müller, Peter; Jara, Alejandro; Hanson, Tim
2015-01-01
This book reviews nonparametric Bayesian methods and models that have proven useful in the context of data analysis. Rather than providing an encyclopedic review of probability models, the book’s structure follows a data analysis perspective. As such, the chapters are organized by traditional data analysis problems. In selecting specific nonparametric models, simpler and more traditional models are favored over specialized ones. The discussed methods are illustrated with a wealth of examples, including applications ranging from stylized examples to case studies from recent literature. The book also includes an extensive discussion of computational methods and details on their implementation. R code for many examples is included in on-line software pages.
Lohmann, Ulrike; Rotstayn, Leon; Storelvmo, Trude; Jones, Andrew; Menon, Surabi; Quaas, Johannes; Ekman, Annica M. L.; Koch, Dorothy; Ruedy, Reto A.
2015-01-01
Uncertainties in aerosol radiative forcings, especially those associated with clouds, contribute to a large extent to uncertainties in the total anthropogenic forcing. The interaction of aerosols with clouds and radiation introduces feedbacks which can affect the rate of precipitation formation. In former assessments of aerosol radiative forcings, these effects have not been quantified. Also, with global aerosol-climate models simulating interactively aerosols and cloud microphysical prope...
Classification using Bayesian neural nets
J.C. Bioch (Cor); O. van der Meer; R. Potharst (Rob)
1995-01-01
textabstractRecently, Bayesian methods have been proposed for neural networks to solve regression and classification problems. These methods claim to overcome some difficulties encountered in the standard approach such as overfitting. However, an implementation of the full Bayesian approach to
Bayesian Data Analysis (lecture 1)
CERN. Geneva
2018-01-01
framework but we will also go into more detail and discuss for example the role of the prior. The second part of the lecture will cover further examples and applications that heavily rely on the bayesian approach, as well as some computational tools needed to perform a bayesian analysis.
Bayesian Data Analysis (lecture 2)
CERN. Geneva
2018-01-01
framework but we will also go into more detail and discuss for example the role of the prior. The second part of the lecture will cover further examples and applications that heavily rely on the bayesian approach, as well as some computational tools needed to perform a bayesian analysis.
Development of an aerosol decontamination factor evaluation method using an aerosol spectrometer
Energy Technology Data Exchange (ETDEWEB)
Kanai, Taizo, E-mail: t-kanai@criepi.denken.or.jp; Furuya, Masahiro, E-mail: furuya@criepi.denken.or.jp; Arai, Takahiro, E-mail: t-arai@criepi.denken.or.jp; Nishi, Yoshihisa, E-mail: y-nishi@criepi.denken.or.jp
2016-07-15
Highlights: • Aerosol DF of each diameter is evaluable by using optical scattering method. • Outlet aerosol concentration shows exponential decay by the submergence. • This decay constant depends on the aerosol diameter. • Aerosol DF at water scrubber is described by simple equation. - Abstract: During a severe nuclear power plant accident, the release of fission products into containment and an increase in containment pressure are assumed to be possible. When the containment is damaged by excess pressure or temperature, radioactive materials are released. Pressure suppression pools, containment spray systems and a filtered containment venting system (FCVS) reduce containment pressure and reduce the radioactive release into the environment. These devices remove radioactive materials via various mechanisms. Pressure suppression pools remove radioactive materials by pool scrubbing. Spray systems remove radioactive materials by droplet−aerosol interaction. FCVS, which is installed in the exhaust system, comprises multi-scrubbers (venturi-scrubber, pool scrubbing, static mixer, metal−fiber filter and molecular sieve). For the particulate radioactive materials, its size affects the removal performance and a number of studies have been performed on the removal effect of radioactive materials. This study has developed a new means of evaluating aerosol removal efficiency. The aerosol number density of each effective diameter (light scattering equivalent diameter) is measured using an optical method, while the decontamination factor (DF) of each effective diameter is evaluated by the inlet outlet number density ratio. While the applicable scope is limited to several conditions (geometry of test section: inner diameter 500 mm × height 8.0 m, nozzle shape and air-water ambient pressure conditions), this study has developed a numerical model which defines aerosol DF as a function of aerosol diameter (d) and submergences (x).
Model and Computing Experiment for Research and Aerosols Usage Management
Directory of Open Access Journals (Sweden)
Daler K. Sharipov
2012-09-01
Full Text Available The article deals with a math model for research and management of aerosols released into the atmosphere as well as numerical algorithm used as hardware and software systems for conducting computing experiment.
The Bayesian Covariance Lasso.
Khondker, Zakaria S; Zhu, Hongtu; Chu, Haitao; Lin, Weili; Ibrahim, Joseph G
2013-04-01
Estimation of sparse covariance matrices and their inverse subject to positive definiteness constraints has drawn a lot of attention in recent years. The abundance of high-dimensional data, where the sample size ( n ) is less than the dimension ( d ), requires shrinkage estimation methods since the maximum likelihood estimator is not positive definite in this case. Furthermore, when n is larger than d but not sufficiently larger, shrinkage estimation is more stable than maximum likelihood as it reduces the condition number of the precision matrix. Frequentist methods have utilized penalized likelihood methods, whereas Bayesian approaches rely on matrix decompositions or Wishart priors for shrinkage. In this paper we propose a new method, called the Bayesian Covariance Lasso (BCLASSO), for the shrinkage estimation of a precision (covariance) matrix. We consider a class of priors for the precision matrix that leads to the popular frequentist penalties as special cases, develop a Bayes estimator for the precision matrix, and propose an efficient sampling scheme that does not precalculate boundaries for positive definiteness. The proposed method is permutation invariant and performs shrinkage and estimation simultaneously for non-full rank data. Simulations show that the proposed BCLASSO performs similarly as frequentist methods for non-full rank data.
Approximate Bayesian computation.
Directory of Open Access Journals (Sweden)
Mikael Sunnåker
Full Text Available Approximate Bayesian computation (ABC constitutes a class of computational methods rooted in Bayesian statistics. In all model-based statistical inference, the likelihood function is of central importance, since it expresses the probability of the observed data under a particular statistical model, and thus quantifies the support data lend to particular values of parameters and to choices among different models. For simple models, an analytical formula for the likelihood function can typically be derived. However, for more complex models, an analytical formula might be elusive or the likelihood function might be computationally very costly to evaluate. ABC methods bypass the evaluation of the likelihood function. In this way, ABC methods widen the realm of models for which statistical inference can be considered. ABC methods are mathematically well-founded, but they inevitably make assumptions and approximations whose impact needs to be carefully assessed. Furthermore, the wider application domain of ABC exacerbates the challenges of parameter estimation and model selection. ABC has rapidly gained popularity over the last years and in particular for the analysis of complex problems arising in biological sciences (e.g., in population genetics, ecology, epidemiology, and systems biology.
Bayesian inference with ecological applications
Link, William A
2009-01-01
This text is written to provide a mathematically sound but accessible and engaging introduction to Bayesian inference specifically for environmental scientists, ecologists and wildlife biologists. It emphasizes the power and usefulness of Bayesian methods in an ecological context. The advent of fast personal computers and easily available software has simplified the use of Bayesian and hierarchical models . One obstacle remains for ecologists and wildlife biologists, namely the near absence of Bayesian texts written specifically for them. The book includes many relevant examples, is supported by software and examples on a companion website and will become an essential grounding in this approach for students and research ecologists. Engagingly written text specifically designed to demystify a complex subject Examples drawn from ecology and wildlife research An essential grounding for graduate and research ecologists in the increasingly prevalent Bayesian approach to inference Companion website with analyt...
Bayesian Inference on Gravitational Waves
Directory of Open Access Journals (Sweden)
Asad Ali
2015-12-01
Full Text Available The Bayesian approach is increasingly becoming popular among the astrophysics data analysis communities. However, the Pakistan statistics communities are unaware of this fertile interaction between the two disciplines. Bayesian methods have been in use to address astronomical problems since the very birth of the Bayes probability in eighteenth century. Today the Bayesian methods for the detection and parameter estimation of gravitational waves have solid theoretical grounds with a strong promise for the realistic applications. This article aims to introduce the Pakistan statistics communities to the applications of Bayesian Monte Carlo methods in the analysis of gravitational wave data with an overview of the Bayesian signal detection and estimation methods and demonstration by a couple of simplified examples.
Pollution metallique relargable par les aerosols d'origine autoroutiere
Lebreton , Laurent; Thevenot , Daniel ,
1992-01-01
International audience; Because they are highly contaminated by heavy metals, road aerosols may pollute runoff waters. To estimate the mobility of some toxic metals such as Zn, Pb or Cd, these aerosols have been submited to a range of sequential chemical extraction (chemical speciation) and to laboratory release experiments. Both chemical speciation and reactor experiments show similar metal behaviour. Zn and Cd are extremely mobile (60 % released) while Pb, highly bound to particles, needs a...
Aerosol typing - key information from aerosol studies
Mona, Lucia; Kahn, Ralph; Papagiannopoulos, Nikolaos; Holzer-Popp, Thomas; Pappalardo, Gelsomina
2016-04-01
Aerosol typing is a key source of aerosol information from ground-based and satellite-borne instruments. Depending on the specific measurement technique, aerosol typing can be used as input for retrievals or represents an output for other applications. Typically aerosol retrievals require some a priori or external aerosol type information. The accuracy of the derived aerosol products strongly depends on the reliability of these assumptions. Different sensors can make use of different aerosol type inputs. A critical review and harmonization of these procedures could significantly reduce related uncertainties. On the other hand, satellite measurements in recent years are providing valuable information about the global distribution of aerosol types, showing for example the main source regions and typical transport paths. Climatological studies of aerosol load at global and regional scales often rely on inferred aerosol type. There is still a high degree of inhomogeneity among satellite aerosol typing schemes, which makes the use different sensor datasets in a consistent way difficult. Knowledge of the 4d aerosol type distribution at these scales is essential for understanding the impact of different aerosol sources on climate, precipitation and air quality. All this information is needed for planning upcoming aerosol emissions policies. The exchange of expertise and the communication among satellite and ground-based measurement communities is fundamental for improving long-term dataset consistency, and for reducing aerosol type distribution uncertainties. Aerosol typing has been recognized as one of its high-priority activities of the AEROSAT (International Satellite Aerosol Science Network, http://aero-sat.org/) initiative. In the AEROSAT framework, a first critical review of aerosol typing procedures has been carried out. The review underlines the high heterogeneity in many aspects: approach, nomenclature, assumed number of components and parameters used for the
Aerosol-type retrieval and uncertainty quantification from OMI data
Kauppi, Anu; Kolmonen, Pekka; Laine, Marko; Tamminen, Johanna
2017-11-01
We discuss uncertainty quantification for aerosol-type selection in satellite-based atmospheric aerosol retrieval. The retrieval procedure uses precalculated aerosol microphysical models stored in look-up tables (LUTs) and top-of-atmosphere (TOA) spectral reflectance measurements to solve the aerosol characteristics. The forward model approximations cause systematic differences between the modelled and observed reflectance. Acknowledging this model discrepancy as a source of uncertainty allows us to produce more realistic uncertainty estimates and assists the selection of the most appropriate LUTs for each individual retrieval.This paper focuses on the aerosol microphysical model selection and characterisation of uncertainty in the retrieved aerosol type and aerosol optical depth (AOD). The concept of model evidence is used as a tool for model comparison. The method is based on Bayesian inference approach, in which all uncertainties are described as a posterior probability distribution. When there is no single best-matching aerosol microphysical model, we use a statistical technique based on Bayesian model averaging to combine AOD posterior probability densities of the best-fitting models to obtain an averaged AOD estimate. We also determine the shared evidence of the best-matching models of a certain main aerosol type in order to quantify how plausible it is that it represents the underlying atmospheric aerosol conditions.The developed method is applied to Ozone Monitoring Instrument (OMI) measurements using a multiwavelength approach for retrieving the aerosol type and AOD estimate with uncertainty quantification for cloud-free over-land pixels. Several larger pixel set areas were studied in order to investigate the robustness of the developed method. We evaluated the retrieved AOD by comparison with ground-based measurements at example sites. We found that the uncertainty of AOD expressed by posterior probability distribution reflects the difficulty in model
Aerosol-type retrieval and uncertainty quantification from OMI data
Directory of Open Access Journals (Sweden)
A. Kauppi
2017-11-01
Full Text Available We discuss uncertainty quantification for aerosol-type selection in satellite-based atmospheric aerosol retrieval. The retrieval procedure uses precalculated aerosol microphysical models stored in look-up tables (LUTs and top-of-atmosphere (TOA spectral reflectance measurements to solve the aerosol characteristics. The forward model approximations cause systematic differences between the modelled and observed reflectance. Acknowledging this model discrepancy as a source of uncertainty allows us to produce more realistic uncertainty estimates and assists the selection of the most appropriate LUTs for each individual retrieval.This paper focuses on the aerosol microphysical model selection and characterisation of uncertainty in the retrieved aerosol type and aerosol optical depth (AOD. The concept of model evidence is used as a tool for model comparison. The method is based on Bayesian inference approach, in which all uncertainties are described as a posterior probability distribution. When there is no single best-matching aerosol microphysical model, we use a statistical technique based on Bayesian model averaging to combine AOD posterior probability densities of the best-fitting models to obtain an averaged AOD estimate. We also determine the shared evidence of the best-matching models of a certain main aerosol type in order to quantify how plausible it is that it represents the underlying atmospheric aerosol conditions.The developed method is applied to Ozone Monitoring Instrument (OMI measurements using a multiwavelength approach for retrieving the aerosol type and AOD estimate with uncertainty quantification for cloud-free over-land pixels. Several larger pixel set areas were studied in order to investigate the robustness of the developed method. We evaluated the retrieved AOD by comparison with ground-based measurements at example sites. We found that the uncertainty of AOD expressed by posterior probability distribution reflects the
Bayesian nonparametric hierarchical modeling.
Dunson, David B
2009-04-01
In biomedical research, hierarchical models are very widely used to accommodate dependence in multivariate and longitudinal data and for borrowing of information across data from different sources. A primary concern in hierarchical modeling is sensitivity to parametric assumptions, such as linearity and normality of the random effects. Parametric assumptions on latent variable distributions can be challenging to check and are typically unwarranted, given available prior knowledge. This article reviews some recent developments in Bayesian nonparametric methods motivated by complex, multivariate and functional data collected in biomedical studies. The author provides a brief review of flexible parametric approaches relying on finite mixtures and latent class modeling. Dirichlet process mixture models are motivated by the need to generalize these approaches to avoid assuming a fixed finite number of classes. Focusing on an epidemiology application, the author illustrates the practical utility and potential of nonparametric Bayes methods.
DEFF Research Database (Denmark)
Hartelius, Karsten; Carstensen, Jens Michael
2003-01-01
A method for locating distorted grid structures in images is presented. The method is based on the theories of template matching and Bayesian image restoration. The grid is modeled as a deformable template. Prior knowledge of the grid is described through a Markov random field (MRF) model which...... represents the spatial coordinates of the grid nodes. Knowledge of how grid nodes are depicted in the observed image is described through the observation model. The prior consists of a node prior and an arc (edge) prior, both modeled as Gaussian MRFs. The node prior models variations in the positions of grid...... nodes and the arc prior models variations in row and column spacing across the grid. Grid matching is done by placing an initial rough grid over the image and applying an ensemble annealing scheme to maximize the posterior distribution of the grid. The method can be applied to noisy images with missing...
Bayesian supervised dimensionality reduction.
Gönen, Mehmet
2013-12-01
Dimensionality reduction is commonly used as a preprocessing step before training a supervised learner. However, coupled training of dimensionality reduction and supervised learning steps may improve the prediction performance. In this paper, we introduce a simple and novel Bayesian supervised dimensionality reduction method that combines linear dimensionality reduction and linear supervised learning in a principled way. We present both Gibbs sampling and variational approximation approaches to learn the proposed probabilistic model for multiclass classification. We also extend our formulation toward model selection using automatic relevance determination in order to find the intrinsic dimensionality. Classification experiments on three benchmark data sets show that the new model significantly outperforms seven baseline linear dimensionality reduction algorithms on very low dimensions in terms of generalization performance on test data. The proposed model also obtains the best results on an image recognition task in terms of classification and retrieval performances.
Bayesian Geostatistical Design
DEFF Research Database (Denmark)
Diggle, Peter; Lophaven, Søren Nymand
2006-01-01
This paper describes the use of model-based geostatistics for choosing the set of sampling locations, collectively called the design, to be used in a geostatistical analysis. Two types of design situation are considered. These are retrospective design, which concerns the addition of sampling...... locations to, or deletion of locations from, an existing design, and prospective design, which consists of choosing positions for a new set of sampling locations. We propose a Bayesian design criterion which focuses on the goal of efficient spatial prediction whilst allowing for the fact that model...... parameter values are unknown. The results show that in this situation a wide range of interpoint distances should be included in the design, and the widely used regular design is often not the best choice....
Release fraction of PWR after severe accidents. Vol. 4
International Nuclear Information System (INIS)
Aziz, M.; El-Messeiry, A.M.
1996-01-01
Fission fragments and gases are emitted after accidents as a result of core meltdown and core concrete interactions. These aerosols are transported and fill the reactor containment. With increasing the pressure above pressure design bases, a failure of containment may occur and subsequently these aerosols will release into the external environment leading to a source term of radioactivity that affects the safety of workers and public. The amount of aerosol which escapes to the environment can be described by the release fraction which is defined as the total accumulated aerosol which initially enters the containment. The factors that affect the release fraction is studied, and the aerosol dynamics equation is used to model the release of aerosol to the outside atmosphere. These factors are containment pressure, failure time,break area, the size of aerosol particle. It found that early failure time and higher pressure increase the release fraction, also the release faction is affected by the area and the aerosol particle size. 7 figs., 2 tabs
Protection of air in premises and environment against beryllium aerosols
Energy Technology Data Exchange (ETDEWEB)
Bitkolov, N.Z.; Vishnevsky, E.P.; Krupkin, A.V. [Research Inst. of Industrial and Marine Medicine, St. Petersburg (Russian Federation)
1998-01-01
First and foremost, the danger of beryllium aerosols concerns a possibility of their inhalation. The situation is aggravated with high biological activity of the beryllium in a human lung. The small allowable beryllium aerosols` concentration in air poses a rather complex and expensive problem of the pollution prevention and clearing up of air. The delivery and transportation of beryllium aerosols from sites of their formation are defined by the circuit of ventilation, that forms aerodynamics of air flows in premises, and aerodynamic links between premises. The causes of aerosols release in air of premises from hoods, isolated and hermetically sealed vessels can be vibrations, as well as pulses of temperature and pressure. Furthermore, it is possible the redispersion of aerosols from dirty surfaces. The effective protection of air against beryllium aerosols at industrial plants is provided by a complex of hygienic measures: from individual means of breath protection up to collective means of the prevention of air pollution. (J.P.N.)
Bayesian adaptive methods for clinical trials
Berry, Scott M; Muller, Peter
2010-01-01
Already popular in the analysis of medical device trials, adaptive Bayesian designs are increasingly being used in drug development for a wide variety of diseases and conditions, from Alzheimer's disease and multiple sclerosis to obesity, diabetes, hepatitis C, and HIV. Written by leading pioneers of Bayesian clinical trial designs, Bayesian Adaptive Methods for Clinical Trials explores the growing role of Bayesian thinking in the rapidly changing world of clinical trial analysis. The book first summarizes the current state of clinical trial design and analysis and introduces the main ideas and potential benefits of a Bayesian alternative. It then gives an overview of basic Bayesian methodological and computational tools needed for Bayesian clinical trials. With a focus on Bayesian designs that achieve good power and Type I error, the next chapters present Bayesian tools useful in early (Phase I) and middle (Phase II) clinical trials as well as two recent Bayesian adaptive Phase II studies: the BATTLE and ISP...
Current trends in Bayesian methodology with applications
Upadhyay, Satyanshu K; Dey, Dipak K; Loganathan, Appaia
2015-01-01
Collecting Bayesian material scattered throughout the literature, Current Trends in Bayesian Methodology with Applications examines the latest methodological and applied aspects of Bayesian statistics. The book covers biostatistics, econometrics, reliability and risk analysis, spatial statistics, image analysis, shape analysis, Bayesian computation, clustering, uncertainty assessment, high-energy astrophysics, neural networking, fuzzy information, objective Bayesian methodologies, empirical Bayes methods, small area estimation, and many more topics.Each chapter is self-contained and focuses on
Measurements provide fundamental information for evaluating and managing the impact of aerosols on air quality. Specific measurements of aerosol concentration and their physical and chemical properties are required by different users to meet different user-community needs. Befo...
Aerosols and environmental pollution.
Colbeck, Ian; Lazaridis, Mihalis
2010-02-01
The number of publications on atmospheric aerosols has dramatically increased in recent years. This review, predominantly from a European perspective, summarizes the current state of knowledge of the role played by aerosols in environmental pollution and, in addition, highlights gaps in our current knowledge. Aerosol particles are ubiquitous in the Earth's atmosphere and are central to many environmental issues; ranging from the Earth's radiative budget to human health. Aerosol size distribution and chemical composition are crucial parameters that determine their dynamics in the atmosphere. Sources of aerosols are both anthropogenic and natural ranging from vehicular emissions to dust resuspension. Ambient concentrations of aerosols are elevated in urban areas with lower values at rural sites. A comprehensive understanding of aerosol ambient characteristics requires a combination of measurements and modeling tools. Legislation for ambient aerosols has been introduced at national and international levels aiming to protect human health and the environment.
Bayesian Inference: with ecological applications
Link, William A.; Barker, Richard J.
2010-01-01
This text provides a mathematically rigorous yet accessible and engaging introduction to Bayesian inference with relevant examples that will be of interest to biologists working in the fields of ecology, wildlife management and environmental studies as well as students in advanced undergraduate statistics.. This text opens the door to Bayesian inference, taking advantage of modern computational efficiencies and easily accessible software to evaluate complex hierarchical models.
Bayesian image restoration, using configurations
Thorarinsdottir, Thordis
2006-01-01
In this paper, we develop a Bayesian procedure for removing noise from images that can be viewed as noisy realisations of random sets in the plane. The procedure utilises recent advances in configuration theory for noise free random sets, where the probabilities of observing the different boundary configurations are expressed in terms of the mean normal measure of the random set. These probabilities are used as prior probabilities in a Bayesian image restoration approach. Estimation of the re...
Engineering of aerosol nanoparticle architectures
Jiang, Xingmao
Aerosol-assisted evaporation-induced self-assembly has been applied to fabricate a wide range of nanoparticle architectures. Ordered core-shell Ce/silica particles are effective for corrosion inhibition of aluminum alloy AA2024-T3. Higher hydrophobicity derived by increasing methyltrimethoxysilane/tetramethoxysilane ratio in the precursor delays the release in water and improves the hydrothermal stability significantly. Long-term corrosion inhibition can be realized using microporous encapsulating materials. A mathematical model has been developed to evaluate the release behavior and obtain the effective diffusion coefficient. To realize a long-term controlled release, low diffusivity and low solubility of the encapsulated cerium compound in the release medium are desirable. To maintain an effective cerium concentration for corrosion protection, a proper formulation of quick-release particles and slow-release particles may be strategically necessary. NaCl is selected as a model salt to investigate the diffusion of ions in nanoporous silica and the formation mechanism for the core-shell structure. Ordered nonporous silica with single crystal NaCl core has also been prepared. Azobenzene ligands have been uniformly anchored to the pore surfaces of the nanoporous silica particles by reacting with 4-(3-triethoxysilylpropylureido) azobenzene (TSUA). The functionalization of pore surfaces by organic groups regulates the hydrophobicity and therefore the release behavior. The modified particles demonstrate a photo controlled release by trans/cis isomerization of azobenzene moieties. Long molecule solvents or polymers can be used as blockers to adjust the release behavior for a long-term controlled release. We have developed a valid simulation method and computer code for the evaporation of ethanol-water-NaCl droplets. Various parameters such as droplet size and surrounding gas temperature and pressure have been examined. The code clearly demonstrates the evolution of
Indian Academy of Sciences (India)
atmosphere, aerosols have the potential to significantly influ- ence the climate. The global impact of aerosol is assessed as the change imposed on planetary radiation measured in Wm-2, which alters the global temperature. Effect of aerosols on the solar radiation (also called radiative forcing) can be broadly classified into ...
Indian Academy of Sciences (India)
Large warming by elevated aerosols · AERONET – Global network (NASA) · Slide 25 · Slide 26 · Slide 27 · Slide 28 · Slide 29 · Slide 30 · Slide 31 · Long-term trends - Trivandrum · Enhanced warming over Himalayan-Gangetic region · Aerosol Radiative Forcing Over India _ Regional Aerosol Warming Experiment ...
Indian Academy of Sciences (India)
Aerosols and Climate · Slide 2 · Slide 3 · Slide 4 · Slide 5 · Slide 6 · Principal efforts in improving the understanding of Climate impact of aerosols - · Slide 8 · Observations of Aerosol – from space (Spatial variation) · AOD around Indian region from AVHRR · Dust absorption efficiency over Great Indian Desert from Satellite ...
Cox, S. J.; Stackhouse, P. W., Jr.; Mikovitz, J. C.; Zhang, T.
2017-12-01
The NASA/GEWEX Surface Radiation Budget (SRB) project produces shortwave and longwave surface and top of atmosphere radiative fluxes for the 1983-near present time period. Spatial resolution is 1 degree. The new Release 4 uses the newly processed ISCCP HXS product as its primary input for cloud and radiance data. The ninefold increase in pixel number compared to the previous ISCCP DX allows finer gradations in cloud fraction in each grid box. It will also allow higher spatial resolutions (0.5 degree) in future releases. In addition to the input data improvements, several important algorithm improvements have been made since Release 3. These include recalculated atmospheric transmissivities and reflectivities yielding a less transmissive atmosphere. The calculations also include variable aerosol composition, allowing for the use of a detailed aerosol history from the Max Planck Institut Aerosol Climatology (MAC). Ocean albedo and snow/ice albedo are also improved from Release 3. Total solar irradiance is now variable, averaging 1361 Wm-2. Water vapor is taken from ISCCP's nnHIRS product. Results from GSW Release 4 are presented and analyzed. Early comparison to surface measurements show improved agreement.
A Bayesian Analysis of the Flood Frequency Hydrology Concept
2016-02-01
ERDC/CHL CHETN-X-1 February 2016 Approved for public release; distribution is unlimited. A Bayesian Analysis of the Flood Frequency Hydrology ...flood frequency hydrology concept as a formal probabilistic-based means by which to coherently combine and also evaluate the worth of different types...and development. INTRODUCTION: Merz and Blöschl (2008a,b) proposed the concept of flood frequency hydrology , which emphasizes the importance of
Investigation on aerosol transport in containment cracks
International Nuclear Information System (INIS)
Parozzi, F.; Chatzidakis, S.; Housiadas, C.; Gelain, T.; Nahas, G.; Plumecocq, W.; Vendel, J.; Herranz, L.E.; Hinis, E.; Journeau, C.; Piluso, P.; Malgarida, E.
2005-01-01
Under severe accident conditions, the containment leak-tightness could be threatened by energetic phenomena that could yield a release to the environment of nuclear aerosols through penetrating concrete cracks. As few data are presently available to quantify this aerosol leakage, a specific action was launched in the framework of the Santar Project of the European 6 th Framework Programme. In this context, both theoretical and experimental investigations have been managed to develop a model that can readily be applied within a code like Aster (Accident Source Term Evaluation Code). Particle diffusion, settling, turbulent deposition, diffusiophoresis and thermophoresis have been considered as deposition mechanisms inside the crack path. They have been encapsulated in numerical models set up to reproduce experiments with small tubes and capillaries and simulate the plug formation. Then, an original lagrangian approach has been used to evaluate the crack retention under typical PWR accident conditions, comparing its predictions with those given by the eulerian approach implemented in the ECART code. On the experimental side, the paper illustrates an aerosol production and measurement system developed to validate aerosol deposition models into cracks and the results that can be obtained: a series of tests were performed with monodispersed fluorescein aerosols injected into a cracked concrete sample. A key result that should be further explored refers to the high enhancement of aerosol retention that could be due to steam condensation. Recommendations concerning future experimentation are also given in the paper. (author)
Results from DEMONA aerosol experiments
International Nuclear Information System (INIS)
Kanzleiter, T.; Wolf, L.; Schoeck, W.
1987-01-01
In the DEMONA experiments the behavior of aerosols released into a large dry PWR containment during a core meltdown accident was investigated by means of a 640 m 3 model containment. After performance of eight experiments with quasi-single-compartment containment geometry and one multi-compartment experiment, the DEMONA project is being completed by evaluating and recalculating the measured results. Up to now, good agreement between experiment and model calculations was achieved only for simple test conditions. Adequate modeling of more complicated test conditions requires additional analytical efforts
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
Bayesian microsaccade detection
Mihali, Andra; van Opheusden, Bas; Ma, Wei Ji
2017-01-01
Microsaccades are high-velocity fixational eye movements, with special roles in perception and cognition. The default microsaccade detection method is to determine when the smoothed eye velocity exceeds a threshold. We have developed a new method, Bayesian microsaccade detection (BMD), which performs inference based on a simple statistical model of eye positions. In this model, a hidden state variable changes between drift and microsaccade states at random times. The eye position is a biased random walk with different velocity distributions for each state. BMD generates samples from the posterior probability distribution over the eye state time series given the eye position time series. Applied to simulated data, BMD recovers the “true” microsaccades with fewer errors than alternative algorithms, especially at high noise. Applied to EyeLink eye tracker data, BMD detects almost all the microsaccades detected by the default method, but also apparent microsaccades embedded in high noise—although these can also be interpreted as false positives. Next we apply the algorithms to data collected with a Dual Purkinje Image eye tracker, whose higher precision justifies defining the inferred microsaccades as ground truth. When we add artificial measurement noise, the inferences of all algorithms degrade; however, at noise levels comparable to EyeLink data, BMD recovers the “true” microsaccades with 54% fewer errors than the default algorithm. Though unsuitable for online detection, BMD has other advantages: It returns probabilities rather than binary judgments, and it can be straightforwardly adapted as the generative model is refined. We make our algorithm available as a software package. PMID:28114483
Kernel Bayesian ART and ARTMAP.
Masuyama, Naoki; Loo, Chu Kiong; Dawood, Farhan
2018-02-01
Adaptive Resonance Theory (ART) is one of the successful approaches to resolving "the plasticity-stability dilemma" in neural networks, and its supervised learning model called ARTMAP is a powerful tool for classification. Among several improvements, such as Fuzzy or Gaussian based models, the state of art model is Bayesian based one, while solving the drawbacks of others. However, it is known that the Bayesian approach for the high dimensional and a large number of data requires high computational cost, and the covariance matrix in likelihood becomes unstable. This paper introduces Kernel Bayesian ART (KBA) and ARTMAP (KBAM) by integrating Kernel Bayes' Rule (KBR) and Correntropy Induced Metric (CIM) to Bayesian ART (BA) and ARTMAP (BAM), respectively, while maintaining the properties of BA and BAM. The kernel frameworks in KBA and KBAM are able to avoid the curse of dimensionality. In addition, the covariance-free Bayesian computation by KBR provides the efficient and stable computational capability to KBA and KBAM. Furthermore, Correntropy-based similarity measurement allows improving the noise reduction ability even in the high dimensional space. The simulation experiments show that KBA performs an outstanding self-organizing capability than BA, and KBAM provides the superior classification ability than BAM, respectively. Copyright © 2017 Elsevier Ltd. All rights reserved.
Influence of moisture on the behavior of aerosols
International Nuclear Information System (INIS)
Adams, R.E.; Longest, A.W.; Tobias, M.L.
1986-01-01
The behavior of aerosols assumed to be characteristic of those generated during light water reactor (LWR) accident sequences and released into containment has been studied in the Nuclear Safety Pilot Plant (NSPP) located at the Oak Ridge National Laboratory (ORNL). It has been observed that in a saturated steam-air environment a change occurs in the shape of aerosol agglomerates of U 3 O 8 aerosol, Fe 2 O 3 aerosol, and mixed U 3 O 8 -Fe 2 O 3 aerosol from branched-chain to spherical, and that the rate of reduction in the airborne aerosol mass concentration is increased relative to the rate observed in a dry atmosphere. The effect of a steam-air environment on the behavior of concrete aerosol is different. The shape of the agglomerated concrete aerosol is intermediate between branched-chain and spherical and the effect on the rate of reduction in airborne mass concentration appears to be slight. In a related project the shape of an agglomerated Fe 2 O 3 aerosol was observed to change from branched-chain to spherical at, or near, 100% relative humidity
Global estimate of aerosol direct radiative forcing from satellite measurements.
Bellouin, Nicolas; Boucher, Olivier; Haywood, Jim; Reddy, M Shekar
2005-12-22
Atmospheric aerosols cause scattering and absorption of incoming solar radiation. Additional anthropogenic aerosols released into the atmosphere thus exert a direct radiative forcing on the climate system. The degree of present-day aerosol forcing is estimated from global models that incorporate a representation of the aerosol cycles. Although the models are compared and validated against observations, these estimates remain uncertain. Previous satellite measurements of the direct effect of aerosols contained limited information about aerosol type, and were confined to oceans only. Here we use state-of-the-art satellite-based measurements of aerosols and surface wind speed to estimate the clear-sky direct radiative forcing for 2002, incorporating measurements over land and ocean. We use a Monte Carlo approach to account for uncertainties in aerosol measurements and in the algorithm used. Probability density functions obtained for the direct radiative forcing at the top of the atmosphere give a clear-sky, global, annual average of -1.9 W m(-2) with standard deviation, +/- 0.3 W m(-2). These results suggest that present-day direct radiative forcing is stronger than present model estimates, implying future atmospheric warming greater than is presently predicted, as aerosol emissions continue to decline.
Bayesian analysis of CCDM models
Energy Technology Data Exchange (ETDEWEB)
Jesus, J.F. [Universidade Estadual Paulista (Unesp), Câmpus Experimental de Itapeva, Rua Geraldo Alckmin 519, Vila N. Sra. de Fátima, Itapeva, SP, 18409-010 Brazil (Brazil); Valentim, R. [Departamento de Física, Instituto de Ciências Ambientais, Químicas e Farmacêuticas—ICAQF, Universidade Federal de São Paulo (UNIFESP), Unidade José Alencar, Rua São Nicolau No. 210, Diadema, SP, 09913-030 Brazil (Brazil); Andrade-Oliveira, F., E-mail: jfjesus@itapeva.unesp.br, E-mail: valentim.rodolfo@unifesp.br, E-mail: felipe.oliveira@port.ac.uk [Institute of Cosmology and Gravitation—University of Portsmouth, Burnaby Road, Portsmouth, PO1 3FX United Kingdom (United Kingdom)
2017-09-01
Creation of Cold Dark Matter (CCDM), in the context of Einstein Field Equations, produces a negative pressure term which can be used to explain the accelerated expansion of the Universe. In this work we tested six different spatially flat models for matter creation using statistical criteria, in light of SNe Ia data: Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC) and Bayesian Evidence (BE). These criteria allow to compare models considering goodness of fit and number of free parameters, penalizing excess of complexity. We find that JO model is slightly favoured over LJO/ΛCDM model, however, neither of these, nor Γ = 3α H {sub 0} model can be discarded from the current analysis. Three other scenarios are discarded either because poor fitting or because of the excess of free parameters. A method of increasing Bayesian evidence through reparameterization in order to reducing parameter degeneracy is also developed.
International Nuclear Information System (INIS)
Firnhaber, M.; Kanzleiter, T.F.; Schwarz, S.; Weber, G.
1996-12-01
This paper presents the results and assessment of the 'open' ISP37, which deals with the containment thermal-hydraulics and aerosol behavior during an unmitigated severe LWR accident with core melt-down and steam and aerosol release into the containment. Representatives of 22 organizations participated to the ISP37 using the codes CONTAIN, FIPLOC, MELCOR, RALOC, FUMO, MACRES, REMOVAL etc. The containment and aerosol behavior experiment VANAM M3 was selected as experimental comparison basis. The main phenomena investigated are the thermal behavior of a multi-compartment containment, e.g. pressure, temperature and the distribution and depletion of a soluble aerosol. The ISP37 has demonstrated that the codes used could calculate the thermal-hydraulic containment behavior in general with sufficient accuracy. But with respect to the needs of aerosol behavior analysis the accuracies, both analytical and experimental as well, for specific thermal-hydraulic variables should be improved. Although large progress has been made in the simulation of aerosol behavior in multi-compartment geometries the calculated local aerosol concentrations scatter widely. However, the aerosol source term to the environment is overestimated in general. The largest uncertainty concerning the aerosol results is caused by a limited number of thermal hydraulic variables like relative humidity, volume condensation rate and atmospheric flow rate. In some codes also a solubility model is missing
Bayesian modeling using WinBUGS
Ntzoufras, Ioannis
2009-01-01
A hands-on introduction to the principles of Bayesian modeling using WinBUGS Bayesian Modeling Using WinBUGS provides an easily accessible introduction to the use of WinBUGS programming techniques in a variety of Bayesian modeling settings. The author provides an accessible treatment of the topic, offering readers a smooth introduction to the principles of Bayesian modeling with detailed guidance on the practical implementation of key principles. The book begins with a basic introduction to Bayesian inference and the WinBUGS software and goes on to cover key topics, including: Markov Chain Monte Carlo algorithms in Bayesian inference Generalized linear models Bayesian hierarchical models Predictive distribution and model checking Bayesian model and variable evaluation Computational notes and screen captures illustrate the use of both WinBUGS as well as R software to apply the discussed techniques. Exercises at the end of each chapter allow readers to test their understanding of the presented concepts and all ...
3D Bayesian contextual classifiers
DEFF Research Database (Denmark)
Larsen, Rasmus
2000-01-01
We extend a series of multivariate Bayesian 2-D contextual classifiers to 3-D by specifying a simultaneous Gaussian distribution for the feature vectors as well as a prior distribution of the class variables of a pixel and its 6 nearest 3-D neighbours.......We extend a series of multivariate Bayesian 2-D contextual classifiers to 3-D by specifying a simultaneous Gaussian distribution for the feature vectors as well as a prior distribution of the class variables of a pixel and its 6 nearest 3-D neighbours....
Bayesian image restoration, using configurations
DEFF Research Database (Denmark)
Thorarinsdottir, Thordis Linda
2006-01-01
In this paper, we develop a Bayesian procedure for removing noise from images that can be viewed as noisy realisations of random sets in the plane. The procedure utilises recent advances in configuration theory for noise free random sets, where the probabilities of observing the different boundary...... configurations are expressed in terms of the mean normal measure of the random set. These probabilities are used as prior probabilities in a Bayesian image restoration approach. Estimation of the remaining parameters in the model is outlined for the salt and pepper noise. The inference in the model is discussed...
Bayesian image restoration, using configurations
DEFF Research Database (Denmark)
Thorarinsdottir, Thordis
In this paper, we develop a Bayesian procedure for removing noise from images that can be viewed as noisy realisations of random sets in the plane. The procedure utilises recent advances in configuration theory for noise free random sets, where the probabilities of observing the different boundary...... configurations are expressed in terms of the mean normal measure of the random set. These probabilities are used as prior probabilities in a Bayesian image restoration approach. Estimation of the remaining parameters in the model is outlined for salt and pepper noise. The inference in the model is discussed...
Bayesian variable selection in regression
Energy Technology Data Exchange (ETDEWEB)
Mitchell, T.J.; Beauchamp, J.J.
1987-01-01
This paper is concerned with the selection of subsets of ''predictor'' variables in a linear regression model for the prediction of a ''dependent'' variable. We take a Bayesian approach and assign a probability distribution to the dependent variable through a specification of prior distributions for the unknown parameters in the regression model. The appropriate posterior probabilities are derived for each submodel and methods are proposed for evaluating the family of prior distributions. Examples are given that show the application of the Bayesian methodology. 23 refs., 3 figs.
Inference in hybrid Bayesian networks
DEFF Research Database (Denmark)
Lanseth, Helge; Nielsen, Thomas Dyhre; Rumí, Rafael
2009-01-01
Since the 1980s, Bayesian Networks (BNs) have become increasingly popular for building statistical models of complex systems. This is particularly true for boolean systems, where BNs often prove to be a more efficient modelling framework than traditional reliability-techniques (like fault trees a...... decade's research on inference in hybrid Bayesian networks. The discussions are linked to an example model for estimating human reliability....... and reliability block diagrams). However, limitations in the BNs' calculation engine have prevented BNs from becoming equally popular for domains containing mixtures of both discrete and continuous variables (so-called hybrid domains). In this paper we focus on these difficulties, and summarize some of the last...
Bayesian methods for proteomic biomarker development
Directory of Open Access Journals (Sweden)
Belinda Hernández
2015-12-01
In this review we provide an introduction to Bayesian inference and demonstrate some of the advantages of using a Bayesian framework. We summarize how Bayesian methods have been used previously in proteomics and other areas of bioinformatics. Finally, we describe some popular and emerging Bayesian models from the statistical literature and provide a worked tutorial including code snippets to show how these methods may be applied for the evaluation of proteomic biomarkers.
Bayesian variable order Markov models: Towards Bayesian predictive state representations
Dimitrakakis, C.
2009-01-01
We present a Bayesian variable order Markov model that shares many similarities with predictive state representations. The resulting models are compact and much easier to specify and learn than classical predictive state representations. Moreover, we show that they significantly outperform a more
The humble Bayesian : Model checking from a fully Bayesian perspective
Morey, Richard D.; Romeijn, Jan-Willem; Rouder, Jeffrey N.
Gelman and Shalizi (2012) criticize what they call the usual story in Bayesian statistics: that the distribution over hypotheses or models is the sole means of statistical inference, thus excluding model checking and revision, and that inference is inductivist rather than deductivist. They present
Brooks, Sarah D; Thornton, Daniel C O
2018-01-03
The role of marine bioaerosols in cloud formation and climate is currently so uncertain that even the sign of the climate forcing is unclear. Marine aerosols form through direct emissions and through the conversion of gas-phase emissions to aerosols in the atmosphere. The composition and size of aerosols determine how effective they are in catalyzing the formation of water droplets and ice crystals in clouds by acting as cloud condensation nuclei and ice nucleating particles, respectively. Marine organic aerosols may be sourced both from recent regional phytoplankton blooms that add labile organic matter to the surface ocean and from long-term global processes, such as the upwelling of old refractory dissolved organic matter from the deep ocean. Understanding the formation of marine aerosols and their propensity to catalyze cloud formation processes are challenges that must be addressed given the major uncertainties associated with aerosols in climate models.
Brooks, Sarah D.; Thornton, Daniel C. O.
2018-01-01
The role of marine bioaerosols in cloud formation and climate is currently so uncertain that even the sign of the climate forcing is unclear. Marine aerosols form through direct emissions and through the conversion of gas-phase emissions to aerosols in the atmosphere. The composition and size of aerosols determine how effective they are in catalyzing the formation of water droplets and ice crystals in clouds by acting as cloud condensation nuclei and ice nucleating particles, respectively. Marine organic aerosols may be sourced both from recent regional phytoplankton blooms that add labile organic matter to the surface ocean and from long-term global processes, such as the upwelling of old refractory dissolved organic matter from the deep ocean. Understanding the formation of marine aerosols and their propensity to catalyze cloud formation processes are challenges that must be addressed given the major uncertainties associated with aerosols in climate models.
Bayesian Model Averaging for Propensity Score Analysis
Kaplan, David; Chen, Jianshen
2013-01-01
The purpose of this study is to explore Bayesian model averaging in the propensity score context. Previous research on Bayesian propensity score analysis does not take into account model uncertainty. In this regard, an internally consistent Bayesian framework for model building and estimation must also account for model uncertainty. The…
Bayesian models in cognitive neuroscience: A tutorial
O'Reilly, J.X.; Mars, R.B.
2015-01-01
This chapter provides an introduction to Bayesian models and their application in cognitive neuroscience. The central feature of Bayesian models, as opposed to other classes of models, is that Bayesian models represent the beliefs of an observer as probability distributions, allowing them to
A Bayesian framework for risk perception
van Erp, H.R.N.
2017-01-01
We present here a Bayesian framework of risk perception. This framework encompasses plausibility judgments, decision making, and question asking. Plausibility judgments are modeled by way of Bayesian probability theory, decision making is modeled by way of a Bayesian decision theory, and relevancy
LMFBR source term experiments in the Fuel Aerosol Simulant Test (FAST) facility
Energy Technology Data Exchange (ETDEWEB)
Petrykowski, J.C.; Longest, A.W.
1985-01-01
The transport of uranium dioxide (UO/sub 2/) aerosol through liquid sodium was studied in a series of ten experiments in the Fuel Aerosol Simulant Test (FAST) facility at Oak Ridge National Laboratory (ORNL). The experiments were designed to provide a mechanistic basis for evaluating the radiological source term associated with a postulated, energetic core disruptive accident (CDA) in a liquid metal fast breeder reactor (LMFBR). Aerosol was generated by capacitor discharge vaporization of UO/sub 2/ pellets which were submerged in a sodium pool under an argon cover gas. Measurements of the pool and cover gas pressures were used to study the transport of aerosol contained by vapor bubbles within the pool. Samples of cover gas were filtered to determine the quantity of aerosol released from the pool. The depth at which the aerosol was generated was found to be the most critical parameter affecting release. The largest release was observed in the baseline experiment where the sample was vaporized above the sodium pool. In the nine ''undersodium'' experiments aerosol was generated beneath the surface of the pool at depths varying from 30 to 1060 mm. The mass of aerosol released from the pool was found to be a very small fraction of the original specimen. It appears that the bulk of aerosol was contained by bubbles which collapsed within the pool. 18 refs., 11 figs., 4 tabs.
Substantial convection and precipitation enhancements by ultrafine aerosol particles
Fan, Jiwen; Rosenfeld, Daniel; Zhang, Yuwei; Giangrande, Scott E.; Li, Zhanqing; Machado, Luiz A. T.; Martin, Scot T.; Yang, Yan; Wang, Jian; Artaxo, Paulo; Barbosa, Henrique M. J.; Braga, Ramon C.; Comstock, Jennifer M.; Feng, Zhe; Gao, Wenhua; Gomes, Helber B.; Mei, Fan; Pöhlker, Christopher; Pöhlker, Mira L.; Pöschl, Ulrich; de Souza, Rodrigo A. F.
2018-01-01
Ultrafine aerosol particles (smaller than 50 nanometers in diameter) have been thought to be too small to affect cloud formation. Fan et al. show that this is not the case. They studied the effect of urban pollution transported into the otherwise nearly pristine atmosphere of the Amazon. Condensational growth of water droplets around the tiny particles releases latent heat, thereby intensifying atmospheric convection. Thus, anthropogenic ultrafine aerosol particles may exert a more important influence on cloud formation processes than previously believed.
Small scale studies of production of fissium aerosols
International Nuclear Information System (INIS)
Lindqvist, O.; Rydberg, J.
1983-02-01
A small scale study concerning the production and analysis of fission product aerosols formed at various temperatures as a function of the chemical composition of the fissium/corium mixture at the source is presented. CsOH, CsJ and Te are the main aerosol components to be expected. The thermodynamic characterization of occuring Te-iodides and other phases is of great importance for reactor core meltdown chemistry and for the evaluation of the aerosol transport tests. Elemental iodine seems not to be released in significant amounts in reducing atmosphere. Analysis data concerning elements, phases, themral analysis and gases are presented. (G.B.)
Electrospray ionizer for mass spectrometry of aerosol particles
He, Siqin; Hogan, Chris; Li, Lin; Liu, Benjamin Y. H.; Naqwi, Amir; Romay, Francisco
2017-09-19
A device and method are disclosed to apply ESI-based mass spectroscopy to submicrometer and nanometer scale aerosol particles. Unipolar ionization is utilized to charge the particles in order to collect them electrostatically on the tip of a tungsten rod. Subsequently, the species composing the collected particles are dissolved by making a liquid flow over the tungsten rod. This liquid with dissolved aerosol contents is formed into highly charged droplets, which release unfragmented ions for mass spectroscopy, such as time-of-flight mass spectroscopy. The device is configured to operate in a switching mode, wherein aerosol deposition occurs while solvent delivery is turned off and vice versa.
Differentiated Bayesian Conjoint Choice Designs
Z. Sándor (Zsolt); M. Wedel (Michel)
2003-01-01
textabstractPrevious conjoint choice design construction procedures have produced a single design that is administered to all subjects. This paper proposes to construct a limited set of different designs. The designs are constructed in a Bayesian fashion, taking into account prior uncertainty about
Bayesian networks in levee reliability
Roscoe, K.; Hanea, A.
2015-01-01
We applied a Bayesian network to a system of levees for which the results of traditional reliability analysis showed high failure probabilities, which conflicted with the intuition and experience of those managing the levees. We made use of forty proven strength observations - high water levels with
Bayesian Classification of Image Structures
DEFF Research Database (Denmark)
Goswami, Dibyendu; Kalkan, Sinan; Krüger, Norbert
2009-01-01
In this paper, we describe work on Bayesian classi ers for distinguishing between homogeneous structures, textures, edges and junctions. We build semi-local classiers from hand-labeled images to distinguish between these four different kinds of structures based on the concept of intrinsic...... dimensionality. The built classi er is tested on standard and non-standard images...
Computational Neuropsychology and Bayesian Inference.
Parr, Thomas; Rees, Geraint; Friston, Karl J
2018-01-01
Computational theories of brain function have become very influential in neuroscience. They have facilitated the growth of formal approaches to disease, particularly in psychiatric research. In this paper, we provide a narrative review of the body of computational research addressing neuropsychological syndromes, and focus on those that employ Bayesian frameworks. Bayesian approaches to understanding brain function formulate perception and action as inferential processes. These inferences combine 'prior' beliefs with a generative (predictive) model to explain the causes of sensations. Under this view, neuropsychological deficits can be thought of as false inferences that arise due to aberrant prior beliefs (that are poor fits to the real world). This draws upon the notion of a Bayes optimal pathology - optimal inference with suboptimal priors - and provides a means for computational phenotyping. In principle, any given neuropsychological disorder could be characterized by the set of prior beliefs that would make a patient's behavior appear Bayes optimal. We start with an overview of some key theoretical constructs and use these to motivate a form of computational neuropsychology that relates anatomical structures in the brain to the computations they perform. Throughout, we draw upon computational accounts of neuropsychological syndromes. These are selected to emphasize the key features of a Bayesian approach, and the possible types of pathological prior that may be present. They range from visual neglect through hallucinations to autism. Through these illustrative examples, we review the use of Bayesian approaches to understand the link between biology and computation that is at the heart of neuropsychology.
Bayesian Alternation During Tactile Augmentation
Directory of Open Access Journals (Sweden)
Caspar Mathias Goeke
2016-10-01
Full Text Available A large number of studies suggest that the integration of multisensory signals by humans is well described by Bayesian principles. However, there are very few reports about cue combination between a native and an augmented sense. In particular, we asked the question whether adult participants are able to integrate an augmented sensory cue with existing native sensory information. Hence for the purpose of this study we build a tactile augmentation device. Consequently, we compared different hypotheses of how untrained adult participants combine information from a native and an augmented sense. In a two-interval forced choice (2 IFC task, while subjects were blindfolded and seated on a rotating platform, our sensory augmentation device translated information on whole body yaw rotation to tactile stimulation. Three conditions were realized: tactile stimulation only (augmented condition, rotation only (native condition, and both augmented and native information (bimodal condition. Participants had to choose one out of two consecutive rotations with higher angular rotation. For the analysis, we fitted the participants’ responses with a probit model and calculated the just notable difference (JND. Then we compared several models for predicting bimodal from unimodal responses. An objective Bayesian alternation model yielded a better prediction (χred2 = 1.67 than the Bayesian integration model (χred2= 4.34. Slightly higher accuracy showed a non-Bayesian winner takes all model (χred2= 1.64, which either used only native or only augmented values per subject for prediction. However the performance of the Bayesian alternation model could be substantially improved (χred2= 1.09 utilizing subjective weights obtained by a questionnaire. As a result, the subjective Bayesian alternation model predicted bimodal performance most accurately among all tested models. These results suggest that information from augmented and existing sensory modalities in
The impact of precipitation evaporation on the atmospheric aerosol distribution in EC-Earth v3.2.0
de Bruine, Marco; Krol, Maarten; van Noije, Twan; Le Sager, Philippe; Röckmann, Thomas
2018-04-01
The representation of aerosol-cloud interaction in global climate models (GCMs) remains a large source of uncertainty in climate projections. Due to its complexity, precipitation evaporation is either ignored or taken into account in a simplified manner in GCMs. This research explores various ways to treat aerosol resuspension and determines the possible impact of precipitation evaporation and subsequent aerosol resuspension on global aerosol burdens and distribution. The representation of aerosol wet deposition by large-scale precipitation in the EC-Earth model has been improved by utilising additional precipitation-related 3-D fields from the dynamical core, the Integrated Forecasting System (IFS) general circulation model, in the chemistry and aerosol module Tracer Model, version 5 (TM5). A simple approach of scaling aerosol release with evaporated precipitation fraction leads to an increase in the global aerosol burden (+7.8 to +15 % for different aerosol species). However, when taking into account the different sizes and evaporation rate of raindrops following Gong et al. (2006), the release of aerosols is strongly reduced, and the total aerosol burden decreases by -3.0 to -8.5 %. Moreover, inclusion of cloud processing based on observations by Mitra et al. (1992) transforms scavenged small aerosol to coarse particles, which enhances removal by sedimentation and hence leads to a -10 to -11 % lower aerosol burden. Finally, when these two effects are combined, the global aerosol burden decreases by -11 to -19 %. Compared to the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite observations, aerosol optical depth (AOD) is generally underestimated in most parts of the world in all configurations of the TM5 model and although the representation is now physically more realistic, global AOD shows no large improvements in spatial patterns. Similarly, the agreement of the vertical profile with Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP
Topics in Bayesian statistics and maximum entropy
International Nuclear Information System (INIS)
Mutihac, R.; Cicuttin, A.; Cerdeira, A.; Stanciulescu, C.
1998-12-01
Notions of Bayesian decision theory and maximum entropy methods are reviewed with particular emphasis on probabilistic inference and Bayesian modeling. The axiomatic approach is considered as the best justification of Bayesian analysis and maximum entropy principle applied in natural sciences. Particular emphasis is put on solving the inverse problem in digital image restoration and Bayesian modeling of neural networks. Further topics addressed briefly include language modeling, neutron scattering, multiuser detection and channel equalization in digital communications, genetic information, and Bayesian court decision-making. (author)
Evaluation of a radioactive aerosol surveillance system
International Nuclear Information System (INIS)
Scripsick, R.C.; Stafford, R.G.; Beckman, R.J.; Tillery, M.I.; Romero, P.O.
Measurements of the dilution of air contaminants between worker breathing zone and area air samplers were made by releasing a test aerosol in a workroom equipped with an aerosol surveillance system. The data were used to evaluate performance, and suggest improvements in design of the workroom's alarming air monitor system. It was found that a breathing zone concentration of 960 times the maximum permissible concentration in air (MPC/sub a/) for a half-hour was required to trigger alarms of the existing monitoring system under some release conditions. Alternative air monitor placement, suggested from dilution measurements, would reduce this average triggering concentration to 354 MPC/sub a/. Deployment of additional air monitors could further reduce the average triggering concentration to 241 MPC/sub a/. The relation between number of monitors and triggering concentration was studied. No significant decrease in average triggering concentration was noted for arrays containing greater than five monitors
Bayesian analysis of rare events
Straub, Daniel; Papaioannou, Iason; Betz, Wolfgang
2016-06-01
In many areas of engineering and science there is an interest in predicting the probability of rare events, in particular in applications related to safety and security. Increasingly, such predictions are made through computer models of physical systems in an uncertainty quantification framework. Additionally, with advances in IT, monitoring and sensor technology, an increasing amount of data on the performance of the systems is collected. This data can be used to reduce uncertainty, improve the probability estimates and consequently enhance the management of rare events and associated risks. Bayesian analysis is the ideal method to include the data into the probabilistic model. It ensures a consistent probabilistic treatment of uncertainty, which is central in the prediction of rare events, where extrapolation from the domain of observation is common. We present a framework for performing Bayesian updating of rare event probabilities, termed BUS. It is based on a reinterpretation of the classical rejection-sampling approach to Bayesian analysis, which enables the use of established methods for estimating probabilities of rare events. By drawing upon these methods, the framework makes use of their computational efficiency. These methods include the First-Order Reliability Method (FORM), tailored importance sampling (IS) methods and Subset Simulation (SuS). In this contribution, we briefly review these methods in the context of the BUS framework and investigate their applicability to Bayesian analysis of rare events in different settings. We find that, for some applications, FORM can be highly efficient and is surprisingly accurate, enabling Bayesian analysis of rare events with just a few model evaluations. In a general setting, BUS implemented through IS and SuS is more robust and flexible.
Polytomies and Bayesian phylogenetic inference.
Lewis, Paul O; Holder, Mark T; Holsinger, Kent E
2005-04-01
Bayesian phylogenetic analyses are now very popular in systematics and molecular evolution because they allow the use of much more realistic models than currently possible with maximum likelihood methods. There are, however, a growing number of examples in which large Bayesian posterior clade probabilities are associated with very short branch lengths and low values for non-Bayesian measures of support such as nonparametric bootstrapping. For the four-taxon case when the true tree is the star phylogeny, Bayesian analyses become increasingly unpredictable in their preference for one of the three possible resolved tree topologies as data set size increases. This leads to the prediction that hard (or near-hard) polytomies in nature will cause unpredictable behavior in Bayesian analyses, with arbitrary resolutions of the polytomy receiving very high posterior probabilities in some cases. We present a simple solution to this problem involving a reversible-jump Markov chain Monte Carlo (MCMC) algorithm that allows exploration of all of tree space, including unresolved tree topologies with one or more polytomies. The reversible-jump MCMC approach allows prior distributions to place some weight on less-resolved tree topologies, which eliminates misleadingly high posteriors associated with arbitrary resolutions of hard polytomies. Fortunately, assigning some prior probability to polytomous tree topologies does not appear to come with a significant cost in terms of the ability to assess the level of support for edges that do exist in the true tree. Methods are discussed for applying arbitrary prior distributions to tree topologies of varying resolution, and an empirical example showing evidence of polytomies is analyzed and discussed.
Bayesian methods for measures of agreement
Broemeling, Lyle D
2009-01-01
Using WinBUGS to implement Bayesian inferences of estimation and testing hypotheses, Bayesian Methods for Measures of Agreement presents useful methods for the design and analysis of agreement studies. It focuses on agreement among the various players in the diagnostic process.The author employs a Bayesian approach to provide statistical inferences based on various models of intra- and interrater agreement. He presents many examples that illustrate the Bayesian mode of reasoning and explains elements of a Bayesian application, including prior information, experimental information, the likelihood function, posterior distribution, and predictive distribution. The appendices provide the necessary theoretical foundation to understand Bayesian methods as well as introduce the fundamentals of programming and executing the WinBUGS software.Taking a Bayesian approach to inference, this hands-on book explores numerous measures of agreement, including the Kappa coefficient, the G coefficient, and intraclass correlation...
Konovalov, Igor B.; Beekmann, Matthias; Berezin, Evgeny V.; Formenti, Paola; Andreae, Meinrat O.
2017-04-01
Carbonaceous aerosol released into the atmosphere from open biomass burning (BB) is known to undergo considerable chemical and physical transformations (aging). However, there is substantial controversy about the nature and observable effects of these transformations. A shortage of consistent observational evidence on BB aerosol aging processes under different environmental conditions and at various temporal scales hinders development of their adequate representations in chemistry transport models (CTMs). In this study, we obtain insights into the BB aerosol dynamics by using available satellite measurements of aerosol optical depth (AOD) and carbon monoxide (CO). The basic concept of our method is to consider AOD as a function of the BB aerosol photochemical age (that is, the time period characterizing the exposure of BB aerosol emissions to atmospheric oxidation reactions) predicted by means of model tracers. We evaluate the AOD enhancement ratio (ER) defined as the ratio of optical depth of actual BB aerosol with respect to that of a modeled aerosol tracer that is assumed to originate from the same fires as the real BB aerosol but that is not affected by any aging processes. To limit possible effects of model transport errors, the AOD measurements are normalized to CO column amounts that are also retrieved from satellite measurements. The method is applied to the analysis of the meso- and synoptic-scale evolution of aerosol in smoke plumes from major wildfires that occurred in Siberia in summer 2012. AOD and CO retrievals from MODIS and IASI measurements, respectively, are used in combination with simulations performed with the CHIMERE CTM. The analysis indicates that aging processes strongly affected the evolution of BB aerosol in the situation considered, especially in dense plumes (with spatial average PM2. 5 concentration exceeding 100 µg m-3). For such plumes, the ER is found to increase almost 2-fold on the scale of ˜ 10 h of daytime aerosol evolution
Passive self-cleaning aerosol scrubber
International Nuclear Information System (INIS)
Postma, A.K.
1981-01-01
A hybrid gas scrubbing system is described, which includes features of both a pool type scrubber and a sand or ground filter, for use on nuclear reactor containment buildings to limit release of aerosol particles and absorbable gases, including radio-active materials, during postulated major accidents. The system requires no energy while in the passive state and no active energy other than pressurization of the stream of gas being scrubbed. (U.K.)
Hamill, P.; Thomason, L. W.; Peter, T.
2002-05-01
Stratospheric Processes and their Role in Climate (SPARC), a project of the WMO/ICSU/IOC World Climate Research Programme, was responsible for the recent SPARC Assessment of Upper Tropospheric and Stratospheric Water Vapour. SPARC has now decided to generate an analogous document for the stratospheric aerosol, using many of the measurements that have been developed in the last twenty years, but relying heavily on the SAGE II data set. The stratospheric aerosol assessment involves a large international collection of atmospheric scientists whose special area of expertise is the stratospheric aerosol. Key questions that have been identified as requiring answers include: How have aerosol properties such as surface area density varied with time? How representative are satellite-based climatologies? What is the non-volcanic bacground for stratospheric aerosol and can a trend in it be detected? How well can models reproduce observed aerosol properties? We report on the ``kickoff" workshop that was held at the CNES headquarters in Paris on November 4-6, 2001. We shall describe some of the interesting results that were based on the SAGE II data set. The assessment will be carried out by five working groups each focussing on one of the following aspects: processes, aerosol precursors, climatology, trends and modeling. The long records from SAGE II, HALOE and other space based instruments will play a prominent role in construction of a climatology. It is expected that a valuable result of this assessment will be a set of ``standard" stratospheric aerosol parameters for use by modelers.
DEFF Research Database (Denmark)
Nielsen, Ingeborg Elbæk
2017-01-01
carbon, which is the most efficient aerosol to absorb radiation, is found to be one of the largest contributors to global warming. Aerosols are emitted from both anthropogenic and natural sources and the major components of atmospheric particulate matter include sulfate, organic aerosols, nitrate...... at the Villum Research Station, Station Nord in North Greenland. Laboratory studies of a conventional wood stove showed that particle emissions were strongly dependent on the intensity of burn rate. The burning cycle was divided into three phases, where the first phase, the fuel addition, resulted in short-lived...... but high emissions of levoglucosan and organic aerosols. The second phase, the intermediate phase, was dominated by black carbon and only to a minor extent organic aerosols and levoglucosan. The final burn out phase was generally represented by low concentrations of all species and overall the full cycle...
Physico-chemical characterization of aerosols produced by a PWR control rods vaporization
International Nuclear Information System (INIS)
Rabu, B.; Pagano, C.; Tourasse, M.; Gros d'Aillon, L.; Boucenna, A.; Boulaud, D.; Dubourg, R.
2000-01-01
During a PWR type reactor accident, the aerosols produced by the vaporization of the control rods condition the released fission products evolution, for instance, the iodine or the tellurium. The EMAIC experiment has to characterize the aerosols emitted during the core degradation. The IPSN and EDF finances this program, realized at the CEA Grenoble. The results should allow the simulation of the aerosols source resulting from the vaporization to introduce in the ASTEC code, serious accident codes system. (A.L.B.)
Aqueous aerosol SOA formation: impact on aerosol physical properties.
Woo, Joseph L; Kim, Derek D; Schwier, Allison N; Li, Ruizhi; McNeill, V Faye
2013-01-01
Organic chemistry in aerosol water has recently been recognized as a potentially important source of secondary organic aerosol (SOA) material. This SOA material may be surface-active, therefore potentially affecting aerosol heterogeneous activity, ice nucleation, and CCN activity. Aqueous aerosol chemistry has also been shown to be a potential source of light-absorbing products ("brown carbon"). We present results on the formation of secondary organic aerosol material in aerosol water and the associated changes in aerosol physical properties from GAMMA (Gas-Aerosol Model for Mechanism Analysis), a photochemical box model with coupled gas and detailed aqueous aerosol chemistry. The detailed aerosol composition output from GAMMA was coupled with two recently developed modules for predicting a) aerosol surface tension and b) the UV-Vis absorption spectrum of the aerosol, based on our previous laboratory observations. The simulation results suggest that the formation of oligomers and organic acids in bulk aerosol water is unlikely to perturb aerosol surface tension significantly. Isoprene-derived organosulfates are formed in high concentrations in acidic aerosols under low-NO(x) conditions, but more experimental data are needed before the potential impact of these species on aerosol surface tension may be evaluated. Adsorption of surfactants from the gas phase may further suppress aerosol surface tension. Light absorption by aqueous aerosol SOA material is driven by dark glyoxal chemistry and is highest under high-NO(x) conditions, at high relative humidity, in the early morning hours. The wavelength dependence of the predicted absorption spectra is comparable to field observations and the predicted mass absorption efficiencies suggest that aqueous aerosol chemistry can be a significant source of aerosol brown carbon under urban conditions.
Bayesian Model Averaging for Propensity Score Analysis.
Kaplan, David; Chen, Jianshen
2014-01-01
This article considers Bayesian model averaging as a means of addressing uncertainty in the selection of variables in the propensity score equation. We investigate an approximate Bayesian model averaging approach based on the model-averaged propensity score estimates produced by the R package BMA but that ignores uncertainty in the propensity score. We also provide a fully Bayesian model averaging approach via Markov chain Monte Carlo sampling (MCMC) to account for uncertainty in both parameters and models. A detailed study of our approach examines the differences in the causal estimate when incorporating noninformative versus informative priors in the model averaging stage. We examine these approaches under common methods of propensity score implementation. In addition, we evaluate the impact of changing the size of Occam's window used to narrow down the range of possible models. We also assess the predictive performance of both Bayesian model averaging propensity score approaches and compare it with the case without Bayesian model averaging. Overall, results show that both Bayesian model averaging propensity score approaches recover the treatment effect estimates well and generally provide larger uncertainty estimates, as expected. Both Bayesian model averaging approaches offer slightly better prediction of the propensity score compared with the Bayesian approach with a single propensity score equation. Covariate balance checks for the case study show that both Bayesian model averaging approaches offer good balance. The fully Bayesian model averaging approach also provides posterior probability intervals of the balance indices.
Interactions of fission product vapours with aerosols
Energy Technology Data Exchange (ETDEWEB)
Benson, C.G.; Newland, M.S. [AEA Technology, Winfrith (United Kingdom)
1996-12-01
Reactions between structural and reactor materials aerosols and fission product vapours released during a severe accident in a light water reactor (LWR) will influence the magnitude of the radiological source term ultimately released to the environment. The interaction of cadmium aerosol with iodine vapour at different temperatures has been examined in a programme of experiments designed to characterise the kinetics of the system. Laser induced fluorescence (LIF) is a technique that is particularly amenable to the study of systems involving elemental iodine because of the high intensity of the fluorescence lines. Therefore this technique was used in the experiments to measure the decrease in the concentration of iodine vapour as the reaction with cadmium proceeded. Experiments were conducted over the range of temperatures (20-350{sup o}C), using calibrated iodine vapour and cadmium aerosol generators that gave well-quantified sources. The LIF results provided information on the kinetics of the process, whilst examination of filter samples gave data on the composition and morphology of the aerosol particles that were formed. The results showed that the reaction of cadmium with iodine was relatively fast, giving reaction half-lives of approximately 0.3 s. This suggests that the assumption used by primary circuit codes such as VICTORIA that reaction rates are mass-transfer limited, is justified for the cadmium-iodine reaction. The reaction was first order with respect to both cadmium and iodine, and was assigned as pseudo second order overall. However, there appeared to be a dependence of aerosol surface area on the overall rate constant, making the precise order of the reaction difficult to assign. The relatively high volatility of the cadmium iodide formed in the reaction played an important role in determining the composition of the particles. (author) 23 figs., 7 tabs., 22 refs.
Pedestrian dynamics via Bayesian networks
Venkat, Ibrahim; Khader, Ahamad Tajudin; Subramanian, K. G.
2014-06-01
Studies on pedestrian dynamics have vital applications in crowd control management relevant to organizing safer large scale gatherings including pilgrimages. Reasoning pedestrian motion via computational intelligence techniques could be posed as a potential research problem within the realms of Artificial Intelligence. In this contribution, we propose a "Bayesian Network Model for Pedestrian Dynamics" (BNMPD) to reason the vast uncertainty imposed by pedestrian motion. With reference to key findings from literature which include simulation studies, we systematically identify: What are the various factors that could contribute to the prediction of crowd flow status? The proposed model unifies these factors in a cohesive manner using Bayesian Networks (BNs) and serves as a sophisticated probabilistic tool to simulate vital cause and effect relationships entailed in the pedestrian domain.
Bayesian Networks and Influence Diagrams
DEFF Research Database (Denmark)
Kjærulff, Uffe Bro; Madsen, Anders Læsø
Probabilistic networks, also known as Bayesian networks and influence diagrams, have become one of the most promising technologies in the area of applied artificial intelligence, offering intuitive, efficient, and reliable methods for diagnosis, prediction, decision making, classification......, troubleshooting, and data mining under uncertainty. Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis provides a comprehensive guide for practitioners who wish to understand, construct, and analyze intelligent systems for decision support based on probabilistic networks. Intended...... primarily for practitioners, this book does not require sophisticated mathematical skills or deep understanding of the underlying theory and methods nor does it discuss alternative technologies for reasoning under uncertainty. The theory and methods presented are illustrated through more than 140 examples...
BAYESIAN IMAGE RESTORATION, USING CONFIGURATIONS
Directory of Open Access Journals (Sweden)
Thordis Linda Thorarinsdottir
2011-05-01
Full Text Available In this paper, we develop a Bayesian procedure for removing noise from images that can be viewed as noisy realisations of random sets in the plane. The procedure utilises recent advances in configuration theory for noise free random sets, where the probabilities of observing the different boundary configurations are expressed in terms of the mean normal measure of the random set. These probabilities are used as prior probabilities in a Bayesian image restoration approach. Estimation of the remaining parameters in the model is outlined for salt and pepper noise. The inference in the model is discussed in detail for 3 X 3 and 5 X 5 configurations and examples of the performance of the procedure are given.
Bayesian Inference on Proportional Elections
Brunello, Gabriel Hideki Vatanabe; Nakano, Eduardo Yoshio
2015-01-01
Polls for majoritarian voting systems usually show estimates of the percentage of votes for each candidate. However, proportional vote systems do not necessarily guarantee the candidate with the most percentage of votes will be elected. Thus, traditional methods used in majoritarian elections cannot be applied on proportional elections. In this context, the purpose of this paper was to perform a Bayesian inference on proportional elections considering the Brazilian system of seats distribution. More specifically, a methodology to answer the probability that a given party will have representation on the chamber of deputies was developed. Inferences were made on a Bayesian scenario using the Monte Carlo simulation technique, and the developed methodology was applied on data from the Brazilian elections for Members of the Legislative Assembly and Federal Chamber of Deputies in 2010. A performance rate was also presented to evaluate the efficiency of the methodology. Calculations and simulations were carried out using the free R statistical software. PMID:25786259
Bayesian analyses of cognitive architecture.
Houpt, Joseph W; Heathcote, Andrew; Eidels, Ami
2017-06-01
The question of cognitive architecture-how cognitive processes are temporally organized-has arisen in many areas of psychology. This question has proved difficult to answer, with many proposed solutions turning out to be spurious. Systems factorial technology (Townsend & Nozawa, 1995) provided the first rigorous empirical and analytical method of identifying cognitive architecture, using the survivor interaction contrast (SIC) to determine when people are using multiple sources of information in parallel or in series. Although the SIC is based on rigorous nonparametric mathematical modeling of response time distributions, for many years inference about cognitive architecture has relied solely on visual assessment. Houpt and Townsend (2012) recently introduced null hypothesis significance tests, and here we develop both parametric and nonparametric (encompassing prior) Bayesian inference. We show that the Bayesian approaches can have considerable advantages. (PsycINFO Database Record (c) 2017 APA, all rights reserved).
Deep Learning and Bayesian Methods
Directory of Open Access Journals (Sweden)
Prosper Harrison B.
2017-01-01
Full Text Available A revolution is underway in which deep neural networks are routinely used to solve diffcult problems such as face recognition and natural language understanding. Particle physicists have taken notice and have started to deploy these methods, achieving results that suggest a potentially significant shift in how data might be analyzed in the not too distant future. We discuss a few recent developments in the application of deep neural networks and then indulge in speculation about how such methods might be used to automate certain aspects of data analysis in particle physics. Next, the connection to Bayesian methods is discussed and the paper ends with thoughts on a significant practical issue, namely, how, from a Bayesian perspective, one might optimize the construction of deep neural networks.
Bayesian inference on proportional elections.
Directory of Open Access Journals (Sweden)
Gabriel Hideki Vatanabe Brunello
Full Text Available Polls for majoritarian voting systems usually show estimates of the percentage of votes for each candidate. However, proportional vote systems do not necessarily guarantee the candidate with the most percentage of votes will be elected. Thus, traditional methods used in majoritarian elections cannot be applied on proportional elections. In this context, the purpose of this paper was to perform a Bayesian inference on proportional elections considering the Brazilian system of seats distribution. More specifically, a methodology to answer the probability that a given party will have representation on the chamber of deputies was developed. Inferences were made on a Bayesian scenario using the Monte Carlo simulation technique, and the developed methodology was applied on data from the Brazilian elections for Members of the Legislative Assembly and Federal Chamber of Deputies in 2010. A performance rate was also presented to evaluate the efficiency of the methodology. Calculations and simulations were carried out using the free R statistical software.
Space Shuttle RTOS Bayesian Network
Morris, A. Terry; Beling, Peter A.
2001-01-01
With shrinking budgets and the requirements to increase reliability and operational life of the existing orbiter fleet, NASA has proposed various upgrades for the Space Shuttle that are consistent with national space policy. The cockpit avionics upgrade (CAU), a high priority item, has been selected as the next major upgrade. The primary functions of cockpit avionics include flight control, guidance and navigation, communication, and orbiter landing support. Secondary functions include the provision of operational services for non-avionics systems such as data handling for the payloads and caution and warning alerts to the crew. Recently, a process to selection the optimal commercial-off-the-shelf (COTS) real-time operating system (RTOS) for the CAU was conducted by United Space Alliance (USA) Corporation, which is a joint venture between Boeing and Lockheed Martin, the prime contractor for space shuttle operations. In order to independently assess the RTOS selection, NASA has used the Bayesian network-based scoring methodology described in this paper. Our two-stage methodology addresses the issue of RTOS acceptability by incorporating functional, performance and non-functional software measures related to reliability, interoperability, certifiability, efficiency, correctness, business, legal, product history, cost and life cycle. The first stage of the methodology involves obtaining scores for the various measures using a Bayesian network. The Bayesian network incorporates the causal relationships between the various and often competing measures of interest while also assisting the inherently complex decision analysis process with its ability to reason under uncertainty. The structure and selection of prior probabilities for the network is extracted from experts in the field of real-time operating systems. Scores for the various measures are computed using Bayesian probability. In the second stage, multi-criteria trade-off analyses are performed between the scores
Multiview Bayesian Correlated Component Analysis
DEFF Research Database (Denmark)
Kamronn, Simon Due; Poulsen, Andreas Trier; Hansen, Lars Kai
2015-01-01
are identical. Here we propose a hierarchical probabilistic model that can infer the level of universality in such multiview data, from completely unrelated representations, corresponding to canonical correlation analysis, to identical representations as in correlated component analysis. This new model, which...... we denote Bayesian correlated component analysis, evaluates favorably against three relevant algorithms in simulated data. A well-established benchmark EEG data set is used to further validate the new model and infer the variability of spatial representations across multiple subjects....
Reliability analysis with Bayesian networks
Zwirglmaier, Kilian Martin
2017-01-01
Bayesian networks (BNs) represent a probabilistic modeling tool with large potential for reliability engineering. While BNs have been successfully applied to reliability engineering, there are remaining issues, some of which are addressed in this work. Firstly a classification of BN elicitation approaches is proposed. Secondly two approximate inference approaches, one of which is based on discretization and the other one on sampling, are proposed. These approaches are applicable to hybrid/con...
Interim Bayesian Persuasion: First Steps
Perez, Eduardo
2015-01-01
This paper makes a first attempt at building a theory of interim Bayesian persuasion. I work in a minimalist model where a low or high type sender seeks validation from a receiver who is willing to validate high types exclusively. After learning her type, the sender chooses a complete conditional information structure for the receiver from a possibly restricted feasible set. I suggest a solution to this game that takes into account the signaling potential of the sender's choice.
Bayesian Sampling using Condition Indicators
DEFF Research Database (Denmark)
Faber, Michael H.; Sørensen, John Dalsgaard
2002-01-01
. This allows for a Bayesian formulation of the indicators whereby the experience and expertise of the inspection personnel may be fully utilized and consistently updated as frequentistic information is collected. The approach is illustrated on an example considering a concrete structure subject to corrosion....... It is shown how half-cell potential measurements may be utilized to update the probability of excessive repair after 50 years....
Computational Neuropsychology and Bayesian Inference
Directory of Open Access Journals (Sweden)
Thomas Parr
2018-02-01
Full Text Available Computational theories of brain function have become very influential in neuroscience. They have facilitated the growth of formal approaches to disease, particularly in psychiatric research. In this paper, we provide a narrative review of the body of computational research addressing neuropsychological syndromes, and focus on those that employ Bayesian frameworks. Bayesian approaches to understanding brain function formulate perception and action as inferential processes. These inferences combine ‘prior’ beliefs with a generative (predictive model to explain the causes of sensations. Under this view, neuropsychological deficits can be thought of as false inferences that arise due to aberrant prior beliefs (that are poor fits to the real world. This draws upon the notion of a Bayes optimal pathology – optimal inference with suboptimal priors – and provides a means for computational phenotyping. In principle, any given neuropsychological disorder could be characterized by the set of prior beliefs that would make a patient’s behavior appear Bayes optimal. We start with an overview of some key theoretical constructs and use these to motivate a form of computational neuropsychology that relates anatomical structures in the brain to the computations they perform. Throughout, we draw upon computational accounts of neuropsychological syndromes. These are selected to emphasize the key features of a Bayesian approach, and the possible types of pathological prior that may be present. They range from visual neglect through hallucinations to autism. Through these illustrative examples, we review the use of Bayesian approaches to understand the link between biology and computation that is at the heart of neuropsychology.
Bayesian methods applied to GWAS.
Fernando, Rohan L; Garrick, Dorian
2013-01-01
Bayesian multiple-regression methods are being successfully used for genomic prediction and selection. These regression models simultaneously fit many more markers than the number of observations available for the analysis. Thus, the Bayes theorem is used to combine prior beliefs of marker effects, which are expressed in terms of prior distributions, with information from data for inference. Often, the analyses are too complex for closed-form solutions and Markov chain Monte Carlo (MCMC) sampling is used to draw inferences from posterior distributions. This chapter describes how these Bayesian multiple-regression analyses can be used for GWAS. In most GWAS, false positives are controlled by limiting the genome-wise error rate, which is the probability of one or more false-positive results, to a small value. As the number of test in GWAS is very large, this results in very low power. Here we show how in Bayesian GWAS false positives can be controlled by limiting the proportion of false-positive results among all positives to some small value. The advantage of this approach is that the power of detecting associations is not inversely related to the number of markers.
Development of Multi-Wavelength Raman Lidar and its Application on Aerosol and Cloud Research
Directory of Open Access Journals (Sweden)
Liu Dong
2016-01-01
Full Text Available A movable multi-wavelength Raman lidar (TMPRL was built in Hefei, China. Emitting with three wavelengths at 1064, 532, and 355nm, receiving three above Mie scattering signals and two nitrogen Raman signals at 386 and 607nm, and depolarization signal at 532nm, TMPRL has the capacity to investigate the height resolved optical and microphysical properties of aerosol and cloud. The retrieval algorithms of optical parameters base on Mie-Raman technique and the microphysical parameters based on Bayesian optimization method were also developed and applied to observed lidar data. Designing to make unattended operation and 24/7 continuous working, TMPRL has joined several field campaigns to study on the aerosol, cloud and their interaction researches. Some observed results of aerosol and cloud optical properties and the first attempt to validate the vertical aerosol size distribution retrieved by TMPRL and in-situ measurement by airplane are presented and discussed.
12th Brazilian Meeting on Bayesian Statistics
Louzada, Francisco; Rifo, Laura; Stern, Julio; Lauretto, Marcelo
2015-01-01
Through refereed papers, this volume focuses on the foundations of the Bayesian paradigm; their comparison to objectivistic or frequentist Statistics counterparts; and the appropriate application of Bayesian foundations. This research in Bayesian Statistics is applicable to data analysis in biostatistics, clinical trials, law, engineering, and the social sciences. EBEB, the Brazilian Meeting on Bayesian Statistics, is held every two years by the ISBrA, the International Society for Bayesian Analysis, one of the most active chapters of the ISBA. The 12th meeting took place March 10-14, 2014 in Atibaia. Interest in foundations of inductive Statistics has grown recently in accordance with the increasing availability of Bayesian methodological alternatives. Scientists need to deal with the ever more difficult choice of the optimal method to apply to their problem. This volume shows how Bayes can be the answer. The examination and discussion on the foundations work towards the goal of proper application of Bayesia...
Compiling Relational Bayesian Networks for Exact Inference
DEFF Research Database (Denmark)
Jaeger, Manfred; Chavira, Mark; Darwiche, Adnan
2004-01-01
We describe a system for exact inference with relational Bayesian networks as defined in the publicly available \\primula\\ tool. The system is based on compiling propositional instances of relational Bayesian networks into arithmetic circuits and then performing online inference by evaluating...... and differentiating these circuits in time linear in their size. We report on experimental results showing the successful compilation, and efficient inference, on relational Bayesian networks whose {\\primula}--generated propositional instances have thousands of variables, and whose jointrees have clusters...
Bayesian Posterior Distributions Without Markov Chains
Cole, Stephen R.; Chu, Haitao; Greenland, Sander; Hamra, Ghassan; Richardson, David B.
2012-01-01
Bayesian posterior parameter distributions are often simulated using Markov chain Monte Carlo (MCMC) methods. However, MCMC methods are not always necessary and do not help the uninitiated understand Bayesian inference. As a bridge to understanding Bayesian inference, the authors illustrate a transparent rejection sampling method. In example 1, they illustrate rejection sampling using 36 cases and 198 controls from a case-control study (1976–1983) assessing the relation between residential ex...
Lessons learned from case studies of worker exposures to radioactive aerosols
International Nuclear Information System (INIS)
Hoover, M.D.; Guilmette, R.A.; Scott, B.R.
1995-01-01
Considerable efforts in the aerosol science and health protection communities are devoted to developing a defensible technical basis for measuring, modeling, and mitigating toxic aerosols. These efforts involve understanding aerosol source terms, projecting potential aerosol releases, describing their behavior in the workplace and environment, developing instruments and techniques to measure the aerosols, designing ways to contain or control the aerosols, modeling and measuring uptake by workers and other people, estimating health effects, and planning appropriate responses. To help in this effort, we have compiled a data base of case studies involving releases of aerosols and worker exposures in a wide range of industries. Sources of information have included personal communications, limited distribution reports, open literature publications, and reports of abnormal occurrences in U.S. Department of Energy facilities and among licensees of the U.S. Nuclear Regulatory Commission. The data base currently includes more than 100 cases. The case studies have been organized according to the radionuclides involved and the circumstances and consequences of the release. This information has been used to address a number of important questions, such as the adequacy of current aerosol sampling and monitoring procedures, areas needing improvement, and strategies for planning for or responding to accidents. One area of particular interest is related to strategies for prospective or retrospective characterization of aerosol source terms. In some cases, worker exposures have involved aerosols that are similar in particle size distribution, composition, and solubility to aerosols routinely produced in the normal process activities. In such cases, prospective characterization of aerosol source terms has provided relevant and useful information
Preliminary results of the aerosol optical depth retrieval in Johor, Malaysia
International Nuclear Information System (INIS)
Lim, H Q; Lau, A M S; Kanniah, K D
2014-01-01
Monitoring of atmospheric aerosols over the urban area is important as tremendous amounts of pollutants are released by industrial activities and heavy traffic flow. Air quality monitoring by satellite observation provides better spatial coverage, however, detailed aerosol properties retrieval remains a challenge. This is due to the limitation of aerosol retrieval algorithm on high reflectance (bright surface) areas. The aim of this study is to retrieve aerosol optical depth over urban areas of Iskandar Malaysia; the main southern development zone in Johor state, using Moderate Resolution Imaging Spectroradiometer (MODIS) 500 m resolution data. One of the important steps is the aerosol optical depth retrieval is to characterise different types of aerosols in the study area. This information will be used to construct a Look Up Table containing the simulated aerosol reflectance and corresponding aerosol optical depth. Thus, in this study we have characterised different aerosol types in the study area using Aerosol Robotic Network (AERONET) data. These data were processed using cluster analysis and the preliminary results show that the area is consisting of coastal urban (65%), polluted urban (27.5%), dust particles (6%) and heavy pollution (1.5%) aerosols
Preliminary results of the aerosol optical depth retrieval in Johor, Malaysia
Lim, H. Q.; Kanniah, K. D.; Lau, A. M. S.
2014-02-01
Monitoring of atmospheric aerosols over the urban area is important as tremendous amounts of pollutants are released by industrial activities and heavy traffic flow. Air quality monitoring by satellite observation provides better spatial coverage, however, detailed aerosol properties retrieval remains a challenge. This is due to the limitation of aerosol retrieval algorithm on high reflectance (bright surface) areas. The aim of this study is to retrieve aerosol optical depth over urban areas of Iskandar Malaysia; the main southern development zone in Johor state, using Moderate Resolution Imaging Spectroradiometer (MODIS) 500 m resolution data. One of the important steps is the aerosol optical depth retrieval is to characterise different types of aerosols in the study area. This information will be used to construct a Look Up Table containing the simulated aerosol reflectance and corresponding aerosol optical depth. Thus, in this study we have characterised different aerosol types in the study area using Aerosol Robotic Network (AERONET) data. These data were processed using cluster analysis and the preliminary results show that the area is consisting of coastal urban (65%), polluted urban (27.5%), dust particles (6%) and heavy pollution (1.5%) aerosols.
Attachment of gaseous fission products to aerosols
International Nuclear Information System (INIS)
Skyrme, G.
1985-01-01
Accidents may occur in which the integrity of fuel cladding is breached and volatile fission products are released to the containment atmosphere. In order to assess the magnitude of the subsequent radiological hazard it is necessary to know the transport behaviour of such fission products. It is frequently assumed that the fission products remain in the gaseous phase. There is a possibility, however, that they may attach themselves to particles and hence substantially modify their transport properties. This paper provides a theoretical assessment of the conditions under which gaseous fission products may be attached to aerosol particles. Specific topics discussed are: the mass transfer of a gaseous fission product to an isolated aerosol particle in an infinite medium; the rate at which the concentration of fission products in the gas phase diminishes within a container as a result of deposition on a population of particles; and the distribution of deposited fission product between different particle sizes in a log-normal distribution. It is shown that, for a given mass, small particles are more efficient for fission product attachment, and that only small concentrations of such particles may be necessary to achieve rapid attachment. Conditions under which gaseous fission products are not attached to particles are also considered, viz, the competing processes of deposition onto the containment walls and onto aerosol particles, and the possibility of the removal of aerosols from the containment by various deposition processes, or agglomeration, before attachment takes place. (author)
3rd Bayesian Young Statisticians Meeting
Lanzarone, Ettore; Villalobos, Isadora; Mattei, Alessandra
2017-01-01
This book is a selection of peer-reviewed contributions presented at the third Bayesian Young Statisticians Meeting, BAYSM 2016, Florence, Italy, June 19-21. The meeting provided a unique opportunity for young researchers, M.S. students, Ph.D. students, and postdocs dealing with Bayesian statistics to connect with the Bayesian community at large, to exchange ideas, and to network with others working in the same field. The contributions develop and apply Bayesian methods in a variety of fields, ranging from the traditional (e.g., biostatistics and reliability) to the most innovative ones (e.g., big data and networks).
Learning dynamic Bayesian networks with mixed variables
DEFF Research Database (Denmark)
Bøttcher, Susanne Gammelgaard
This paper considers dynamic Bayesian networks for discrete and continuous variables. We only treat the case, where the distribution of the variables is conditional Gaussian. We show how to learn the parameters and structure of a dynamic Bayesian network and also how the Markov order can be learn....... An automated procedure for specifying prior distributions for the parameters in a dynamic Bayesian network is presented. It is a simple extension of the procedure for the ordinary Bayesian networks. Finally the W¨olfer?s sunspot numbers are analyzed....
Aerosols from biomass combustion
Energy Technology Data Exchange (ETDEWEB)
Nussbaumer, T.
2001-07-01
This report is the proceedings of a seminar on biomass combustion and aerosol production organised jointly by the International Energy Agency's (IEA) Task 32 on bio energy and the Swiss Federal Office of Energy (SFOE). This collection of 16 papers discusses the production of aerosols and fine particles by the burning of biomass and their effects. Expert knowledge on the environmental impact of aerosols, formation mechanisms, measurement technologies, methods of analysis and measures to be taken to reduce such emissions is presented. The seminar, visited by 50 participants from 11 countries, shows, according to the authors, that the reduction of aerosol emissions resulting from biomass combustion will remain a challenge for the future.
Sodium oxide aerosol filtration
International Nuclear Information System (INIS)
Duverger de Cuy, G.
1979-01-01
In the scope of the sodium aerosol trapping research effort by the CEA/DSN, the retention capacity and yield were measured for very high efficiency fiberglass filters and several types of prefilters (cyclone agglomerator, fabric prefilters, water scrubbers). (author)
MBIS: multivariate Bayesian image segmentation tool.
Esteban, Oscar; Wollny, Gert; Gorthi, Subrahmanyam; Ledesma-Carbayo, María-J; Thiran, Jean-Philippe; Santos, Andrés; Bach-Cuadra, Meritxell
2014-07-01
We present MBIS (Multivariate Bayesian Image Segmentation tool), a clustering tool based on the mixture of multivariate normal distributions model. MBIS supports multichannel bias field correction based on a B-spline model. A second methodological novelty is the inclusion of graph-cuts optimization for the stationary anisotropic hidden Markov random field model. Along with MBIS, we release an evaluation framework that contains three different experiments on multi-site data. We first validate the accuracy of segmentation and the estimated bias field for each channel. MBIS outperforms a widely used segmentation tool in a cross-comparison evaluation. The second experiment demonstrates the robustness of results on atlas-free segmentation of two image sets from scan-rescan protocols on 21 healthy subjects. Multivariate segmentation is more replicable than the monospectral counterpart on T1-weighted images. Finally, we provide a third experiment to illustrate how MBIS can be used in a large-scale study of tissue volume change with increasing age in 584 healthy subjects. This last result is meaningful as multivariate segmentation performs robustly without the need for prior knowledge. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.
Effect of relative humidity on growth of sodium oxide aerosols
International Nuclear Information System (INIS)
Sundarajan, A.R.; Mitragotri, D.S.; Mukunda Rao, S.R.
1982-01-01
Behavior of aerosol resulting from sodium fires in a closed vessel is investigated and the changes in the particle size distribution of the aerosol due to coagulation and humidity have been studied. The initial mass concentration is in the range of 80 -- 500 mg/m 3 and the relative humidity is varied between 50 to 98%. The initial size of the released aerosol is found to be 0.9 μm. Equilibrium diameters of particles growing in humid air have been computed for various humidity levels using water activity of sodium hydroxide. Both theoretical and experimental results have yielded growth ratios of about 3 at about 95% relative humidity. It is recommended that the computer codes dealing with aerosol coagulation behavior in reactor containment should include an appropriate humidity-growth function. (author)
Winkler, A.; Harazim, S.; Collins, D.J.; Br?nig, R.; Schmidt, H.; Menzel, S.B.
2017-01-01
In this work, we discuss and demonstrate the principle features of surface acoustic wave (SAW) aerosol generation, based on the properties of the fluid supply, the acoustic wave field and the acoustowetting phenomena. Furthermore, we demonstrate a compact SAW-based aerosol generator amenable to mass production fabricated using simple techniques including photolithography, computerized numerical control (CNC) milling and printed circuit board (PCB) manufacturing. Using this device, we present ...
Emergency protection from aerosols
International Nuclear Information System (INIS)
Cristy, G.A.; Chester, C.V.
1981-07-01
Expedient methods were developed that could be used by an average person, using only materials readily available, to protect himself and his family from injury by toxic (e.g., radioactive) aerosols. The most effective means of protection was the use of a household vacuum cleaner to maintain a small positive pressure on a closed house during passage of the aerosol cloud. Protection factors of 800 and above were achieved
Määttä, Anu; Laine, Marko; Tamminen, Johanna
2015-04-01
This study aims to characterize the uncertainty related to the aerosol microphysical model selection and the modelling error due to approximations in the forward modelling. Many satellite aerosol retrieval algorithms rely on pre-calculated look-up tables of model parameters representing various atmospheric conditions. In the retrieval we need to choose the most appropriate aerosol microphysical models from the pre-defined set of models by fitting them to the observations. The aerosol properties, e.g. AOD, are then determined from the best models. This choice of an appropriate aerosol model composes a notable part in the AOD retrieval uncertainty. The motivation in our study was to account these two sources in the total uncertainty budget: uncertainty in selecting the most appropriate model, and uncertainty resulting from the approximations in the pre-calculated aerosol microphysical model. The systematic model error was analysed by studying the behaviour of the model residuals, i.e. the differences between modelled and observed reflectances, by statistical methods. We utilised Gaussian processes to characterize the uncertainty related to approximations in aerosol microphysics modelling due to use of look-up tables and other non-modelled systematic features in the Level 1 data. The modelling error is described by a non-diagonal covariance matrix parameterised by correlation length, which is estimated from the residuals using computational tools from spatial statistics. In addition, we utilised Bayesian model selection and model averaging methods to account the uncertainty due to aerosol model selection. By acknowledging the modelling error as a source of uncertainty in the retrieval of AOD from observed spectral reflectance, we allow the observed values to deviate from the modelled values within limits determined by both the measurement and modelling errors. This results in a more realistic uncertainty level of the retrieved AOD. The method is illustrated by both
Bayesian phylogeography finds its roots.
Directory of Open Access Journals (Sweden)
Philippe Lemey
2009-09-01
Full Text Available As a key factor in endemic and epidemic dynamics, the geographical distribution of viruses has been frequently interpreted in the light of their genetic histories. Unfortunately, inference of historical dispersal or migration patterns of viruses has mainly been restricted to model-free heuristic approaches that provide little insight into the temporal setting of the spatial dynamics. The introduction of probabilistic models of evolution, however, offers unique opportunities to engage in this statistical endeavor. Here we introduce a Bayesian framework for inference, visualization and hypothesis testing of phylogeographic history. By implementing character mapping in a Bayesian software that samples time-scaled phylogenies, we enable the reconstruction of timed viral dispersal patterns while accommodating phylogenetic uncertainty. Standard Markov model inference is extended with a stochastic search variable selection procedure that identifies the parsimonious descriptions of the diffusion process. In addition, we propose priors that can incorporate geographical sampling distributions or characterize alternative hypotheses about the spatial dynamics. To visualize the spatial and temporal information, we summarize inferences using virtual globe software. We describe how Bayesian phylogeography compares with previous parsimony analysis in the investigation of the influenza A H5N1 origin and H5N1 epidemiological linkage among sampling localities. Analysis of rabies in West African dog populations reveals how virus diffusion may enable endemic maintenance through continuous epidemic cycles. From these analyses, we conclude that our phylogeographic framework will make an important asset in molecular epidemiology that can be easily generalized to infer biogeogeography from genetic data for many organisms.
Bayesian flood forecasting methods: A review
Han, Shasha; Coulibaly, Paulin
2017-08-01
Over the past few decades, floods have been seen as one of the most common and largely distributed natural disasters in the world. If floods could be accurately forecasted in advance, then their negative impacts could be greatly minimized. It is widely recognized that quantification and reduction of uncertainty associated with the hydrologic forecast is of great importance for flood estimation and rational decision making. Bayesian forecasting system (BFS) offers an ideal theoretic framework for uncertainty quantification that can be developed for probabilistic flood forecasting via any deterministic hydrologic model. It provides suitable theoretical structure, empirically validated models and reasonable analytic-numerical computation method, and can be developed into various Bayesian forecasting approaches. This paper presents a comprehensive review on Bayesian forecasting approaches applied in flood forecasting from 1999 till now. The review starts with an overview of fundamentals of BFS and recent advances in BFS, followed with BFS application in river stage forecasting and real-time flood forecasting, then move to a critical analysis by evaluating advantages and limitations of Bayesian forecasting methods and other predictive uncertainty assessment approaches in flood forecasting, and finally discusses the future research direction in Bayesian flood forecasting. Results show that the Bayesian flood forecasting approach is an effective and advanced way for flood estimation, it considers all sources of uncertainties and produces a predictive distribution of the river stage, river discharge or runoff, thus gives more accurate and reliable flood forecasts. Some emerging Bayesian forecasting methods (e.g. ensemble Bayesian forecasting system, Bayesian multi-model combination) were shown to overcome limitations of single model or fixed model weight and effectively reduce predictive uncertainty. In recent years, various Bayesian flood forecasting approaches have been
Bayesian inference for Hawkes processes
DEFF Research Database (Denmark)
Rasmussen, Jakob Gulddahl
2013-01-01
The Hawkes process is a practically and theoretically important class of point processes, but parameter-estimation for such a process can pose various problems. In this paper we explore and compare two approaches to Bayesian inference. The first approach is based on the so-called conditional...... intensity function, while the second approach is based on an underlying clustering and branching structure in the Hawkes process. For practical use, MCMC (Markov chain Monte Carlo) methods are employed. The two approaches are compared numerically using three examples of the Hawkes process....
Bayesian inference for Hawkes processes
DEFF Research Database (Denmark)
Rasmussen, Jakob Gulddahl
The Hawkes process is a practically and theoretically important class of point processes, but parameter-estimation for such a process can pose various problems. In this paper we explore and compare two approaches to Bayesian inference. The first approach is based on the so-called conditional...... intensity function, while the second approach is based on an underlying clustering and branching structure in the Hawkes process. For practical use, MCMC (Markov chain Monte Carlo) methods are employed. The two approaches are compared numerically using three examples of the Hawkes process....
Attention in a bayesian framework
DEFF Research Database (Denmark)
Whiteley, Louise Emma; Sahani, Maneesh
2012-01-01
, and include both selective phenomena, where attention is invoked by cues that point to particular stimuli, and integrative phenomena, where attention is invoked dynamically by endogenous processing. However, most previous Bayesian accounts of attention have focused on describing relatively simple experimental...... settings, where cues shape expectations about a small number of upcoming stimuli and thus convey "prior" information about clearly defined objects. While operationally consistent with the experiments it seeks to describe, this view of attention as prior seems to miss many essential elements of both its...
Organic aerosols from biomass burning in Amazonian rain forest and their impact onto the environment
International Nuclear Information System (INIS)
Cecinato, A.; Mabilia, R.; De Castro Vasconcellos, P.
2001-01-01
A field campaign performed in Southern Brazilian Amazonia in 1993 has proved that this region is subjected to fallout of particulated exhausts released by fires of forestal biomass. In fact, organic content of aerosols collected at urban sites located on the border of pluvial forest, about 50 km from fires, was similar to that of biomass burning exhausts. Aerosol composition is indicative of dolous origin of fires. However, organic contents seems to be influenced by two additional sources, i. e. motor vehicle and high vegetation emission. Chemical pattern of organic aerosols released by biomass burning of forest seems to promote occurrence of photochemical smog episodes in that region [it
Characterisation of Aerosols from Simulated Radiological Dispersion Events
Di Lemma, F.G.
2015-01-01
The research described in this thesis aims at improving the evaluation of the radiaoctive aerosol release from different Radiological Dispersion Events (RDE's), such as accidents and sabotage involving radioactive and nuclear materials. These studies help in a better assessment of the source term as
Can Biomass Burning Explain Isotopically Light Fe in Marine Aerosols?
Sherry, A. M.; Anbar, A. D.; Herckes, P.; Romaniello, S. J.
2016-02-01
Iron (Fe) is an important micronutrient that limits primary productivity in large parts of the ocean. In these regions, atmospheric aerosol deposition is an important source of Fe to the surface ocean and thus has a critical impact on ocean biogeochemistry. Fe-bearing aerosols originate from many sources with potentially distinct Fe isotopic compositions. Consequently, Fe isotopes may provide a new tool to trace the sources of aerosol Fe to the oceans. Mead et al. (2013) first discovered that Fe in the fine fraction of Bermuda aerosols is often isotopically lighter than Fe from known anthropogenic and crustal sources. 1 These authors suggested that this light isotopic signature was likely the result of biomass burning, since Fe in plants is the only known source of isotopically light Fe. More recently, Conway et al. found that Fe in the soluble fraction of aerosols collected during 2010-2011 North Atlantic GEOTRACES cruises also showed light isotope values, which they likewise attributed to biomass burning.2 These studies are further supported by new modeling work which suggests that biomass burning aerosols should contribute significant amounts of soluble Fe to tropical and southern oceans.3To test if biomass burning releases aerosols with a light Fe isotope composition, we are conducting lab-scale biomass burning experiments using natural samples of vegetation and leaf litter. Burn aerosols were collected on cellulose filters, then digested and analyzed for trace metal concentrations using inductively-coupled mass spectrometry (ICP-MS). Fe isotopes were determined by using multiple collector ICP-MS following separation and purification of Fe using anion exchange chromatography. We will discuss metal concentration and isotope data from these experiments with implications for the interpretation of Fe isotope signals in aerosol samples. 1Mead, C et al. GRL, 2013, 40, 5722-5727. 2 Conway, T et al. Goldschmidt Abs 2015 593. 3Ito, A. ES&T Lett, 2015, 2, 70-75.
Robust bayesian inference of generalized Pareto distribution ...
African Journals Online (AJOL)
Abstract. In this work, robust Bayesian estimation of the generalized Pareto distribution is proposed. The methodology is presented in terms of oscillation of posterior risks of the Bayesian estimators. By using a Monte Carlo simulation study, we show that, under a suitable generalized loss function, we can obtain a robust ...
Bayesian Decision Theoretical Framework for Clustering
Chen, Mo
2011-01-01
In this thesis, we establish a novel probabilistic framework for the data clustering problem from the perspective of Bayesian decision theory. The Bayesian decision theory view justifies the important questions: what is a cluster and what a clustering algorithm should optimize. We prove that the spectral clustering (to be specific, the…
Using Bayesian belief networks in adaptive management.
J.B. Nyberg; B.G. Marcot; R. Sulyma
2006-01-01
Bayesian belief and decision networks are relatively new modeling methods that are especially well suited to adaptive-management applications, but they appear not to have been widely used in adaptive management to date. Bayesian belief networks (BBNs) can serve many purposes for practioners of adaptive management, from illustrating system relations conceptually to...
Calibration in a Bayesian modelling framework
Jansen, M.J.W.; Hagenaars, T.H.J.
2004-01-01
Bayesian statistics may constitute the core of a consistent and comprehensive framework for the statistical aspects of modelling complex processes that involve many parameters whose values are derived from many sources. Bayesian statistics holds great promises for model calibration, provides the
Particle identification in ALICE: a Bayesian approach
Adam, J.; Adamova, D.; Aggarwal, M. M.; Rinella, G. Aglieri; Agnello, M.; Agrawal, N.; Ahammed, Z.; Ahn, S. U.; Aiola, S.; Akindinov, A.; Alam, S. N.; Albuquerque, D. S. D.; Aleksandrov, D.; Alessandro, B.; Alexandre, D.; Alfaro Molina, R.; Alici, A.; Alkin, A.; Almaraz, J. R. M.; Alme, J.; Alt, T.; Altinpinar, S.; Altsybeev, I.; Alves Garcia Prado, C.; Andrei, C.; Andronic, A.; Anguelov, V.; Anticic, T.; Antinori, F.; Antonioli, P.; Aphecetche, L.; Appelshaeuser, H.; Arcelli, S.; Arnaldi, R.; Arnold, O. W.; Arsene, I. C.; Arslandok, M.; Audurier, B.; Augustinus, A.; Averbeck, R.; Azmi, M. D.; Badala, A.; Baek, Y. W.; Bagnasco, S.; Bailhache, R.; Bala, R.; Balasubramanian, S.; Baldisseri, A.; Baral, R. C.; Barbano, A. M.; Barbera, R.; Barile, F.; Barnafoeldi, G. G.; Barnby, L. S.; Barret, V.; Bartalini, P.; Barth, K.; Bartke, J.; Bartsch, E.; Basile, M.; Bastid, N.; Bathen, B.; Batigne, G.; Camejo, A. Batista; Batyunya, B.; Batzing, P. C.; Bearden, I. G.; Beck, H.; Bedda, C.; Behera, N. K.; Belikov, I.; Bellini, F.; Bello Martinez, H.; Bellwied, R.; Belmont, R.; Belmont-Moreno, E.; Belyaev, V.; Benacek, P.; Bencedi, G.; Beole, S.; Berceanu, I.; Bercuci, A.; Berdnikov, Y.; Berenyi, D.; Bertens, R. A.; Berzano, D.; Betev, L.; Bhasin, A.; Bhat, I. R.; Bhati, A. K.; Bhattacharjee, B.; Bhom, J.; Bianchi, L.; Bianchi, N.; Bianchin, C.; Bielcik, J.; Bielcikova, J.; Bilandzic, A.; Biro, G.; Biswas, R.; Biswas, S.; Bjelogrlic, S.; Blair, J. T.; Blau, D.; Blume, C.; Bock, F.; Bogdanov, A.; Boggild, H.; Boldizsar, L.; Bombara, M.; Book, J.; Borel, H.; Borissov, A.; Borri, M.; Bossu, F.; Botta, E.; Bourjau, C.; Braun-Munzinger, P.; Bregant, M.; Breitner, T.; Broker, T. A.; Browning, T. A.; Broz, M.; Brucken, E. J.; Bruna, E.; Bruno, G. E.; Budnikov, D.; Buesching, H.; Bufalino, S.; Buncic, P.; Busch, O.; Buthelezi, Z.; Butt, J. B.; Buxton, J. T.; Cabala, J.; Caffarri, D.; Cai, X.; Caines, H.; Diaz, L. Calero; Caliva, A.; Calvo Villar, E.; Camerini, P.; Carena, F.; Carena, W.; Carnesecchi, F.; Castellanos, J. Castillo; Castro, A. J.; Casula, E. A. R.; Sanchez, C. Ceballos; Cepila, J.; Cerello, P.; Cerkala, J.; Chang, B.; Chapeland, S.; Chartier, M.; Charvet, J. L.; Chattopadhyay, S.; Chattopadhyay, S.; Chauvin, A.; Chelnokov, V.; Cherney, M.; Cheshkov, C.; Cheynis, B.; Barroso, V. Chibante; Chinellato, D. D.; Cho, S.; Chochula, P.; Choi, K.; Chojnacki, M.; Choudhury, S.; Christakoglou, P.; Christensen, C. H.; Christiansen, P.; Chujo, T.; Cicalo, C.; Cifarelli, L.; Cindolo, F.; Cleymans, J.; Colamaria, F.; Colella, D.; Collu, A.; Colocci, M.; Balbastre, G. Conesa; del Valle, Z. Conesa; Connors, M. E.; Contreras, J. G.; Cormier, T. M.; Morales, Y. Corrales; Cortes Maldonado, I.; Cortese, P.; Cosentino, M. R.; Costa, F.; Crochet, P.; Cruz Albino, R.; Cuautle, E.; Cunqueiro, L.; Dahms, T.; Dainese, A.; Danisch, M. C.; Danu, A.; Das, I.; Das, S.; Dash, A.; Dash, S.; De, S.; De Caro, A.; de Cataldo, G.; de Conti, C.; de Cuveland, J.; De Falco, A.; De Gruttola, D.; De Marco, N.; De Pasquale, S.; Deisting, A.; Deloff, A.; Denes, E.; Deplano, C.; Dhankher, P.; Di Bari, D.; Di Mauro, A.; Di Nezza, P.; Corchero, M. A. Diaz; Dietel, T.; Dillenseger, P.; Divia, R.; Djuvsland, O.; Dobrin, A.; Gimenez, D. Domenicis; Doenigus, B.; Dordic, O.; Drozhzhova, T.; Dubey, A. K.; Dubla, A.; Ducroux, L.; Dupieux, P.; Ehlers, R. J.; Elia, D.; Endress, E.; Engel, H.; Epple, E.; Erazmus, B.; Erdemir, I.; Erhardt, F.; Espagnon, B.; Estienne, M.; Esumi, S.; Eum, J.; Evans, D.; Evdokimov, S.; Eyyubova, G.; Fabbietti, L.; Fabris, D.; Faivre, J.; Fantoni, A.; Fasel, M.; Feldkamp, L.; Feliciello, A.; Feofilov, G.; Ferencei, J.; Fernandez Tellez, A.; Ferreiro, E. G.; Ferretti, A.; Festanti, A.; Feuillard, V. J. G.; Figiel, J.; Figueredo, M. A. S.; Filchagin, S.; Finogeev, D.; Fionda, F. M.; Fiore, E. M.; Fleck, M. G.; Floris, M.; Foertsch, S.; Foka, P.; Fokin, S.; Fragiacomo, E.; Francescon, A.; Frankenfeld, U.; Fronze, G. G.; Fuchs, U.; Furget, C.; Furs, A.; Girard, M. Fusco; Gaardhoje, J. J.; Gagliardi, M.; Gago, A. M.; Gallio, M.; Gangadharan, D. R.; Ganoti, P.; Gao, C.; Garabatos, C.; Garcia-Solis, E.; Gargiulo, C.; Gasik, P.; Gauger, E. F.; Germain, M.; Gheata, A.; Gheata, M.; Gianotti, P.; Giubellino, P.; Giubilato, P.; Gladysz-Dziadus, E.; Glaessel, P.; Gomez Coral, D. M.; Ramirez, A. Gomez; Gonzalez, A. S.; Gonzalez, V.; Gonzalez-Zamora, P.; Gorbunov, S.; Goerlich, L.; Gotovac, S.; Grabski, V.; Grachov, O. A.; Graczykowski, L. K.; Graham, K. L.; Grelli, A.; Grigoras, A.; Grigoras, C.; Grigoriev, V.; Grigoryan, A.; Grigoryan, S.; Grinyov, B.; Grion, N.; Gronefeld, J. M.; Grosse-Oetringhaus, J. F.; Grosso, R.; Guber, F.; Guernane, R.; Guerzoni, B.; Gulbrandsen, K.; Gunji, T.; Gupta, A.; Haake, R.; Haaland, O.; Hadjidakis, C.; Haiduc, M.; Hamagaki, H.; Hamar, G.; Hamon, J. C.; Harris, J. W.; Harton, A.; Hatzifotiadou, D.; Hayashi, S.; Heckel, S. T.; Hellbaer, E.; Helstrup, H.; Herghelegiu, A.; Herrera Corral, G.; Hess, B. A.; Hetland, K. F.; Hillemanns, H.; Hippolyte, B.; Horak, D.; Hosokawa, R.; Hristov, P.; Humanic, T. J.; Hussain, N.; Hussain, T.; Hutter, D.; Hwang, D. S.; Ilkaev, R.; Inaba, M.; Incani, E.; Ippolitov, M.; Irfan, M.; Ivanov, M.; Ivanov, V.; Izucheev, V.; Jacazio, N.; Jadhav, M. B.; Jadlovska, S.; Jadlovsky, J.; Jahnke, C.; Jakubowska, M. J.; Jang, H. J.; Janik, M. A.; Jayarathna, P. H. S. Y.; Jena, C.; Jena, S.; Bustamante, R. T. Jimenez; Jones, P. G.; Jusko, A.; Kalinak, P.; Kalweit, A.; Kamin, J.; Kaplin, V.; Kar, S.; Uysal, A. Karasu; Karavichev, O.; Karavicheva, T.; Karayan, L.; Karpechev, E.; Kebschull, U.; Keidel, R.; Keijdener, D. L. D.; Keil, M.; Khan, M. Mohisin; Khan, P.; Khan, S. A.; Khanzadeev, A.; Kharlov, Y.; Kileng, B.; Kim, D. W.; Kim, D. J.; Kim, D.; Kim, J. S.; Kim, M.; Kim, T.; Kirsch, S.; Kisel, I.; Kiselev, S.; Kisiel, A.; Kiss, G.; Klay, J. L.; Klein, C.; Klein-Boesing, C.; Klewin, S.; Kluge, A.; Knichel, M. L.; Knospe, A. G.; Kobdaj, C.; Kofarago, M.; Kollegger, T.; Kolojvari, A.; Kondratiev, V.; Kondratyeva, N.; Kondratyuk, E.; Konevskikh, A.; Kopcik, M.; Kostarakis, P.; Kour, M.; Kouzinopoulos, C.; Kovalenko, O.; Kovalenko, V.; Kowalski, M.; Meethaleveedu, G. Koyithatta; Kralik, I.; Kravcakova, A.; Krivda, M.; Krizek, F.; Kryshen, E.; Krzewicki, M.; Kubera, A. M.; Kucera, V.; Kuijer, P. G.; Kumar, J.; Kumar, L.; Kumar, S.; Kurashvili, P.; Kurepin, A.; Kurepin, A. B.; Kuryakin, A.; Kweon, M. J.; Kwon, Y.; La Pointe, S. L.; La Rocca, P.; Ladron de Guevara, P.; Lagana Fernandes, C.; Lakomov, I.; Langoy, R.; Lara, C.; Lardeux, A.; Lattuca, A.; Laudi, E.; Lea, R.; Leardini, L.; Lee, G. R.; Lee, S.; Lehas, F.; Lemmon, R. C.; Lenti, V.; Leogrande, E.; Monzon, I. Leon; Leon Vargas, H.; Leoncino, M.; Levai, P.; Lien, J.; Lietava, R.; Lindal, S.; Lindenstruth, V.; Lippmann, C.; Lisa, M. A.; Ljunggren, H. M.; Lodato, D. F.; Loenne, P. I.; Loginov, V.; Loizides, C.; Lopez, X.; Torres, E. Lopez; Lowe, A.; Luettig, P.; Lunardon, M.; Luparello, G.; Lutz, T. H.; Maevskaya, A.; Mager, M.; Mahajan, S.; Mahmood, S. M.; Maire, A.; Majka, R. D.; Malaev, M.; Maldonado Cervantes, I.; Malinina, L.; Mal'Kevich, D.; Malzacher, P.; Mamonov, A.; Manko, V.; Manso, F.; Manzari, V.; Marchisone, M.; Mares, J.; Margagliotti, G. V.; Margotti, A.; Margutti, J.; Marin, A.; Markert, C.; Marquard, M.; Martin, N. A.; Blanco, J. Martin; Martinengo, P.; Martinez, M. I.; Garcia, G. Martinez; Pedreira, M. Martinez; Mas, A.; Masciocchi, S.; Masera, M.; Masoni, A.; Mastroserio, A.; Matyja, A.; Mayer, C.; Mazer, J.; Mazzoni, M. A.; Mcdonald, D.; Meddi, F.; Melikyan, Y.; Menchaca-Rocha, A.; Meninno, E.; Perez, J. Mercado; Meres, M.; Miake, Y.; Mieskolainen, M. M.; Mikhaylov, K.; Milano, L.; Milosevic, J.; Mischke, A.; Mishra, A. N.; Miskowiec, D.; Mitra, J.; Mitu, C. M.; Mohammadi, N.; Mohanty, B.; Molnar, L.; Montano Zetina, L.; Montes, E.; De Godoy, D. A. Moreira; Moreno, L. A. P.; Moretto, S.; Morreale, A.; Morsch, A.; Muccifora, V.; Mudnic, E.; Muehlheim, D.; Muhuri, S.; Mukherjee, M.; Mulligan, J. D.; Munhoz, M. G.; Munzer, R. H.; Murakami, H.; Murray, S.; Musa, L.; Musinsky, J.; Naik, B.; Nair, R.; Nandi, B. K.; Nania, R.; Nappi, E.; Naru, M. U.; Natal da Luz, H.; Nattrass, C.; Navarro, S. R.; Nayak, K.; Nayak, R.; Nayak, T. K.; Nazarenko, S.; Nedosekin, A.; Nellen, L.; Ng, F.; Nicassio, M.; Niculescu, M.; Niedziela, J.; Nielsen, B. S.; Nikolaev, S.; Nikulin, S.; Nikulin, V.; Noferini, F.; Nomokonov, P.; Nooren, G.; Noris, J. C. C.; Norman, J.; Nyanin, A.; Nystrand, J.; Oeschler, H.; Oh, S.; Oh, S. K.; Ohlson, A.; Okatan, A.; Okubo, T.; Olah, L.; Oleniacz, J.; Oliveira Da Silva, A. C.; Oliver, M. H.; Onderwaater, J.; Oppedisano, C.; Orava, R.; Oravec, M.; Ortiz Velasquez, A.; Oskarsson, A.; Otwinowski, J.; Oyama, K.; Ozdemir, M.; Pachmayer, Y.; Pagano, D.; Pagano, P.; Paic, G.; Pal, S. K.; Pan, J.; Papikyan, V.; Pappalardo, G. S.; Pareek, P.; Park, W. J.; Parmar, S.; Passfeld, A.; Paticchio, V.; Patra, R. N.; Paul, B.; Pei, H.; Peitzmann, T.; Da Costa, H. Pereira; Peresunko, D.; Lara, C. E. Perez; Lezama, E. Perez; Peskov, V.; Pestov, Y.; Petracek, V.; Petrov, V.; Petrovici, M.; Petta, C.; Piano, S.; Pikna, M.; Pillot, P.; Pimentel, L. O. D. L.; Pinazza, O.; Pinsky, L.; Piyarathna, D. B.; Ploskon, M.; Planinic, M.; Pluta, J.; Pochybova, S.; Podesta-Lerma, P. L. M.; Poghosyan, M. G.; Polichtchouk, B.; Poljak, N.; Poonsawat, W.; Pop, A.; Porteboeuf-Houssais, S.; Porter, J.; Pospisil, J.; Prasad, S. K.; Preghenella, R.; Prino, F.; Pruneau, C. A.; Pshenichnov, I.; Puccio, M.; Puddu, G.; Pujahari, P.; Punin, V.; Putschke, J.; Qvigstad, H.; Rachevski, A.; Raha, S.; Rajput, S.; Rak, J.; Rakotozafindrabe, A.; Ramello, L.; Rami, F.; Raniwala, R.; Raniwala, S.; Raesaenen, S. S.; Rascanu, B. T.; Rathee, D.; Read, K. F.; Redlich, K.; Reed, R. J.; Reichelt, P.; Reidt, F.; Ren, X.; Renfordt, R.; Reolon, A. R.; Reshetin, A.; Reygers, K.; Riabov, V.; Ricci, R. A.; Richert, T.; Richter, M.; Riedler, P.; Riegler, W.; Riggi, F.; Ristea, C.; Rocco, E.; Rodriguez Cahuantzi, M.; Manso, A. Rodriguez; Roed, K.; Rogochaya, E.; Rohr, D.; Roehrich, D.; Ronchetti, F.; Ronflette, L.; Rosnet, P.; Rossi, A.; Roukoutakis, F.; Roy, A.; Roy, C.; Roy, P.; Montero, A. J. Rubio; Rui, R.; Russo, R.; Ryabinkin, E.; Ryabov, Y.; Rybicki, A.; Saarinen, S.; Sadhu, S.; Sadovsky, S.; Safarik, K.; Sahlmuller, B.; Sahoo, P.; Sahoo, R.; Sahoo, S.; Sahu, P. K.; Saini, J.; Sakai, S.; Saleh, M. A.; Salzwedel, J.; Sambyal, S.; Samsonov, V.; Sandor, L.; Sandoval, A.; Sano, M.; Sarkar, D.; Sarkar, N.; Sarma, P.; Scapparone, E.; Scarlassara, F.; Schiaua, C.; Schicker, R.; Schmidt, C.; Schmidt, H. R.; Schuchmann, S.; Schukraft, J.; Schulc, M.; Schutz, Y.; Schwarz, K.; Schweda, K.; Scioli, G.; Scomparin, E.; Scott, R.; Sefcik, M.; Seger, J. E.; Sekiguchi, Y.; Sekihata, D.; Selyuzhenkov, I.; Senosi, K.; Senyukov, S.; Serradilla, E.; Sevcenco, A.; Shabanov, A.; Shabetai, A.; Shadura, O.; Shahoyan, R.; Shahzad, M. I.; Shangaraev, A.; Sharma, M.; Sharma, M.; Sharma, N.; Sheikh, A. I.; Shigaki, K.; Shou, Q.; Shtejer, K.; Sibiriak, Y.; Siddhanta, S.; Sielewicz, K. M.; Siemiarczuk, T.; Silvermyr, D.; Silvestre, C.; Simatovic, G.; Simonetti, G.; Singaraju, R.; Singh, R.; Singha, S.; Singhal, V.; Sinha, B. C.; Sinha, T.; Sitar, B.; Sitta, M.; Skaali, T. B.; Slupecki, M.; Smirnov, N.; Snellings, R. J. M.; Snellman, T. W.; Song, J.; Song, M.; Song, Z.; Soramel, F.; Sorensen, S.; de Souza, R. D.; Sozzi, F.; Spacek, M.; Spiriti, E.; Sputowska, I.; Spyropoulou-Stassinaki, M.; Stachel, J.; Stan, I.; Stankus, P.; Stenlund, E.; Steyn, G.; Stiller, J. H.; Stocco, D.; Strmen, P.; Suaide, A. A. P.; Sugitate, T.; Suire, C.; Suleymanov, M.; Suljic, M.; Sultanov, R.; Sumbera, M.; Sumowidagdo, S.; Szabo, A.; Szanto de Toledo, A.; Szarka, I.; Szczepankiewicz, A.; Szymanski, M.; Tabassam, U.; Takahashi, J.; Tambave, G. J.; Tanaka, N.; Tarhini, M.; Tariq, M.; Tarzila, M. G.; Tauro, A.; Tejeda Munoz, G.; Telesca, A.; Terasaki, K.; Terrevoli, C.; Teyssier, B.; Thaeder, J.; Thakur, D.; Thomas, D.; Tieulent, R.; Timmins, A. R.; Toia, A.; Trogolo, S.; Trombetta, G.; Trubnikov, V.; Trzaska, W. H.; Tsuji, T.; Tumkin, A.; Turrisi, R.; Tveter, T. S.; Ullaland, K.; Uras, A.; Usai, G. L.; Utrobicic, A.; Vala, M.; Palomo, L. Valencia; Vallero, S.; Van Der Maarel, J.; Van Hoorne, J. W.; van Leeuwen, M.; Vanat, T.; Vyvre, P. Vande; Varga, D.; Vargas, A.; Vargyas, M.; Varma, R.; Vasileiou, M.; Vasiliev, A.; Vauthier, A.; Vechernin, V.; Veen, A. M.; Veldhoen, M.; Velure, A.; Vercellin, E.; Vergara Limon, S.; Vernet, R.; Verweij, M.; Vickovic, L.; Viesti, G.; Viinikainen, J.; Vilakazi, Z.; Baillie, O. Villalobos; Villatoro Tello, A.; Vinogradov, A.; Vinogradov, L.; Vinogradov, Y.; Virgili, T.; Vislavicius, V.; Viyogi, Y. P.; Vodopyanov, A.; Voelkl, M. A.; Voloshin, K.; Voloshin, S. A.; Volpe, G.; von Haller, B.; Vorobyev, I.; Vranic, D.; Vrlakova, J.; Vulpescu, B.; Wagner, B.; Wagner, J.; Wang, H.; Watanabe, D.; Watanabe, Y.; Weiser, D. F.; Westerhoff, U.; Whitehead, A. M.; Wiechula, J.; Wikne, J.; Wilk, G.; Wilkinson, J.; Williams, M. C. S.; Windelband, B.; Winn, M.; Yang, H.; Yano, S.; Yasin, Z.; Yokoyama, H.; Yoo, I. -K.; Yoon, J. H.; Yurchenko, V.; Yushmanov, I.; Zaborowska, A.; Zaccolo, V.; Zaman, A.; Zampolli, C.; Zanoli, H. J. C.; Zaporozhets, S.; Zardoshti, N.; Zarochentsev, A.; Zavada, P.; Zaviyalov, N.; Zbroszczyk, H.; Zgura, I. S.; Zhalov, M.; Zhang, C.; Zhao, C.; Zhigareva, N.; Zhou, Y.; Zhou, Z.; Zhu, H.; Zichichi, A.; Zimmermann, A.; Zimmermann, M. B.; Zinovjev, G.; Zyzak, M.; Collaboration, ALICE
2016-01-01
We present a Bayesian approach to particle identification (PID) within the ALICE experiment. The aim is to more effectively combine the particle identification capabilities of its various detectors. After a brief explanation of the adopted methodology and formalism, the performance of the Bayesian
Bayesian Network for multiple hypthesis tracking
Zajdel, W.P.; Kröse, B.J.A.; Blockeel, H.; Denecker, M.
2002-01-01
For a flexible camera-to-camera tracking of multiple objects we model the objects behavior with a Bayesian network and combine it with the multiple hypohesis framework that associates observations with objects. Bayesian networks offer a possibility to factor complex, joint distributions into a
Bayesian learning theory applied to human cognition.
Jacobs, Robert A; Kruschke, John K
2011-01-01
Probabilistic models based on Bayes' rule are an increasingly popular approach to understanding human cognition. Bayesian models allow immense representational latitude and complexity. Because they use normative Bayesian mathematics to process those representations, they define optimal performance on a given task. This article focuses on key mechanisms of Bayesian information processing, and provides numerous examples illustrating Bayesian approaches to the study of human cognition. We start by providing an overview of Bayesian modeling and Bayesian networks. We then describe three types of information processing operations-inference, parameter learning, and structure learning-in both Bayesian networks and human cognition. This is followed by a discussion of the important roles of prior knowledge and of active learning. We conclude by outlining some challenges for Bayesian models of human cognition that will need to be addressed by future research. WIREs Cogn Sci 2011 2 8-21 DOI: 10.1002/wcs.80 For further resources related to this article, please visit the WIREs website. Copyright © 2010 John Wiley & Sons, Ltd.
Properties of the Bayesian Knowledge Tracing Model
van de Sande, Brett
2013-01-01
Bayesian Knowledge Tracing is used very widely to model student learning. It comes in two different forms: The first form is the Bayesian Knowledge Tracing "hidden Markov model" which predicts the probability of correct application of a skill as a function of the number of previous opportunities to apply that skill and the model…
Plug & Play object oriented Bayesian networks
DEFF Research Database (Denmark)
Bangsø, Olav; Flores, J.; Jensen, Finn Verner
2003-01-01
and secondly, to gain efficiency during modification of an object oriented Bayesian network. To accomplish these two goals we have exploited a mechanism allowing local triangulation of instances to develop a method for updating the junction trees associated with object oriented Bayesian networks in highly...
Using Bayesian Networks to Improve Knowledge Assessment
Millan, Eva; Descalco, Luis; Castillo, Gladys; Oliveira, Paula; Diogo, Sandra
2013-01-01
In this paper, we describe the integration and evaluation of an existing generic Bayesian student model (GBSM) into an existing computerized testing system within the Mathematics Education Project (PmatE--Projecto Matematica Ensino) of the University of Aveiro. This generic Bayesian student model had been previously evaluated with simulated…
Bayesian models: A statistical primer for ecologists
Hobbs, N. Thompson; Hooten, Mevin B.
2015-01-01
Bayesian modeling has become an indispensable tool for ecological research because it is uniquely suited to deal with complexity in a statistically coherent way. This textbook provides a comprehensive and accessible introduction to the latest Bayesian methods—in language ecologists can understand. Unlike other books on the subject, this one emphasizes the principles behind the computations, giving ecologists a big-picture understanding of how to implement this powerful statistical approach.Bayesian Models is an essential primer for non-statisticians. It begins with a definition of probability and develops a step-by-step sequence of connected ideas, including basic distribution theory, network diagrams, hierarchical models, Markov chain Monte Carlo, and inference from single and multiple models. This unique book places less emphasis on computer coding, favoring instead a concise presentation of the mathematical statistics needed to understand how and why Bayesian analysis works. It also explains how to write out properly formulated hierarchical Bayesian models and use them in computing, research papers, and proposals.This primer enables ecologists to understand the statistical principles behind Bayesian modeling and apply them to research, teaching, policy, and management.Presents the mathematical and statistical foundations of Bayesian modeling in language accessible to non-statisticiansCovers basic distribution theory, network diagrams, hierarchical models, Markov chain Monte Carlo, and moreDeemphasizes computer coding in favor of basic principlesExplains how to write out properly factored statistical expressions representing Bayesian models
Modeling Diagnostic Assessments with Bayesian Networks
Almond, Russell G.; DiBello, Louis V.; Moulder, Brad; Zapata-Rivera, Juan-Diego
2007-01-01
This paper defines Bayesian network models and examines their applications to IRT-based cognitive diagnostic modeling. These models are especially suited to building inference engines designed to be synchronous with the finer grained student models that arise in skills diagnostic assessment. Aspects of the theory and use of Bayesian network models…
Physical metrology of aerosols; Metrologie physique des aerosols
Energy Technology Data Exchange (ETDEWEB)
Boulaud, D.; Vendel, J. [CEA Saclay, 91 - Gif-sur-Yvette (France). Inst. de Protection et de Surete Nucleaire
1996-12-31
The various detection and measuring methods for aerosols are presented, and their selection is related to aerosol characteristics (size range, concentration or mass range), thermo-hydraulic conditions (carrier fluid temperature, pressure and flow rate) and to the measuring system conditions (measuring frequency, data collection speed, cost...). Methods based on aerosol dynamic properties (inertial, diffusional and electrical methods) and aerosol optical properties (localized and integral methods) are described and their performances and applications are compared
Flexible Bayesian Human Fecundity Models.
Kim, Sungduk; Sundaram, Rajeshwari; Buck Louis, Germaine M; Pyper, Cecilia
2012-12-01
Human fecundity is an issue of considerable interest for both epidemiological and clinical audiences, and is dependent upon a couple's biologic capacity for reproduction coupled with behaviors that place a couple at risk for pregnancy. Bayesian hierarchical models have been proposed to better model the conception probabilities by accounting for the acts of intercourse around the day of ovulation, i.e., during the fertile window. These models can be viewed in the framework of a generalized nonlinear model with an exponential link. However, a fixed choice of link function may not always provide the best fit, leading to potentially biased estimates for probability of conception. Motivated by this, we propose a general class of models for fecundity by relaxing the choice of the link function under the generalized nonlinear model framework. We use a sample from the Oxford Conception Study (OCS) to illustrate the utility and fit of this general class of models for estimating human conception. Our findings reinforce the need for attention to be paid to the choice of link function in modeling conception, as it may bias the estimation of conception probabilities. Various properties of the proposed models are examined and a Markov chain Monte Carlo sampling algorithm was developed for implementing the Bayesian computations. The deviance information criterion measure and logarithm of pseudo marginal likelihood are used for guiding the choice of links. The supplemental material section contains technical details of the proof of the theorem stated in the paper, and contains further simulation results and analysis.
Bayesian Nonparametric Longitudinal Data Analysis.
Quintana, Fernando A; Johnson, Wesley O; Waetjen, Elaine; Gold, Ellen
2016-01-01
Practical Bayesian nonparametric methods have been developed across a wide variety of contexts. Here, we develop a novel statistical model that generalizes standard mixed models for longitudinal data that include flexible mean functions as well as combined compound symmetry (CS) and autoregressive (AR) covariance structures. AR structure is often specified through the use of a Gaussian process (GP) with covariance functions that allow longitudinal data to be more correlated if they are observed closer in time than if they are observed farther apart. We allow for AR structure by considering a broader class of models that incorporates a Dirichlet Process Mixture (DPM) over the covariance parameters of the GP. We are able to take advantage of modern Bayesian statistical methods in making full predictive inferences and about characteristics of longitudinal profiles and their differences across covariate combinations. We also take advantage of the generality of our model, which provides for estimation of a variety of covariance structures. We observe that models that fail to incorporate CS or AR structure can result in very poor estimation of a covariance or correlation matrix. In our illustration using hormone data observed on women through the menopausal transition, biology dictates the use of a generalized family of sigmoid functions as a model for time trends across subpopulation categories.
BELM: Bayesian extreme learning machine.
Soria-Olivas, Emilio; Gómez-Sanchis, Juan; Martín, José D; Vila-Francés, Joan; Martínez, Marcelino; Magdalena, José R; Serrano, Antonio J
2011-03-01
The theory of extreme learning machine (ELM) has become very popular on the last few years. ELM is a new approach for learning the parameters of the hidden layers of a multilayer neural network (as the multilayer perceptron or the radial basis function neural network). Its main advantage is the lower computational cost, which is especially relevant when dealing with many patterns defined in a high-dimensional space. This brief proposes a bayesian approach to ELM, which presents some advantages over other approaches: it allows the introduction of a priori knowledge; obtains the confidence intervals (CIs) without the need of applying methods that are computationally intensive, e.g., bootstrap; and presents high generalization capabilities. Bayesian ELM is benchmarked against classical ELM in several artificial and real datasets that are widely used for the evaluation of machine learning algorithms. Achieved results show that the proposed approach produces a competitive accuracy with some additional advantages, namely, automatic production of CIs, reduction of probability of model overfitting, and use of a priori knowledge.
2nd Bayesian Young Statisticians Meeting
Bitto, Angela; Kastner, Gregor; Posekany, Alexandra
2015-01-01
The Second Bayesian Young Statisticians Meeting (BAYSM 2014) and the research presented here facilitate connections among researchers using Bayesian Statistics by providing a forum for the development and exchange of ideas. WU Vienna University of Business and Economics hosted BAYSM 2014 from September 18th to 19th. The guidance of renowned plenary lecturers and senior discussants is a critical part of the meeting and this volume, which follows publication of contributions from BAYSM 2013. The meeting's scientific program reflected the variety of fields in which Bayesian methods are currently employed or could be introduced in the future. Three brilliant keynote lectures by Chris Holmes (University of Oxford), Christian Robert (Université Paris-Dauphine), and Mike West (Duke University), were complemented by 24 plenary talks covering the major topics Dynamic Models, Applications, Bayesian Nonparametrics, Biostatistics, Bayesian Methods in Economics, and Models and Methods, as well as a lively poster session ...
Bayesian natural language semantics and pragmatics
Zeevat, Henk
2015-01-01
The contributions in this volume focus on the Bayesian interpretation of natural languages, which is widely used in areas of artificial intelligence, cognitive science, and computational linguistics. This is the first volume to take up topics in Bayesian Natural Language Interpretation and make proposals based on information theory, probability theory, and related fields. The methodologies offered here extend to the target semantic and pragmatic analyses of computational natural language interpretation. Bayesian approaches to natural language semantics and pragmatics are based on methods from signal processing and the causal Bayesian models pioneered by especially Pearl. In signal processing, the Bayesian method finds the most probable interpretation by finding the one that maximizes the product of the prior probability and the likelihood of the interpretation. It thus stresses the importance of a production model for interpretation as in Grice's contributions to pragmatics or in interpretation by abduction.
Crystal structure prediction accelerated by Bayesian optimization
Yamashita, Tomoki; Sato, Nobuya; Kino, Hiori; Miyake, Takashi; Tsuda, Koji; Oguchi, Tamio
2018-01-01
We propose a crystal structure prediction method based on Bayesian optimization. Our method is classified as a selection-type algorithm which is different from evolution-type algorithms such as an evolutionary algorithm and particle swarm optimization. Crystal structure prediction with Bayesian optimization can efficiently select the most stable structure from a large number of candidate structures with a lower number of searching trials using a machine learning technique. Crystal structure prediction using Bayesian optimization combined with random search is applied to known systems such as NaCl and Y2Co17 to discuss the efficiency of Bayesian optimization. These results demonstrate that Bayesian optimization can significantly reduce the number of searching trials required to find the global minimum structure by 30-40% in comparison with pure random search, which leads to much less computational cost.
Investigation of air pollution and regional climate change due to anthropogenic aerosols
Nakata, Makiko; Sano, Itaru; Mukai, Sonoyo
2016-10-01
Increased emissions of anthropogenic aerosols associated with economic growth can lead to increased concentrations of hazardous air pollutants. In particular, large cities in East Asia have experienced numerous heavy haze episodes. Atmospheric aerosol distributions in East Asia are complex, being influenced by both natural phenomena and human activity, with urban areas in particular being dominated by fine anthropogenic aerosols released from diesel-powered vehicles and industrial activity. In Japan, air pollution levels have been reduced; nevertheless, in recent years, there is increasing concern regarding air pollution caused by fine particulate matter. The origins of air pollution were examined, focusing on the comparison between aerosol properties observed from satellites and that on the ground. Because of their short life spans, concentrations of anthropogenic aerosols are highest over the source regions, and as a result, the climatic impacts of anthropogenic aerosols are also found to be most pronounced in these regions. In this study, aerosol impacts on climate are assessed by numerical model simulations. The direct effects of aerosols include reduced solar radiation, and hence a decrease in surface temperatures. In addition to these changes in the radiation budget, aerosols have a significant potential to change cloud and precipitation fields. These climatic responses to aerosols can manifest far from their source regions with high industrial activities.
Stable generator of polydisperse aerosol
Czech Academy of Sciences Publication Activity Database
Mikuška, Pavel
2001-01-01
Roč. 32, Suppl. 1 (2001), s. S823-S824 ISSN 0021-8502. [European Aerosol Conference 2001. Leipzig, 03.09.2001-07.09.2001] R&D Projects: GA AV ČR IAA4031105 Institutional research plan: CEZ:AV0Z4031919 Keywords : aerosol generator * fine aerosol * polydisperse aerosol Subject RIV: CB - Analytical Chemistry, Separation Impact factor: 1.605, year: 2001
Energy Technology Data Exchange (ETDEWEB)
Kellogg, W W
1980-01-01
The atmospheric burden of particles, or aerosols, has been measurably increased by human activities, especially in industrialized regions and those where slash-burn agricultural practices are followed. Some of these aerosols are directly produced when fossil fuels or other materials are burned (soot, smoke, fly ash); others are the result of photochemical reactions involving organic molecules, oxides of nitrogen, and sunlight (smog); and a third source is the oxidation of sulfur dioxide, produced when sulfur-bearing fuel is burned, to sulfuric acid thereby forming sulfate particles of droplets. In all cases, the resulting aerosols scatter and absorb both solar and infrared radiation, and therefore they influence the atmospheric heat balance. The question is the way in which they influence it, and the geographical and extent of this influence.
Radioactive aerosol inhalation apparatus
International Nuclear Information System (INIS)
Bordoni, M.E.; Lieberman, E.
1987-01-01
An aerosol inhalation apparatus for supplying an aerosol mist containing radioactive tagged particles to a subject is described comprising a reusable radiation-shielding container having lid means. The contents of the container are readily accessible. A radioactive aerosol inhalation device includes first and second conduit means in the container and passing therethrough, means for communicating with an air passageway of a subject connected to the first and second conduit means externally of the container. Valve means control exhalation from the second conduit means. A nebulizer is within the container connected to the first conduit means. Means are positioned at least in part within the container and in fluid communication with the nebulizer for allowing introduction of radioactive solution from outside the container into the nebulizer
DEFF Research Database (Denmark)
Butcher, Andrew Charles
entrainment may account for the large discrepancy in energy input for the two systems. In the third study, the temperature dependence of sea spray aerosol production is probed with the use of a highly stable temperature controlled plunging jet. Similar to previous studies, particle production increases...... of a cloud condensation nuclei ounter. Proxy solutions with high inorganic salt concentrations and some organics produce sea spray aerosol particles with little change in cloud condensation activity relative to pure salts. Comparison is made between a frit based method for bubble production and a plunging...... a relationship between plunging jet particle ux, oceanic particle ux, and energy dissipation rate in both systems. Previous sea spray aerosol studies dissipate an order of magnitude more energy for the same particle ux production as the open ocean. A scaling factor related to the energy expended in air...
Aerosols behavior inside a PWR during an accident
International Nuclear Information System (INIS)
Hervouet, C.
1983-01-01
During very hypothetical accidents occurring in a pressurized water ractor, radioactive aerosols can be released, during core-melt, inside the reactor containment building. A good knowledge of their behavior in the humid containment atmosphere (mass concentration and size distribution) is essential in order to evaluate their harmfulness in case of environment contamination and to design possible filtration devices. Accordingly the Safety Analysis Department of the Atomic Energy Commission uses several computer models, describing the particle formation (BOIL/MARCH), then behavior in the primary circuits (TRAP-MELT), and in the reactor containment building (AEROSOLS-PARFDISEKO-III B). On the one hand, these models have been improved, in particular the one related to the aerosol formation (nature and mass of released particles) using recent experimental results. On the other hand, sensitivity analyses have been performed with the AEROSOLS code which emphasize the particle coagulation parameters: agglomerate shape factors and collision efficiency. Finally, the different computer models have been applied to the study of aerosol behavior during a 900 MWe PWR accident: loss-of-coolant-accident (small break with failure of all safety systems) [fr
OMPS Limb Profiler: Extending SAGE and CALIPSO Stratospheric Aerosol Records
Taha, G.; Bhartia, P. K.; Chen, Z.; Xu, P.; Loughman, R. P.; Jaross, G.
2017-12-01
The OMPS LP instrument is designed to provide high vertical resolution ozone and aerosol profiles from measurements of the scattered solar radiation in the 290-1000 nm spectral range. It collected its first Earth limb measurement in January 10, 2012, and continues to provide daily global measurements of ozone and aerosol profiles from the cloud top up to 60 km and 40 km respectively. The relatively high vertical and spatial sampling allow detection and tracking periodic events when aerosol particles are injected into the stratosphere, such as volcanic eruptions or meteor explosions. OMPS LP can extend the long-term records of stratospheric aerosol at high vertical resolution produced by variety of sensors, such as SAGEII, GOMOS, OSIRIS and CALIPSO. Most of these instruments ceased to operate or well beyond their designed lifetime. After an absence of over a decade, SAGE III/ISS was launched earlier this year and expected to resume the high quality aerosol data record. OMPS LP is also schedule to fly on JPSS-2 and 3. In this study we will examine the suitability of using LP profiles to continue the stratospheric aerosol records beyond SAGE, OSIRIS, and CALIPSO. We will compare OMPS LP released V1.0 aerosol extinction measurements to OSIRIS and CALIPSO. Initial results shows good agreement with OSIRIS measurements to within 20%, with larger bias in the southern hemisphere. To test the effect of the assumed aerosol size model (ASD) and phase function, we compare measurements taken at similar location and time with different viewing geometry. Comparison of ascending and descending aerosol extinction daily zonal means at high latitudes shows systematic bias that is well correlated with the solar scattering angle, indicating ASD uncertainties up to 30%. In addition, results showing latitudinal, and temporal variability of stratospheric aerosol extinction and optical depth for the three instruments will also be presented and compared. We will also present OMPS LP aerosol
Dry season aerosol iron solubility in tropical northern Australia
Directory of Open Access Journals (Sweden)
V. H. L. Winton
2016-10-01
Full Text Available Marine nitrogen fixation is co-limited by the supply of iron (Fe and phosphorus in large regions of the global ocean. The deposition of soluble aerosol Fe can initiate nitrogen fixation and trigger toxic algal blooms in nitrate-poor tropical waters. We present dry season soluble Fe data from the Savannah Fires in the Early Dry Season (SAFIRED campaign in northern Australia that reflects coincident dust and biomass burning sources of soluble aerosol Fe. The mean soluble and total aerosol Fe concentrations were 40 and 500 ng m−3 respectively. Our results show that while biomass burning species may not be a direct source of soluble Fe, biomass burning may substantially enhance the solubility of mineral dust. We observed fractional Fe solubility up to 12 % in mixed aerosols. Thus, Fe in dust may be more soluble in the tropics compared to higher latitudes due to higher concentrations of biomass-burning-derived reactive organic species in the atmosphere. In addition, biomass-burning-derived particles can act as a surface for aerosol Fe to bind during atmospheric transport and subsequently be released to the ocean upon deposition. As the aerosol loading is dominated by biomass burning emissions over the tropical waters in the dry season, additions of biomass-burning-derived soluble Fe could have harmful consequences for initiating nitrogen-fixing toxic algal blooms. Future research is required to quantify biomass-burning-derived particle sources of soluble Fe over tropical waters.
MODIS 3km Aerosol Product: Algorithm and Global Perspective
Remer, L. A.; Mattoo, S.; Levy, R. C.; Munchak, L.
2013-01-01
After more than a decade of producing a nominal 10 km aerosol product based on the dark target method, the MODIS aerosol team will be releasing a nominal 3 km product as part of their Collection 6 release. The new product differs from the original 10 km product only in the manner in which reflectance pixels are ingested, organized and selected by the aerosol algorithm. Overall, the 3 km product closely mirrors the 10 km product. However, the finer resolution product is able to retrieve over ocean closer to islands and coastlines, and is better able to resolve fine aerosol features such as smoke plumes over both ocean and land. In some situations, it provides retrievals over entire regions that the 10 km product barely samples. In situations traditionally difficult for the dark target algorithm, such as over bright or urban surfaces the 3 km product introduces isolated spikes of artificially high aerosol optical depth (AOD) that the 10 km algorithm avoids. Over land, globally, the 3 km product appears to be 0.01 to 0.02 higher than the 10 km product, while over ocean, the 3 km algorithm is retrieving a proportionally greater number of very low aerosol loading situations. Based on collocations with ground-based observations for only six months, expected errors associated with the 3 km land product are determined to be greater than for the 10 km product: 0.05 0.25 AOD. Over ocean, the suggestion is for expected errors to be the same as the 10 km product: 0.03 0.05 AOD. The advantage of the product is on the local scale, which will require continued evaluation not addressed here. Nevertheless, the new 3 km product is expected to provide important information complementary to existing satellite-derived products and become an important tool for the aerosol community.
Bayesian Approach to Inverse Problems
2008-01-01
Many scientific, medical or engineering problems raise the issue of recovering some physical quantities from indirect measurements; for instance, detecting or quantifying flaws or cracks within a material from acoustic or electromagnetic measurements at its surface is an essential problem of non-destructive evaluation. The concept of inverse problems precisely originates from the idea of inverting the laws of physics to recover a quantity of interest from measurable data.Unfortunately, most inverse problems are ill-posed, which means that precise and stable solutions are not easy to devise. Regularization is the key concept to solve inverse problems.The goal of this book is to deal with inverse problems and regularized solutions using the Bayesian statistical tools, with a particular view to signal and image estimation
Bayesian modelling of fusion diagnostics
Fischer, R.; Dinklage, A.; Pasch, E.
2003-07-01
Integrated data analysis of fusion diagnostics is the combination of different, heterogeneous diagnostics in order to improve physics knowledge and reduce the uncertainties of results. One example is the validation of profiles of plasma quantities. Integration of different diagnostics requires systematic and formalized error analysis for all uncertainties involved. The Bayesian probability theory (BPT) allows a systematic combination of all information entering the measurement descriptive model that considers all uncertainties of the measured data, calibration measurements, physical model parameters and measurement nuisance parameters. A sensitivity analysis of model parameters allows crucial uncertainties to be found, which has an impact on both diagnostic improvement and design. The systematic statistical modelling within the BPT is used for reconstructing electron density and electron temperature profiles from Thomson scattering data from the Wendelstein 7-AS stellarator. The inclusion of different diagnostics and first-principle information is discussed in terms of improvements.
Bayesian networks in educational assessment
Almond, Russell G; Steinberg, Linda S; Yan, Duanli; Williamson, David M
2015-01-01
Bayesian inference networks, a synthesis of statistics and expert systems, have advanced reasoning under uncertainty in medicine, business, and social sciences. This innovative volume is the first comprehensive treatment exploring how they can be applied to design and analyze innovative educational assessments. Part I develops Bayes nets’ foundations in assessment, statistics, and graph theory, and works through the real-time updating algorithm. Part II addresses parametric forms for use with assessment, model-checking techniques, and estimation with the EM algorithm and Markov chain Monte Carlo (MCMC). A unique feature is the volume’s grounding in Evidence-Centered Design (ECD) framework for assessment design. This “design forward” approach enables designers to take full advantage of Bayes nets’ modularity and ability to model complex evidentiary relationships that arise from performance in interactive, technology-rich assessments such as simulations. Part III describes ECD, situates Bayes nets as ...
Bayesian Networks and Influence Diagrams
DEFF Research Database (Denmark)
Kjærulff, Uffe Bro; Madsen, Anders Læsø
Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis, Second Edition, provides a comprehensive guide for practitioners who wish to understand, construct, and analyze intelligent systems for decision support based on probabilistic networks. This new edition contains six new...... sections, in addition to fully-updated examples, tables, figures, and a revised appendix. Intended primarily for practitioners, this book does not require sophisticated mathematical skills or deep understanding of the underlying theory and methods nor does it discuss alternative technologies for reasoning...... under uncertainty. The theory and methods presented are illustrated through more than 140 examples, and exercises are included for the reader to check his or her level of understanding. The techniques and methods presented on model construction and verification, modeling techniques and tricks, learning...
On Bayesian System Reliability Analysis
Energy Technology Data Exchange (ETDEWEB)
Soerensen Ringi, M.
1995-05-01
The view taken in this thesis is that reliability, the probability that a system will perform a required function for a stated period of time, depends on a person`s state of knowledge. Reliability changes as this state of knowledge changes, i.e. when new relevant information becomes available. Most existing models for system reliability prediction are developed in a classical framework of probability theory and they overlook some information that is always present. Probability is just an analytical tool to handle uncertainty, based on judgement and subjective opinions. It is argued that the Bayesian approach gives a much more comprehensive understanding of the foundations of probability than the so called frequentistic school. A new model for system reliability prediction is given in two papers. The model encloses the fact that component failures are dependent because of a shared operational environment. The suggested model also naturally permits learning from failure data of similar components in non identical environments. 85 refs.
Nonparametric Bayesian inference in biostatistics
Müller, Peter
2015-01-01
As chapters in this book demonstrate, BNP has important uses in clinical sciences and inference for issues like unknown partitions in genomics. Nonparametric Bayesian approaches (BNP) play an ever expanding role in biostatistical inference from use in proteomics to clinical trials. Many research problems involve an abundance of data and require flexible and complex probability models beyond the traditional parametric approaches. As this book's expert contributors show, BNP approaches can be the answer. Survival Analysis, in particular survival regression, has traditionally used BNP, but BNP's potential is now very broad. This applies to important tasks like arrangement of patients into clinically meaningful subpopulations and segmenting the genome into functionally distinct regions. This book is designed to both review and introduce application areas for BNP. While existing books provide theoretical foundations, this book connects theory to practice through engaging examples and research questions. Chapters c...
Bayesian Kernel Mixtures for Counts.
Canale, Antonio; Dunson, David B
2011-12-01
Although Bayesian nonparametric mixture models for continuous data are well developed, there is a limited literature on related approaches for count data. A common strategy is to use a mixture of Poissons, which unfortunately is quite restrictive in not accounting for distributions having variance less than the mean. Other approaches include mixing multinomials, which requires finite support, and using a Dirichlet process prior with a Poisson base measure, which does not allow smooth deviations from the Poisson. As a broad class of alternative models, we propose to use nonparametric mixtures of rounded continuous kernels. An efficient Gibbs sampler is developed for posterior computation, and a simulation study is performed to assess performance. Focusing on the rounded Gaussian case, we generalize the modeling framework to account for multivariate count data, joint modeling with continuous and categorical variables, and other complications. The methods are illustrated through applications to a developmental toxicity study and marketing data. This article has supplementary material online.
On Bayesian System Reliability Analysis
International Nuclear Information System (INIS)
Soerensen Ringi, M.
1995-01-01
The view taken in this thesis is that reliability, the probability that a system will perform a required function for a stated period of time, depends on a person's state of knowledge. Reliability changes as this state of knowledge changes, i.e. when new relevant information becomes available. Most existing models for system reliability prediction are developed in a classical framework of probability theory and they overlook some information that is always present. Probability is just an analytical tool to handle uncertainty, based on judgement and subjective opinions. It is argued that the Bayesian approach gives a much more comprehensive understanding of the foundations of probability than the so called frequentistic school. A new model for system reliability prediction is given in two papers. The model encloses the fact that component failures are dependent because of a shared operational environment. The suggested model also naturally permits learning from failure data of similar components in non identical environments. 85 refs
A Bayesian Reflection on Surfaces
Directory of Open Access Journals (Sweden)
David R. Wolf
1999-10-01
Full Text Available Abstract: The topic of this paper is a novel Bayesian continuous-basis field representation and inference framework. Within this paper several problems are solved: The maximally informative inference of continuous-basis fields, that is where the basis for the field is itself a continuous object and not representable in a finite manner; the tradeoff between accuracy of representation in terms of information learned, and memory or storage capacity in bits; the approximation of probability distributions so that a maximal amount of information about the object being inferred is preserved; an information theoretic justification for multigrid methodology. The maximally informative field inference framework is described in full generality and denoted the Generalized Kalman Filter. The Generalized Kalman Filter allows the update of field knowledge from previous knowledge at any scale, and new data, to new knowledge at any other scale. An application example instance, the inference of continuous surfaces from measurements (for example, camera image data, is presented.
Energy Technology Data Exchange (ETDEWEB)
Fields, D.E.; Cooper, A.C.; Miller, C.W.
1987-02-01
This report describes the methodology used and results obtained in efforts to estimate the sodium aerosol concentrations at air intake ports of a liquid-metal cooled, fast-breeder nuclear reactor. An earlier version of this methodology has been previously discussed (Fields and Miller, 1985). A range of wind speeds from 2 to 10 m/s is assumed, and an effort is made to include building wake effects which, in many cases, dominate the dispersal of aerosols near buildings. For relatively small release rates, on the order of 1 to 10 kg/s, the plume rise is small and estimates of aerosol concentrations are derived using the methodology of Wilson and Britter (1982), which describes releases from surface vents. For release rates on the order of 100 kg/s much higher release velocities are expected, and plume rise is considered. An effective increase in release height is computed using the Split-H methodology with a parameterization suggested by Ramsdell (1983), and the release source strength is transformed to rooftop level. Evaluation of the acute release aerosol concentration is then based on the methodology for releases from a surface release of this transformed source strength. For a horizontal release, a methodology is developed to chart the plume path as a function of release and site meteorology parameters. Results described herein must be regarded as maximum aerosol concentrations, based on models derived from generic wind tunnel studies. More accurate and site-specific results may be obtained through wind tunnel simulations and through simulating emissions from release points other than those assumed here.
Atmospheric aerosol system: An overview
International Nuclear Information System (INIS)
Prospero, J.M.; Charlson, R.J.; Mohnen, V.; Jaenicke, R.; Delany, A.C.; Moyers, J.; Zoller, W.; Rahn, K.
1983-01-01
Aerosols could play a critical role in many processes which impact on our lives either indirectly (e.g., climate) or directly (e.g., health). However, our ability to assess these possible impacts is constrained by our limited knowledge of the physical and chemical properties of aerosols, both anthropogenic and natural. This deficiency is attributable in part to the fact that aerosols are the end product of a vast array of chemical and physical processes. Consequently, the properties of the aerosol can exhibit a great deal of variability in both time and space. Furthermore, most aerosol studies have focused on measurements of a single aerosol characteristic such as composition or size distribution. Such information is generally not useful for the assessment of impacts because the degree of impact may depend on the integral properties of the aerosol, for example, the aerosol composition as a function of particle size. In this overview we discuss recent work on atmospheric aerosols that illustrates the complex nature of the aerosol chemical and physical system, and we suggest strategies for future research. A major conclusion is that man has had a great impact on the global budgets of certain species, especially sulfur and nitrogen, that play a dominant role in the atmospheric aerosol system. These changes could conceivably affect climate. Large-scale impacts are implied because it has recently been demonstrated that natural and pollutant aerosol episodes can be propagated over great distances. However, at present there is no evidence linking anthropogenic activities with a persistent increase in aerosol concentrations on a global scale. A major problem in assessing man's impact on the atmospheric aerosol system and on global budgets is the absence of aerosol measurements in remote marine and continental areas
GRIP LANGLEY AEROSOL RESEARCH GROUP EXPERIMENT (LARGE) V1
National Aeronautics and Space Administration — Langley Aerosol Research Group Experiment (LARGE) measures ultrafine aerosol number density, total and non-volatile aerosol number density, dry aerosol size...
On the New Satellite Aerosol Measurements for Atmospheric Applications: VIIRS Aerosol Products
Huang, H.; Laszlo, I.; Kondragunta, S.; Liu, H.; Huang, J.; Cronk, H. Q.; Remer, L. A.; Sayer, A. M.
2012-12-01
The Joint Polar Satellite System (JPSS) is the USA's next generation polar-orbiting operational environmental satellite system. JPSS will provide operational continuity of satellite-based observations and products for the Suomi National Polar-orbiting Partnership (NPP) mission. JPSS/NPP will provide a continuation of major long-term observations by the Earth Observing System such as Terra, Aqua, and Aura to enhance our understanding of the climate change processes. Visible Infrared Imaging Radiometer Suite (VIIRS) is a multi-spectral scanning radiometer (22 bands between 0.4μm and 12μm) on-board JPSS/NPP with spatial resolution for 16 bands at 750m and 5 bands at 325m. The spatial resolution of Aerosol Optical Thickness (AOT) Environment Data Record (EDR) is 6 km at nadir compared to 10km at nadir for Moderate-Resolution Imaging Spectroradiometer (MODIS). Separate algorithms are used for aerosol retrieval over land and ocean. The over-land aerosol algorithm is based on MODIS surface Reflectance (MOD09 Collection 5) algorithm and the over-ocean algorithm is derived from the MODIS Aerosol (MOD04 Collection 5) algorithm. AOT and aerosol type are retrieved simultaneously by minimizing the difference between observed and calculated reflectance in multiple channels. VIIRS aerosol products include AOT, Aerosol Particle Size Parameter (APSP), and suspended matter. At the time of this presentation, the beta release of VIIRS aerosol data is expected to be available to the community to allow users to gain familiarity with data formats and parameters. The beta release of VIIRS aerosol products is expected to include AOT EDR of both over land and ocean and the APSP (Angstrom Exponent) EDR over ocean. To understand the present performance of VIIRS aerosol products at beta level and the difference of AOT retrievals between VIIRS and MODIS for long-term climatic applications, this study will focus on comparisons between VIIRS AOT and that of AErosol RObotic NETwork (AERONET
Chamber lidar measurements of biological aerosols.
Brown, David M; Thrush, Evan; Thomas, Michael E
2011-02-10
In order to determine the performance of standoff sensors against agents, there is a need to develop methods to characterize the optical properties of biological warfare agents. The goal of this work is to develop a methodology that would allow the characterization of agent optical cross sections from the UV to the longwave IR. The present work demonstrates an optical measurement architecture based on lidar technology, allowing the measurement of backscatter and depolarization ratio from biological aerosols (either simulants or agents) released in a refereed, 1m3 chamber. Measured results on simulant materials are calibrated and compared to theoretical simulations of the cross sections.
Robust bayesian analysis of an autoregressive model with ...
African Journals Online (AJOL)
In this work, robust Bayesian analysis of the Bayesian estimation of an autoregressive model with exponential innovations is performed. Using a Bayesian robustness methodology, we show that, using a suitable generalized quadratic loss, we obtain optimal Bayesian estimators of the parameters corresponding to the ...
Bayesian models a statistical primer for ecologists
Hobbs, N Thompson
2015-01-01
Bayesian modeling has become an indispensable tool for ecological research because it is uniquely suited to deal with complexity in a statistically coherent way. This textbook provides a comprehensive and accessible introduction to the latest Bayesian methods-in language ecologists can understand. Unlike other books on the subject, this one emphasizes the principles behind the computations, giving ecologists a big-picture understanding of how to implement this powerful statistical approach. Bayesian Models is an essential primer for non-statisticians. It begins with a definition of probabili
Compiling Relational Bayesian Networks for Exact Inference
DEFF Research Database (Denmark)
Jaeger, Manfred; Darwiche, Adnan; Chavira, Mark
2006-01-01
We describe in this paper a system for exact inference with relational Bayesian networks as defined in the publicly available PRIMULA tool. The system is based on compiling propositional instances of relational Bayesian networks into arithmetic circuits and then performing online inference...... by evaluating and differentiating these circuits in time linear in their size. We report on experimental results showing successful compilation and efficient inference on relational Bayesian networks, whose PRIMULA--generated propositional instances have thousands of variables, and whose jointrees have clusters...
Indian Academy of Sciences (India)
exam~le, the transport of dust from Sahara desert over the. Atlantic Ocean by winds. Most of the aerosol sources are located near the Earth's surface and hence their concentration (mass per unit volume) is larger near the surface. Occasionally there may be layers aloft as well depending upon the atmospheric condi- tions.
Bayesian estimation and modeling: Editorial to the second special issue on Bayesian data analysis.
Chow, Sy-Miin; Hoijtink, Herbert
2017-12-01
This editorial accompanies the second special issue on Bayesian data analysis published in this journal. The emphases of this issue are on Bayesian estimation and modeling. In this editorial, we outline the basics of current Bayesian estimation techniques and some notable developments in the statistical literature, as well as adaptations and extensions by psychological researchers to better tailor to the modeling applications in psychology. We end with a discussion on future outlooks of Bayesian data analysis in psychology. (PsycINFO Database Record (c) 2017 APA, all rights reserved).
A Bayesian approach to model uncertainty
International Nuclear Information System (INIS)
Buslik, A.
1994-01-01
A Bayesian approach to model uncertainty is taken. For the case of a finite number of alternative models, the model uncertainty is equivalent to parameter uncertainty. A derivation based on Savage's partition problem is given
Bayesian analysis for the social sciences
Jackman, Simon
2009-01-01
Bayesian methods are increasingly being used in the social sciences, as the problems encountered lend themselves so naturally to the subjective qualities of Bayesian methodology. This book provides an accessible introduction to Bayesian methods, tailored specifically for social science students. It contains lots of real examples from political science, psychology, sociology, and economics, exercises in all chapters, and detailed descriptions of all the key concepts, without assuming any background in statistics beyond a first course. It features examples of how to implement the methods using WinBUGS - the most-widely used Bayesian analysis software in the world - and R - an open-source statistical software. The book is supported by a Website featuring WinBUGS and R code, and data sets.
Bayesian optimization for computationally extensive probability distributions.
Tamura, Ryo; Hukushima, Koji
2018-01-01
An efficient method for finding a better maximizer of computationally extensive probability distributions is proposed on the basis of a Bayesian optimization technique. A key idea of the proposed method is to use extreme values of acquisition functions by Gaussian processes for the next training phase, which should be located near a local maximum or a global maximum of the probability distribution. Our Bayesian optimization technique is applied to the posterior distribution in the effective physical model estimation, which is a computationally extensive probability distribution. Even when the number of sampling points on the posterior distributions is fixed to be small, the Bayesian optimization provides a better maximizer of the posterior distributions in comparison to those by the random search method, the steepest descent method, or the Monte Carlo method. Furthermore, the Bayesian optimization improves the results efficiently by combining the steepest descent method and thus it is a powerful tool to search for a better maximizer of computationally extensive probability distributions.
新家, 健精
1991-01-01
© 2012 Springer Science+Business Media, LLC. All rights reserved. Article Outline: Glossary Definition of the Subject and Introduction The Bayesian Statistical Paradigm Three Examples Comparison with the Frequentist Statistical Paradigm Future Directions Bibliography
An overview on Approximate Bayesian computation*
Directory of Open Access Journals (Sweden)
Baragatti Meïli
2014-01-01
Full Text Available Approximate Bayesian computation techniques, also called likelihood-free methods, are one of the most satisfactory approach to intractable likelihood problems. This overview presents recent results since its introduction about ten years ago in population genetics.
Implementing the Bayesian paradigm in risk analysis
International Nuclear Information System (INIS)
Aven, T.; Kvaloey, J.T.
2002-01-01
The Bayesian paradigm comprises a unified and consistent framework for analyzing and expressing risk. Yet, we see rather few examples of applications where the full Bayesian setting has been adopted with specifications of priors of unknown parameters. In this paper, we discuss some of the practical challenges of implementing Bayesian thinking and methods in risk analysis, emphasizing the introduction of probability models and parameters and associated uncertainty assessments. We conclude that there is a need for a pragmatic view in order to 'successfully' apply the Bayesian approach, such that we can do the assignments of some of the probabilities without adopting the somewhat sophisticated procedure of specifying prior distributions of parameters. A simple risk analysis example is presented to illustrate ideas
A Bayesian concept learning approach to crowdsourcing
DEFF Research Database (Denmark)
Viappiani, P.; Zilles, S.; Hamilton, H.J.
2011-01-01
We develop a Bayesian approach to concept learning for crowdsourcing applications. A probabilistic belief over possible concept definitions is maintained and updated according to (noisy) observations from experts, whose behaviors are modeled using discrete types. We propose recommendation...
An Intuitive Dashboard for Bayesian Network Inference
International Nuclear Information System (INIS)
Reddy, Vikas; Farr, Anna Charisse; Wu, Paul; Mengersen, Kerrie; Yarlagadda, Prasad K D V
2014-01-01
Current Bayesian network software packages provide good graphical interface for users who design and develop Bayesian networks for various applications. However, the intended end-users of these networks may not necessarily find such an interface appealing and at times it could be overwhelming, particularly when the number of nodes in the network is large. To circumvent this problem, this paper presents an intuitive dashboard, which provides an additional layer of abstraction, enabling the end-users to easily perform inferences over the Bayesian networks. Unlike most software packages, which display the nodes and arcs of the network, the developed tool organises the nodes based on the cause-and-effect relationship, making the user-interaction more intuitive and friendly. In addition to performing various types of inferences, the users can conveniently use the tool to verify the behaviour of the developed Bayesian network. The tool has been developed using QT and SMILE libraries in C++
A Bayesian Network Approach to Ontology Mapping
National Research Council Canada - National Science Library
Pan, Rong; Ding, Zhongli; Yu, Yang; Peng, Yun
2005-01-01
.... In this approach, the source and target ontologies are first translated into Bayesian networks (BN); the concept mapping between the two ontologies are treated as evidential reasoning between the two translated BNs...
Learning Bayesian networks for discrete data
Liang, Faming
2009-02-01
Bayesian networks have received much attention in the recent literature. In this article, we propose an approach to learn Bayesian networks using the stochastic approximation Monte Carlo (SAMC) algorithm. Our approach has two nice features. Firstly, it possesses the self-adjusting mechanism and thus avoids essentially the local-trap problem suffered by conventional MCMC simulation-based approaches in learning Bayesian networks. Secondly, it falls into the class of dynamic importance sampling algorithms; the network features can be inferred by dynamically weighted averaging the samples generated in the learning process, and the resulting estimates can have much lower variation than the single model-based estimates. The numerical results indicate that our approach can mix much faster over the space of Bayesian networks than the conventional MCMC simulation-based approaches. © 2008 Elsevier B.V. All rights reserved.
Sampling and characterization of aerosols formed in the atmospheric hydrolysis of UF6
International Nuclear Information System (INIS)
Bostick, W.D.; McCulla, W.H.; Pickrell, P.W.; Branam, D.A.
1983-01-01
When gaseous UF 6 is released into the atmosphere, it rapidly reacts with ambient moisture to form an aerosol of uranyl fluoride and HF. As part of our Safety Analysis program, we have performed several experimental releases of UF 6 (from natural uranium) in contained volumes in order to investigate techniques for sampling and characterizing the aerosol materials. The aggregrate particle morphology and size distribution have been found to be dependent upon several conditions, including the relative humidity at the time of the release and the elapse time after the release. Aerosol composition and settling rate have been investigated using isokinetic samplers for the separate collection of UO 2 F 2 and HF, and via laser spectroscopic remote sensing (Mie scatter and infrared spectroscopy). 8 references
Bayesian networks for management of industrial risk
International Nuclear Information System (INIS)
Munteanu, P.; Debache, G.; Duval, C.
2008-01-01
This article presents the outlines of Bayesian networks modelling and argues for their interest in the probabilistic studies of industrial risk and reliability. A practical case representative of this type of study is presented in support of the argumentation. The article concludes on some research tracks aiming at improving the performances of the methods relying on Bayesian networks and at widening their application area in risk management. (authors)
MCMC for parameters estimation by bayesian approach
International Nuclear Information System (INIS)
Ait Saadi, H.; Ykhlef, F.; Guessoum, A.
2011-01-01
This article discusses the parameter estimation for dynamic system by a Bayesian approach associated with Markov Chain Monte Carlo methods (MCMC). The MCMC methods are powerful for approximating complex integrals, simulating joint distributions, and the estimation of marginal posterior distributions, or posterior means. The MetropolisHastings algorithm has been widely used in Bayesian inference to approximate posterior densities. Calibrating the proposal distribution is one of the main issues of MCMC simulation in order to accelerate the convergence.
Fully probabilistic design of hierarchical Bayesian models
Czech Academy of Sciences Publication Activity Database
Quinn, A.; Kárný, Miroslav; Guy, Tatiana Valentine
2016-01-01
Roč. 369, č. 1 (2016), s. 532-547 ISSN 0020-0255 R&D Projects: GA ČR GA13-13502S Institutional support: RVO:67985556 Keywords : Fully probabilistic design * Ideal distribution * Minimum cross- entropy principle * Bayesian conditioning * Kullback-Leibler divergence * Bayesian nonparametric modelling Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 4.832, year: 2016 http://library.utia.cas.cz/separaty/2016/AS/karny-0463052.pdf
Capturing Business Cycles from a Bayesian Viewpoint
大鋸, 崇
2011-01-01
This paper is a survey of empirical studies analyzing business cycles from the perspective of Bayesian econometrics. Kim and Nelson (1998) use a hybrid model; Dynamic factor model of Stock and Watson (1989) and Markov switching model of Hamilton (1989). From the point of view, it is more important dealing with non-linear and non-Gaussian econometric models, recently. Although the classical econometric approaches have difficulty in these models, the Bayesian's do easily. The fact leads heavy u...
Variations on Bayesian Prediction and Inference
2016-05-09
inference 2.2.1 Background There are a number of statistical inference problems that are not generally formulated via a full probability model...problem of inference about an unknown parameter, the Bayesian approach requires a full probability 1. REPORT DATE (DD-MM-YYYY) 4. TITLE AND...the problem of inference about an unknown parameter, the Bayesian approach requires a full probability model/likelihood which can be an obstacle
A Bayesian classifier for symbol recognition
Barrat , Sabine; Tabbone , Salvatore; Nourrissier , Patrick
2007-01-01
URL : http://www.buyans.com/POL/UploadedFile/134_9977.pdf; International audience; We present in this paper an original adaptation of Bayesian networks to symbol recognition problem. More precisely, a descriptor combination method, which enables to improve significantly the recognition rate compared to the recognition rates obtained by each descriptor, is presented. In this perspective, we use a simple Bayesian classifier, called naive Bayes. In fact, probabilistic graphical models, more spec...
Dai, Mengyan; Liu, Jianghai; Cui, Jianlin; Chen, Chunsheng; Jia, Peng
2017-10-01
In order to solve the problem of the quantitative test of spectrum and color of aerosol, the measurement method of spectrum of aerosol based on human visual system was proposed. The spectrum characteristics and color parameters of three different aerosols were tested, and the color differences were calculated according to the CIE1976-L*a*b* color difference formula. Three tested powders (No 1# No 2# and No 3# ) were dispersed in a plexglass box and turned into aerosol. The powder sample was released by an injector with different dosages in each experiment. The spectrum and color of aerosol were measured by the PRO 6500 Fiber Optic Spectrometer. The experimental results showed that the extinction performance of aerosol became stronger and stronger with the increase of concentration of aerosol. While the chromaticity value differences of aerosols in the experiment were so small, luminance was verified to be the main influence factor of human eye visual perception and contributed most in the three factors of the color difference calculation. The extinction effect of No 3# aerosol was the strongest of all and caused the biggest change of luminance and color difference which would arouse the strongest human visual perception. According to the sensation level of chromatic color by Chinese, recognition color difference would be produced when the dosage of No 1# powder was more than 0.10 gram, the dosage of No 2# powder was more than 0.15 gram, and the dosage of No 3# powder was more than 0.05 gram.
Evaluating MODIS Collection 6 Dark Target Over Water Aerosol Products for Multi-sensor Data Fusion
Shi, Y.; Zhang, J.; Reid, J. S.; Hyer, E. J.; McHardy, T. M.; Lee, L.
2014-12-01
The Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol products have been widely used in aerosol related climate, visibility, and air quality studies for more than a decade. Recently, the MODIS collection 6 (c6) aerosol products from MODIS-Aqua have been released. The reported changes between Collection 5 and Collection 6 include updates in the retrieving algorithms and a new cloud filtering process for the over-ocean products. Thus it is necessary to fully evaluate the collection 6 products for applications that require high quality MODIS aerosol optical depth data, such as operational aerosol data assimilation. The uncertainties in the MODIS c6 DT over ocean products are studied through both inter-comparing with the Multi-angle Imaging Spectroradiometer (MISR) aerosol products and by evaluation against ground truth. Special attention is given to the low bias in MODIS DT products due to the misclassifications of heavy aerosol plumes as clouds. Finally, a quality assured data assimilation grade aerosol optical product is constructed for aerosol data assimilation related applications.
American Association for Aerosol Research (AAAR) `95
Energy Technology Data Exchange (ETDEWEB)
NONE
1995-12-31
The Fourteenth annual meeting of the American Association for Aerosol Research was held October 9-13, 1995 at Westin William Penn Hotel in Pittsburgh, PA. This volume contains the abstracts of the papers and poster sessions presented at this meeting, grouped by the session in which they were presented as follows: Radiation Effects; Aerosol Deposition; Collision Simulations and Microphysical Behavior; Filtration Theory and Measurements; Materials Synthesis; Radioactive and Nuclear Aerosols; Aerosol Formation, Thermodynamic Properties, and Behavior; Particle Contamination Issues in the Computer Industry; Pharmaceutical Aerosol Technology; Modeling Global/Regional Aerosols; Visibility; Respiratory Deposition; Biomass and Biogenic Aerosols; Aerosol Dynamics; Atmospheric Aerosols.
Bayesian Inference of Tumor Hypoxia
Gunawan, R.; Tenti, G.; Sivaloganathan, S.
2009-12-01
Tumor hypoxia is a state of oxygen deprivation in tumors. It has been associated with aggressive tumor phenotypes and with increased resistance to conventional cancer therapies. In this study, we report on the application of Bayesian sequential analysis in estimating the most probable value of tumor hypoxia quantification based on immunohistochemical assays of a biomarker. The `gold standard' of tumor hypoxia assessment is a direct measurement of pO2 in vivo by the Eppendorf polarographic electrode, which is an invasive technique restricted to accessible sites and living tissues. An attractive alternative is immunohistochemical staining to detect proteins expressed by cells during hypoxia. Carbonic anhydrase IX (CAIX) is an enzyme expressed on the cell membrane during hypoxia to balance the immediate extracellular microenvironment. CAIX is widely regarded as a surrogate marker of chronic hypoxia in various cancers. The study was conducted with two different experimental procedures. The first data set was a group of three patients with invasive cervical carcinomas, from which five biopsies were obtained. Each of the biopsies was fully sectioned and from each section, the proportion of CAIX-positive cells was estimated. Measurements were made by image analysis of multiple deep sections cut through these biopsies, labeled for CAIX using both immunofluorescence and immunohistochemical techniques [1]. The second data set was a group of 24 patients, also with invasive cervical carcinomas, from which two biopsies were obtained. Bayesian parameter estimation was applied to obtain a reliable inference about the proportion of CAIX-positive cells within the carcinomas, based on the available biopsies. From the first data set, two to three biopsies were found to be sufficient to infer the overall CAIX percentage in the simple form: best estimate±uncertainty. The second data-set led to a similar result in 70% of the cases. In the remaining cases Bayes' theorem warned us
Philosophy and the practice of Bayesian statistics.
Gelman, Andrew; Shalizi, Cosma Rohilla
2013-02-01
A substantial school in the philosophy of science identifies Bayesian inference with inductive inference and even rationality as such, and seems to be strengthened by the rise and practical success of Bayesian statistics. We argue that the most successful forms of Bayesian statistics do not actually support that particular philosophy but rather accord much better with sophisticated forms of hypothetico-deductivism. We examine the actual role played by prior distributions in Bayesian models, and the crucial aspects of model checking and model revision, which fall outside the scope of Bayesian confirmation theory. We draw on the literature on the consistency of Bayesian updating and also on our experience of applied work in social science. Clarity about these matters should benefit not just philosophy of science, but also statistical practice. At best, the inductivist view has encouraged researchers to fit and compare models without checking them; at worst, theorists have actively discouraged practitioners from performing model checking because it does not fit into their framework. © 2012 The British Psychological Society.
Philosophy and the practice of Bayesian statistics
Gelman, Andrew; Shalizi, Cosma Rohilla
2015-01-01
A substantial school in the philosophy of science identifies Bayesian inference with inductive inference and even rationality as such, and seems to be strengthened by the rise and practical success of Bayesian statistics. We argue that the most successful forms of Bayesian statistics do not actually support that particular philosophy but rather accord much better with sophisticated forms of hypothetico-deductivism. We examine the actual role played by prior distributions in Bayesian models, and the crucial aspects of model checking and model revision, which fall outside the scope of Bayesian confirmation theory. We draw on the literature on the consistency of Bayesian updating and also on our experience of applied work in social science. Clarity about these matters should benefit not just philosophy of science, but also statistical practice. At best, the inductivist view has encouraged researchers to fit and compare models without checking them; at worst, theorists have actively discouraged practitioners from performing model checking because it does not fit into their framework. PMID:22364575
Modification of combustion aerosols in the atmosphere
Energy Technology Data Exchange (ETDEWEB)
Weingartner, E. [Paul Scherrer Inst. (PSI), Villigen (Switzerland)
1996-07-01
Combustion aerosols particles are released on large scale into the atmosphere in the industrialized regions as well as in the tropics (by wood fires). The particles are subjected to various aging processes which depend on the size, morphology, and chemical composition of the particles. The interaction of combustion particles with sunlight and humidity as well as adsorption and desorption of volatile material to or from the particles considerably changes their physical and chemical properties and thus their residence time in the atmosphere. This is of importance because combustion particles are known to have a variety of health effects on people. Moreover, atmospheric aerosol particles have an influence on climate, directly through the reflection and absorption of solar radiation and indirectly through modifying the optical properties and lifetime of clouds. In a first step, a field experiment was carried out to study the sources and characteristics of combustion aerosols that are emitted from vehicles in a road tunnel. It was found that most of the fine particles were tail pipe emissions of diesel powered vehicles. The calculation shows that on an average these vehicles emit about 300 mg fine particulate matter per driven kilometer. This emission factor is at least 100 times higher than the mean emission factor estimated for gasoline powered vehicles. Furthermore, it is found that during their residence time in the tunnel, the particles undergo significant changes: The particles change towards a more compact structure. The conclusion is reached that this is mainly due to adsorption of volatile material from the gas phase to the particle surface. In the atmosphere, the life cycle as well as the radiative and chemical properties of an aerosol particle is strongly dependent on its response to humidity. Therefore the hygroscopic behavior of combustion particles emitted from single sources (i.e. from a gasoline and a diesel engine) were studied in laboratory experiments.
Evaporation of droplets in a Champagne wine aerosol
Ghabache, Elisabeth; Liger-Belair, Gérard; Antkowiak, Arnaud; Séon, Thomas
2016-01-01
In a single glass of champagne about a million bubbles nucleate on the wall and rise towards the surface. When these bubbles reach the surface and rupture, they project a multitude of tiny droplets in the form of a particular aerosol holding a concentrate of wine aromas. Based on the model experiment of a single bubble bursting in idealized champagnes, the key features of the champagne aerosol are identified. In particular, we show that film drops, critical in sea spray for example, are here nonexistent. We then demonstrate that compared to a still wine, champagne fizz drastically enhances the transfer of liquid into the atmosphere. There, conditions on bubble radius and wine viscosity that optimize aerosol evaporation are provided. These results pave the way towards the fine tuning of flavor release during sparkling wine tasting, a major issue for the sparkling wine industry. PMID:27125240
EXONEST: The Bayesian Exoplanetary Explorer
Directory of Open Access Journals (Sweden)
Kevin H. Knuth
2017-10-01
Full Text Available The fields of astronomy and astrophysics are currently engaged in an unprecedented era of discovery as recent missions have revealed thousands of exoplanets orbiting other stars. While the Kepler Space Telescope mission has enabled most of these exoplanets to be detected by identifying transiting events, exoplanets often exhibit additional photometric effects that can be used to improve the characterization of exoplanets. The EXONEST Exoplanetary Explorer is a Bayesian exoplanet inference engine based on nested sampling and originally designed to analyze archived Kepler Space Telescope and CoRoT (Convection Rotation et Transits planétaires exoplanet mission data. We discuss the EXONEST software package and describe how it accommodates plug-and-play models of exoplanet-associated photometric effects for the purpose of exoplanet detection, characterization and scientific hypothesis testing. The current suite of models allows for both circular and eccentric orbits in conjunction with photometric effects, such as the primary transit and secondary eclipse, reflected light, thermal emissions, ellipsoidal variations, Doppler beaming and superrotation. We discuss our new efforts to expand the capabilities of the software to include more subtle photometric effects involving reflected and refracted light. We discuss the EXONEST inference engine design and introduce our plans to port the current MATLAB-based EXONEST software package over to the next generation Exoplanetary Explorer, which will be a Python-based open source project with the capability to employ third-party plug-and-play models of exoplanet-related photometric effects.
Maximum entropy and Bayesian methods
International Nuclear Information System (INIS)
Smith, C.R.; Erickson, G.J.; Neudorfer, P.O.
1992-01-01
Bayesian probability theory and Maximum Entropy methods are at the core of a new view of scientific inference. These 'new' ideas, along with the revolution in computational methods afforded by modern computers allow astronomers, electrical engineers, image processors of any type, NMR chemists and physicists, and anyone at all who has to deal with incomplete and noisy data, to take advantage of methods that, in the past, have been applied only in some areas of theoretical physics. The title workshops have been the focus of a group of researchers from many different fields, and this diversity is evident in this book. There are tutorial and theoretical papers, and applications in a very wide variety of fields. Almost any instance of dealing with incomplete and noisy data can be usefully treated by these methods, and many areas of theoretical research are being enhanced by the thoughtful application of Bayes' theorem. Contributions contained in this volume present a state-of-the-art overview that will be influential and useful for many years to come
Aerosol behavior during SIC control rod failure in QUENCH-13 test
Energy Technology Data Exchange (ETDEWEB)
Lind, Terttaliisa, E-mail: terttaliisa.lind@psi.c [Paul Scherrer Institut, Villigen (Switzerland); Csordas, Anna Pinter; Nagy, Imre [HAS KFKI Atomic Energy Research Institute, Budapest (Hungary); Stuckert, Juri [Forschungszentrum Karlsruhe, Karlsruhe (Germany)
2010-02-15
In a nuclear reactor severe accident, radioactive fission products as well as structural materials are released from the core by evaporation, and the released gases form particles by nucleation and condensation. In addition, aerosol particles may be generated by droplet formation and fragmentation of the core. In pressurized water reactors (PWR), a commonly used control rod material is silver-indium-cadmium (SIC) covered with stainless steel cladding. The control rod elements, Cd, In and Ag, have relatively low melting temperatures, and especially Cd has also a very low boiling point. Control rods are likely to fail early on in the accident due to melting of the stainless steel cladding which can be accelerated by eutectic interaction between stainless steel and the surrounding Zircaloy guide tube. The release of the control rod materials would follow the cladding failure thus affecting aerosol source term as well as fuel rod degradation. The QUENCH experimental program at Forschungszentrum Karlsruhe investigates phenomena associated with reflood of a degrading core under postulated severe accident conditions. QUENCH-13 test was the first in this program to include a silver-indium-cadmium control rod of prototypic PWR design. To characterize the extent of aerosol release during the control rod failure, aerosol particle size distribution and concentration measurements in the off-gas pipe of the QUENCH facility were carried out. For the first time, it was possible to determine on-line the aerosol concentration and size distribution released from the core. These results are of prime importance for model development for the proper calculation of the source term resulting from control rod failure. The on-line measurement showed that the main aerosol release started at the bundle temperature maximum of T approx 1570 K at hottest bundle elevation. A very large burst of aerosols was detected 660 s later at the bundle temperature maximum of T approx 1650 K, followed by a
Special aerosol sources for certification and test of aerosol radiometers
International Nuclear Information System (INIS)
Belkina, S.K.; Zalmanzon, Y.E.; Kuznetsov, Y.V.; Rizin, A.I.; Fertman, D.E.
1991-01-01
The results are presented of the development and practical application of new radionuclide source types (Special Aerosol Sources (SAS)), that meet the international standard recommendations, which are used for certification and test of aerosol radiometers (monitors) using model aerosols of plutonium-239, strontium-yttrium-90 or uranium of natural isotope composition and certified against Union of Soviet Socialist Republics USSR national radioactive aerosol standard or by means of a reference radiometer. The original technology for source production allows the particular features of sampling to be taken into account as well as geometry and conditions of radionuclides radiation registration in the sample for the given type of radiometer. (author)
Special aerosol sources for certification and test of aerosol radiometers
Energy Technology Data Exchange (ETDEWEB)
Belkina, S.K.; Zalmanzon, Y.E.; Kuznetsov, Y.V.; Rizin, A.I.; Fertman, D.E. (Union Research Institute of Instrumentation, Moscow (USSR))
1991-01-01
The results are presented of the development and practical application of new radionuclide source types (Special Aerosol Sources (SAS)), that meet the international standard recommendations, which are used for certification and test of aerosol radiometers (monitors) using model aerosols of plutonium-239, strontium-yttrium-90 or uranium of natural isotope composition and certified against Union of Soviet Socialist Republics USSR national radioactive aerosol standard or by means of a reference radiometer. The original technology for source production allows the particular features of sampling to be taken into account as well as geometry and conditions of radionuclides radiation registration in the sample for the given type of radiometer. (author).
DEFF Research Database (Denmark)
Butcher, Andrew Charles
emissions produced directly from bubble bursting as the result of air entrainment from breaking waves and particles generated from secondary emissions of volatile organic compounds. In the first paper, we study the chemical properties of particles produced from several sea water proxies with the use...... of a cloud condensation nuclei ounter. Proxy solutions with high inorganic salt concentrations and some organics produce sea spray aerosol particles with little change in cloud condensation activity relative to pure salts. Comparison is made between a frit based method for bubble production and a plunging...... a relationship between plunging jet particle ux, oceanic particle ux, and energy dissipation rate in both systems. Previous sea spray aerosol studies dissipate an order of magnitude more energy for the same particle ux production as the open ocean. A scaling factor related to the energy expended in air...
A New Stratospheric Aerosol Product from CALIPSO Lidar Measurements
Kar, J.; Vaughan, M.; Trepte, C. R.; Winker, D. M.; Vernier, J. P.; Pitts, M. C.; Young, S. A.; Liu, Z.; Lucker, P.; Tackett, J. L.; Omar, A. H.
2014-12-01
Stratospheric aerosols are derived from precursor SO2 and OCS gases transported from the lower troposphere. Volcanic injections can also enhance aerosol loadings far above background levels. The latter can exert a significant influence on the Earth's radiation budget for major and even minor eruptions. Careful measurements are needed, therefore, to monitor the distribution and evolution of stratospheric aerosols for climate related studies. The Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) mission has been acquiring profile measurements of clouds and aerosols since 2006, leading to major advances in our understanding of tropospheric aerosol and cloud properties and the processes that control them. The CALIPSO products have also enabled new insights into polar stratospheric clouds and stratospheric aerosols. Vernier et al (2009,JGR,114,D00H10) reported on the construction of a modified CALIPSO lidar product that corrected minor artifacts with the original lidar calibration that affected stratospheric aerosol investigations. A significantly improved CALIPSO Lidar Version 4 Level 1 product has been recently released addressing these calibration issues and has resulted in enhanced signal levels and a highly stable record over the span of the mission. Based on this product, a new 3D gridded stratospheric CALIPSO data product is under development and being targeted for release in 2015. A key emphasis of this new product is to bridge the measurement gap between the SAGE II and SAGE III data record (1984-2005) and the start of measurements from the new SAGE III instrument to be deployed on the International Space Station in 2016. The primary parameters delivered in the CALIPSO stratospheric data products will be attenuated scattering ratio and aerosol extinction profiles, both averaged over one month intervals and binned into an equal angle grid of constant latitude and longitude with a vertical resolution of 900m. We will present the overall
Fission product vapour - aerosol interactions in the containment: simulant fuel studies
International Nuclear Information System (INIS)
Beard, A.M.; Benson, C.G.; Bowsher, B.R.
1988-12-01
Experiments have been conducted in the Falcon facility to study the interaction of fission product vapours released from simulant fuel samples with control rod aerosols. The aerosols generated from both the control rod and fuel sample were chemically distinct and had different deposition characteristics. Extensive interaction was observed between the fission product vapours and the control rod aerosol. The two dominant mechanisms were condensation of the vapours onto the aerosol, and chemical reactions between the two components; sorption phenomena were believed to be only of secondary importance. The interaction of fission product vapours and reactor materials aerosols could have a major impact on the transport characteristics of the radioactive emission from a degrading core. (author)
Energy Technology Data Exchange (ETDEWEB)
Fukuda, M.; Yamaoka, S.; Miyazaki, T.; Oka, M.
1982-01-01
The distribution and chemical composition of acidic aerosol in Osaka City were investigated. Samples were collected at five sites in the city from June to September, 1979. Acidic aerosol was determined by the acid-base titration method, sulfate ion by barium chloride turbidimetry, nitrate ion by the xylenol method, and chloride ion by the mercury thiocyanate method. The concentration of acidic aerosol at five sites ranged from 7.7 micrograms per cubic meter to 10.0 micrograms per cubic meter, but mean concentrations in the residential area were slightly higher than those in the industrial area. When acidic aerosol concentrations were compared with concentrations of sulfate, nitrate, and chloride ions, a significant correlation was found between acidic aerosol and sulfate ion. The sum of the ion equivalents of the three types showed good correlation with the acidic aerosol equivalent during the whole period.
Aerosol characterization during project POLINAT
Energy Technology Data Exchange (ETDEWEB)
Hagen, D.E.; Hopkins, A.R.; Paladino, J.D.; Whitefield, P.D. [Missouri Univ., Rolla, MO (United States). Cloud and Aerosol Sciences Lab.; Lilenfeld, H.V. [McDonnell Douglas Aerospace-East, St. Louis, MO (United States)
1997-12-31
The objectives of the aerosol/particulate characterization measurements of project POLINAT (POLlution from aircraft emissions In the North ATlantic flight corridor) are: to search for aerosol/particulate signatures of air traffic emissions in the region of the North Atlantic Flight Corridor; to search for the aerosol/particulate component of large scale enhancement (`corridor effects`) of air traffic related species in the North Atlantic region; to determine the effective emission indices for the aerosol/particulate component of engine exhaust in both the near and far field of aircraft exhaust plumes; to measure the dispersion and transformation of the aerosol/particulate component of aircraft emissions as a function of ambient condition; to characterize background levels of aerosol/particulate concentrations in the North Atlantic Region; and to determine effective emission indices for engine exhaust particulates for regimes beyond the jet phase of plume expansion. (author) 10 refs.
Aerosol Observing System (AOS) Handbook
Energy Technology Data Exchange (ETDEWEB)
Jefferson, A
2011-01-17
The Aerosol Observing System (AOS) is a suite of in situ surface measurements of aerosol optical and cloud-forming properties. The instruments measure aerosol properties that influence the earth’s radiative balance. The primary optical measurements are those of the aerosol scattering and absorption coefficients as a function of particle size and radiation wavelength and cloud condensation nuclei (CCN) measurements as a function of percent supersaturation. Additional measurements include those of the particle number concentration and scattering hygroscopic growth. Aerosol optical measurements are useful for calculating parameters used in radiative forcing calculations such as the aerosol single-scattering albedo, asymmetry parameter, mass scattering efficiency, and hygroscopic growth. CCN measurements are important in cloud microphysical models to predict droplet formation.
Characterization of aerosols from RDD surrogate compounds produced by fast thermal transients
International Nuclear Information System (INIS)
Di Lemma, Fidelma Giulia; Colle, Jean-Yves; Ernstberger, Markus; Konings, Rudy J.M.
2016-01-01
Experimental tests have been performed to characterize the aerosols representative of radiological dispersion devices (RDDs, a.k.a. “dirty bombs”) by applying to chosen surrogate compound rapid high temperature transients, vaporizing the sample and forming aerosols mainly by rapid cooling of the vapour. The materials, which were tested in their non-radioactive form, had been chosen from the radioactive sources widely used in industries and nuclear medicine applications, Co, CsCl, Ir and SrTiO 3 . Our analyses permitted the characterization of the inhalable fraction of the aerosols released, and the study of the influence of cladding materials on the aerosol release and on its characteristics. (author)
Generator of fine polydisperse aerosol
Czech Academy of Sciences Publication Activity Database
Mikuška, Pavel
2004-01-01
Roč. 69, č. 7 (2004), s. 1453-1463 ISSN 0010-0765 R&D Projects: GA AV ČR IAA4031105; GA ČR GA203/98/0943 Grant - others:INCO COPERNICUS(BE) SUB AERO-EVK2-CT-1999-0052 Institutional research plan: CEZ:AV0Z4031919 Keywords : aerosol generator * polydisperse aerosol * fine aerosol Subject RIV: CB - Analytical Chemistry, Separation Impact factor: 1.062, year: 2004
A Bayesian Algorithm for Assessing Uncertainty in Radionuclide Source Terms
Robins, Peter
2015-04-01
Inferring source term parameters for a radionuclide release is difficult, due to the large uncertainties in forward dispersion modelling as a consequence of imperfect knowledge pertaining to wind vector fields and turbulent diffusion in the Earth's atmosphere. Additional sources of error include the radionuclide measurements obtained from sensors. These measurements may either be subject to random fluctuations or are simple indications that the true, unobserved quantity is below a detection limit. Consequent large reconstruction uncertainties can render a "best" estimate meaningless. A Markov Chain Monte Carlo (MCMC) Bayesian Algorithm is presented that attempts to account for uncertainties in atmospheric transport modelling and radionuclide sensor measurements to quantify uncertainties in radionuclide release source term parameters. Prior probability distributions are created for likely release locations at existing nuclear facilities and seismic events. Likelihood models are constructed using CTBTO adjoint modelling output and probability distributions of sensor response. Samples from the resulting multi-isotope source term parameters posterior probability distribution are generated that can be used to make probabilistic statements about the source term. Examples are given of marginal probability distributions obtained from simulated sensor data. The consequences of errors in numerical weather prediction wind fields are demonstrated with a reconstruction of the Fukushima nuclear reactor accident from International Monitoring System radionuclide particulate sensor data.
Topics in current aerosol research
Hidy, G M
1971-01-01
Topics in Current Aerosol Research deals with the fundamental aspects of aerosol science, with emphasis on experiment and theory describing highly dispersed aerosols (HDAs) as well as the dynamics of charged suspensions. Topics covered range from the basic properties of HDAs to their formation and methods of generation; sources of electric charges; interactions between fluid and aerosol particles; and one-dimensional motion of charged cloud of particles. This volume is comprised of 13 chapters and begins with an introduction to the basic properties of HDAs, followed by a discussion on the form
Resolution and Content Improvements to MISR Aerosol and Land Surface Products
Garay, M. J.; Bull, M. A.; Diner, D. J.; Hansen, E. G.; Kalashnikova, O. V.
2015-12-01
Since early 2000, the Multi-angle Imaging SpectroRadiometer (MISR) instrument on NASA's Terra satellite has been providing operational Level 2 (swath-based) aerosol optical depth (AOD) and particle property retrievals at 17.6 km spatial resolution and atmospherically corrected land surface products at 1.1 km resolution. The performance of the aerosol product has been validated against ground-based Aerosol Robotic Network (AERONET) observations, model comparisons, and climatological assessments. This product has played a major role in studies of the impacts of aerosols on climate and air quality. The surface product has found a variety of uses, particularly at regional scales for assessing vegetation and land surface change. A major development effort has led to the release of an update to the operational (Version 22) MISR Level 2 aerosol and land surface retrieval products, which has been in production since December 2007. The new release is designated Version 23. The resolution of the aerosol product has been increased to 4.4 km, allowing more detailed characterization of aerosol spatial variability, especially near local sources and in urban areas. The product content has been simplified and updated to include more robust measures of retrieval uncertainty and other fields to benefit users. The land surface product has also been updated to incorporate the Version 23 aerosol product as input and to improve spatial coverage, particularly over mountainous terrain and snow/ice-covered surfaces. We will describe the major upgrades incorporated in Version 23 and present validation of the aerosol product against both the standard AERONET historical database, as well as high spatial density AERONET-DRAGON deployments. Comparisons will also be shown relative to the Version 22 aerosol and land surface products. Applications enabled by these product updates will be discussed.
International Nuclear Information System (INIS)
Artaxo, P.; Martins, J.V.; Yamasoe, M.A.; Gerab, F.; Kocinas, S.
1994-01-01
In order to study the natural release of aerosol particles by the Amazon Basin tropical rain forest, the composition and size distribution of biogenic aerosol particles were analyzed. The role of the atmospheric emissions from the Amazon Basin rain forest in the global atmosphere will be investigated. The atmosphere was studied in long-term sampling stations in three different locations. The elemental composition of aerosol particles released during biomass burning was also measured in several different ecosystems, from primary forest to Savannah. One of the main focuses was to identify and quantify important physical and chemical processes in the generation, transformation and deposition of aerosol particles. Also important was to obtain a better understanding of natural aerosol sources concerning identification, their characteristics and strength, to be able to understand the natural chemistry in the atmosphere on a global scale. 36 refs, 3 figs, 3 tabs
Bayesian tomographic reconstruction of microsystems
International Nuclear Information System (INIS)
Salem, Sofia Fekih; Vabre, Alexandre; Mohammad-Djafari, Ali
2007-01-01
The microtomography by X ray transmission plays an increasingly dominating role in the study and the understanding of microsystems. Within this framework, an experimental setup of high resolution X ray microtomography was developed at CEA-List to quantify the physical parameters related to the fluids flow in microsystems. Several difficulties rise from the nature of experimental data collected on this setup: enhanced error measurements due to various physical phenomena occurring during the image formation (diffusion, beam hardening), and specificities of the setup (limited angle, partial view of the object, weak contrast).To reconstruct the object we must solve an inverse problem. This inverse problem is known to be ill-posed. It therefore needs to be regularized by introducing prior information. The main prior information we account for is that the object is composed of a finite known number of different materials distributed in compact regions. This a priori information is introduced via a Gauss-Markov field for the contrast distributions with a hidden Potts-Markov field for the class materials in the Bayesian estimation framework. The computations are done by using an appropriate Markov Chain Monte Carlo (MCMC) technique.In this paper, we present first the basic steps of the proposed algorithms. Then we focus on one of the main steps in any iterative reconstruction method which is the computation of forward and adjoint operators (projection and backprojection). A fast implementation of these two operators is crucial for the real application of the method. We give some details on the fast computation of these steps and show some preliminary results of simulations
Combustion Aerosols from Full-Scale Suspension-Firing of Wood Pellets
DEFF Research Database (Denmark)
Damø, Anne Juul; Wu, Hao; Frandsen, Flemming
2012-01-01
.03 – 12.7 μm) was used to sample aerosols in the flue gas, in the top of the boiler before the SCR (Tfluegas ~350 oC). The collected aerosols were subsequently characterized with respect to particle size distribution, morphology, and chemical composition. The mass-based size distribution of the aerosols...... for the experiments with coal fly ash addition. This indicates that the coal fly ash is effective in capturing volatile alkalis released from the wood during combustion, thus suppressing the homogeneous nucleation of alkali-salts. SEM/EDS and TEM/EDS analysis revealed that the large condensation peak from pure wood...
Dimensionality reduction in Bayesian estimation algorithms
Directory of Open Access Journals (Sweden)
G. W. Petty
2013-09-01
Full Text Available An idealized synthetic database loosely resembling 3-channel passive microwave observations of precipitation against a variable background is employed to examine the performance of a conventional Bayesian retrieval algorithm. For this dataset, algorithm performance is found to be poor owing to an irreconcilable conflict between the need to find matches in the dependent database versus the need to exclude inappropriate matches. It is argued that the likelihood of such conflicts increases sharply with the dimensionality of the observation space of real satellite sensors, which may utilize 9 to 13 channels to retrieve precipitation, for example. An objective method is described for distilling the relevant information content from N real channels into a much smaller number (M of pseudochannels while also regularizing the background (geophysical plus instrument noise component. The pseudochannels are linear combinations of the original N channels obtained via a two-stage principal component analysis of the dependent dataset. Bayesian retrievals based on a single pseudochannel applied to the independent dataset yield striking improvements in overall performance. The differences between the conventional Bayesian retrieval and reduced-dimensional Bayesian retrieval suggest that a major potential problem with conventional multichannel retrievals – whether Bayesian or not – lies in the common but often inappropriate assumption of diagonal error covariance. The dimensional reduction technique described herein avoids this problem by, in effect, recasting the retrieval problem in a coordinate system in which the desired covariance is lower-dimensional, diagonal, and unit magnitude.
Dimensionality reduction in Bayesian estimation algorithms
Petty, G. W.
2013-09-01
An idealized synthetic database loosely resembling 3-channel passive microwave observations of precipitation against a variable background is employed to examine the performance of a conventional Bayesian retrieval algorithm. For this dataset, algorithm performance is found to be poor owing to an irreconcilable conflict between the need to find matches in the dependent database versus the need to exclude inappropriate matches. It is argued that the likelihood of such conflicts increases sharply with the dimensionality of the observation space of real satellite sensors, which may utilize 9 to 13 channels to retrieve precipitation, for example. An objective method is described for distilling the relevant information content from N real channels into a much smaller number (M) of pseudochannels while also regularizing the background (geophysical plus instrument) noise component. The pseudochannels are linear combinations of the original N channels obtained via a two-stage principal component analysis of the dependent dataset. Bayesian retrievals based on a single pseudochannel applied to the independent dataset yield striking improvements in overall performance. The differences between the conventional Bayesian retrieval and reduced-dimensional Bayesian retrieval suggest that a major potential problem with conventional multichannel retrievals - whether Bayesian or not - lies in the common but often inappropriate assumption of diagonal error covariance. The dimensional reduction technique described herein avoids this problem by, in effect, recasting the retrieval problem in a coordinate system in which the desired covariance is lower-dimensional, diagonal, and unit magnitude.
Classifying emotion in Twitter using Bayesian network
Surya Asriadie, Muhammad; Syahrul Mubarok, Mohamad; Adiwijaya
2018-03-01
Language is used to express not only facts, but also emotions. Emotions are noticeable from behavior up to the social media statuses written by a person. Analysis of emotions in a text is done in a variety of media such as Twitter. This paper studies classification of emotions on twitter using Bayesian network because of its ability to model uncertainty and relationships between features. The result is two models based on Bayesian network which are Full Bayesian Network (FBN) and Bayesian Network with Mood Indicator (BNM). FBN is a massive Bayesian network where each word is treated as a node. The study shows the method used to train FBN is not very effective to create the best model and performs worse compared to Naive Bayes. F1-score for FBN is 53.71%, while for Naive Bayes is 54.07%. BNM is proposed as an alternative method which is based on the improvement of Multinomial Naive Bayes and has much lower computational complexity compared to FBN. Even though it’s not better compared to FBN, the resulting model successfully improves the performance of Multinomial Naive Bayes. F1-Score for Multinomial Naive Bayes model is 51.49%, while for BNM is 52.14%.
How few? Bayesian statistics in injury biomechanics.
Cutcliffe, Hattie C; Schmidt, Allison L; Lucas, Joseph E; Bass, Cameron R
2012-10-01
In injury biomechanics, there are currently no general a priori estimates of how few specimens are necessary to obtain sufficiently accurate injury risk curves for a given underlying distribution. Further, several methods are available for constructing these curves, and recent methods include Bayesian survival analysis. This study used statistical simulations to evaluate the fidelity of different injury risk methods using limited sample sizes across four different underlying distributions. Five risk curve techniques were evaluated, including Bayesian techniques. For the Bayesian analyses, various prior distributions were assessed, each incorporating more accurate information. Simulated subject injury and biomechanical input values were randomly sampled from each underlying distribution, and injury status was determined by comparing these values. Injury risk curves were developed for this data using each technique for various small sample sizes; for each, analyses on 2000 simulated data sets were performed. Resulting median predicted risk values and confidence intervals were compared with the underlying distributions. Across conditions, the standard and Bayesian survival analyses better represented the underlying distributions included in this study, especially for extreme (1, 10, and 90%) risk. This study demonstrates that the value of the Bayesian analysis is the use of informed priors. As the mean of the prior approaches the actual value, the sample size necessary for good reproduction of the underlying distribution with small confidence intervals can be as small as 2. This study provides estimates of confidence intervals and number of samples to allow the selection of the most appropriate sample sizes given known information.
A default Bayesian hypothesis test for mediation.
Nuijten, Michèle B; Wetzels, Ruud; Matzke, Dora; Dolan, Conor V; Wagenmakers, Eric-Jan
2015-03-01
In order to quantify the relationship between multiple variables, researchers often carry out a mediation analysis. In such an analysis, a mediator (e.g., knowledge of a healthy diet) transmits the effect from an independent variable (e.g., classroom instruction on a healthy diet) to a dependent variable (e.g., consumption of fruits and vegetables). Almost all mediation analyses in psychology use frequentist estimation and hypothesis-testing techniques. A recent exception is Yuan and MacKinnon (Psychological Methods, 14, 301-322, 2009), who outlined a Bayesian parameter estimation procedure for mediation analysis. Here we complete the Bayesian alternative to frequentist mediation analysis by specifying a default Bayesian hypothesis test based on the Jeffreys-Zellner-Siow approach. We further extend this default Bayesian test by allowing a comparison to directional or one-sided alternatives, using Markov chain Monte Carlo techniques implemented in JAGS. All Bayesian tests are implemented in the R package BayesMed (Nuijten, Wetzels, Matzke, Dolan, & Wagenmakers, 2014).
Computationally efficient Bayesian inference for inverse problems.
Energy Technology Data Exchange (ETDEWEB)
Marzouk, Youssef M.; Najm, Habib N.; Rahn, Larry A.
2007-10-01
Bayesian statistics provides a foundation for inference from noisy and incomplete data, a natural mechanism for regularization in the form of prior information, and a quantitative assessment of uncertainty in the inferred results. Inverse problems - representing indirect estimation of model parameters, inputs, or structural components - can be fruitfully cast in this framework. Complex and computationally intensive forward models arising in physical applications, however, can render a Bayesian approach prohibitive. This difficulty is compounded by high-dimensional model spaces, as when the unknown is a spatiotemporal field. We present new algorithmic developments for Bayesian inference in this context, showing strong connections with the forward propagation of uncertainty. In particular, we introduce a stochastic spectral formulation that dramatically accelerates the Bayesian solution of inverse problems via rapid evaluation of a surrogate posterior. We also explore dimensionality reduction for the inference of spatiotemporal fields, using truncated spectral representations of Gaussian process priors. These new approaches are demonstrated on scalar transport problems arising in contaminant source inversion and in the inference of inhomogeneous material or transport properties. We also present a Bayesian framework for parameter estimation in stochastic models, where intrinsic stochasticity may be intermingled with observational noise. Evaluation of a likelihood function may not be analytically tractable in these cases, and thus several alternative Markov chain Monte Carlo (MCMC) schemes, operating on the product space of the observations and the parameters, are introduced.
International Nuclear Information System (INIS)
Hilliard, R.K.; McCormack, J.D.; Muhlestein, L.D.
1985-10-01
A program for aerosol behavior validation and evaluation (ABCOVE) has been developed in accordance with the LMFBR Safety Program Plan. The ABCOVE program is a cooperative effort between the USDOE, the USNRC, and their contractor organizations currently involved in aerosol code development, testing or application. The third large-scale test in the ABCOVE program, AB7, was performed in the 850-m 3 CSTF vessel with a two-species test aerosol. The test conditions involved the release of a simulated fission product aerosol, NaI, into the containment atmosphere after the end of a small sodium pool fire. Four organizations made pretest predictions of aerosol behavior using five computer codes. Two of the codes (QUICKM and CONTAIN) were discrete, multiple species codes, while three (HAA-3, HAA-4, and HAARM-3) were log-normal codes which assume uniform coagglomeration of different aerosol species. Detailed test results are presented and compared with the code predictions for eight key aerosol behavior parameters. 11 refs., 44 figs., 35 tabs
Aerosol sampler for analysis of fine and ultrafine aerosols
Czech Academy of Sciences Publication Activity Database
Mikuška, Pavel; Čapka, Lukáš; Večeřa, Zbyněk
2018-01-01
Roč. 1020 (2018), s. 123-133 ISSN 0003-2670 R&D Projects: GA ČR(CZ) GA14-25558S Institutional support: RVO:68081715 Keywords : atmospheric aerosols * aerosol collection * chemical composition Subject RIV: CB - Analytical Chemistry, Separation OBOR OECD: Analytical chemistry Impact factor: 4.950, year: 2016
International Nuclear Information System (INIS)
Scripsick, R.C.; Gray, D.C.; Tillery, M.I.; Stafford, R.G.; Romero, P.O.
1978-11-01
Biphasic dissolution of both laboratory-produced and field plutonium aerosols was studied to further understand the relation between long-term and initial dissolution. These studies indicate that initial phase duration and cumulative activity eluted during this phase are directly related to long-term dissolution. A rapid method of sizing plutonium aerosol was developed using autoradiography. This method uses the rapidly available size information from the larger particles of a size distribution in determining size distribution parameters. A comparison of autoradiographic sizing to impactor sizing showed that count median aerodynamic diameter (CMAD) of aerosol collection on impactor stages predicted by impactor theory was 1.2 times the CMAD found by autoradiographic sizing. Retrospective study of airborne contamination records and simulated glove box release studies have indicated large variabilities associated with aerosol sampling. Release studies have also indicated that parameters of breathing zone concentration required to trigger alarms and probability of aerosol surveillance system to detect release are important in evaluation of aerosol surveillance system performance. Computer modeling of room airflow patterns predicted flow patterns found through smoke tube studies of room airflow
Directory of Open Access Journals (Sweden)
G.-J. Roelofs
2010-08-01
Full Text Available In May 2008, the measurement campaign IMPACT for observation of atmospheric aerosol and cloud properties was conducted in Cabauw, The Netherlands. With a nudged version of the coupled aerosol-climate model ECHAM5-HAM we simulate the size distribution and chemical composition of the aerosol and the associated aerosol optical thickness (AOT for the campaign period. Synoptic scale meteorology is represented realistically through nudging of the vorticity, the divergence, the temperature and the surface pressure. Simulated concentrations of aerosol sulfate and organics at the surface are generally within a factor of two from observed values. The monthly averaged AOT from the model is 0.33, about 20% larger than observed. For selected periods of the month with relatively dry and moist conditions discrepancies are approximately −30% and +15%, respectively. Discrepancies during the dry period are partly caused by inaccurate representation of boundary layer (BL dynamics by the model affecting the simulated AOT. The model simulates too strong exchange between the BL and the free troposphere, resulting in weaker concentration gradients at the BL top than observed for aerosol and humidity, while upward mixing from the surface layers into the BL appears to be underestimated. The results indicate that beside aerosol sulfate and organics also aerosol ammonium and nitrate significantly contribute to aerosol water uptake. The simulated day-to-day variability of AOT follows synoptic scale advection of humidity rather than particle concentration. Even for relatively dry conditions AOT appears to be strongly influenced by the diurnal cycle of RH in the lower boundary layer, further enhanced by uptake and release of nitric acid and ammonia by aerosol water.
Aerosol effects in radiation transfer
International Nuclear Information System (INIS)
Binenko, V.I.; Harshvardhan, H.
1993-01-01
The radiative properties and effects of aerosols are assessed for the following aerosol sources: relatively clean background aerosol, dust storms and dust outbreaks, anthropogenic pollution, and polluted cloud layers. Studies show it is the submicron aerosol fraction that plays a dominant radiative role in the atmosphere. The radiative effect of the aerosol depends not only on its loading but also on the underlying surface albedo and on solar zenith angle. It is only with highly reflecting surfaces such as Arctic ice that aerosols have a warming effect. Radiometric, microphysical, mineral composition, and refractive index measurements are presented for dust and in particular for the Saharan aerosol layer (SAL). Short-wave radiative heating of the atmosphere is caused by the SAL and is due mainly to absorption. However, the SAL does not contribute significantly to the long-wave thermal radiation budget. Field program studies of the radiative effects of aerosols are described. Anthropogenic aerosols deplete the incoming solar radiation. A case field study for a regional Ukrainian center is discussed. The urban aerosol causes a cooling of metropolitan centers, compared with outlying areas, during the day, which is followed by a warming trend at night. In another study, an increase in turbidity by a factor of 3 due to increased industrialization for Mexico City is noted, together with a drop in atmospheric transmission by 10% over a 50-year period. Numerous studies are cited that demonstrate that anthropogenic aerosols affect both the microphysical and radiative properties of clouds, which in turn affect regional climate. Particles acting as cloud nuclei are considered to have the greatest indirect effect on cloud absorptivity of short-wave radiation. Satellite observations show that low-level stratus clouds contaminated by ship exhaust at sea lead to an increase in cloud albedo
Vance, Marina E; Pegues, Valerie; Van Montfrans, Schuyler; Leng, Weinan; Marr, Linsey C
2017-09-05
Three-dimensional (3D) printers are known to emit aerosols, but questions remain about their composition and the fundamental processes driving emissions. The objective of this work was to characterize the aerosol emissions from the operation of a fuse-deposition modeling 3D printer. We modeled the time- and size-resolved emissions of submicrometer aerosols from the printer in a chamber study, gained insight into the chemical composition of emitted aerosols using Raman spectroscopy, and measured the potential for exposure to the aerosols generated by 3D printers under real-use conditions in a variety of indoor environments. The average aerosol emission rates ranged from ∼10 8 to ∼10 11 particles min -1 , and the rates varied over the course of a print job. Acrylonitrile butadiene styrene (ABS) filaments generated the largest number of aerosols, and wood-infused polylactic acid (PLA) filaments generated the smallest amount. The emission factors ranged from 6 × 10 8 to 6 × 10 11 per gram of printed part, depending on the type of filament used. For ABS, the Raman spectra of the filament and the printed part were indistinguishable, while the aerosol spectra lacked important peaks corresponding to styrene and acrylonitrile, which are both present in ABS. This observation suggests that aerosols are not a result of volatilization and subsequent nucleation of ABS or direct release of ABS aerosols.
Characterization of aerosols in Beijing during severe aerosol loadings
Chen, Hao; Cheng, Tianhai; Gu, Xingfa; Wu, Yu
2015-10-01
Severe aerosol pollutions in China significantly impact radiative forcing of climate at regional and global scales. Until now, the uncertainties in net climate forcing from severe aerosol pollutions in China are substantial, largely due to the lack of detailed knowledge of radiative properties of severe aerosol pollutions. Here the characteristics of aerosols under severe aerosol pollution days (APs) in Beijing are studied by analyzing the ground-based radiance measurements during the period from 2002 to 2014. We show that the mean single scattering albedo (SSA) values increase by 0.03-0.06 (7%) in APs, and the mean asymmetry (ASY) parameter values increase by 0.03-0.04 (6%) for the four wavelengths of 440-1020 nm. The atmospheric forcing of the APs is 2 times higher than that in other days. Contrary to the RF values, the radiative forcing efficiencies in the APs are 38% lower than those in the other days. Larger values of SSA and ASY under APs represent larger presence of more scattering aerosols and irregular-sized aerosols such as dust and non-absorbing fine mode particles. These particles are also verified by the much lower radiative forcing efficiency values. Analyses are applied on the dataset of the APs over Beijing, to group them into four discrete clusters. The two fine-size absorbing aerosols show larger mean atmospheric radiative forcing values (152.5 W/m2 and 184.5 W/m2 respectively) and forcing efficiency values (83.5 W/m2 and 108.5 W/m2 respectively). The non-absorbing aerosols and coarse aerosols exert large planetary cooling (-86.7 W/m2 and -77.3 W/m2) and low atmospheric heating effect.
Bayesian analysis of MEG visual evoked responses
Energy Technology Data Exchange (ETDEWEB)
Schmidt, D.M.; George, J.S.; Wood, C.C.
1999-04-01
The authors developed a method for analyzing neural electromagnetic data that allows probabilistic inferences to be drawn about regions of activation. The method involves the generation of a large number of possible solutions which both fir the data and prior expectations about the nature of probable solutions made explicit by a Bayesian formalism. In addition, they have introduced a model for the current distributions that produce MEG and (EEG) data that allows extended regions of activity, and can easily incorporate prior information such as anatomical constraints from MRI. To evaluate the feasibility and utility of the Bayesian approach with actual data, they analyzed MEG data from a visual evoked response experiment. They compared Bayesian analyses of MEG responses to visual stimuli in the left and right visual fields, in order to examine the sensitivity of the method to detect known features of human visual cortex organization. They also examined the changing pattern of cortical activation as a function of time.
Empirical Bayesian inference and model uncertainty
International Nuclear Information System (INIS)
Poern, K.
1994-01-01
This paper presents a hierarchical or multistage empirical Bayesian approach for the estimation of uncertainty concerning the intensity of a homogeneous Poisson process. A class of contaminated gamma distributions is considered to describe the uncertainty concerning the intensity. These distributions in turn are defined through a set of secondary parameters, the knowledge of which is also described and updated via Bayes formula. This two-stage Bayesian approach is an example where the modeling uncertainty is treated in a comprehensive way. Each contaminated gamma distributions, represented by a point in the 3D space of secondary parameters, can be considered as a specific model of the uncertainty about the Poisson intensity. Then, by the empirical Bayesian method each individual model is assigned a posterior probability
Bayesian modeling of unknown diseases for biosurveillance.
Shen, Yanna; Cooper, Gregory F
2009-11-14
This paper investigates Bayesian modeling of unknown causes of events in the context of disease-outbreak detection. We introduce a Bayesian approach that models and detects both (1) known diseases (e.g., influenza and anthrax) by using informative prior probabilities and (2) unknown diseases (e.g., a new, highly contagious respiratory virus that has never been seen before) by using relatively non-informative prior probabilities. We report the results of simulation experiments which support that this modeling method can improve the detection of new disease outbreaks in a population. A key contribution of this paper is that it introduces a Bayesian approach for jointly modeling both known and unknown causes of events. Such modeling has broad applicability in medical informatics, where the space of known causes of outcomes of interest is seldom complete.
Bayesian disease mapping: hierarchical modeling in spatial epidemiology
National Research Council Canada - National Science Library
Lawson, Andrew
2013-01-01
.... Exploring these new developments, Bayesian Disease Mapping: Hierarchical Modeling in Spatial Epidemiology, Second Edition provides an up-to-date, cohesive account of the full range of Bayesian disease mapping methods and applications...
Bayesian Inference in Polling Technique: 1992 Presidential Polls.
Satake, Eiki
1994-01-01
Explores the potential utility of Bayesian statistical methods in determining the predictability of multiple polls. Compares Bayesian techniques to the classical statistical method employed by pollsters. Considers these questions in the context of the 1992 presidential elections. (HB)
The Bayesian Approach to Association
Arora, N. S.
2017-12-01
The Bayesian approach to Association focuses mainly on quantifying the physics of the domain. In the case of seismic association for instance let X be the set of all significant events (above some threshold) and their attributes, such as location, time, and magnitude, Y1 be the set of detections that are caused by significant events and their attributes such as seismic phase, arrival time, amplitude etc., Y2 be the set of detections that are not caused by significant events, and finally Y be the set of observed detections We would now define the joint distribution P(X, Y1, Y2, Y) = P(X) P(Y1 | X) P(Y2) I(Y = Y1 + Y2) ; where the last term simply states that Y1 and Y2 are a partitioning of Y. Given the above joint distribution the inference problem is simply to find the X, Y1, and Y2 that maximizes posterior probability P(X, Y1, Y2| Y) which reduces to maximizing P(X) P(Y1 | X) P(Y2) I(Y = Y1 + Y2). In this expression P(X) captures our prior belief about event locations. P(Y1 | X) captures notions of travel time, residual error distributions as well as detection and mis-detection probabilities. While P(Y2) captures the false detection rate of our seismic network. The elegance of this approach is that all of the assumptions are stated clearly in the model for P(X), P(Y1|X) and P(Y2). The implementation of the inference is merely a by-product of this model. In contrast some of the other methods such as GA hide a number of assumptions in the implementation details of the inference - such as the so called "driver cells." The other important aspect of this approach is that all seismic knowledge including knowledge from other domains such as infrasound and hydroacoustic can be included in the same model. So, we don't need to separately account for misdetections or merge seismic and infrasound events as a separate step. Finally, it should be noted that the objective of automatic association is to simplify the job of humans who are publishing seismic bulletins based on this
Aerosol impacts on scene contrast
Eijk, A.M.J. van; Piazzolla, J.; Tedeschi, G.; Stein, K.
2016-01-01
Atmospheric aerosols scatter and absorb radiation, which impacts greatly on the amount of solar radiation reaching a surface, thereby changing the amount of radiation available for heating up a target. The presence of aerosols also reduces the amount of target radiation that reaches the sensor, and
Preliminary aerosol generator design studies
Stampfer, J. F., Jr.
1976-01-01
The design and construction of a prototype vaporization generator for highly dispersed sodium chloride aerosols is described. The aerosol generating system is to be used in the Science Simulator of the Cloud Physics Laboratory Project and as part of the Cloud Physics Laboratory payload to be flown on the shuttle/spacelab.
AEROSOL VARIABILITY OBSERVED WITH RPAS
Directory of Open Access Journals (Sweden)
B. Altstädter
2013-08-01
Full Text Available To observe the origin, vertical and horizontal distribution and variability of aerosol particles, and especially ultrafine particles recently formed, we plan to employ the remotely piloted aircraft system (RPAS Carolo-P360 "ALADINA" of TU Braunschweig. The goal of the presented project is to investigate the vertical and horizontal distribution, transport and small-scale variability of aerosol particles in the atmospheric boundary layer using RPAS. Two additional RPAS of type MASC of Tübingen University equipped with turbulence instrumentation add the opportunity to study the interaction of the aerosol concentration with turbulent transport and exchange processes of the surface and the atmosphere. The combination of different flight patterns of the three RPAS allows new insights in atmospheric boundary layer processes. Currently, the different aerosol sensors are miniaturized at the Leibniz Institute for Tropospheric Research, Leipzig and together with the TU Braunschweig adapted to fit into the RPAS. Moreover, an additional meteorological payload for measuring temperature, humidity and turbulence properties is constructed by Tübingen University. Two condensation particle counters determine the total aerosol number with a different lower detection threshold in order to investigate the horizontal and vertical aerosol variability and new particle formation (aerosol particles of some nm diameter. Further the aerosol size distribution in the range from about 0.300 to ~5 μm is given by an optical particle counter.
Aerosol extinction in coastal zone
Piazzola, J.; Kaloshin, G.; Leeuw, G. de; Eijk, A.M.J. van
2004-01-01
The performance of electro-optical systems can be substantially affected by aerosol particles that scatter and absorb electromagnetic radiation. A few years ago, an empirical model was developed describing the aerosol size distributions in the Mediterranean coastal atmosphere near Toulon (France).
Mount Saint Helens aerosol evolution
Oberbeck, V. R.; Farlow, N. H.; Fong, W.; Snetsinger, K. G.; Ferry, G. V.; Hayes, D. M.
1982-09-01
Stratospheric aerosol samples were collected using a wire impactor during the year following the eruption of Mt. St. Helens. Analysis of samples shows that aerosol volume increased for 6 months due to gas-to-particle conversion and then decreased to background levels in the following 6 months.
Bayesian estimation and tracking a practical guide
Haug, Anton J
2012-01-01
A practical approach to estimating and tracking dynamic systems in real-worl applications Much of the literature on performing estimation for non-Gaussian systems is short on practical methodology, while Gaussian methods often lack a cohesive derivation. Bayesian Estimation and Tracking addresses the gap in the field on both accounts, providing readers with a comprehensive overview of methods for estimating both linear and nonlinear dynamic systems driven by Gaussian and non-Gaussian noices. Featuring a unified approach to Bayesian estimation and tracking, the book emphasizes the derivation
Nonparametric Bayesian Modeling of Complex Networks
DEFF Research Database (Denmark)
Schmidt, Mikkel Nørgaard; Mørup, Morten
2013-01-01
an infinite mixture model as running example, we go through the steps of deriving the model as an infinite limit of a finite parametric model, inferring the model parameters by Markov chain Monte Carlo, and checking the model?s fit and predictive performance. We explain how advanced nonparametric models......Modeling structure in complex networks using Bayesian nonparametrics makes it possible to specify flexible model structures and infer the adequate model complexity from the observed data. This article provides a gentle introduction to nonparametric Bayesian modeling of complex networks: Using...
Motion Learning Based on Bayesian Program Learning
Directory of Open Access Journals (Sweden)
Cheng Meng-Zhen
2017-01-01
Full Text Available The concept of virtual human has been highly anticipated since the 1980s. By using computer technology, Human motion simulation could generate authentic visual effect, which could cheat human eyes visually. Bayesian Program Learning train one or few motion data, generate new motion data by decomposing and combining. And the generated motion will be more realistic and natural than the traditional one.In this paper, Motion learning based on Bayesian program learning allows us to quickly generate new motion data, reduce workload, improve work efficiency, reduce the cost of motion capture, and improve the reusability of data.
Bayesian inference and the parametric bootstrap
Efron, Bradley
2013-01-01
The parametric bootstrap can be used for the efficient computation of Bayes posterior distributions. Importance sampling formulas take on an easy form relating to the deviance in exponential families, and are particularly simple starting from Jeffreys invariant prior. Because of the i.i.d. nature of bootstrap sampling, familiar formulas describe the computational accuracy of the Bayes estimates. Besides computational methods, the theory provides a connection between Bayesian and frequentist analysis. Efficient algorithms for the frequentist accuracy of Bayesian inferences are developed and demonstrated in a model selection example. PMID:23843930
Length Scales in Bayesian Automatic Adaptive Quadrature
Directory of Open Access Journals (Sweden)
Adam Gh.
2016-01-01
Full Text Available Two conceptual developments in the Bayesian automatic adaptive quadrature approach to the numerical solution of one-dimensional Riemann integrals [Gh. Adam, S. Adam, Springer LNCS 7125, 1–16 (2012] are reported. First, it is shown that the numerical quadrature which avoids the overcomputing and minimizes the hidden floating point loss of precision asks for the consideration of three classes of integration domain lengths endowed with specific quadrature sums: microscopic (trapezoidal rule, mesoscopic (Simpson rule, and macroscopic (quadrature sums of high algebraic degrees of precision. Second, sensitive diagnostic tools for the Bayesian inference on macroscopic ranges, coming from the use of Clenshaw-Curtis quadrature, are derived.
Length Scales in Bayesian Automatic Adaptive Quadrature
Adam, Gh.; Adam, S.
2016-02-01
Two conceptual developments in the Bayesian automatic adaptive quadrature approach to the numerical solution of one-dimensional Riemann integrals [Gh. Adam, S. Adam, Springer LNCS 7125, 1-16 (2012)] are reported. First, it is shown that the numerical quadrature which avoids the overcomputing and minimizes the hidden floating point loss of precision asks for the consideration of three classes of integration domain lengths endowed with specific quadrature sums: microscopic (trapezoidal rule), mesoscopic (Simpson rule), and macroscopic (quadrature sums of high algebraic degrees of precision). Second, sensitive diagnostic tools for the Bayesian inference on macroscopic ranges, coming from the use of Clenshaw-Curtis quadrature, are derived.
Prior approval: the growth of Bayesian methods in psychology.
Andrews, Mark; Baguley, Thom
2013-02-01
Within the last few years, Bayesian methods of data analysis in psychology have proliferated. In this paper, we briefly review the history or the Bayesian approach to statistics, and consider the implications that Bayesian methods have for the theory and practice of data analysis in psychology.
A Fast Iterative Bayesian Inference Algorithm for Sparse Channel Estimation
DEFF Research Database (Denmark)
Pedersen, Niels Lovmand; Manchón, Carles Navarro; Fleury, Bernard Henri
2013-01-01
representation of the Bessel K probability density function; a highly efficient, fast iterative Bayesian inference method is then applied to the proposed model. The resulting estimator outperforms other state-of-the-art Bayesian and non-Bayesian estimators, either by yielding lower mean squared estimation error...
A Gentle Introduction to Bayesian Analysis : Applications to Developmental Research
Van de Schoot, Rens|info:eu-repo/dai/nl/304833207; Kaplan, David; Denissen, Jaap; Asendorpf, Jens B.; Neyer, Franz J.; van Aken, Marcel A G|info:eu-repo/dai/nl/081831218
2014-01-01
Bayesian statistical methods are becoming ever more popular in applied and fundamental research. In this study a gentle introduction to Bayesian analysis is provided. It is shown under what circumstances it is attractive to use Bayesian estimation, and how to interpret properly the results. First,
A gentle introduction to Bayesian analysis : Applications to developmental research
van de Schoot, R.; Kaplan, D.; Denissen, J.J.A.; Asendorpf, J.B.; Neyer, F.J.; van Aken, M.A.G.
2014-01-01
Bayesian statistical methods are becoming ever more popular in applied and fundamental research. In this study a gentle introduction to Bayesian analysis is provided. It is shown under what circumstances it is attractive to use Bayesian estimation, and how to interpret properly the results. First,
Atmospheric and aerosol chemistry
International Nuclear Information System (INIS)
McNeill, V. Faye; Ariya, Parisa A.; McGill Univ. Montreal, QC
2014-01-01
This series presents critical reviews of the present position and future trends in modern chemical research. Short and concise reports on chemistry, each written by the world renowned experts. Still valid and useful after 5 or 10 years. More information as well as the electronic version of the whole content available at: springerlink.com. Christian George, Barbara D'Anna, Hartmut Herrmann, Christian Weller, Veronica Vaida, D. J. Donaldson, Thorsten Bartels-Rausch, Markus Ammann Emerging Areas in Atmospheric Photochemistry. Lisa Whalley, Daniel Stone, Dwayne Heard New Insights into the Tropospheric Oxidation of Isoprene: Combining Field Measurements, Laboratory Studies, Chemical Modelling and Quantum Theory. Neil M. Donahue, Allen L. Robinson, Erica R. Trump, Ilona Riipinen, Jesse H. Kroll Volatility and Aging of Atmospheric Organic Aerosol. P. A. Ariya, G. Kos, R. Mortazavi, E. D. Hudson, V. Kanthasamy, N. Eltouny, J. Sun, C. Wilde Bio-Organic Materials in the Atmosphere and Snow: Measurement and Characterization V. Faye McNeill, Neha Sareen, Allison N. Schwier Surface-Active Organics in Atmospheric Aerosols.
Atmospheric and aerosol chemistry
Energy Technology Data Exchange (ETDEWEB)
McNeill, V. Faye [Columbia Univ., New York, NY (United States). Dept. of Chemical Engineering; Ariya, Parisa A. (ed.) [McGill Univ. Montreal, QC (Canada). Dept. of Chemistry; McGill Univ. Montreal, QC (Canada). Dept. of Atmospheric and Oceanic Sciences
2014-09-01
This series presents critical reviews of the present position and future trends in modern chemical research. Short and concise reports on chemistry, each written by the world renowned experts. Still valid and useful after 5 or 10 years. More information as well as the electronic version of the whole content available at: springerlink.com. Christian George, Barbara D'Anna, Hartmut Herrmann, Christian Weller, Veronica Vaida, D. J. Donaldson, Thorsten Bartels-Rausch, Markus Ammann Emerging Areas in Atmospheric Photochemistry. Lisa Whalley, Daniel Stone, Dwayne Heard New Insights into the Tropospheric Oxidation of Isoprene: Combining Field Measurements, Laboratory Studies, Chemical Modelling and Quantum Theory. Neil M. Donahue, Allen L. Robinson, Erica R. Trump, Ilona Riipinen, Jesse H. Kroll Volatility and Aging of Atmospheric Organic Aerosol. P. A. Ariya, G. Kos, R. Mortazavi, E. D. Hudson, V. Kanthasamy, N. Eltouny, J. Sun, C. Wilde Bio-Organic Materials in the Atmosphere and Snow: Measurement and Characterization V. Faye McNeill, Neha Sareen, Allison N. Schwier Surface-Active Organics in Atmospheric Aerosols.
Preparatory studies for modelling steam condensation on soluble aerosols
International Nuclear Information System (INIS)
Dunbar, I.H.
1988-01-01
Of the fission products which would be released from the core of an LWR in the event of a severe accident, only the noble gases and possibly some of the iodine (depending on chemical form) are likely not to be in the form of aerosols when they reach the containment building atmosphere. Therefore in order to predict the extent of fission product retention on containment building internal structures, one needs to have a good understanding of aerosol deposition processes and of the factors which affect them. Following a severe accident in an LWR, a major component of the containment atmosphere will be steam. If the thermodynamic conditions allow condensation of this steam, this condensation is most likely to occur on the aerosol particles. A major component of the aerosol formed during the in-vessel release following a severe reactor accident will be fission product caesium. It is believed that much of this will enter the containment in the form of the hydroxide which has a great affinity for water, so particle growth due to steam condensation is likely to be a very important mechanism for retaining radioactive caesium within the containment builing. The author provides a systematic review of the basic chemical and physical issues which must be addressed if the phenomena are to be modelled accurately, and gives recommendations on how computer models of condensation onto soluble aerosols should be constructed. He proposes also to perform a systematic review of the existing literature and to perform small-scale thermodynamic experiments wherever important gaps in the data base are discovered
Atmospheric dispersion of sodium aerosol due to a sodium leak in a fast breeder reactor complex
International Nuclear Information System (INIS)
Punitha, G.; Sudha, A. Jasmin; Kasinathan, N.; Rajan, M.
2008-01-01
Liquid sodium at high temperatures (470 K to 825 K) is used as the primary and secondary coolant in Liquid Metal cooled Fast Breeder Reactors (LMFBR). In the event of a postulated sodium leak in the Steam Generator Building (SGB) of a LMFBR, sodium readily combusts in the ambient air, especially at temperatures above 523 K. Intense sodium fire results and sodium oxide fumes are released as sodium aerosols. Sodium oxides are readily converted to sodium hydroxide in air due to the presence of moisture in it. Hence, sodium aerosols are invariably in the form of particulate sodium hydroxide. These aerosols damage not only the equipment and instruments due to their corrosive nature but also pose health hazard to humans. Hence, it is essential to estimate the concentration of sodium aerosols within the plant boundary for a sodium leak event. The Gaussian Plume Dispersion Model can obtain the atmospheric dispersion of sodium aerosols in an open terrain. However, this model dose not give accurate results for dispersion in spaces close to the point of release and with buildings in between. The velocity field due to the wind is altered to a large extent by the intervening buildings and structures. Therefore, a detailed 3-D estimation of the velocity field and concentration has to be obtained through rigorous computational fluid dynamics (CFD) approach. PHOENICS code has been employed to determine concentration of sodium aerosols at various distances from the point of release. The dispersion studies have been carried out for the release of sodium aerosols at different elevations from the ground and for different wind directions. (author)
A Bayesian perspective on some replacement strategies
International Nuclear Information System (INIS)
Mazzuchi, Thomas A.; Soyer, Refik
1996-01-01
In this paper we present a Bayesian decision theoretic approach for determining optimal replacement strategies. This approach enables us to formally incorporate, express, and update our uncertainty when determining optimal replacement strategies. We develop relevant expressions for both the block replacement protocol with minimal repair and the age replacement protocol and illustrate the use of our approach with real data
Posterior Predictive Model Checking in Bayesian Networks
Crawford, Aaron
2014-01-01
This simulation study compared the utility of various discrepancy measures within a posterior predictive model checking (PPMC) framework for detecting different types of data-model misfit in multidimensional Bayesian network (BN) models. The investigated conditions were motivated by an applied research program utilizing an operational complex…
Sequential Bayesian technique: An alternative approach for ...
Indian Academy of Sciences (India)
This paper proposes a sequential Bayesian approach similar to Kalman ﬁlter for estimating reliability growth or decay of software. The main advantage of proposed method is that it shows the variation of the parameter over a time, as new failure data become available. The usefulness of the method is demonstrated with ...
Sequential Bayesian technique: An alternative approach for ...
Indian Academy of Sciences (India)
MS received 8 October 2007; revised 15 July 2008. Abstract. This paper proposes a sequential Bayesian approach similar to Kalman filter for estimating reliability growth or decay of software. The main advantage of proposed method is that it shows the variation of the parameter over a time, as new failure data become ...
Theory change and Bayesian statistical inference
Romeijn, Jan-Willem
2005-01-01
This paper addresses the problem that Bayesian statistical inference cannot accommodate theory change, and proposes a framework for dealing with such changes. It first presents a scheme for generating predictions from observations by means of hypotheses. An example shows how the hypotheses represent
Bayesian mixture models for partially verified data
DEFF Research Database (Denmark)
Kostoulas, Polychronis; Browne, William J.; Nielsen, Søren Saxmose
2013-01-01
Bayesian mixture models can be used to discriminate between the distributions of continuous test responses for different infection stages. These models are particularly useful in case of chronic infections with a long latent period, like Mycobacterium avium subsp. paratuberculosis (MAP) infection...
Non-Linear Approximation of Bayesian Update
Litvinenko, Alexander
2016-06-23
We develop a non-linear approximation of expensive Bayesian formula. This non-linear approximation is applied directly to Polynomial Chaos Coefficients. In this way, we avoid Monte Carlo sampling and sampling error. We can show that the famous Kalman Update formula is a particular case of this update.
Bayesian approach and application to operation safety
International Nuclear Information System (INIS)
Procaccia, H.; Suhner, M.Ch.
2003-01-01
The management of industrial risks requires the development of statistical and probabilistic analyses which use all the available convenient information in order to compensate the insufficient experience feedback in a domain where accidents and incidents remain too scarce to perform a classical statistical frequency analysis. The Bayesian decision approach is well adapted to this problem because it integrates both the expertise and the experience feedback. The domain of knowledge is widen, the forecasting study becomes possible and the decisions-remedial actions are strengthen thanks to risk-cost-benefit optimization analyzes. This book presents the bases of the Bayesian approach and its concrete applications in various industrial domains. After a mathematical presentation of the industrial operation safety concepts and of the Bayesian approach principles, this book treats of some of the problems that can be solved thanks to this approach: softwares reliability, controls linked with the equipments warranty, dynamical updating of databases, expertise modeling and weighting, Bayesian optimization in the domains of maintenance, quality control, tests and design of new equipments. A synthesis of the mathematical formulae used in this approach is given in conclusion. (J.S.)
Comparison between Fisherian and Bayesian approach to ...
African Journals Online (AJOL)
... of its simplicity and optimality properties is normally used for two group cases. However, Bayesian approach is found to be better than Fisher's approach because of its low misclassification error rate. Keywords: variance-covariance matrices, centroids, prior probability, mahalanobis distance, probability of misclassification ...
Neural network classification - A Bayesian interpretation
Wan, Eric A.
1990-01-01
The relationship between minimizing a mean squared error and finding the optimal Bayesian classifier is reviewed. This provides a theoretical interpretation for the process by which neural networks are used in classification. A number of confidence measures are proposed to evaluate the performance of the neural network classifier within a statistical framework.
Bayesian Estimation of Item Response Curves.
Tsutakawa, Robert K.; Lin, Hsin Ying
1986-01-01
Item response curves for a set of binary responses are studied from a Bayesian viewpoint of estimating the item parameters. For the two-parameter logistic model with normally distributed ability, restricted bivariate beta priors are used to illustrate the computation of the posterior mode via the EM algorithm. (Author/LMO)
Speech Segmentation Using Bayesian Autoregressive Changepoint Detector
Directory of Open Access Journals (Sweden)
P. Sovka
1998-12-01
Full Text Available This submission is devoted to the study of the Bayesian autoregressive changepoint detector (BCD and its use for speech segmentation. Results of the detector application to autoregressive signals as well as to real speech are given. BCD basic properties are described and discussed. The novel two-step algorithm consisting of cepstral analysis and BCD for automatic speech segmentation is suggested.
Bayesian networks: a combined tuning heuristic
Bolt, J.H.
2016-01-01
One of the issues in tuning an output probability of a Bayesian network by changing multiple parameters is the relative amount of the individual parameter changes. In an existing heuristic parameters are tied such that their changes induce locally a maximal change of the tuned probability. This
Exploiting structure in cooperative Bayesian games
Oliehoek, F.A.; Whiteson, S.; Spaan, M.T.J.; de Freitas, N.; Murphy, K.
2012-01-01
Cooperative Bayesian games (BGs) can model decision-making problems for teams of agents under imperfect information, but require space and computation time that is exponential in the number of agents. While agent independence has been used to mitigate these problems in perfect information settings,
BAYESIAN ESTIMATION OF THERMONUCLEAR REACTION RATES
Energy Technology Data Exchange (ETDEWEB)
Iliadis, C.; Anderson, K. S. [Department of Physics and Astronomy, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599-3255 (United States); Coc, A. [Centre de Sciences Nucléaires et de Sciences de la Matière (CSNSM), CNRS/IN2P3, Univ. Paris-Sud, Université Paris–Saclay, Bâtiment 104, F-91405 Orsay Campus (France); Timmes, F. X.; Starrfield, S., E-mail: iliadis@unc.edu [School of Earth and Space Exploration, Arizona State University, Tempe, AZ 85287-1504 (United States)
2016-11-01
The problem of estimating non-resonant astrophysical S -factors and thermonuclear reaction rates, based on measured nuclear cross sections, is of major interest for nuclear energy generation, neutrino physics, and element synthesis. Many different methods have been applied to this problem in the past, almost all of them based on traditional statistics. Bayesian methods, on the other hand, are now in widespread use in the physical sciences. In astronomy, for example, Bayesian statistics is applied to the observation of extrasolar planets, gravitational waves, and Type Ia supernovae. However, nuclear physics, in particular, has been slow to adopt Bayesian methods. We present astrophysical S -factors and reaction rates based on Bayesian statistics. We develop a framework that incorporates robust parameter estimation, systematic effects, and non-Gaussian uncertainties in a consistent manner. The method is applied to the reactions d(p, γ ){sup 3}He, {sup 3}He({sup 3}He,2p){sup 4}He, and {sup 3}He( α , γ ){sup 7}Be, important for deuterium burning, solar neutrinos, and Big Bang nucleosynthesis.
An Approximate Bayesian Fundamental Frequency Estimator
DEFF Research Database (Denmark)
Nielsen, Jesper Kjær; Christensen, Mads Græsbøll; Jensen, Søren Holdt
2012-01-01
Joint fundamental frequency and model order estimation is an important problem in several applications such as speech and music processing. In this paper, we develop an approximate estimation algorithm of these quantities using Bayesian inference. The inference about the fundamental frequency...
Erratum Bayesian and Dempster–Shafer fusion
Indian Academy of Sciences (India)
(1) The paper “Bayesian and Dempster–Shafer fusion” contains a mistake in Appendix A, although this has not affected anything in the body of the paper. On page 172, the authors state correctly that the matrix F is, in general, not square, but then in (A.22) they take its determinant. This confusion resulted because the ...
On local optima in learning bayesian networks
DEFF Research Database (Denmark)
Dalgaard, Jens; Kocka, Tomas; Pena, Jose
2003-01-01
This paper proposes and evaluates the k-greedy equivalence search algorithm (KES) for learning Bayesian networks (BNs) from complete data. The main characteristic of KES is that it allows a trade-off between greediness and randomness, thus exploring different good local optima. When greediness...
Comprehension and computation in Bayesian problem solving
Directory of Open Access Journals (Sweden)
Eric D. Johnson
2015-07-01
Full Text Available Humans have long been characterized as poor probabilistic reasoners when presented with explicit numerical information. Bayesian word problems provide a well-known example of this, where even highly educated and cognitively skilled individuals fail to adhere to mathematical norms. It is widely agreed that natural frequencies can facilitate Bayesian reasoning relative to normalized formats (e.g. probabilities, percentages, both by clarifying logical set-subset relations and by simplifying numerical calculations. Nevertheless, between-study performance on transparent Bayesian problems varies widely, and generally remains rather unimpressive. We suggest there has been an over-focus on this representational facilitator (i.e. transparent problem structures at the expense of the specific logical and numerical processing requirements and the corresponding individual abilities and skills necessary for providing Bayesian-like output given specific verbal and numerical input. We further suggest that understanding this task-individual pair could benefit from considerations from the literature on mathematical cognition, which emphasizes text comprehension and problem solving, along with contributions of online executive working memory, metacognitive regulation, and relevant stored knowledge and skills. We conclude by offering avenues for future research aimed at identifying the stages in problem solving at which correct versus incorrect reasoners depart, and how individual difference might influence this time point.
Combining morphological analysis and Bayesian networks for ...
African Journals Online (AJOL)
... how these two computer aided methods may be combined to better facilitate modelling procedures. A simple example is presented, concerning a recent application in the field of environmental decision support. Keywords: Morphological analysis, Bayesian networks, strategic decision support. ORiON Vol. 23 (2) 2007: pp.
Approximate Bayesian evaluations of measurement uncertainty
Possolo, Antonio; Bodnar, Olha
2018-04-01
The Guide to the Expression of Uncertainty in Measurement (GUM) includes formulas that produce an estimate of a scalar output quantity that is a function of several input quantities, and an approximate evaluation of the associated standard uncertainty. This contribution presents approximate, Bayesian counterparts of those formulas for the case where the output quantity is a parameter of the joint probability distribution of the input quantities, also taking into account any information about the value of the output quantity available prior to measurement expressed in the form of a probability distribution on the set of possible values for the measurand. The approximate Bayesian estimates and uncertainty evaluations that we present have a long history and illustrious pedigree, and provide sufficiently accurate approximations in many applications, yet are very easy to implement in practice. Differently from exact Bayesian estimates, which involve either (analytical or numerical) integrations, or Markov Chain Monte Carlo sampling, the approximations that we describe involve only numerical optimization and simple algebra. Therefore, they make Bayesian methods widely accessible to metrologists. We illustrate the application of the proposed techniques in several instances of measurement: isotopic ratio of silver in a commercial silver nitrate; odds of cryptosporidiosis in AIDS patients; height of a manometer column; mass fraction of chromium in a reference material; and potential-difference in a Zener voltage standard.
Bayesian Meta-Analysis of Coefficient Alpha
Brannick, Michael T.; Zhang, Nanhua
2013-01-01
The current paper describes and illustrates a Bayesian approach to the meta-analysis of coefficient alpha. Alpha is the most commonly used estimate of the reliability or consistency (freedom from measurement error) for educational and psychological measures. The conventional approach to meta-analysis uses inverse variance weights to combine…
Theory Change and Bayesian Statistical Inference
Romeyn, Jan-Willem
2008-01-01
This paper addresses the problem that Bayesian statistical inference cannot accommodate theory change, and proposes a framework for dealing with such changes. It first presents a scheme for generating predictions from observations by means of hypotheses. An example shows how the hypotheses represent
Heuristics as Bayesian inference under extreme priors.
Parpart, Paula; Jones, Matt; Love, Bradley C
2018-05-01
Simple heuristics are often regarded as tractable decision strategies because they ignore a great deal of information in the input data. One puzzle is why heuristics can outperform full-information models, such as linear regression, which make full use of the available information. These "less-is-more" effects, in which a relatively simpler model outperforms a more complex model, are prevalent throughout cognitive science, and are frequently argued to demonstrate an inherent advantage of simplifying computation or ignoring information. In contrast, we show at the computational level (where algorithmic restrictions are set aside) that it is never optimal to discard information. Through a formal Bayesian analysis, we prove that popular heuristics, such as tallying and take-the-best, are formally equivalent to Bayesian inference under the limit of infinitely strong priors. Varying the strength of the prior yields a continuum of Bayesian models with the heuristics at one end and ordinary regression at the other. Critically, intermediate models perform better across all our simulations, suggesting that down-weighting information with the appropriate prior is preferable to entirely ignoring it. Rather than because of their simplicity, our analyses suggest heuristics perform well because they implement strong priors that approximate the actual structure of the environment. We end by considering how new heuristics could be derived by infinitely strengthening the priors of other Bayesian models. These formal results have implications for work in psychology, machine learning and economics. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.
Default Bayesian Estimation of the Fundamental Frequency
DEFF Research Database (Denmark)
Nielsen, Jesper Kjær; Christensen, Mads Græsbøll; Jensen, Søren Holdt
2013-01-01
Joint fundamental frequency and model order esti- mation is an important problem in several applications. In this paper, a default estimation algorithm based on a minimum of prior information is presented. The algorithm is developed in a Bayesian framework, and it can be applied to both real...
Error probabilities in default Bayesian hypothesis testing
Gu, Xin; Hoijtink, Herbert; Mulder, J,
2016-01-01
This paper investigates the classical type I and type II error probabilities of default Bayes factors for a Bayesian t test. Default Bayes factors quantify the relative evidence between the null hypothesis and the unrestricted alternative hypothesis without needing to specify prior distributions for
Forecasting nuclear power supply with Bayesian autoregression
International Nuclear Information System (INIS)
Beck, R.; Solow, J.L.
1994-01-01
We explore the possibility of forecasting the quarterly US generation of electricity from nuclear power using a Bayesian autoregression model. In terms of forecasting accuracy, this approach compares favorably with both the Department of Energy's current forecasting methodology and their more recent efforts using ARIMA models, and it is extremely easy and inexpensive to implement. (author)
Bayesian Benefits for the Pragmatic Researcher
Wagenmakers, E.-J.; Morey, R.D.; Lee, M.D.
2016-01-01
The practical advantages of Bayesian inference are demonstrated here through two concrete examples. In the first example, we wish to learn about a criminal’s IQ: a problem of parameter estimation. In the second example, we wish to quantify and track support in favor of the null hypothesis that Adam
Bayesian evaluation of inequality constrained hypotheses
Gu, X.; Mulder, J.; Deković, M.; Hoijtink, H.
2014-01-01
Bayesian evaluation of inequality constrained hypotheses enables researchers to investigate their expectations with respect to the structure among model parameters. This article proposes an approximate Bayes procedure that can be used for the selection of the best of a set of inequality constrained
Bayesian calibration for forensic age estimation.
Ferrante, Luigi; Skrami, Edlira; Gesuita, Rosaria; Cameriere, Roberto
2015-05-10
Forensic medicine is increasingly called upon to assess the age of individuals. Forensic age estimation is mostly required in relation to illegal immigration and identification of bodies or skeletal remains. A variety of age estimation methods are based on dental samples and use of regression models, where the age of an individual is predicted by morphological tooth changes that take place over time. From the medico-legal point of view, regression models, with age as the dependent random variable entail that age tends to be overestimated in the young and underestimated in the old. To overcome this bias, we describe a new full Bayesian calibration method (asymmetric Laplace Bayesian calibration) for forensic age estimation that uses asymmetric Laplace distribution as the probability model. The method was compared with three existing approaches (two Bayesian and a classical method) using simulated data. Although its accuracy was comparable with that of the other methods, the asymmetric Laplace Bayesian calibration appears to be significantly more reliable and robust in case of misspecification of the probability model. The proposed method was also applied to a real dataset of values of the pulp chamber of the right lower premolar measured on x-ray scans of individuals of known age. Copyright © 2015 John Wiley & Sons, Ltd.
Low Complexity Bayesian Single Channel Source Separation
DEFF Research Database (Denmark)
Beierholm, Thomas; Pedersen, Brian Dam; Winther, Ole
2004-01-01
We propose a simple Bayesian model for performing single channel speech separation using factorized source priors in a sliding window linearly transformed domain. Using a one dimensional mixture of Gaussians to model each band source leads to fast tractable inference for the source signals. Simul...
Evidence Estimation for Bayesian Partially Observed MRFs
Chen, Y.; Welling, M.
2013-01-01
Bayesian estimation in Markov random fields is very hard due to the intractability of the partition function. The introduction of hidden units makes the situation even worse due to the presence of potentially very many modes in the posterior distribution. For the first time we propose a
Quantifying Registration Uncertainty With Sparse Bayesian Modelling.
Le Folgoc, Loic; Delingette, Herve; Criminisi, Antonio; Ayache, Nicholas
2017-02-01
We investigate uncertainty quantification under a sparse Bayesian model of medical image registration. Bayesian modelling has proven powerful to automate the tuning of registration hyperparameters, such as the trade-off between the data and regularization functionals. Sparsity-inducing priors have recently been used to render the parametrization itself adaptive and data-driven. The sparse prior on transformation parameters effectively favors the use of coarse basis functions to capture the global trends in the visible motion while finer, highly localized bases are introduced only in the presence of coherent image information and motion. In earlier work, approximate inference under the sparse Bayesian model was tackled in an efficient Variational Bayes (VB) framework. In this paper we are interested in the theoretical and empirical quality of uncertainty estimates derived under this approximate scheme vs. under the exact model. We implement an (asymptotically) exact inference scheme based on reversible jump Markov Chain Monte Carlo (MCMC) sampling to characterize the posterior distribution of the transformation and compare the predictions of the VB and MCMC based methods. The true posterior distribution under the sparse Bayesian model is found to be meaningful: orders of magnitude for the estimated uncertainty are quantitatively reasonable, the uncertainty is higher in textureless regions and lower in the direction of strong intensity gradients.
Adaptive bayesian analysis for binomial proportions
CSIR Research Space (South Africa)
Das, Sonali
2008-10-01
Full Text Available The authors consider the problem of statistical inference of binomial proportions for non-matched, correlated samples, under the Bayesian framework. Such inference can arise when the same group is observed at a different number of times with the aim...
Inverse Problems in a Bayesian Setting
Matthies, Hermann G.
2016-02-13
In a Bayesian setting, inverse problems and uncertainty quantification (UQ)—the propagation of uncertainty through a computational (forward) model—are strongly connected. In the form of conditional expectation the Bayesian update becomes computationally attractive. We give a detailed account of this approach via conditional approximation, various approximations, and the construction of filters. Together with a functional or spectral approach for the forward UQ there is no need for time-consuming and slowly convergent Monte Carlo sampling. The developed sampling-free non-linear Bayesian update in form of a filter is derived from the variational problem associated with conditional expectation. This formulation in general calls for further discretisation to make the computation possible, and we choose a polynomial approximation. After giving details on the actual computation in the framework of functional or spectral approximations, we demonstrate the workings of the algorithm on a number of examples of increasing complexity. At last, we compare the linear and nonlinear Bayesian update in form of a filter on some examples.
Jones, Matt; Love, Bradley C
2011-08-01
The prominence of Bayesian modeling of cognition has increased recently largely because of mathematical advances in specifying and deriving predictions from complex probabilistic models. Much of this research aims to demonstrate that cognitive behavior can be explained from rational principles alone, without recourse to psychological or neurological processes and representations. We note commonalities between this rational approach and other movements in psychology - namely, Behaviorism and evolutionary psychology - that set aside mechanistic explanations or make use of optimality assumptions. Through these comparisons, we identify a number of challenges that limit the rational program's potential contribution to psychological theory. Specifically, rational Bayesian models are significantly unconstrained, both because they are uninformed by a wide range of process-level data and because their assumptions about the environment are generally not grounded in empirical measurement. The psychological implications of most Bayesian models are also unclear. Bayesian inference itself is conceptually trivial, but strong assumptions are often embedded in the hypothesis sets and the approximation algorithms used to derive model predictions, without a clear delineation between psychological commitments and implementational details. Comparing multiple Bayesian models of the same task is rare, as is the realization that many Bayesian models recapitulate existing (mechanistic level) theories. Despite the expressive power of current Bayesian models, we argue they must be developed in conjunction with mechanistic considerations to offer substantive explanations of cognition. We lay out several means for such an integration, which take into account the representations on which Bayesian inference operates, as well as the algorithms and heuristics that carry it out. We argue this unification will better facilitate lasting contributions to psychological theory, avoiding the pitfalls
CATS Cloud and Aerosol Level 2 Heritage Edition Data Products.
Rodier, S. D.; Vaughan, M.; Yorks, J. E.; Palm, S. P.; Selmer, P. A.; Hlavka, D. L.; McGill, M. J.; Trepte, C. R.
2017-12-01
The Cloud-Aerosol Transport System (CATS) instrument was developed at NASA's Goddard Space Flight Center (GSFC) and deployed to the International Space Station (ISS) in January 2015. The CATS elastic backscatter lidars have been operating continuously in one of two science modes since February 2015. One of the primary science objectives of CATS is to continue the CALIPSO aerosol and cloud profile data record to provide continuity of lidar climate observations during the transition from CALIPSO to EarthCARE. To accomplish this, the CATS project at NASA's Goddard Space Flight Center (GSFC) and the CALIPSO project at NASA's Langley Research Center (LaRC) closely collaborated to develop and deliver a full suite of CALIPSO-like level 2 data products using the latest version of the CALIPSO level 2 Version 4 algorithms for the CATS data acquired while operating in science mode 1 (Multi-beam backscatter detection at 1064 and 532 nm, with depolarization measurement at both wavelengths). In this work, we present the current status of the CATS Heritage (i.e. CALIPSO-like) level 2 data products derived from the recent released CATS Level 1B V2-08 data. Extensive comparisons are performed between the three data sets (CALIPSO V4.10 Level 2, CATS Level 2 Operational V2-00 and CATS Heritage V1.00) for cloud and aerosol measurements (e.g., cloud-top height cloud-phase, cloud-layer occurrence frequency and cloud-aerosol discrimination) along the ISS path. In addition, global comparisons (between 52°S and 52°N) of aerosol extinction profiles derived from the CATS Level 2 Operational products and CALIOP V4 Level 2 products are presented. Comparisons of aerosol optical depths retrieved from active sensors (CATS and CALIOP) and passive sensors (MODIS) will provide context for the extinction profile comparisons.
The GRAPE aerosol retrieval algorithm
Directory of Open Access Journals (Sweden)
G. E. Thomas
2009-11-01
Full Text Available The aerosol component of the Oxford-Rutherford Aerosol and Cloud (ORAC combined cloud and aerosol retrieval scheme is described and the theoretical performance of the algorithm is analysed. ORAC is an optimal estimation retrieval scheme for deriving cloud and aerosol properties from measurements made by imaging satellite radiometers and, when applied to cloud free radiances, provides estimates of aerosol optical depth at a wavelength of 550 nm, aerosol effective radius and surface reflectance at 550 nm. The aerosol retrieval component of ORAC has several incarnations – this paper addresses the version which operates in conjunction with the cloud retrieval component of ORAC (described by Watts et al., 1998, as applied in producing the Global Retrieval of ATSR Cloud Parameters and Evaluation (GRAPE data-set.
The algorithm is described in detail and its performance examined. This includes a discussion of errors resulting from the formulation of the forward model, sensitivity of the retrieval to the measurements and a priori constraints, and errors resulting from assumptions made about the atmospheric/surface state.
Davies, Andrew J; Hope, Max J
2015-07-15
Contingency plans are essential in guiding the response to marine oil spills. However, they are written before the pollution event occurs so must contain some degree of assumption and prediction and hence may be unsuitable for a real incident when it occurs. The use of Bayesian networks in ecology, environmental management, oil spill contingency planning and post-incident analysis is reviewed and analysed to establish their suitability for use as real-time environmental decision support systems during an oil spill response. It is demonstrated that Bayesian networks are appropriate for facilitating the re-assessment and re-validation of contingency plans following pollutant release, thus helping ensure that the optimum response strategy is adopted. This can minimise the possibility of sub-optimal response strategies causing additional environmental and socioeconomic damage beyond the original pollution event. Copyright © 2015 Elsevier Ltd. All rights reserved.
Learning Local Components to Understand Large Bayesian Networks
DEFF Research Database (Denmark)
Zeng, Yifeng; Xiang, Yanping; Cordero, Jorge
2009-01-01
Bayesian networks are known for providing an intuitive and compact representation of probabilistic information and allowing the creation of models over a large and complex domain. Bayesian learning and reasoning are nontrivial for a large Bayesian network. In parallel, it is a tough job for users...... in a domain. We evaluate its performance on three benchmark Bayesian networks and provide results in support. We further show that the learned components may represent local knowledge more precisely in comparison to the full Bayesian networks when working with a small amount of data....
Bayesian Correlation Analysis for Sequence Count Data.
Directory of Open Access Journals (Sweden)
Daniel Sánchez-Taltavull
Full Text Available Evaluating the similarity of different measured variables is a fundamental task of statistics, and a key part of many bioinformatics algorithms. Here we propose a Bayesian scheme for estimating the correlation between different entities' measurements based on high-throughput sequencing data. These entities could be different genes or miRNAs whose expression is measured by RNA-seq, different transcription factors or histone marks whose expression is measured by ChIP-seq, or even combinations of different types of entities. Our Bayesian formulation accounts for both measured signal levels and uncertainty in those levels, due to varying sequencing depth in different experiments and to varying absolute levels of individual entities, both of which affect the precision of the measurements. In comparison with a traditional Pearson correlation analysis, we show that our Bayesian correlation analysis retains high correlations when measurement confidence is high, but suppresses correlations when measurement confidence is low-especially for entities with low signal levels. In addition, we consider the influence of priors on the Bayesian correlation estimate. Perhaps surprisingly, we show that naive, uniform priors on entities' signal levels can lead to highly biased correlation estimates, particularly when different experiments have widely varying sequencing depths. However, we propose two alternative priors that provably mitigate this problem. We also prove that, like traditional Pearson correlation, our Bayesian correlation calculation constitutes a kernel in the machine learning sense, and thus can be used as a similarity measure in any kernel-based machine learning algorithm. We demonstrate our approach on two RNA-seq datasets and one miRNA-seq dataset.
Instrumentation for tropospheric aerosol characterization
Energy Technology Data Exchange (ETDEWEB)
Shi, Z.; Young, S.E.; Becker, C.H.; Coggiola, M.J. [SRI International, Menlo Park, CA (United States); Wollnik, H. [Giessen Univ. (Germany)
1997-12-31
A new instrument has been developed that determines the abundance, size distribution, and chemical composition of tropospheric and lower stratospheric aerosols with diameters down to 0.2 {mu}m. In addition to aerosol characterization, the instrument also monitors the chemical composition of the ambient gas. More than 25.000 aerosol particle mass spectra were recorded during the NASA-sponsored Subsonic Aircraft: Contrail and Cloud Effects Special Study (SUCCESS) field program using NASA`s DC-8 research aircraft. (author) 7 refs.
Energy Technology Data Exchange (ETDEWEB)
Venzie, J. [Savannah River Site (SRS), Aiken, SC (United States). Savannah River National Lab. (SRNL)
2015-10-13
The eDPS Aerosol Collection project studies the fundamental physics of electrostatic aerosol collection for national security applications. The interpretation of aerosol data requires understanding and correcting for biases introduced from particle genesis through collection and analysis. The research and development undertaken in this project provides the basis for both the statistical correction of existing equipment and techniques; as well as, the development of new collectors and analytical techniques designed to minimize unwanted biases while improving the efficiency of locating and measuring individual particles of interest.
Optical properties and source analysis of aerosols over a desert area in Dunhuang, Northwest china
Ma, Yongjing; Xin, Jinyuan; Ma, Yining; Kong, Lingbin; Zhang, Kequan; Zhang, Wenyu; Wang, Yuesi; Wang, Xiuqin; Zhu, Yongfeng
2017-08-01
Aerosol observational data for 2012 obtained from Dunhuang Station of CARE-China (Campaign on Atmospheric Aerosol Research Network of China) were analyzed to achieve in-depth knowledge of aerosol optical properties over Dunhuang region. The results showed that the annual average aerosol optical depth (AOD) at 500 nm was 0.32±0.06, and the Ångström exponent ( α) was 0.73 ± 0.27. Aerosol optical properties revealed significant seasonal characteristics. Frequent sandstorms in MAM (March-April-May) resulted in the seasonal maximum AOD, 0.41 ± 0.04, and a relatively smaller α value, 0.44±0.04. The tourism seasons, JJA (June-July-August) and SON (September-October-November) coincide with serious emissions of small anthropogenic aerosols. While in DJF (December-January-February), the composition of the atmosphere was a mixture of dust particles and polluted aerosols released by domestic heating; the average AOD and α were 0.29 ± 0.02 and 0.66 ± 0.17, respectively. Different air masses exhibited different degrees of influence on the aerosol concentration over Dunhuang in different seasons. During MAM, ranges of AOD (0.11-1.18) and α (0.06-0.82) were the largest under the dust influence of northwest-short-distance air mass in the four trajectories. Urban aerosols transported by northwest-short-distance air mass accounted for a very large proportion in JJA and the mixed aerosols observed in SON were mainly conveyed by air masses from the west. In DJF, the similar ranges of AOD and α under the three air mass demonstrated the analogous diffusion effects on regional pollutants over Dunhuang.
Energy Technology Data Exchange (ETDEWEB)
Lerner, Chad A.; Rutagarama, Pierrot; Ahmad, Tanveer; Sundar, Isaac K.; Elder, Alison; Rahman, Irfan, E-mail: irfan_rahman@urmc.rochester.edu
2016-09-02
Oxidants or nanoparticles have recently been identified as constituents of aerosols released from various styles of electronic cigarettes (E-cigs). Cells in the lung may be directly exposed to these constituents and harbor reactive properties capable of incurring acute cell injury. Our results show mitochondria are sensitive to both E-cig aerosols and aerosol containing copper nanoparticles when exposed to human lung fibroblasts (HFL-1) using an Air-Liquid Interface culture system, evident by elevated levels of mitochondrial ROS (mtROS). Increased mtROS after aerosol exposure is associated with reduced stability of OxPhos electron transport chain (ETC) complex IV subunit and nuclear DNA fragmentation. Increased levels of IL-8 and IL-6 in HFL-1 conditioned media were also observed. These findings reveal both mitochondrial, genotoxic, and inflammatory stresses are features of direct cell exposure to E-cig aerosols which are ensued by inflammatory duress, raising a concern on deleterious effect of vaping. - Graphical abstract: Oxidants and possibly reactive properties of metal particles in E-cig aerosols impart mitochondrial oxidative stress and DNA damage. These biological effects accompany inflammatory response which may raise concern regarding long term E-cig use. Mitochondria may be particularly sensitive to reactive properties of E-cig aerosols in addition to the potential for them to induce genotoxic stress by generating increased ROS. - Highlights: • Mitochondria are sensitive to both E-cig aerosols and metal nanoparticles. • Increased mtROS by E-cig aerosol is associated with disrupted mitochondrial energy. • E-cig causes nuclear DNA fragmentation. • E-cig aerosols induce pro-inflammatory response in human fibroblasts.
National Aeronautics and Space Administration — The Aura Ozone Monitoring Instrument level-2 near UV Aerosol data product 'OMAERUV', recently re-processed using an enhanced algorithm, is now released (April 2012)...
OMI/Aura Near UV Aerosol Optical Depth and Single Scattering Albedo 1-orbit L2 Swath 13x24 km V003
National Aeronautics and Space Administration — The OMI/Aura level-2 near UV Aerosol data product 'OMAERUV', recently re-processed using an enhanced algorithm, is now released (April 2012) to the public. The data...
National Aeronautics and Space Administration — The OMI/Aura level-2 near UV Aerosol data product 'OMAERUV', recently re-processed using an enhanced algorithm, is now released (April 2012) to the public. The data...
Bayesian structural equation modeling in sport and exercise psychology.
Stenling, Andreas; Ivarsson, Andreas; Johnson, Urban; Lindwall, Magnus
2015-08-01
Bayesian statistics is on the rise in mainstream psychology, but applications in sport and exercise psychology research are scarce. In this article, the foundations of Bayesian analysis are introduced, and we will illustrate how to apply Bayesian structural equation modeling in a sport and exercise psychology setting. More specifically, we contrasted a confirmatory factor analysis on the Sport Motivation Scale II estimated with the most commonly used estimator, maximum likelihood, and a Bayesian approach with weakly informative priors for cross-loadings and correlated residuals. The results indicated that the model with Bayesian estimation and weakly informative priors provided a good fit to the data, whereas the model estimated with a maximum likelihood estimator did not produce a well-fitting model. The reasons for this discrepancy between maximum likelihood and Bayesian estimation are discussed as well as potential advantages and caveats with the Bayesian approach.
Aerosol Transmission of Filoviruses
Directory of Open Access Journals (Sweden)
Berhanu Mekibib
2016-05-01
Full Text Available Filoviruses have become a worldwide public health concern because of their potential for introductions into non-endemic countries through international travel and the international transport of infected animals or animal products. Since it was first identified in 1976, in the Democratic Republic of Congo (formerly Zaire and Sudan, the 2013–2015 western African Ebola virus disease (EVD outbreak is the largest, both by number of cases and geographical extension, and deadliest, recorded so far in medical history. The source of ebolaviruses for human index case(s in most outbreaks is presumptively associated with handling of bush meat or contact with fruit bats. Transmission among humans occurs easily when a person comes in contact with contaminated body fluids of patients, but our understanding of other transmission routes is still fragmentary. This review deals with the controversial issue of aerosol transmission of filoviruses.
2002-01-01
This Sea-viewing Wide Field-of-view Sensor (SeaWiFS) image of eastern Asia from October 14, 2001, shows large amounts of aerosol in the air. A few possible point sources of smoke, probably fires, are visible north of the Amur River at the very top of the image. One of the larger of these plumes can be seen down river of the confluence of the Songhua and Amur rivers. At lower left, the Yangtze River plume in the East China Sea is also very prominent. Sediment suspended in the ocean water is quite brown near the shore, but becomes much greener as it diffuses into the water. The increasing greenness of the river plume is probably an indication of enhanced phytoplankton growth driven by the nutrients in the river runoff. Image courtesy the SeaWiFS Project, NASA/Goddard Space Flight Center, and ORBIMAGE
Stratospheric aerosol geoengineering
Energy Technology Data Exchange (ETDEWEB)
Robock, Alan [Department of Environmental Sciences, Rutgers University, 14 College Farm Road, New Brunswick, NJ 08901 (United States)
2015-03-30
The Geoengineering Model Intercomparison Project, conducting climate model experiments with standard stratospheric aerosol injection scenarios, has found that insolation reduction could keep the global average temperature constant, but global average precipitation would reduce, particularly in summer monsoon regions around the world. Temperature changes would also not be uniform; the tropics would cool, but high latitudes would warm, with continuing, but reduced sea ice and ice sheet melting. Temperature extremes would still increase, but not as much as without geoengineering. If geoengineering were halted all at once, there would be rapid temperature and precipitation increases at 5–10 times the rates from gradual global warming. The prospect of geoengineering working may reduce the current drive toward reducing greenhouse gas emissions, and there are concerns about commercial or military control. Because geoengineering cannot safely address climate change, global efforts to reduce greenhouse gas emissions and to adapt are crucial to address anthropogenic global warming.
Stratospheric aerosol geoengineering
International Nuclear Information System (INIS)
Robock, Alan
2015-01-01
The Geoengineering Model Intercomparison Project, conducting climate model experiments with standard stratospheric aerosol injection scenarios, has found that insolation reduction could keep the global average temperature constant, but global average precipitation would reduce, particularly in summer monsoon regions around the world. Temperature changes would also not be uniform; the tropics would cool, but high latitudes would warm, with continuing, but reduced sea ice and ice sheet melting. Temperature extremes would still increase, but not as much as without geoengineering. If geoengineering were halted all at once, there would be rapid temperature and precipitation increases at 5–10 times the rates from gradual global warming. The prospect of geoengineering working may reduce the current drive toward reducing greenhouse gas emissions, and there are concerns about commercial or military control. Because geoengineering cannot safely address climate change, global efforts to reduce greenhouse gas emissions and to adapt are crucial to address anthropogenic global warming
Aerosol Transmission of Filoviruses.
Mekibib, Berhanu; Ariën, Kevin K
2016-05-23
Filoviruses have become a worldwide public health concern because of their potential for introductions into non-endemic countries through international travel and the international transport of infected animals or animal products. Since it was first identified in 1976, in the Democratic Republic of Congo (formerly Zaire) and Sudan, the 2013-2015 western African Ebola virus disease (EVD) outbreak is the largest, both by number of cases and geographical extension, and deadliest, recorded so far in medical history. The source of ebolaviruses for human index case(s) in most outbreaks is presumptively associated with handling of bush meat or contact with fruit bats. Transmission among humans occurs easily when a person comes in contact with contaminated body fluids of patients, but our understanding of other transmission routes is still fragmentary. This review deals with the controversial issue of aerosol transmission of filoviruses.
Redemann, J.; Livingston, J.; Shinozuka, Y.; Kacenelenbogen, M.; Russell, P.; LeBlanc, S.; Vaughan, M.; Ferrare, R.; Hostetler, C.; Rogers, R.;
2014-01-01
We have developed a technique for combining CALIOP aerosol backscatter, MODIS spectral AOD (aerosol optical depth), and OMI AAOD (absorption aerosol optical depth) retrievals for the purpose of estimating full spectral sets of aerosol radiative properties, and ultimately for calculating the 3-D distribution of direct aerosol radiative forcing. We present results using one year of data collected in 2007 and show comparisons of the aerosol radiative property estimates to collocated AERONET retrievals. Use of the recently released MODIS Collection 6 data for aerosol optical depths derived with the dark target and deep blue algorithms has extended the coverage of the multi-sensor estimates towards higher latitudes. We compare the spatio-temporal distribution of our multi-sensor aerosol retrievals and calculations of seasonal clear-sky aerosol radiative forcing based on the aerosol retrievals to values derived from four models that participated in the latest AeroCom model intercomparison initiative. We find significant inter-model differences, in particular for the aerosol single scattering albedo, which can be evaluated using the multi-sensor A-Train retrievals. We discuss the major challenges that exist in extending our clear-sky results to all-sky conditions. On the basis of comparisons to suborbital measurements, we present some of the limitations of the MODIS and CALIOP retrievals in the presence of adjacent or underlying clouds. Strategies for meeting these challenges are discussed.
Direct Aerosol Radiative Forcing from Combined A-Train Observations - Preliminary Comparisons with AeroCom Models and Pathways to Observationally Based All-sky Estimates
Redemann, J.; Livingston, J. M.; Shinozuka, Y.; Kacenelenbogen, M. S.; Russell, P. B.; LeBlanc, S. E.; Vaughan, M.; Ferrare, R. A.; Hostetler, C. A.; Rogers, R. R.; Burton, S. P.; Torres, O.; Remer, L. A.; Stier, P.; Schutgens, N.
2014-12-01
We describe a technique for combining CALIOP aerosol backscatter, MODIS spectral AOD (aerosol optical depth), and OMI AAOD (absorption aerosol optical depth) retrievals for the purpose of estimating full spectral sets of aerosol radiative properties, and ultimately for calculating the 3-D distribution of direct aerosol radiative forcing. We present results using one year of data collected in 2007 and show comparisons of the aerosol radiative property estimates to collocated AERONET retrievals. Use of the recently released MODIS Collection 6 data for aerosol optical depths derived with the dark target and deep blue algorithms has extended the coverage of the multi-sensor estimates towards higher latitudes. Initial calculations of seasonal clear-sky aerosol radiative forcing based on our multi-sensor aerosol retrievals compare well with over-ocean and top of the atmosphere IPCC-2007 model-based results, and with more recent assessments in the "Climate Change Science Program Report: Atmospheric Aerosol Properties and Climate Impacts" (2009). For the first time, we present comparisons of our multi-sensor aerosol direct radiative forcing estimates to values derived from a subset of models that participated in the latest AeroCom initiative. We discuss the major challenges that exist in extending our clear-sky results to all-sky conditions. On the basis of comparisons to suborbital measurements, we present some of the limitations of the MODIS and CALIOP retrievals in the presence of adjacent or underlying clouds. Strategies for meeting these challenges are discussed.
Black carbon aerosols and the third polar ice cap
Energy Technology Data Exchange (ETDEWEB)
Menon, Surabi; Koch, Dorothy; Beig, Gufran; Sahu, Saroj; Fasullo, John; Orlikowski, Daniel
2010-04-15
Recent thinning of glaciers over the Himalayas (sometimes referred to as the third polar region) have raised concern on future water supplies since these glaciers supply water to large river systems that support millions of people inhabiting the surrounding areas. Black carbon (BC) aerosols, released from incomplete combustion, have been increasingly implicated as causing large changes in the hydrology and radiative forcing over Asia and its deposition on snow is thought to increase snow melt. In India BC emissions from biofuel combustion is highly prevalent and compared to other regions, BC aerosol amounts are high. Here, we quantify the impact of BC aerosols on snow cover and precipitation from 1990 to 2010 over the Indian subcontinental region using two different BC emission inventories. New estimates indicate that Indian BC emissions from coal and biofuel are large and transport is expected to expand rapidly in coming years. We show that over the Himalayas, from 1990 to 2000, simulated snow/ice cover decreases by {approx}0.9% due to aerosols. The contribution of the enhanced Indian BC to this decline is {approx}36%, similar to that simulated for 2000 to 2010. Spatial patterns of modeled changes in snow cover and precipitation are similar to observations (from 1990 to 2000), and are mainly obtained with the newer BC estimates.
Aerosol Inlet Characterization Experiment Report
Energy Technology Data Exchange (ETDEWEB)
Bullard, Robert L. [Brookhaven National Lab. (BNL), Upton, NY (United States); Kuang, Chongai [Brookhaven National Lab. (BNL), Upton, NY (United States); Uin, Janek [Brookhaven National Lab. (BNL), Upton, NY (United States); Smith, Scott [Brookhaven National Lab. (BNL), Upton, NY (United States); Springston, Stephen R. [Brookhaven National Lab. (BNL), Upton, NY (United States)
2017-05-01
The U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Climate Research Facility Aerosol Observation System inlet stack was characterized for particle penetration efficiency from 10 nm to 20 μm in diameter using duplicate scanning mobility particle sizers (10 nm-450 nm), ultra-high-sensitivity aerosol spectrometers (60 nm-μm), and aerodynamic particle sizers (0.5 μm-20 μm). Results show good model-measurement agreement and unit transmission efficiency of aerosols from 10 nm to 4 μm in diameter. Large uncertainties in the measured transmission efficiency exist above 4 μm due to low ambient aerosol signal in that size range.
Earth Observatory Aerosol Optical Depth
National Aeronautics and Space Administration — Tiny solid and liquid particles suspended in the atmosphere are called aerosols. Windblown dust, sea salts, volcanic ash, smoke from wildfires, and pollution from...
Aerosol Size Distributions In Auckland.
Czech Academy of Sciences Publication Activity Database
Coulson, G.; Olivares, G.; Talbot, Nicholas
2016-01-01
Roč. 50, č. 1 (2016), s. 23-28 E-ISSN 1836-5876 Institutional support: RVO:67985858 Keywords : aerosol size distribution * particle number concentration * roadside Subject RIV: CF - Physical ; Theoretical Chemistry
Aerosol model selection and uncertainty modelling by adaptive MCMC technique
Directory of Open Access Journals (Sweden)
M. Laine
2008-12-01
Full Text Available We present a new technique for model selection problem in atmospheric remote sensing. The technique is based on Monte Carlo sampling and it allows model selection, calculation of model posterior probabilities and model averaging in Bayesian way.
The algorithm developed here is called Adaptive Automatic Reversible Jump Markov chain Monte Carlo method (AARJ. It uses Markov chain Monte Carlo (MCMC technique and its extension called Reversible Jump MCMC. Both of these techniques have been used extensively in statistical parameter estimation problems in wide area of applications since late 1990's. The novel feature in our algorithm is the fact that it is fully automatic and easy to use.
We show how the AARJ algorithm can be implemented and used for model selection and averaging, and to directly incorporate the model uncertainty. We demonstrate the technique by applying it to the statistical inversion problem of gas profile retrieval of GOMOS instrument on board the ENVISAT satellite. Four simple models are used simultaneously to describe the dependence of the aerosol cross-sections on wavelength. During the AARJ estimation all the models are used and we obtain a probability distribution characterizing how probable each model is. By using model averaging, the uncertainty related to selecting the aerosol model can be taken into account in assessing the uncertainty of the estimates.
Directory of Open Access Journals (Sweden)
F. Immler
2005-01-01
Full Text Available With a lidar system that was installed in Lindenberg/Germany, we observed in June 2003 an extended aerosol layer at 13km altitude in the lowermost stratosphere. This layer created an inelastic backscatter signal that we detected with a water vapour Raman channel, but that was not produced by Raman scattering. Also, we find evidence for inelastic scattering from a smoke plume from a forest fire that we observed in the troposphere. We interpret the unexpected properties of these aerosols as fluorescence induced by the laser beam at organic components of the aerosol particles. Fluorescence from ambient aerosol had not yet been considered detectable by lidar systems. However, organic compounds such as polycyclic aromatic hydrocarbons sticking to the aerosol particles, or bioaerosol such as bacteria, spores or pollen fluoresce when excited with UV-radiation in a way that is detectable by our lidar system. Therefore, we conclude that fluorescence from organic material released by biomass burning creates, inelastic backscatter signals that we measured with our instrument and thus demonstrate a new and powerful way to characterize aerosols by a remote sensing technique. The stratospheric aerosol layer that we have observed in Lindenberg for three consecutive days is likely to be a remnant from Siberian forest fire plumes lifted across the tropopause and transported around the globe.
Release of Streptomyces albus propagules from contaminated surfaces
International Nuclear Information System (INIS)
Gorny, R.L.; Mainelis, Gediminas; Grinshpun, Sergey A.; Willeke, Klaus; Dutkiewicz, Jacek; Reponen, Tiina
2003-01-01
The release of Streptomyces albus propagules from contaminated agar an ceiling tile surfaces was studied under controlled environmental condition in a newly developed aerosolization chamber. The experiments revealed tha both spores and cell fragments can be simultaneously released from the colonized surface by relatively gentle air currents of 0.3 m s -1 . A 100x increase of the air velocity can result in a 50-fold increase in the number of released propagules. The aerosolization rate depends strongly on the typ and roughness of the contaminated surface. Up to 90% of available actinomycete propagules can become airborne during the first 10 min of th release process. Application of vibration to the surface did not reveal an influence on the aerosolization process of S. albus propagules under th tested conditions. This study has shown that propagules in the fine particle size range can be released in large amounts from contaminated surfaces Measurement of the number of S. albus fragments in the vicinity of contaminated area, as an alternative to conventional air or surface sampling appears to be a promising approach for quantitative exposure assessment
Bayesian networks in overlay recipe optimization
Binns, Lewis A.; Reynolds, Greg; Rigden, Timothy C.; Watkins, Stephen; Soroka, Andrew
2005-05-01
Currently, overlay measurements are characterized by "recipe", which defines both physical parameters such as focus, illumination et cetera, and also the software parameters such as algorithm to be used and regions of interest. Setting up these recipes requires both engineering time and wafer availability on an overlay tool, so reducing these requirements will result in higher tool productivity. One of the significant challenges to automating this process is that the parameters are highly and complexly correlated. At the same time, a high level of traceability and transparency is required in the recipe creation process, so a technique that maintains its decisions in terms of well defined physical parameters is desirable. Running time should be short, given the system (automatic recipe creation) is being implemented to reduce overheads. Finally, a failure of the system to determine acceptable parameters should be obvious, so a certainty metric is also desirable. The complex, nonlinear interactions make solution by an expert system difficult at best, especially in the verification of the resulting decision network. The transparency requirements tend to preclude classical neural networks and similar techniques. Genetic algorithms and other "global minimization" techniques require too much computational power (given system footprint and cost requirements). A Bayesian network, however, provides a solution to these requirements. Such a network, with appropriate priors, can be used during recipe creation / optimization not just to select a good set of parameters, but also to guide the direction of search, by evaluating the network state while only incomplete information is available. As a Bayesian network maintains an estimate of the probability distribution of nodal values, a maximum-entropy approach can be utilized to obtain a working recipe in a minimum or near-minimum number of steps. In this paper we discuss the potential use of a Bayesian network in such a capacity
Can natural selection encode Bayesian priors?
Ramírez, Juan Camilo; Marshall, James A R
2017-08-07
The evolutionary success of many organisms depends on their ability to make decisions based on estimates of the state of their environment (e.g., predation risk) from uncertain information. These decision problems have optimal solutions and individuals in nature are expected to evolve the behavioural mechanisms to make decisions as if using the optimal solutions. Bayesian inference is the optimal method to produce estimates from uncertain data, thus natural selection is expected to favour individuals with the behavioural mechanisms to make decisions as if they were computing Bayesian estimates in typically-experienced environments, although this does not necessarily imply that favoured decision-makers do perform Bayesian computations exactly. Each individual should evolve to behave as if updating a prior estimate of the unknown environment variable to a posterior estimate as it collects evidence. The prior estimate represents the decision-maker's default belief regarding the environment variable, i.e., the individual's default 'worldview' of the environment. This default belief has been hypothesised to be shaped by natural selection and represent the environment experienced by the individual's ancestors. We present an evolutionary model to explore how accurately Bayesian prior estimates can be encoded genetically and shaped by natural selection when decision-makers learn from uncertain information. The model simulates the evolution of a population of individuals that are required to estimate the probability of an event. Every individual has a prior estimate of this probability and collects noisy cues from the environment in order to update its prior belief to a Bayesian posterior estimate with the evidence gained. The prior is inherited and passed on to offspring. Fitness increases with the accuracy of the posterior estimates produced. Simulations show that prior estimates become accurate over evolutionary time. In addition to these 'Bayesian' individuals, we also
Optimal delivery of aerosols to infants during mechanical ventilation.
Longest, P Worth; Azimi, Mandana; Hindle, Michael
2014-10-01
The objective of this study was to determine optimal aerosol delivery conditions for a full-term (3.6 kg) infant receiving invasive mechanical ventilation by evaluating the effects of aerosol particle size, a new wye connector, and timing of aerosol delivery. In vitro experiments used a vibrating mesh nebulizer and evaluated drug deposition fraction and emitted dose through ventilation circuits containing either a commercial (CM) or new streamlined (SL) wye connector and 3-mm endotracheal tube (ETT) for aerosols with mass median aerodynamic diameters of 880 nm, 1.78 μm, and 4.9 μm. The aerosol was released into the circuit either over the full inhalation cycle (T1 delivery) or over the first half of inhalation (T2 delivery). Validated computational fluid dynamics (CFD) simulations and whole-lung model predictions were used to assess lung deposition and exhaled dose during cyclic ventilation. In vitro experiments at a steady-state tracheal flow rate of 5 L/min resulted in 80-90% transmission of the 880-nm and 1.78-μm aerosols from the ETT. Based on CFD simulations with cyclic ventilation, the SL wye design reduced depositional losses in the wye by a factor of approximately 2-4 and improved lung delivery efficiencies by a factor of approximately 2 compared with the CM device. Delivery of the aerosol over the first half of the inspiratory cycle (T2) reduced exhaled dose from the ventilation circuit by a factor of 4 compared with T1 delivery. Optimal lung deposition was achieved with the SL wye connector and T2 delivery, resulting in 45% and 60% lung deposition for optimal polydisperse (∼1.78 μm) and monodisperse (∼2.5 μm) particle sizes, respectively. Optimization of selected factors and use of a new SL wye connector can substantially increase the lung delivery efficiency of medical aerosols to infants from current values of <1-10% to a range of 45-60%.
Rigorous bounds on aerosol optical properties from measurement and/or model constraints
McGraw, Robert; Fierce, Laura
2016-04-01
Sparse-particle aerosol models are an attractive alternative to sectional and modal methods for representation of complex, generally mixed particle populations. In the quadrature method of moments (QMOM) a small set of abscissas and weights, determined from distributional moments, provides the sparse set. Linear programming (LP) yields a generalization of the QMOM that is especially convenient for sparse particle selection. In this paper we use LP to obtain rigorous, nested upper and lower bounds to aerosol optical properties in terms of a prescribed Bayesian-like sequence of model or simulated measurement constraints. Examples of such constraints include remotely-sensed light extinction at different wavelengths, modeled particulate mass, etc. Successive reduction in bound separation with each added constraint provides a quantitative measure of its contextual information content. The present study is focused on univariate populations as a first step towards development of new simulation algorithms for tracking the physical and optical properties of multivariate particle populations.
Comparison of sodium aerosol codes
International Nuclear Information System (INIS)
Dunbar, I.H.; Fermandjian, J.; Bunz, H.; L'homme, A.; Lhiaubet, G.; Himeno, Y.; Kirby, C.R.; Mitsutsuka, N.
1984-01-01
Although hypothetical fast reactor accidents leading to severe core damage are very low probability events, their consequences are to be assessed. During such accidents, one can envisage the ejection of sodium, mixed with fuel and fission products, from the primary circuit into the secondary containment. Aerosols can be formed either by mechanical dispersion of the molten material or as a result of combustion of the sodium in the mixture. Therefore considerable effort has been devoted to study the different sodium aerosol phenomena. To ensure that the problems of describing the physical behaviour of sodium aerosols were adequately understood, a comparison of the codes being developed to describe their behaviour was undertaken. The comparison consists of two parts. The first is a comparative study of the computer codes used to predict aerosol behaviour during a hypothetical accident. It is a critical review of documentation available. The second part is an exercise in which code users have run their own codes with a pre-arranged input. For the critical comparative review of the computer models, documentation has been made available on the following codes: AEROSIM (UK), MAEROS (USA), HAARM-3 (USA), AEROSOLS/A2 (France), AEROSOLS/B1 (France), and PARDISEKO-IIIb (FRG)
Satellite Remote Sensing: Aerosol Measurements
Kahn, Ralph A.
2013-01-01
Aerosols are solid or liquid particles suspended in the air, and those observed by satellite remote sensing are typically between about 0.05 and 10 microns in size. (Note that in traditional aerosol science, the term "aerosol" refers to both the particles and the medium in which they reside, whereas for remote sensing, the term commonly refers to the particles only. In this article, we adopt the remote-sensing definition.) They originate from a great diversity of sources, such as wildfires, volcanoes, soils and desert sands, breaking waves, natural biological activity, agricultural burning, cement production, and fossil fuel combustion. They typically remain in the atmosphere from several days to a week or more, and some travel great distances before returning to Earth's surface via gravitational settling or washout by precipitation. Many aerosol sources exhibit strong seasonal variability, and most experience inter-annual fluctuations. As such, the frequent, global coverage that space-based aerosol remote-sensing instruments can provide is making increasingly important contributions to regional and larger-scale aerosol studies.
Climate forcing by anthropogenic aerosols
Charlson, R. J.; Schwartz, S. E.; Hales, J. M.; Cess, R. D.; Coakley, J. A., Jr.; Hansen, J. E.; Hofmann, D. J.
1992-01-01
Although long considered to be of marginal importance to global climate change, tropospheric aerosol contributes substantially to radiative forcing, and anthropogenic sulfate aerosol, in particular, has imposed a major perturbation to this forcing. Both the direct scattering of short-wavelength solar radiation and the modification of the shortwave reflective properties of clouds by sulfate aerosol particles increase planetary albedo, thereby exerting a cooling influence on the planet. Current climate forcing due to anthropogenic sulfate is estimated to be -1 to -2 watts per square meter, globally averaged. This perturbation is comparable in magnitude to current anthropogenic greenhouse gas forcing but opposite in sign. Thus, the aerosol forcing has likely offset global greenhouse warming to a substantial degree. However, differences in geographical and seasonal distributions of these forcings preclude any simple compensation. Aerosol effects must be taken into account in evaluating anthropogenic influences on past, current, and projected future climate and in formulating policy regarding controls on emission of greenhouse gases and sulfur dioxide. Resolution of such policy issues requires integrated research on the magnitude and geographical distribution of aerosol climate forcing and on the controlling chemical and physical processes.
Climate forcing by anthropogenic aerosols
Energy Technology Data Exchange (ETDEWEB)
Charlson, R.J.; Schwartz, S.E.; Hales, J.M.; Cess, R.D.; Coakley, J.A. Jr.; Hansen, J.E.; Hofmann, D.J. (University of Washington, Seattle, WA (USA). Inst. for Environmental Studies, Dept. of Atmospheric Sciences)
1992-01-24
Although long considered to be of marginal importance to global climate change, tropospheric aerosol contributes substantially to radiative forcing, and anthropogenic sulfate aerosol in particular has imposed a major perturbation to this forcing. Both the direct scattering of short wavelength solar radiation and the modification of the shortwave reflective properties of clouds by sulfate aerosol particles increase planetary albedo, thereby exerting a cooling influence on the planet. Current climate forcing due to anthropogenic sulfate is estimated to be -1 to -2 watts per square metre, globally averaged. This perturbation is comparable in magnitude to current anthropogenic greenhouse gas forcing but opposite in sign. Thus, the aerosol forcing has likely offset global greenhouse warming to a substantial degree. However, differences in geographical and seasonal distributions of these forcings preclude any simple compensation. Aerosol effects must be taken into account in evaluating anthropogenic influences on past, current, and projected future climate and in formulating policy regarding controls on emission of greenhouse gases and sulfur dioxide. Resolution of such policy issues requires integrated research on the magnitude and geographical distribution of aerosol climate forcing and on the controlling chemical and physical processes. 73 refs., 4 figs., 2 tabs.
Devices and methods for generating an aerosol
Bisetti, Fabrizio
2016-03-03
Aerosol generators and methods of generating aerosols are provided. The aerosol can be generated at a stagnation interface between a hot, wet stream and a cold, dry stream. The aerosol has the benefit that the properties of the aerosol can be precisely controlled. The stagnation interface can be generated, for example, by the opposed flow of the hot stream and the cold stream. The aerosol generator and the aerosol generation methods are capable of producing aerosols with precise particle sizes and a narrow size distribution. The properties of the aerosol can be controlled by controlling one or more of the stream temperatures, the saturation level of the hot stream, and the flow times of the streams.
Optical and Chemical Characterization of Aerosols Produced from Cooked Meats
Niedziela, R. F.; Foreman, E.; Blanc, L. E.
2011-12-01
Cooking processes can release a variety compounds into the air immediately above a cooking surface. The distribution of compounds will largely depend on the type of food that is being processed and the temperatures at which the food is prepared. High temperatures release compounds from foods like meats and carry them away from the preparation surface into cooler regions where condensation into particles can occur. Aerosols formed in this manner can impact air quality, particularly in urban areas where the amount of food preparation is high. Reported here are the results of laboratory experiments designed to optically and chemically characterize aerosols derived from cooking several types of meats including ground beef, salmon, chicken, and pork both in an inert atmosphere and in synthetic air. The laboratory-generated aerosols are studied using a laminar flow cell that is configured to accommodate simultaneous optical characterization in the mid-infrared and collection of particles for subsequent chemical analysis by gas chromatography. Preliminary optical results in the visible and ultra-violet will also be presented.
Aerosol Robotic Network (AERONET) Version 3 Aerosol Optical Depth and Inversion Products
Giles, D. M.; Holben, B. N.; Eck, T. F.; Smirnov, A.; Sinyuk, A.; Schafer, J.; Sorokin, M. G.; Slutsker, I.
2017-12-01
The Aerosol Robotic Network (AERONET) surface-based aerosol optical depth (AOD) database has been a principal component of many Earth science remote sensing applications and modelling for more than two decades. During this time, the AERONET AOD database had utilized a semiautomatic quality assurance approach (Smirnov et al., 2000). Data quality automation developed for AERONET Version 3 (V3) was achieved by augmenting and improving upon the combination of Version 2 (V2) automatic and manual procedures to provide a more refined near real time (NRT) and historical worldwide database of AOD. The combined effect of these new changes provides a historical V3 AOD Level 2.0 data set comparable to V2 Level 2.0 AOD. The recently released V3 Level 2.0 AOD product uses Level 1.5 data with automated cloud screening and quality controls and applies pre-field and post-field calibrations and wavelength-dependent temperature characterizations. For V3, the AERONET aerosol retrieval code inverts AOD and almucantar sky radiances using a full vector radiative transfer called Successive ORDers of scattering (SORD; Korkin et al., 2017). The full vector code allows for potentially improving the real part of the complex index of refraction and the sphericity parameter and computing the radiation field in the UV (e.g., 380nm) and degree of linear depolarization. Effective lidar ratio and depolarization ratio products are also available with the V3 inversion release. Inputs to the inversion code were updated to the accommodate H2O, O3 and NO2 absorption to be consistent with the computation of V3 AOD. All of the inversion products are associated with estimated uncertainties that include the random error plus biases due to the uncertainty in measured AOD, absolute sky radiance calibration, and retrieved MODIS BRDF for snow-free and snow covered surfaces. The V3 inversion products use the same data quality assurance criteria as V2 inversions (Holben et al. 2006). The entire AERONET V3
Static and mobile networks design for atmospheric accidental releases monitoring
International Nuclear Information System (INIS)
Abida, R.
2010-01-01
The global context of my PhD thesis work is the optimization of air pollution monitoring networks, but more specifically it concerns the monitoring of accidental releases of radionuclides in air. The optimization problem of air quality measuring networks has been addresses in the literature. However, it has not been addresses in the context of surveillance of accidental atmospheric releases. The first part of my thesis addresses the optimization of a permanent network of monitoring of radioactive aerosols in the air, covering France. The second part concerns the problem of targeting of observations in case of an accidental release of radionuclides from a nuclear plant. (author)
Bayesian Modelling of Functional Whole Brain Connectivity
DEFF Research Database (Denmark)
Røge, Rasmus
This thesis deals with parcellation of whole-brain functional magnetic resonance imaging (fMRI) using Bayesian inference with mixture models tailored to the fMRI data. In the three included papers and manuscripts, we analyze two different approaches to modeling fMRI signal; either we accept...... the prevalent strategy of standardizing of fMRI time series and model data using directional statistics or we model the variability in the signal across the brain and across multiple subjects. In either case, we use Bayesian nonparametric modeling to automatically learn from the fMRI data the number...... of funcional units, i.e. parcels. We benchmark the proposed mixture models against state of the art methods of brain parcellation, both probabilistic and non-probabilistic. The time series of each voxel are most often standardized using z-scoring which projects the time series data onto a hypersphere...
Bayesian Peak Picking for NMR Spectra
Cheng, Yichen
2014-02-01
Protein structure determination is a very important topic in structural genomics, which helps people to understand varieties of biological functions such as protein-protein interactions, protein–DNA interactions and so on. Nowadays, nuclear magnetic resonance (NMR) has often been used to determine the three-dimensional structures of protein in vivo. This study aims to automate the peak picking step, the most important and tricky step in NMR structure determination. We propose to model the NMR spectrum by a mixture of bivariate Gaussian densities and use the stochastic approximation Monte Carlo algorithm as the computational tool to solve the problem. Under the Bayesian framework, the peak picking problem is casted as a variable selection problem. The proposed method can automatically distinguish true peaks from false ones without preprocessing the data. To the best of our knowledge, this is the first effort in the literature that tackles the peak picking problem for NMR spectrum data using Bayesian method.
Structure Learning in Bayesian Sensorimotor Integration.
Directory of Open Access Journals (Sweden)
Tim Genewein
2015-08-01
Full Text Available Previous studies have shown that sensorimotor processing can often be described by Bayesian learning, in particular the integration of prior and feedback information depending on its degree of reliability. Here we test the hypothesis that the integration process itself can be tuned to the statistical structure of the environment. We exposed human participants to a reaching task in a three-dimensional virtual reality environment where we could displace the visual feedback of their hand position in a two dimensional plane. When introducing statistical structure between the two dimensions of the displacement, we found that over the course of several days participants adapted their feedback integration process in order to exploit this structure for performance improvement. In control experiments we found that this adaptation process critically depended on performance feedback and could not be induced by verbal instructions. Our results suggest that structural learning is an important meta-learning component of Bayesian sensorimotor integration.
Machine learning a Bayesian and optimization perspective
Theodoridis, Sergios
2015-01-01
This tutorial text gives a unifying perspective on machine learning by covering both probabilistic and deterministic approaches, which rely on optimization techniques, as well as Bayesian inference, which is based on a hierarchy of probabilistic models. The book presents the major machine learning methods as they have been developed in different disciplines, such as statistics, statistical and adaptive signal processing and computer science. Focusing on the physical reasoning behind the mathematics, all the various methods and techniques are explained in depth, supported by examples and problems, giving an invaluable resource to the student and researcher for understanding and applying machine learning concepts. The book builds carefully from the basic classical methods to the most recent trends, with chapters written to be as self-contained as possible, making the text suitable for different courses: pattern recognition, statistical/adaptive signal processing, statistical/Bayesian learning, as well as shor...
Bayesian image reconstruction: Application to emission tomography
Energy Technology Data Exchange (ETDEWEB)
Nunez, J.; Llacer, J.
1989-02-01
In this paper we propose a Maximum a Posteriori (MAP) method of image reconstruction in the Bayesian framework for the Poisson noise case. We use entropy to define the prior probability and likelihood to define the conditional probability. The method uses sharpness parameters which can be theoretically computed or adjusted, allowing us to obtain MAP reconstructions without the problem of the grey'' reconstructions associated with the pre Bayesian reconstructions. We have developed several ways to solve the reconstruction problem and propose a new iterative algorithm which is stable, maintains positivity and converges to feasible images faster than the Maximum Likelihood Estimate method. We have successfully applied the new method to the case of Emission Tomography, both with simulated and real data. 41 refs., 4 figs., 1 tab.
Distributed Bayesian Networks for User Modeling
DEFF Research Database (Denmark)
Tedesco, Roberto; Dolog, Peter; Nejdl, Wolfgang
2006-01-01
The World Wide Web is a popular platform for providing eLearning applications to a wide spectrum of users. However – as users differ in their preferences, background, requirements, and goals – applications should provide personalization mechanisms. In the Web context, user models used...... by such adaptive applications are often partial fragments of an overall user model. The fragments have then to be collected and merged into a global user profile. In this paper we investigate and present algorithms able to cope with distributed, fragmented user models – based on Bayesian Networks – in the context...... of Web-based eLearning platforms. The scenario we are tackling assumes learners who use several systems over time, which are able to create partial Bayesian Networks for user models based on the local system context. In particular, we focus on how to merge these partial user models. Our merge mechanism...
Bayesian long branch attraction bias and corrections.
Susko, Edward
2015-03-01
Previous work on the star-tree paradox has shown that Bayesian methods suffer from a long branch attraction bias. That work is extended to settings involving more taxa and partially resolved trees. The long branch attraction bias is confirmed to arise more broadly and an additional source of bias is found. A by-product of the analysis is methods that correct for biases toward particular topologies. The corrections can be easily calculated using existing Bayesian software. Posterior support for a set of two or more trees can thus be supplemented with corrected versions to cross-check or replace results. Simulations show the corrections to be highly effective. © The Author(s) 2014. Published by Oxford University Press, on behalf of the Society of Systematic Biologists. All rights reserved. For Permissions, please email: journals.permissions@oup.com.
Bayesian Peak Picking for NMR Spectra
Directory of Open Access Journals (Sweden)
Yichen Cheng
2014-02-01
Full Text Available Protein structure determination is a very important topic in structural genomics, which helps people to understand varieties of biological functions such as protein-protein interactions, protein–DNA interactions and so on. Nowadays, nuclear magnetic resonance (NMR has often been used to determine the three-dimensional structures of protein in vivo. This study aims to automate the peak picking step, the most important and tricky step in NMR structure determination. We propose to model the NMR spectrum by a mixture of bivariate Gaussian densities and use the stochastic approximation Monte Carlo algorithm as the computational tool to solve the problem. Under the Bayesian framework, the peak picking problem is casted as a variable selection problem. The proposed method can automatically distinguish true peaks from false ones without preprocessing the data. To the best of our knowledge, this is the first effort in the literature that tackles the peak picking problem for NMR spectrum data using Bayesian method.
Bayesianism and inference to the best explanation
Directory of Open Access Journals (Sweden)
Valeriano IRANZO
2008-01-01
Full Text Available Bayesianism and Inference to the best explanation (IBE are two different models of inference. Recently there has been some debate about the possibility of “bayesianizing” IBE. Firstly I explore several alternatives to include explanatory considerations in Bayes’s Theorem. Then I distinguish two different interpretations of prior probabilities: “IBE-Bayesianism” (IBE-Bay and “frequentist-Bayesianism” (Freq-Bay. After detailing the content of the latter, I propose a rule for assessing the priors. I also argue that Freq-Bay: (i endorses a role for explanatory value in the assessment of scientific hypotheses; (ii avoids a purely subjectivist reading of prior probabilities; and (iii fits better than IBE-Bayesianism with two basic facts about science, i.e., the prominent role played by empirical testing and the existence of many scientific theories in the past that failed to fulfil their promises and were subsequently abandoned.
Software Health Management with Bayesian Networks
Mengshoel, Ole; Schumann, JOhann
2011-01-01
Most modern aircraft as well as other complex machinery is equipped with diagnostics systems for its major subsystems. During operation, sensors provide important information about the subsystem (e.g., the engine) and that information is used to detect and diagnose faults. Most of these systems focus on the monitoring of a mechanical, hydraulic, or electromechanical subsystem of the vehicle or machinery. Only recently, health management systems that monitor software have been developed. In this paper, we will discuss our approach of using Bayesian networks for Software Health Management (SWHM). We will discuss SWHM requirements, which make advanced reasoning capabilities for the detection and diagnosis important. Then we will present our approach to using Bayesian networks for the construction of health models that dynamically monitor a software system and is capable of detecting and diagnosing faults.
Disentangling Complexity in Bayesian Automatic Adaptive Quadrature
Adam, Gheorghe; Adam, Sanda
2018-02-01
The paper describes a Bayesian automatic adaptive quadrature (BAAQ) solution for numerical integration which is simultaneously robust, reliable, and efficient. Detailed discussion is provided of three main factors which contribute to the enhancement of these features: (1) refinement of the m-panel automatic adaptive scheme through the use of integration-domain-length-scale-adapted quadrature sums; (2) fast early problem complexity assessment - enables the non-transitive choice among three execution paths: (i) immediate termination (exceptional cases); (ii) pessimistic - involves time and resource consuming Bayesian inference resulting in radical reformulation of the problem to be solved; (iii) optimistic - asks exclusively for subrange subdivision by bisection; (3) use of the weaker accuracy target from the two possible ones (the input accuracy specifications and the intrinsic integrand properties respectively) - results in maximum possible solution accuracy under minimum possible computing time.
Narrowband interference parameterization for sparse Bayesian recovery
Ali, Anum
2015-09-11
This paper addresses the problem of narrowband interference (NBI) in SC-FDMA systems by using tools from compressed sensing and stochastic geometry. The proposed NBI cancellation scheme exploits the frequency domain sparsity of the unknown signal and adopts a Bayesian sparse recovery procedure. This is done by keeping a few randomly chosen sub-carriers data free to sense the NBI signal at the receiver. As Bayesian recovery requires knowledge of some NBI parameters (i.e., mean, variance and sparsity rate), we use tools from stochastic geometry to obtain analytical expressions for the required parameters. Our simulation results validate the analysis and depict suitability of the proposed recovery method for NBI mitigation. © 2015 IEEE.
Bayesian information fusion networks for biosurveillance applications.
Mnatsakanyan, Zaruhi R; Burkom, Howard S; Coberly, Jacqueline S; Lombardo, Joseph S
2009-01-01
This study introduces new information fusion algorithms to enhance disease surveillance systems with Bayesian decision support capabilities. A detection system was built and tested using chief complaints from emergency department visits, International Classification of Diseases Revision 9 (ICD-9) codes from records of outpatient visits to civilian and military facilities, and influenza surveillance data from health departments in the National Capital Region (NCR). Data anomalies were identified and distribution of time offsets between events in the multiple data streams were established. The Bayesian Network was built to fuse data from multiple sources and identify influenza-like epidemiologically relevant events. Results showed increased specificity compared with the alerts generated by temporal anomaly detection algorithms currently deployed by NCR health departments. Further research should be done to investigate correlations between data sources for efficient fusion of the collected data.
Modelling dependable systems using hybrid Bayesian networks
International Nuclear Information System (INIS)
Neil, Martin; Tailor, Manesh; Marquez, David; Fenton, Norman; Hearty, Peter
2008-01-01
A hybrid Bayesian network (BN) is one that incorporates both discrete and continuous nodes. In our extensive applications of BNs for system dependability assessment, the models are invariably hybrid and the need for efficient and accurate computation is paramount. We apply a new iterative algorithm that efficiently combines dynamic discretisation with robust propagation algorithms on junction tree structures to perform inference in hybrid BNs. We illustrate its use in the field of dependability with two example of reliability estimation. Firstly we estimate the reliability of a simple single system and next we implement a hierarchical Bayesian model. In the hierarchical model we compute the reliability of two unknown subsystems from data collected on historically similar subsystems and then input the result into a reliability block model to compute system level reliability. We conclude that dynamic discretisation can be used as an alternative to analytical or Monte Carlo methods with high precision and can be applied to a wide range of dependability problems
Probabilistic forecasting and Bayesian data assimilation
Reich, Sebastian
2015-01-01
In this book the authors describe the principles and methods behind probabilistic forecasting and Bayesian data assimilation. Instead of focusing on particular application areas, the authors adopt a general dynamical systems approach, with a profusion of low-dimensional, discrete-time numerical examples designed to build intuition about the subject. Part I explains the mathematical framework of ensemble-based probabilistic forecasting and uncertainty quantification. Part II is devoted to Bayesian filtering algorithms, from classical data assimilation algorithms such as the Kalman filter, variational techniques, and sequential Monte Carlo methods, through to more recent developments such as the ensemble Kalman filter and ensemble transform filters. The McKean approach to sequential filtering in combination with coupling of measures serves as a unifying mathematical framework throughout Part II. Assuming only some basic familiarity with probability, this book is an ideal introduction for graduate students in ap...
Bayesian hypothesis testing: Editorial to the Special Issue on Bayesian data analysis.
Hoijtink, Herbert; Chow, Sy-Miin
2017-06-01
In the past 20 years, there has been a steadily increasing attention and demand for Bayesian data analysis across multiple scientific disciplines, including psychology. Bayesian methods and the related Markov chain Monte Carlo sampling techniques offered renewed ways of handling old and challenging new problems that may be difficult or impossible to handle using classical approaches. Yet, such opportunities and potential improvements have not been sufficiently explored and investigated. This is 1 of 2 special issues in Psychological Methods dedicated to the topic of Bayesian data analysis, with an emphasis on Bayesian hypothesis testing, model comparison, and general guidelines for applications in psychology. In this editorial, we provide an overview of the use of Bayesian methods in psychological research and a brief history of the Bayes factor and the posterior predictive p value. Translational abstracts that summarize the articles in this issue in very clear and understandable terms are included in the Appendix. (PsycINFO Database Record (c) 2017 APA, all rights reserved).
Gas/Aerosol partitioning: a simplified method for global modeling
Metzger, S.M.
2000-01-01
The main focus of this thesis is the development of a simplified method to routinely calculate gas/aerosol partitioning of multicomponent aerosols and aerosol associated water within global atmospheric chemistry and climate models. Atmospheric aerosols are usually multicomponent mixtures,
Bayesian estimation of Weibull distribution parameters
International Nuclear Information System (INIS)
Bacha, M.; Celeux, G.; Idee, E.; Lannoy, A.; Vasseur, D.
1994-11-01
In this paper, we expose SEM (Stochastic Expectation Maximization) and WLB-SIR (Weighted Likelihood Bootstrap - Sampling Importance Re-sampling) methods which are used to estimate Weibull distribution parameters when data are very censored. The second method is based on Bayesian inference and allow to take into account available prior informations on parameters. An application of this method, with real data provided by nuclear power plants operation feedback analysis has been realized. (authors). 8 refs., 2 figs., 2 tabs
Essays on portfolio choice with Bayesian methods
Kebabci, Deniz
2007-01-01
How investors should allocate assets to their portfolios in the presence of predictable components in asset returns is a question of great importance in finance. While early studies took the return generating process as given, recent studies have addressed issues such as parameter estimation and model uncertainty. My dissertation develops Bayesian methods for portfolio choice - and industry allocation in particular - under parameter and model uncertainty. The first chapter of my dissertation,...
Characteristic imsets for learning Bayesian network structure
Czech Academy of Sciences Publication Activity Database
Hemmecke, R.; Lindner, S.; Studený, Milan
2012-01-01
Roč. 53, č. 9 (2012), s. 1336-1349 ISSN 0888-613X R&D Projects: GA MŠk(CZ) 1M0572; GA ČR GA201/08/0539 Institutional support: RVO:67985556 Keywords : learning Bayesian network structure * essential graph * standard imset * characteristic imset * LP relaxation of a polytope Subject RIV: BA - General Mathematics Impact factor: 1.729, year: 2012 http://library.utia.cas.cz/separaty/2012/MTR/studeny-0382596.pdf
Centralized Bayesian reliability modelling with sensor networks
Czech Academy of Sciences Publication Activity Database
Dedecius, Kamil; Sečkárová, Vladimíra
2013-01-01
Roč. 19, č. 5 (2013), s. 471-482 ISSN 1387-3954 R&D Projects: GA MŠk 7D12004 Grant - others:GA MŠk(CZ) SVV-265315 Keywords : Bayesian modelling * Sensor network * Reliability Subject RIV: BD - Theory of Information Impact factor: 0.984, year: 2013 http://library.utia.cas.cz/separaty/2013/AS/dedecius-0392551.pdf
SAFETY RISK ASSESSMENT USING BAYESIAN BELIEF NETWORK
Directory of Open Access Journals (Sweden)
Victor M. Rukhlinskiy
2017-01-01
Full Text Available The solution of the problem of modelling and quantitative assessment of flight safety risk is being considered in this paper. The article considers the main groups of mathematical models used to quantify the risks of flight safety, which can be used by providers of aviation services. The authors demonstrate and discuss risk modeling possibilities in the field of flight safety on the basis of Bayesian belief networks.In this paper a mathematical model is built on the basis of identified hazards, and this model allows to determine the level of risk for each hazard and the consequences of their occurrence using Bayesian belief networks, consisting of marginal probability distributions graph and conditional probability tables. This mathematical model allows to determine the following, based on the data on adverse events and hazard identification: the probability of various adverse events in all dangers occurrence, the risk level for each of the identified hazards, the most likely consequences of the given danger oc- currence. For risk modeling in the field of flight safety on the basis of Bayesian belief networks there were used supple- mentary Bayes Net Toolbox for MATLAB with open source. To determine the level of risk in the form specified in ICAO Doc 9859 "Flight Safety Management Manual" of the International Civil Aviation Organization, the authors wrote a func- tion to MATLAB, allowing each pair of probability - to set severity level in line with alphanumeric value and significance of the risk category.Risk model in the field of flight safety on the basis of Bayesian belief networks corresponds to the definition of risk by Kaplan and Garrick. The advantage of the developed risk assessment method over other methods is shown in the paper.
Personalized Audio Systems - a Bayesian Approach
DEFF Research Database (Denmark)
Nielsen, Jens Brehm; Jensen, Bjørn Sand; Hansen, Toke Jansen
2013-01-01
, the present paper presents a general inter-active framework for personalization of such audio systems. The framework builds on Bayesian Gaussian process regression in which a model of the users's objective function is updated sequentially. The parameter setting to be evaluated in a given trial is selected...... are optimized using the proposed framework. Twelve test subjects obtain a personalized setting with the framework, and these settings are signicantly preferred to those obtained with random experimentation....
Bayesian estimation of traffic lane state
Czech Academy of Sciences Publication Activity Database
Nagy, Ivan; Kárný, Miroslav; Nedoma, Petr; Voráčová, Š.
2003-01-01
Roč. 17, č. 1 (2003), s. 51-65 ISSN 0890-6327 R&D Projects: GA ČR GA102/03/0049; GA AV ČR IBS1075351 Institutional research plan: CEZ:AV0Z1075907 Keywords : mixture models * estimation * Bayesian approach Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 0.602, year: 2003 http://library.utia.cas.cz/prace/20030021.ps
Multilevel Monte Carlo in Approximate Bayesian Computation
Jasra, Ajay
2017-02-13
In the following article we consider approximate Bayesian computation (ABC) inference. We introduce a method for numerically approximating ABC posteriors using the multilevel Monte Carlo (MLMC). A sequential Monte Carlo version of the approach is developed and it is shown under some assumptions that for a given level of mean square error, this method for ABC has a lower cost than i.i.d. sampling from the most accurate ABC approximation. Several numerical examples are given.
Dimensionality reduction in Bayesian estimation algorithms
G. W. Petty
2013-01-01
An idealized synthetic database loosely resembling 3-channel passive microwave observations of precipitation against a variable background is employed to examine the performance of a conventional Bayesian retrieval algorithm. For this dataset, algorithm performance is found to be poor owing to an irreconcilable conflict between the need to find matches in the dependent database versus the need to exclude inappropriate matches. It is argued that the likelihood of such conflicts increase...
Dimensionality reduction in Bayesian estimation algorithms
G. W. Petty
2013-01-01
An idealized synthetic database loosely resembling 3-channel passive microwave observations of precipitation against a variable background is employed to examine the performance of a conventional Bayesian retrieval algorithm. For this dataset, algorithm performance is found to be poor owing to an irreconcilable conflict between the need to find matches in the dependent database versus the need to exclude inappropriate matches. It is argued that the likelihood of such conf...
Constrained bayesian inference of project performance models
Sunmola, Funlade
2013-01-01
Project performance models play an important role in the management of project success. When used for monitoring projects, they can offer predictive ability such as indications of possible delivery problems. Approaches for monitoring project performance relies on available project information including restrictions imposed on the project, particularly the constraints of cost, quality, scope and time. We study in this paper a Bayesian inference methodology for project performance modelling in ...
Approximation of Bayesian Inverse Problems for PDEs
Cotter, S. L.; Dashti, M.; Stuart, A. M.
2010-01-01
Inverse problems are often ill posed, with solutions that depend sensitively on data.n any numerical approach to the solution of such problems, regularization of some form is needed to counteract the resulting instability. This paper is based on an approach to regularization, employing a Bayesian formulation of the problem, which leads to a notion of well posedness for inverse problems, at the level of probability measures. The stability which results from this well posedness may be used as t...
Bayesian analysis of Markov point processes
DEFF Research Database (Denmark)
Berthelsen, Kasper Klitgaard; Møller, Jesper
2006-01-01
Recently Møller, Pettitt, Berthelsen and Reeves introduced a new MCMC methodology for drawing samples from a posterior distribution when the likelihood function is only specified up to a normalising constant. We illustrate the method in the setting of Bayesian inference for Markov point processes...... a partially ordered Markov point process as the auxiliary variable. As the method requires simulation from the "unknown" likelihood, perfect simulation algorithms for spatial point processes become useful....
Bayesian Inference Methods for Sparse Channel Estimation
DEFF Research Database (Denmark)
Pedersen, Niels Lovmand
2013-01-01
of Bayesian inference algorithms for sparse channel estimation. Sparse inference methods aim at finding the sparse representation of a signal given in some overcomplete dictionary of basis vectors. Within this context, one of our main contributions to the field of SBL is a hierarchical representation...... and computational complexity. We also analyze the impact of transceiver filters on the sparseness of the channel response, and propose a dictionary design that permits the deployment of sparse inference methods in conditions of low bandwidth....
Measurement of the deposition of aerosol particles to skin, hair and clothing
International Nuclear Information System (INIS)
Bell, K.F.
1999-01-01
In the event of a nuclear accident, there are several routes whereby human populations may receive a radioactive dose from material released to the environment. The dose from radioactive aerosol deposited onto the surfaces of the human body has previously been estimated by assuming that aerosol deposition velocities (defined as the flux of aerosol onto a surface divided by the aerosol concentration above the surface) onto human body surfaces are similar to the values for inanimate surfaces. However, Jones (1990) modelled the effects on health of fallout material deposited on skin and clothing and found that the number of early deaths from skin dose was sensitively dependent on aerosol deposition velocity. He also pointed out that there was a lack of experimentally derived data on aerosol deposition velocities to human body surfaces and that the above mentioned assumption may not be valid. The purpose of the present work is to measure aerosol deposition velocities onto human body surfaces, the resultant data to allow more accurate nuclear accident consequence modelling. Aerosol deposition velocities onto human body surfaces in simulated indoor conditions have been measured by releasing tracer aerosols of three mean particle diameters (2.6, 6.2 and 9.2μm) into a test chamber containing volunteers. The skin, hair and clothing of the volunteers were sampled and analysed for deposited aerosol by Neutron Activation Analysis. Aerosol deposition velocities onto skin in the range 1.3 - 15 x 10 -3 ms -1 were recorded, values which are approximately an order of magnitude higher than the equivalent values onto the floor of the test room. These values suggest that the exposure route of radioactive aerosol particles deposited on the skin may be more significant than hitherto had been assumed. The possible mechanisms leading to this relatively high deposition were investigated experimentally and the results suggested that a combination of factors such as the body's electrostatic
Network structure exploration via Bayesian nonparametric models
International Nuclear Information System (INIS)
Chen, Y; Wang, X L; Xiang, X; Tang, B Z; Bu, J Z
2015-01-01
Complex networks provide a powerful mathematical representation of complex systems in nature and society. To understand complex networks, it is crucial to explore their internal structures, also called structural regularities. The task of network structure exploration is to determine how many groups there are in a complex network and how to group the nodes of the network. Most existing structure exploration methods need to specify either a group number or a certain type of structure when they are applied to a network. In the real world, however, the group number and also the certain type of structure that a network has are usually unknown in advance. To explore structural regularities in complex networks automatically, without any prior knowledge of the group number or the certain type of structure, we extend a probabilistic mixture model that can handle networks with any type of structure but needs to specify a group number using Bayesian nonparametric theory. We also propose a novel Bayesian nonparametric model, called the Bayesian nonparametric mixture (BNPM) model. Experiments conducted on a large number of networks with different structures show that the BNPM model is able to explore structural regularities in networks automatically with a stable, state-of-the-art performance. (paper)
Bayesian Methods for Radiation Detection and Dosimetry
International Nuclear Information System (INIS)
Peter G. Groer
2002-01-01
We performed work in three areas: radiation detection, external and internal radiation dosimetry. In radiation detection we developed Bayesian techniques to estimate the net activity of high and low activity radioactive samples. These techniques have the advantage that the remaining uncertainty about the net activity is described by probability densities. Graphs of the densities show the uncertainty in pictorial form. Figure 1 below demonstrates this point. We applied stochastic processes for a method to obtain Bayesian estimates of 222Rn-daughter products from observed counting rates. In external radiation dosimetry we studied and developed Bayesian methods to estimate radiation doses to an individual with radiation induced chromosome aberrations. We analyzed chromosome aberrations after exposure to gammas and neutrons and developed a method for dose-estimation after criticality accidents. The research in internal radiation dosimetry focused on parameter estimation for compartmental models from observed compartmental activities. From the estimated probability densities of the model parameters we were able to derive the densities for compartmental activities for a two compartment catenary model at different times. We also calculated the average activities and their standard deviation for a simple two compartment model
Bayesian Inference of a Multivariate Regression Model
Directory of Open Access Journals (Sweden)
Marick S. Sinay
2014-01-01
Full Text Available We explore Bayesian inference of a multivariate linear regression model with use of a flexible prior for the covariance structure. The commonly adopted Bayesian setup involves the conjugate prior, multivariate normal distribution for the regression coefficients and inverse Wishart specification for the covariance matrix. Here we depart from this approach and propose a novel Bayesian estimator for the covariance. A multivariate normal prior for the unique elements of the matrix logarithm of the covariance matrix is considered. Such structure allows for a richer class of prior distributions for the covariance, with respect to strength of beliefs in prior location hyperparameters, as well as the added ability, to model potential correlation amongst the covariance structure. The posterior moments of all relevant parameters of interest are calculated based upon numerical results via a Markov chain Monte Carlo procedure. The Metropolis-Hastings-within-Gibbs algorithm is invoked to account for the construction of a proposal density that closely matches the shape of the target posterior distribution. As an application of the proposed technique, we investigate a multiple regression based upon the 1980 High School and Beyond Survey.
Bayesian Analysis of Individual Level Personality Dynamics
Directory of Open Access Journals (Sweden)
Edward Cripps
2016-07-01
Full Text Available A Bayesian technique with analyses of within-person processes at the level of the individual is presented. The approach is used to examine if the patterns of within-person responses on a 12 trial simulation task are consistent with the predictions of ITA theory (Dweck, 1999. ITA theory states that the performance of an individual with an entity theory of ability is more likely to spiral down following a failure experience than the performance of an individual with an incremental theory of ability. This is because entity theorists interpret failure experiences as evidence of a lack of ability, which they believe is largely innate and therefore relatively ﬁxed; whilst incremental theorists believe in the malleability of abilities and interpret failure experiences as evidence of more controllable factors such as poor strategy or lack of effort. The results of our analyses support ITA theory at both the within- and between-person levels of analyses and demonstrate the beneﬁts of Bayesian techniques for the analysis of within-person processes. These include more formal speciﬁcation of the theory and the ability to draw inferences about each individual, which allows for more nuanced interpretations of individuals within a personality category, such as differences in the individual probabilities of spiralling. While Bayesian techniques have many potential advantages for the analyses of within-person processes at the individual level, ease of use is not one of them for psychologists trained in traditional frequentist statistical techniques.
Bayesian methodology for reliability model acceptance
International Nuclear Information System (INIS)
Zhang Ruoxue; Mahadevan, Sankaran
2003-01-01
This paper develops a methodology to assess the reliability computation model validity using the concept of Bayesian hypothesis testing, by comparing the model prediction and experimental observation, when there is only one computational model available to evaluate system behavior. Time-independent and time-dependent problems are investigated, with consideration of both cases: with and without statistical uncertainty in the model. The case of time-independent failure probability prediction with no statistical uncertainty is a straightforward application of Bayesian hypothesis testing. However, for the life prediction (time-dependent reliability) problem, a new methodology is developed in this paper to make the same Bayesian hypothesis testing concept applicable. With the existence of statistical uncertainty in the model, in addition to the application of a predictor estimator of the Bayes factor, the uncertainty in the Bayes factor is explicitly quantified through treating it as a random variable and calculating the probability that it exceeds a specified value. The developed method provides a rational criterion to decision-makers for the acceptance or rejection of the computational model
Bayesian Recurrent Neural Network for Language Modeling.
Chien, Jen-Tzung; Ku, Yuan-Chu
2016-02-01
A language model (LM) is calculated as the probability of a word sequence that provides the solution to word prediction for a variety of information systems. A recurrent neural network (RNN) is powerful to learn the large-span dynamics of a word sequence in the continuous space. However, the training of the RNN-LM is an ill-posed problem because of too many parameters from a large dictionary size and a high-dimensional hidden layer. This paper presents a Bayesian approach to regularize the RNN-LM and apply it for continuous speech recognition. We aim to penalize the too complicated RNN-LM by compensating for the uncertainty of the estimated model parameters, which is represented by a Gaussian prior. The objective function in a Bayesian classification network is formed as the regularized cross-entropy error function. The regularized model is constructed not only by calculating the regularized parameters according to the maximum a posteriori criterion but also by estimating the Gaussian hyperparameter by maximizing the marginal likelihood. A rapid approximation to a Hessian matrix is developed to implement the Bayesian RNN-LM (BRNN-LM) by selecting a small set of salient outer-products. The proposed BRNN-LM achieves a sparser model than the RNN-LM. Experiments on different corpora show the robustness of system performance by applying the rapid BRNN-LM under different conditions.
Particle identification in ALICE: a Bayesian approach
Adam, Jaroslav; Aggarwal, Madan Mohan; Aglieri Rinella, Gianluca; Agnello, Michelangelo; Agrawal, Neelima; Ahammed, Zubayer; Ahmad, Shakeel; Ahn, Sang Un; Aiola, Salvatore; Akindinov, Alexander; Alam, Sk Noor; Silva De Albuquerque, Danilo; Aleksandrov, Dmitry; Alessandro, Bruno; Alexandre, Didier; Alfaro Molina, Jose Ruben; Alici, Andrea; Alkin, Anton; Millan Almaraz, Jesus Roberto; Alme, Johan; Alt, Torsten; Altinpinar, Sedat; Altsybeev, Igor; Alves Garcia Prado, Caio; Andrei, Cristian; Andronic, Anton; Anguelov, Venelin; Anticic, Tome; Antinori, Federico; Antonioli, Pietro; Aphecetche, Laurent Bernard; Appelshaeuser, Harald; Arcelli, Silvia; Arnaldi, Roberta; Arnold, Oliver Werner; Arsene, Ionut Cristian; Arslandok, Mesut; Audurier, Benjamin; Augustinus, Andre; Averbeck, Ralf Peter; Azmi, Mohd Danish; Badala, Angela; Baek, Yong Wook; Bagnasco, Stefano; Bailhache, Raphaelle Marie; Bala, Renu; Balasubramanian, Supraja; Baldisseri, Alberto; Baral, Rama Chandra; Barbano, Anastasia Maria; Barbera, Roberto; Barile, Francesco; Barnafoldi, Gergely Gabor; Barnby, Lee Stuart; Ramillien Barret, Valerie; Bartalini, Paolo; Barth, Klaus; Bartke, Jerzy Gustaw; Bartsch, Esther; Basile, Maurizio; Bastid, Nicole; Basu, Sumit; Bathen, Bastian; Batigne, Guillaume; Batista Camejo, Arianna; Batyunya, Boris; Batzing, Paul Christoph; Bearden, Ian Gardner; Beck, Hans; Bedda, Cristina; Behera, Nirbhay Kumar; Belikov, Iouri; Bellini, Francesca; Bello Martinez, Hector; Bellwied, Rene; Belmont Iii, Ronald John; Belmont Moreno, Ernesto; Belyaev, Vladimir; Benacek, Pavel; Bencedi, Gyula; Beole, Stefania; Berceanu, Ionela; Bercuci, Alexandru; Berdnikov, Yaroslav; Berenyi, Daniel; Bertens, Redmer Alexander; Berzano, Dario; Betev, Latchezar; Bhasin, Anju; Bhat, Inayat Rasool; Bhati, Ashok Kumar; Bhattacharjee, Buddhadeb; Bhom, Jihyun; Bianchi, Livio; Bianchi, Nicola; Bianchin, Chiara; Bielcik, Jaroslav; Bielcikova, Jana; Bilandzic, Ante; Biro, Gabor; Biswas, Rathijit; Biswas, Saikat; Bjelogrlic, Sandro; Blair, Justin Thomas; Blau, Dmitry; Blume, Christoph; Bock, Friederike; Bogdanov, Alexey; Boggild, Hans; Boldizsar, Laszlo; Bombara, Marek; Book, Julian Heinz; Borel, Herve; Borissov, Alexander; Borri, Marcello; Bossu, Francesco; Botta, Elena; Bourjau, Christian; Braun-Munzinger, Peter; Bregant, Marco; Breitner, Timo Gunther; Broker, Theo Alexander; Browning, Tyler Allen; Broz, Michal; Brucken, Erik Jens; Bruna, Elena; Bruno, Giuseppe Eugenio; Budnikov, Dmitry; Buesching, Henner; Bufalino, Stefania; Buncic, Predrag; Busch, Oliver; Buthelezi, Edith Zinhle; Bashir Butt, Jamila; Buxton, Jesse Thomas; Cabala, Jan; Caffarri, Davide; Cai, Xu; Caines, Helen Louise; Calero Diaz, Liliet; Caliva, Alberto; Calvo Villar, Ernesto; Camerini, Paolo; Carena, Francesco; Carena, Wisla; Carnesecchi, Francesca; Castillo Castellanos, Javier Ernesto; Castro, Andrew John; Casula, Ester Anna Rita; Ceballos Sanchez, Cesar; Cepila, Jan; Cerello, Piergiorgio; Cerkala, Jakub; Chang, Beomsu; Chapeland, Sylvain; Chartier, Marielle; Charvet, Jean-Luc Fernand; Chattopadhyay, Subhasis; Chattopadhyay, Sukalyan; Chauvin, Alex; Chelnokov, Volodymyr; Cherney, Michael Gerard; Cheshkov, Cvetan Valeriev; Cheynis, Brigitte; Chibante Barroso, Vasco Miguel; Dobrigkeit Chinellato, David; Cho, Soyeon; Chochula, Peter; Choi, Kyungeon; Chojnacki, Marek; Choudhury, Subikash; Christakoglou, Panagiotis; Christensen, Christian Holm; Christiansen, Peter; Chujo, Tatsuya; Chung, Suh-Urk; Cicalo, Corrado; Cifarelli, Luisa; Cindolo, Federico; Cleymans, Jean Willy Andre; Colamaria, Fabio Filippo; Colella, Domenico; Collu, Alberto; Colocci, Manuel; Conesa Balbastre, Gustavo; Conesa Del Valle, Zaida; Connors, Megan Elizabeth; Contreras Nuno, Jesus Guillermo; Cormier, Thomas Michael; Corrales Morales, Yasser; Cortes Maldonado, Ismael; Cortese, Pietro; Cosentino, Mauro Rogerio; Costa, Filippo; Crochet, Philippe; Cruz Albino, Rigoberto; Cuautle Flores, Eleazar; Cunqueiro Mendez, Leticia; Dahms, Torsten; Dainese, Andrea; Danisch, Meike Charlotte; Danu, Andrea; Das, Debasish; Das, Indranil; Das, Supriya; Dash, Ajay Kumar; Dash, Sadhana; De, Sudipan; De Caro, Annalisa; De Cataldo, Giacinto; De Conti, Camila; De Cuveland, Jan; De Falco, Alessandro; De Gruttola, Daniele; De Marco, Nora; De Pasquale, Salvatore; Deisting, Alexander; Deloff, Andrzej; Denes, Ervin Sandor; Deplano, Caterina; Dhankher, Preeti; Di Bari, Domenico; Di Mauro, Antonio; Di Nezza, Pasquale; Diaz Corchero, Miguel Angel; Dietel, Thomas; Dillenseger, Pascal; Divia, Roberto; Djuvsland, Oeystein; Dobrin, Alexandru Florin; Domenicis Gimenez, Diogenes; Donigus, Benjamin; Dordic, Olja; Drozhzhova, Tatiana; Dubey, Anand Kumar; Dubla, Andrea; Ducroux, Laurent; Dupieux, Pascal; Ehlers Iii, Raymond James; Elia, Domenico; Endress, Eric; Engel, Heiko; Epple, Eliane; Erazmus, Barbara Ewa; Erdemir, Irem; Erhardt, Filip; Espagnon, Bruno; Estienne, Magali Danielle; Esumi, Shinichi; Eum, Jongsik; Evans, David; Evdokimov, Sergey; Eyyubova, Gyulnara; Fabbietti, Laura; Fabris, Daniela; Faivre, Julien; Fantoni, Alessandra; Fasel, Markus; Feldkamp, Linus; Feliciello, Alessandro; Feofilov, Grigorii; Ferencei, Jozef; Fernandez Tellez, Arturo; Gonzalez Ferreiro, Elena; Ferretti, Alessandro; Festanti, Andrea; Feuillard, Victor Jose Gaston; Figiel, Jan; Araujo Silva Figueredo, Marcel; Filchagin, Sergey; Finogeev, Dmitry; Fionda, Fiorella; Fiore, Enrichetta Maria; Fleck, Martin Gabriel; Floris, Michele; Foertsch, Siegfried Valentin; Foka, Panagiota; Fokin, Sergey; Fragiacomo, Enrico; Francescon, Andrea; Frankenfeld, Ulrich Michael; Fronze, Gabriele Gaetano; Fuchs, Ulrich; Furget, Christophe; Furs, Artur; Fusco Girard, Mario; Gaardhoeje, Jens Joergen; Gagliardi, Martino; Gago Medina, Alberto Martin; Gallio, Mauro; Gangadharan, Dhevan Raja; Ganoti, Paraskevi; Gao, Chaosong; Garabatos Cuadrado, Jose; Garcia-Solis, Edmundo Javier; Gargiulo, Corrado; Gasik, Piotr Jan; Gauger, Erin Frances; Germain, Marie; Gheata, Andrei George; Gheata, Mihaela; Ghosh, Premomoy; Ghosh, Sanjay Kumar; Gianotti, Paola; Giubellino, Paolo; Giubilato, Piero; Gladysz-Dziadus, Ewa; Glassel, Peter; Gomez Coral, Diego Mauricio; Gomez Ramirez, Andres; Sanchez Gonzalez, Andres; Gonzalez, Victor; Gonzalez Zamora, Pedro; Gorbunov, Sergey; Gorlich, Lidia Maria; Gotovac, Sven; Grabski, Varlen; Grachov, Oleg Anatolievich; Graczykowski, Lukasz Kamil; Graham, Katie Leanne; Grelli, Alessandro; Grigoras, Alina Gabriela; Grigoras, Costin; Grigoryev, Vladislav; Grigoryan, Ara; Grigoryan, Smbat; Grynyov, Borys; Grion, Nevio; Gronefeld, Julius Maximilian; Grosse-Oetringhaus, Jan Fiete; Grosso, Raffaele; Guber, Fedor; Guernane, Rachid; Guerzoni, Barbara; Gulbrandsen, Kristjan Herlache; Gunji, Taku; Gupta, Anik; Gupta, Ramni; Haake, Rudiger; Haaland, Oystein Senneset; Hadjidakis, Cynthia Marie; Haiduc, Maria; Hamagaki, Hideki; Hamar, Gergoe; Hamon, Julien Charles; Harris, John William; Harton, Austin Vincent; Hatzifotiadou, Despina; Hayashi, Shinichi; Heckel, Stefan Thomas; Hellbar, Ernst; Helstrup, Haavard; Herghelegiu, Andrei Ionut; Herrera Corral, Gerardo Antonio; Hess, Benjamin Andreas; Hetland, Kristin Fanebust; Hillemanns, Hartmut; Hippolyte, Boris; Horak, David; Hosokawa, Ritsuya; Hristov, Peter Zahariev; Humanic, Thomas; Hussain, Nur; Hussain, Tahir; Hutter, Dirk; Hwang, Dae Sung; Ilkaev, Radiy; Inaba, Motoi; Incani, Elisa; Ippolitov, Mikhail; Irfan, Muhammad; Ivanov, Marian; Ivanov, Vladimir; Izucheev, Vladimir; Jacazio, Nicolo; Jacobs, Peter Martin; Jadhav, Manoj Bhanudas; Jadlovska, Slavka; Jadlovsky, Jan; Jahnke, Cristiane; Jakubowska, Monika Joanna; Jang, Haeng Jin; Janik, Malgorzata Anna; Pahula Hewage, Sandun; Jena, Chitrasen; Jena, Satyajit; Jimenez Bustamante, Raul Tonatiuh; Jones, Peter Graham; Jusko, Anton; Kalinak, Peter; Kalweit, Alexander Philipp; Kamin, Jason Adrian; Kang, Ju Hwan; Kaplin, Vladimir; Kar, Somnath; Karasu Uysal, Ayben; Karavichev, Oleg; Karavicheva, Tatiana; Karayan, Lilit; Karpechev, Evgeny; Kebschull, Udo Wolfgang; Keidel, Ralf; Keijdener, Darius Laurens; Keil, Markus; Khan, Mohammed Mohisin; Khan, Palash; Khan, Shuaib Ahmad; Khanzadeev, Alexei; Kharlov, Yury; Kileng, Bjarte; Kim, Do Won; Kim, Dong Jo; Kim, Daehyeok; Kim, Hyeonjoong; Kim, Jinsook; Kim, Minwoo; Kim, Se Yong; Kim, Taesoo; Kirsch, Stefan; Kisel, Ivan; Kiselev, Sergey; Kisiel, Adam Ryszard; Kiss, Gabor; Klay, Jennifer Lynn; Klein, Carsten; Klein, Jochen; Klein-Boesing, Christian; Klewin, Sebastian; Kluge, Alexander; Knichel, Michael Linus; Knospe, Anders Garritt; Kobdaj, Chinorat; Kofarago, Monika; Kollegger, Thorsten; Kolozhvari, Anatoly; Kondratev, Valerii; Kondratyeva, Natalia; Kondratyuk, Evgeny; Konevskikh, Artem; Kopcik, Michal; Kostarakis, Panagiotis; Kour, Mandeep; Kouzinopoulos, Charalampos; Kovalenko, Oleksandr; Kovalenko, Vladimir; Kowalski, Marek; Koyithatta Meethaleveedu, Greeshma; Kralik, Ivan; Kravcakova, Adela; Krivda, Marian; Krizek, Filip; Kryshen, Evgeny; Krzewicki, Mikolaj; Kubera, Andrew Michael; Kucera, Vit; Kuhn, Christian Claude; Kuijer, Paulus Gerardus; Kumar, Ajay; Kumar, Jitendra; Kumar, Lokesh; Kumar, Shyam; Kurashvili, Podist; Kurepin, Alexander; Kurepin, Alexey; Kuryakin, Alexey; Kweon, Min Jung; Kwon, Youngil; La Pointe, Sarah Louise; La Rocca, Paola; Ladron De Guevara, Pedro; Lagana Fernandes, Caio; Lakomov, Igor; Langoy, Rune; Lara Martinez, Camilo Ernesto; Lardeux, Antoine Xavier; Lattuca, Alessandra; Laudi, Elisa; Lea, Ramona; Leardini, Lucia; Lee, Graham Richard; Lee, Seongjoo; Lehas, Fatiha; Lemmon, Roy Crawford; Lenti, Vito; Leogrande, Emilia; Leon Monzon, Ildefonso; Leon Vargas, Hermes; Leoncino, Marco; Levai, Peter; Li, Shuang; Li, Xiaomei; Lien, Jorgen Andre; Lietava, Roman; Lindal, Svein; Lindenstruth, Volker; Lippmann, Christian; Lisa, Michael Annan; Ljunggren, Hans Martin; Lodato, Davide Francesco; Lonne, Per-Ivar; Loginov, Vitaly; Loizides, Constantinos; Lopez, Xavier Bernard; Lopez Torres, Ernesto; Lowe, Andrew John; Luettig, Philipp Johannes; Lunardon, Marcello; Luparello, Grazia; Lutz, Tyler Harrison; Maevskaya, Alla; Mager, Magnus; Mahajan, Sanjay; Mahmood, Sohail Musa; Maire, Antonin; Majka, Richard Daniel; Malaev, Mikhail; Maldonado Cervantes, Ivonne Alicia; Malinina, Liudmila; Mal'Kevich, Dmitry; Malzacher, Peter; Mamonov, Alexander; Manko, Vladislav; Manso, Franck; Manzari, Vito; Marchisone, Massimiliano; Mares, Jiri; Margagliotti, Giacomo Vito; Margotti, Anselmo; Margutti, Jacopo; Marin, Ana Maria; Markert, Christina; Marquard, Marco; Martin, Nicole Alice; Martin Blanco, Javier; Martinengo, Paolo; Martinez Hernandez, Mario Ivan; Martinez-Garcia, Gines; Martinez Pedreira, Miguel; Mas, Alexis Jean-Michel; Masciocchi, Silvia; Masera, Massimo; Masoni, Alberto; Mastroserio, Annalisa; Matyja, Adam Tomasz; Mayer, Christoph; Mazer, Joel Anthony; Mazzoni, Alessandra Maria; Mcdonald, Daniel; Meddi, Franco; Melikyan, Yuri; Menchaca-Rocha, Arturo Alejandro; Meninno, Elisa; Mercado-Perez, Jorge; Meres, Michal; Miake, Yasuo; Mieskolainen, Matti Mikael; Mikhaylov, Konstantin; Milano, Leonardo; Milosevic, Jovan; Mischke, Andre; Mishra, Aditya Nath; Miskowiec, Dariusz Czeslaw; Mitra, Jubin; Mitu, Ciprian Mihai; Mohammadi, Naghmeh; Mohanty, Bedangadas; Molnar, Levente; Montano Zetina, Luis Manuel; Montes Prado, Esther; Moreira De Godoy, Denise Aparecida; Perez Moreno, Luis Alberto; Moretto, Sandra; Morreale, Astrid; Morsch, Andreas; Muccifora, Valeria; Mudnic, Eugen; Muhlheim, Daniel Michael; Muhuri, Sanjib; Mukherjee, Maitreyee; Mulligan, James Declan; Gameiro Munhoz, Marcelo; Munzer, Robert Helmut; Murakami, Hikari; Murray, Sean; Musa, Luciano; Musinsky, Jan; Naik, Bharati; Nair, Rahul; Nandi, Basanta Kumar; Nania, Rosario; Nappi, Eugenio; Naru, Muhammad Umair; Ferreira Natal Da Luz, Pedro Hugo; Nattrass, Christine; Rosado Navarro, Sebastian; Nayak, Kishora; Nayak, Ranjit; Nayak, Tapan Kumar; Nazarenko, Sergey; Nedosekin, Alexander; Nellen, Lukas; Ng, Fabian; Nicassio, Maria; Niculescu, Mihai; Niedziela, Jeremi; Nielsen, Borge Svane; Nikolaev, Sergey; Nikulin, Sergey; Nikulin, Vladimir; Noferini, Francesco; Nomokonov, Petr; Nooren, Gerardus; Cabanillas Noris, Juan Carlos; Norman, Jaime; Nyanin, Alexander; Nystrand, Joakim Ingemar; Oeschler, Helmut Oskar; Oh, Saehanseul; Oh, Sun Kun; Ohlson, Alice Elisabeth; Okatan, Ali; Okubo, Tsubasa; Olah, Laszlo; Oleniacz, Janusz; Oliveira Da Silva, Antonio Carlos; Oliver, Michael Henry; Onderwaater, Jacobus; Oppedisano, Chiara; Orava, Risto; Oravec, Matej; Ortiz Velasquez, Antonio; Oskarsson, Anders Nils Erik; Otwinowski, Jacek Tomasz; Oyama, Ken; Ozdemir, Mahmut; Pachmayer, Yvonne Chiara; Pagano, Davide; Pagano, Paola; Paic, Guy; Pal, Susanta Kumar; Pan, Jinjin; Pandey, Ashutosh Kumar; Papikyan, Vardanush; Pappalardo, Giuseppe; Pareek, Pooja; Park, Woojin; Parmar, Sonia; Passfeld, Annika; Paticchio, Vincenzo; Patra, Rajendra Nath; Paul, Biswarup; Pei, Hua; Peitzmann, Thomas; Pereira Da Costa, Hugo Denis Antonio; Peresunko, Dmitry Yurevich; Perez Lara, Carlos Eugenio; Perez Lezama, Edgar; Peskov, Vladimir; Pestov, Yury; Petracek, Vojtech; Petrov, Viacheslav; Petrovici, Mihai; Petta, Catia; Piano, Stefano; Pikna, Miroslav; Pillot, Philippe; Ozelin De Lima Pimentel, Lais; Pinazza, Ombretta; Pinsky, Lawrence; Piyarathna, Danthasinghe; Ploskon, Mateusz Andrzej; Planinic, Mirko; Pluta, Jan Marian; Pochybova, Sona; Podesta Lerma, Pedro Luis Manuel; Poghosyan, Martin; Polishchuk, Boris; Poljak, Nikola; Poonsawat, Wanchaloem; Pop, Amalia; Porteboeuf, Sarah Julie; Porter, R Jefferson; Pospisil, Jan; Prasad, Sidharth Kumar; Preghenella, Roberto; Prino, Francesco; Pruneau, Claude Andre; Pshenichnov, Igor; Puccio, Maximiliano; Puddu, Giovanna; Pujahari, Prabhat Ranjan; Punin, Valery; Putschke, Jorn Henning; Qvigstad, Henrik; Rachevski, Alexandre; Raha, Sibaji; Rajput, Sonia; Rak, Jan; Rakotozafindrabe, Andry Malala; Ramello, Luciano; Rami, Fouad; Raniwala, Rashmi; Raniwala, Sudhir; Rasanen, Sami Sakari; Rascanu, Bogdan Theodor; Rathee, Deepika; Read, Kenneth Francis; Redlich, Krzysztof; Reed, Rosi Jan; Rehman, Attiq Ur; Reichelt, Patrick Simon; Reidt, Felix; Ren, Xiaowen; Renfordt, Rainer Arno Ernst; Reolon, Anna Rita; Reshetin, Andrey; Reygers, Klaus Johannes; Riabov, Viktor; Ricci, Renato Angelo; Richert, Tuva Ora Herenui; Richter, Matthias Rudolph; Riedler, Petra; Riegler, Werner; Riggi, Francesco; Ristea, Catalin-Lucian; Rocco, Elena; Rodriguez Cahuantzi, Mario; Rodriguez Manso, Alis; Roeed, Ketil; Rogochaya, Elena; Rohr, David Michael; Roehrich, Dieter; Ronchetti, Federico; Ronflette, Lucile; Rosnet, Philippe; Rossi, Andrea; Roukoutakis, Filimon; Roy, Ankhi; Roy, Christelle Sophie; Roy, Pradip Kumar; Rubio Montero, Antonio Juan; Rui, Rinaldo; Russo, Riccardo; Ryabinkin, Evgeny; Ryabov, Yury; Rybicki, Andrzej; Saarinen, Sampo; Sadhu, Samrangy; Sadovskiy, Sergey; Safarik, Karel; Sahlmuller, Baldo; Sahoo, Pragati; Sahoo, Raghunath; Sahoo, Sarita; Sahu, Pradip Kumar; Saini, Jogender; Sakai, Shingo; Saleh, Mohammad Ahmad; Salzwedel, Jai Samuel Nielsen; Sambyal, Sanjeev Singh; Samsonov, Vladimir; Sandor, Ladislav; Sandoval, Andres; Sano, Masato; Sarkar, Debojit; Sarkar, Nachiketa; Sarma, Pranjal; Scapparone, Eugenio; Scarlassara, Fernando; Schiaua, Claudiu Cornel; Schicker, Rainer Martin; Schmidt, Christian Joachim; Schmidt, Hans Rudolf; Schuchmann, Simone; Schukraft, Jurgen; Schulc, Martin; Schutz, Yves Roland; Schwarz, Kilian Eberhard; Schweda, Kai Oliver; Scioli, Gilda; Scomparin, Enrico; Scott, Rebecca Michelle; Sefcik, Michal; Seger, Janet Elizabeth; Sekiguchi, Yuko; Sekihata, Daiki; Selyuzhenkov, Ilya; Senosi, Kgotlaesele; Senyukov, Serhiy; Serradilla Rodriguez, Eulogio; Sevcenco, Adrian; Shabanov, Arseniy; Shabetai, Alexandre; Shadura, Oksana; Shahoyan, Ruben; Shahzad, Muhammed Ikram; Shangaraev, Artem; Sharma, Ankita; Sharma, Mona; Sharma, Monika; Sharma, Natasha; Sheikh, Ashik Ikbal; Shigaki, Kenta; Shou, Qiye; Shtejer Diaz, Katherin; Sibiryak, Yury; Siddhanta, Sabyasachi; Sielewicz, Krzysztof Marek; Siemiarczuk, Teodor; Silvermyr, David Olle Rickard; Silvestre, Catherine Micaela; Simatovic, Goran; Simonetti, Giuseppe; Singaraju, Rama Narayana; Singh, Ranbir; Singha, Subhash; Singhal, Vikas; Sinha, Bikash; Sarkar - Sinha, Tinku; Sitar, Branislav; Sitta, Mario; Skaali, Bernhard; Slupecki, Maciej; Smirnov, Nikolai; Snellings, Raimond; Snellman, Tomas Wilhelm; Song, Jihye; Song, Myunggeun; Song, Zixuan; Soramel, Francesca; Sorensen, Soren Pontoppidan; Derradi De Souza, Rafael; Sozzi, Federica; Spacek, Michal; Spiriti, Eleuterio; Sputowska, Iwona Anna; Spyropoulou-Stassinaki, Martha; Stachel, Johanna; Stan, Ionel; Stankus, Paul; Stenlund, Evert Anders; Steyn, Gideon Francois; Stiller, Johannes Hendrik; Stocco, Diego; Strmen, Peter; Alarcon Do Passo Suaide, Alexandre; Sugitate, Toru; Suire, Christophe Pierre; Suleymanov, Mais Kazim Oglu; Suljic, Miljenko; Sultanov, Rishat; Sumbera, Michal; Sumowidagdo, Suharyo; Szabo, Alexander; Szanto De Toledo, Alejandro; Szarka, Imrich; Szczepankiewicz, Adam; Szymanski, Maciej Pawel; Tabassam, Uzma; Takahashi, Jun; Tambave, Ganesh Jagannath; Tanaka, Naoto; Tarhini, Mohamad; Tariq, Mohammad; Tarzila, Madalina-Gabriela; Tauro, Arturo; Tejeda Munoz, Guillermo; Telesca, Adriana; Terasaki, Kohei; Terrevoli, Cristina; Teyssier, Boris; Thaeder, Jochen Mathias; Thakur, Dhananjaya; Thomas, Deepa; Tieulent, Raphael Noel; Timmins, Anthony Robert; Toia, Alberica; Trogolo, Stefano; Trombetta, Giuseppe; Trubnikov, Victor; Trzaska, Wladyslaw Henryk; Tsuji, Tomoya; Tumkin, Alexandr; Turrisi, Rosario; Tveter, Trine Spedstad; Ullaland, Kjetil; Uras, Antonio; Usai, Gianluca; Utrobicic, Antonija; Vala, Martin; Valencia Palomo, Lizardo; Vallero, Sara; Van Der Maarel, Jasper; Van Hoorne, Jacobus Willem; Van Leeuwen, Marco; Vanat, Tomas; Vande Vyvre, Pierre; Varga, Dezso; Diozcora Vargas Trevino, Aurora; Vargyas, Marton; Varma, Raghava; Vasileiou, Maria; Vasiliev, Andrey; Vauthier, Astrid; Vechernin, Vladimir; Veen, Annelies Marianne; Veldhoen, Misha; Velure, Arild; Vercellin, Ermanno; Vergara Limon, Sergio; Vernet, Renaud; Verweij, Marta; Vickovic, Linda; Viesti, Giuseppe; Viinikainen, Jussi Samuli; Vilakazi, Zabulon; Villalobos Baillie, Orlando; Villatoro Tello, Abraham; Vinogradov, Alexander; Vinogradov, Leonid; Vinogradov, Yury; Virgili, Tiziano; Vislavicius, Vytautas; Viyogi, Yogendra; Vodopyanov, Alexander; Volkl, Martin Andreas; Voloshin, Kirill; Voloshin, Sergey; Volpe, Giacomo; Von Haller, Barthelemy; Vorobyev, Ivan; Vranic, Danilo; Vrlakova, Janka; Vulpescu, Bogdan; Wagner, Boris; Wagner, Jan; Wang, Hongkai; Wang, Mengliang; Watanabe, Daisuke; Watanabe, Yosuke; Weber, Michael; Weber, Steffen Georg; Weiser, Dennis Franz; Wessels, Johannes Peter; Westerhoff, Uwe; Whitehead, Andile Mothegi; Wiechula, Jens; Wikne, Jon; Wilk, Grzegorz Andrzej; Wilkinson, Jeremy John; Williams, Crispin; Windelband, Bernd Stefan; Winn, Michael Andreas; Yang, Hongyan; Yang, Ping; Yano, Satoshi; Yasin, Zafar; Yin, Zhongbao; Yokoyama, Hiroki; Yoo, In-Kwon; Yoon, Jin Hee; Yurchenko, Volodymyr; Yushmanov, Igor; Zaborowska, Anna; Zaccolo, Valentina; Zaman, Ali; Zampolli, Chiara; Correia Zanoli, Henrique Jose; Zaporozhets, Sergey; Zardoshti, Nima; Zarochentsev, Andrey; Zavada, Petr; Zavyalov, Nikolay; Zbroszczyk, Hanna Paulina; Zgura, Sorin Ion; Zhalov, Mikhail; Zhang, Haitao; Zhang, Xiaoming; Zhang, Yonghong; Chunhui, Zhang; Zhang, Zuman; Zhao, Chengxin; Zhigareva, Natalia; Zhou, Daicui; Zhou, You; Zhou, Zhuo; Zhu, Hongsheng; Zhu, Jianhui; Zichichi, Antonino; Zimmermann, Alice; Zimmermann, Markus Bernhard; Zinovjev, Gennady; Zyzak, Maksym
2016-05-25
We present a Bayesian approach to particle identification (PID) within the ALICE experiment. The aim is to more effectively combine the particle identification capabilities of its various detectors. After a brief explanation of the adopted methodology and formalism, the performance of the Bayesian PID approach for charged pions, kaons and protons in the central barrel of ALICE is studied. PID is performed via measurements of specific energy loss (dE/dx) and time-of-flight. PID efficiencies and misidentification probabilities are extracted and compared with Monte Carlo simulations using high purity samples of identified particles in the decay channels ${\\rm K}_{\\rm S}^{\\rm 0}\\rightarrow \\pi^+\\pi^-$, $\\phi\\rightarrow {\\rm K}^-{\\rm K}^+$ and $\\Lambda\\rightarrow{\\rm p}\\pi^-$ in p–Pb collisions at $\\sqrt{s_{\\rm NN}}= 5.02$TeV. In order to thoroughly assess the validity of the Bayesian approach, this methodology was used to obtain corrected $p_{\\rm T}$ spectra of pions, kaons, protons, and D$^0$ mesons in pp coll...
Bayesian posterior distributions without Markov chains.
Cole, Stephen R; Chu, Haitao; Greenland, Sander; Hamra, Ghassan; Richardson, David B
2012-03-01
Bayesian posterior parameter distributions are often simulated using Markov chain Monte Carlo (MCMC) methods. However, MCMC methods are not always necessary and do not help the uninitiated understand Bayesian inference. As a bridge to understanding Bayesian inference, the authors illustrate a transparent rejection sampling method. In example 1, they illustrate rejection sampling using 36 cases and 198 controls from a case-control study (1976-1983) assessing the relation between residential exposure to magnetic fields and the development of childhood cancer. Results from rejection sampling (odds ratio (OR) = 1.69, 95% posterior interval (PI): 0.57, 5.00) were similar to MCMC results (OR = 1.69, 95% PI: 0.58, 4.95) and approximations from data-augmentation priors (OR = 1.74, 95% PI: 0.60, 5.06). In example 2, the authors apply rejection sampling to a cohort study of 315 human immunodeficiency virus seroconverters (1984-1998) to assess the relation between viral load after infection and 5-year incidence of acquired immunodeficiency syndrome, adjusting for (continuous) age at seroconversion and race. In this more complex example, rejection sampling required a notably longer run time than MCMC sampling but remained feasible and again yielded similar results. The transparency of the proposed approach comes at a price of being less broadly applicable than MCMC.
Bayesian Posterior Distributions Without Markov Chains
Cole, Stephen R.; Chu, Haitao; Greenland, Sander; Hamra, Ghassan; Richardson, David B.
2012-01-01
Bayesian posterior parameter distributions are often simulated using Markov chain Monte Carlo (MCMC) methods. However, MCMC methods are not always necessary and do not help the uninitiated understand Bayesian inference. As a bridge to understanding Bayesian inference, the authors illustrate a transparent rejection sampling method. In example 1, they illustrate rejection sampling using 36 cases and 198 controls from a case-control study (1976–1983) assessing the relation between residential exposure to magnetic fields and the development of childhood cancer. Results from rejection sampling (odds ratio (OR) = 1.69, 95% posterior interval (PI): 0.57, 5.00) were similar to MCMC results (OR = 1.69, 95% PI: 0.58, 4.95) and approximations from data-augmentation priors (OR = 1.74, 95% PI: 0.60, 5.06). In example 2, the authors apply rejection sampling to a cohort study of 315 human immunodeficiency virus seroconverters (1984–1998) to assess the relation between viral load after infection and 5-year incidence of acquired immunodeficiency syndrome, adjusting for (continuous) age at seroconversion and race. In this more complex example, rejection sampling required a notably longer run time than MCMC sampling but remained feasible and again yielded similar results. The transparency of the proposed approach comes at a price of being less broadly applicable than MCMC. PMID:22306565
Office of Personnel Management — A press release, news release, media release, press statement is written communication directed at members of the news media for the purpose of announcing programs...
Directory of Open Access Journals (Sweden)
Igor Pujalté
2017-07-01
Full Text Available Nanoparticles (NPs can be released in the air in work settings, but various factors influence the exposure of workers. Controlled inhalation experiments can thus be conducted in an attempt to reproduce real-life exposure conditions and assess inhalation toxicology. Methods exist to generate aerosols, but it remains difficult to obtain nano-sized and stable aerosols suitable for inhalation experiments. The goal of this work was to characterize aerosols of titanium dioxide (TiO2 NPs, generated using a novel inhalation system equipped with three types of generators—a wet collision jet nebulizer, a dry dust jet and an electrospray aerosolizer—with the aim of producing stable aerosols with a nano-diameter average (<100 nm and monodispersed distribution for future rodent exposures and toxicological studies. Results showed the ability of the three generation systems to provide good and stable dispersions of NPs, applicable for acute (continuous up to 8 h and repeated (21-day exposures. In all cases, the generated aerosols were composed mainly of small aggregates/agglomerates (average diameter <100 nm with the electrospray producing the finest (average diameter of 70–75 mm and least concentrated aerosols (between 0.150 and 2.5 mg/m3. The dust jet was able to produce concentrations varying from 1.5 to 150 mg/m3, and hence, the most highly concentrated aerosols. The nebulizer collision jet aerosolizer was the most versatile generator, producing both low (0.5 mg/m3 and relatively high concentrations (30 mg/m3. The three optimized generators appeared suited for possible toxicological studies of inhaled NPs.
Substantial convection and precipitation enhancements by ultrafine aerosol particles
Energy Technology Data Exchange (ETDEWEB)
Fan, Jiwen; Rosenfeld, Daniel; Zhang, Yuwei; Giangrande, Scott E.; Li, Zhanqing; Machado, Luiz A. T.; Martin, Scot T.; Yang, Yan; Wang, Jian; Artaxo, Paulo; Barbosa, Henrique M. J.; Braga, Ramon C.; Comstock, Jennifer M.; Feng, Zhe; Gao, Wenhua; Gomes, Helber B.; Mei, Fan; Pöhlker, Christopher; Pöhlker, Mira L.; Pöschl, Ulrich; de Souza, Rodrigo A. F.
2018-01-25
Aerosol-cloud interaction remains the largest uncertainty in climate projections. Ultrafine aerosol particles (UAP; size <50nm) are considered too small to serve as cloud condensation nuclei conventionally. However, this study provides observational evidence to accompany insights from numerical simulations to support that deep convective clouds (DCCs) over Amazon have strong capability of nucleating UAP from an urban source and forming greater numbers of droplets, because fast drop coalescence in these DCCs reduces drop surface area available for condensation, leading to high vapor supersaturation. The additional droplets subsequently decrease supersaturation and release more condensational latent heating, a dominant contributor to convection intensification, whereas enhanced latent heat from ice-related processes plays a secondary role. Therefore, the addition of anthropogenic UAP may play a much greater role in modulating clouds than previously believed over the Amazon region and possibly in other relatively pristine regions such as maritime and forest locations.
Significant atmospheric aerosol pollution caused by world food cultivation
Bauer, Susanne E.; Tsigaridis, Kostas; Miller, Ron
2017-04-01
Particulate matter is a major concern for public health, causing cancer and cardiopulmonary mortality. Therefore, governments in most industrialized countries monitor and set limits for particulate matter. To assist policy makers, it is important to connect the chemical composition and severity of particulate pollution to it s sources. Here we show how agricultural practices, livestock production, and the use of nitrogen fertilizers impact near-surface air quality. In many densely populated areas, aerosols formed from gases that are released by fertilizer application and animal husbandry dominate over the combined contributions from all other anthropogenic pollution. Here we test reduction scenarios of combustion-based and agricultural emissions that could lower air pollution. For a future scenario, we find opposite trends, decreasing nitrate aerosol formation near the surface while total tropospheric loads increase. This suggests that food production could be increased to match the growing global population without sacrificing air quality if combustion emission is decreased.
Shortwave radiative effects of unactivated aerosol particles in clouds
International Nuclear Information System (INIS)
Ackerman, T.; Baker, M.B.
1977-01-01
Clouds in some polluted areas may contain high concentrations of anthropogenic aerosol particles. The possible role of these particles in perturbing the optical and dynamical properties of the clouds is an important question for climate studies. The direct radiative effects of unactivated aerosol particles in stable stratus clouds have been calculated at lambda=0.5μm. Several simplifying asumptions have been made relating the behavior of such particles in the high humidity enviornment within the cloud to their physicochemical make-up. It is shown that the energy absorbed by particles within the clouds may be, for realistic concentrations, comparable to the latent heat released and thus may play a significant role in cloud dynamics in some areas. These results are shown to be relatively insensitive to the assumptions about the particle properties within the cloud
The role of ammonia in the chemistry of atmospheric aerosols
International Nuclear Information System (INIS)
Brosset, C.
1979-01-01
Data is presented on the concentrations of hydrogen and ammonium ions in aerosol samples taken under various meteorological conditions in different areas of Sweden, and implications for the atmospheric chemistry of aerosols are discussed. Particle compositions at coastal and inland stations were determined during situations when particle concentrations increased as much as a hundred times due to atmospheric transport from Europe or air movements from the east or west. Analysis of particle compositions during both types of particle episodes reveals variations in the H(+)/NH4(+) ratio which indicate that particles present over agricultural areas take up ammonia from the ground and release it over a forest district with acid lakes. The ratio is found to be dependent on the atmospheric partial pressure of ammonia at equilibrium, with the flow of ammonia to or from the ground and transport conditions also likely to influence the ratio
Significant Atmospheric Aerosol Pollution Caused by World Food Cultivation
Bauer, Susanne E.; Tsigaridis, Kostas; Miller, Ron
2016-01-01
Particulate matter is a major concern for public health, causing cancer and cardiopulmonary mortality. Therefore, governments in most industrialized countries monitor and set limits for particulate matter. To assist policy makers, it is important to connect the chemical composition and severity of particulate pollution to its sources. Here we show how agricultural practices, livestock production, and the use of nitrogen fertilizers impact near-surface air quality. In many densely populated areas, aerosols formed from gases that are released by fertilizer application and animal husbandry dominate over the combined contributions from all other anthropogenic pollution. Here we test reduction scenarios of combustion-based and agricultural emissions that could lower air pollution. For a future scenario, we find opposite trends, decreasing nitrate aerosol formation near the surface while total tropospheric loads increase. This suggests that food production could be increased to match the growing global population without sacrificing air quality if combustion emission is decreased.
DEFF Research Database (Denmark)
Fogh, C.L.; Byrne, M.A.; Andersson, Kasper Grann
1999-01-01
the deposition and subsequent fate of contaminant aerosol on skin, hair and clothing. The main technique applied involves the release and subsequent deposition on volunteers in test rooms of particles of differentsizes labelled with neutron activatable rare earth tracers. Experiments indicate that the deposition...
DEFF Research Database (Denmark)
Xu, Sheng; Zhang, Luyuan; Freeman, Stewart P. H. T.
2015-01-01
Aerosol samples were collected from Tsukuba, Japan, soon after the 2011 Fukushima nuclear accident and analyzed for speciation of radiocesium and radioiodine to explore their chemical behavior and isotopic ratios after the release. Most Cs-134 and Cs-137 were bound in organic matter (53...
International Nuclear Information System (INIS)
Petti, D.A.; Hobbins, R.R.; Hagrman, D.L.
1994-01-01
Experimental results on fission product and aerosol release during the Power Burst Facility Severe Fuel Damages (SFD) Test 1-4 are examined to determine the composition of aerosols that would be generated during a severe reactor accident. The SFD 1-4 measured aerosol contained significant quantities of volatile fission products (VFPs) (cesium, iodine, tellurium), control materials (silver and cadmium), and structural materials (tin), indicating that fission product release, vaporization of control material, and release of tin from oxidized Zircaloy were all important aerosol sources. On average the aerosol composition is between one-quarter and one-half VFPs (especially cesium), with the remainder being control material (especially cadmium), and structural material (especially tin). Source term computer codes like CORSOR-M tend to overpredict the release of structural and control rod material relative to fission products by a factor of between 2 and 15 because the models do not account for relocation of molten control, fuel, and structural material during the degradation process, which tends to reduce the aerosol source. The results indicate that the aerosol generation in a severe reactor accident is intimately linked to the core degradation process. They recommend that these results be used to improve the models in source term computer codes
Ruprecht, Heidi; Sigg, Laura
The concentrations of aerosols (NH 4NO 3, (NH 4) 2SO 4 and NH 4Cl) and of gases (HCl (g), HNO 3(g), NH 3(g) were determined by denuder methods under different conditions (in the absence of fog, before, during and after fog events). At this site situated in an urban region, high concentrations of the gaseous strong acids HCl (g) and HNO 3(g) are observed. NH 4Cl and NH 4NO 3 aerosols represent a major fraction of the Cl - and NO 3- aerosols (fogwater and are released again after fog dissipation.
Being Bayesian in a quantum world
International Nuclear Information System (INIS)
Fuchs, C.
2005-01-01
Full text: To be a Bayesian about probability theory is to accept that probabilities represent subjective degrees of belief and nothing more. This is in distinction to the idea that probabilities represent long-term frequencies or objective propensities. But, how can a subjective account of probabilities coexist with the existence of quantum mechanics? To accept quantum mechanics is to accept the calculational apparatus of quantum states and the Born rule for determining probabilities in a quantum measurement. If there ever were a place for probabilities to be objective, it ought to be here. This raises the question of whether Bayesianism and quantum mechanics are compatible at all. For the Bayesian, it only suggests that we should rethink what quantum mechanics is about. Is it 'law of nature' or really more 'law of thought'? From transistors to lasers, the evidence is in that we live in a quantum world. One could infer from this that all the elements in the quantum formalism necessarily mirror nature itself: wave functions are so successful as calculational tools precisely because they represent elements of reality. A more Bayesian-like perspective is that if wave functions generate probabilities, then they too must be Bayesian degrees of belief, with all that such a radical idea entails. In particular, quantum probabilities have no firmer hold on reality than the word 'belief' in 'degrees of belief' already indicates. From this perspective, the only sense in which the quantum formalism mirrors nature is through the constraints it places on gambling agents who would like to better navigate through world. One might think that this is thin information, but it is not insubstantial. To the extent that an agent should use quantum mechanics for his uncertainty accounting rather than some other theory tells us something about the world itself - i.e., the world independent of the agent and his particular beliefs at any moment. In this talk, I will try to shore up these
Global simulations of aerosol processing in clouds
Directory of Open Access Journals (Sweden)
C. Hoose
2008-12-01
Full Text Available An explicit and detailed representation of in-droplet and in-crystal aerosol particles in stratiform clouds has been introduced in the global aerosol-climate model ECHAM5-HAM. The new scheme allows an evaluation of the cloud cycling of aerosols and an estimation of the relative contributions of nucleation and collision scavenging, as opposed to evaporation of hydrometeors in the global aerosol processing by clouds. On average an aerosol particle is cycled through stratiform clouds 0.5 times. The new scheme leads to important changes in the simulated fraction of aerosol scavenged in clouds, and consequently in the aerosol wet deposition. In general, less aerosol is scavenged into clouds with the new prognostic treatment than what is prescribed in standard ECHAM5-HAM. Aerosol concentrations, size distributions, scavenged fractions and cloud droplet concentrations are evaluated and compared to different observations. While the scavenged fraction and the aerosol number concentrations in the marine boundary layer are well represented in the new model, aerosol optical thickness, cloud droplet number concentrations in the marine boundary layer and the aerosol volume in the accumulation and coarse modes over the oceans are overestimated. Sensitivity studies suggest that a better representation of below-cloud scavenging, higher in-cloud collision coefficients, or a reduced water uptake by seasalt aerosols could reduce these biases.
An introduction to using Bayesian linear regression with clinical data.
Baldwin, Scott A; Larson, Michael J
2017-11-01
Statistical training psychology focuses on frequentist methods. Bayesian methods are an alternative to standard frequentist methods. This article provides researchers with an introduction to fundamental ideas in Bayesian modeling. We use data from an electroencephalogram (EEG) and anxiety study to illustrate Bayesian models. Specifically, the models examine the relationship between error-related negativity (ERN), a particular event-related potential, and trait anxiety. Methodological topics covered include: how to set up a regression model in a Bayesian framework, specifying priors, examining convergence of the model, visualizing and interpreting posterior distributions, interval estimates, expected and predicted values, and model comparison tools. We also discuss situations where Bayesian methods can outperform frequentist methods as well has how to specify more complicated regression models. Finally, we conclude with recommendations about reporting guidelines for those using Bayesian methods in their own research. We provide data and R code for replicating our analyses. Copyright © 2017 Elsevier Ltd. All rights reserved.
Open Source Bayesian Models. 1. Application to ADME/Tox and Drug Discovery Datasets
2015-01-01
On the order of hundreds of absorption, distribution, metabolism, excretion, and toxicity (ADME/Tox) models have been described in the literature in the past decade which are more often than not inaccessible to anyone but their authors. Public accessibility is also an issue with computational models for bioactivity, and the ability to share such models still remains a major challenge limiting drug discovery. We describe the creation of a reference implementation of a Bayesian model-building software module, which we have released as an open source component that is now included in the Chemistry Development Kit (CDK) project, as well as implemented in the CDD Vault and in several mobile apps. We use this implementation to build an array of Bayesian models for ADME/Tox, in vitro and in vivo bioactivity, and other physicochemical properties. We show that these models possess cross-validation receiver operator curve values comparable to those generated previously in prior publications using alternative tools. We have now described how the implementation of Bayesian models with FCFP6 descriptors generated in the CDD Vault enables the rapid production of robust machine learning models from public data or the user’s own datasets. The current study sets the stage for generating models in proprietary software (such as CDD) and exporting these models in a format that could be run in open source software using CDK components. This work also demonstrates that we can enable biocomputation across distributed private or public datasets to enhance drug discovery. PMID:25994950
Directory of Open Access Journals (Sweden)
David Lunn
Full Text Available The advantages of Bayesian statistical approaches, such as flexibility and the ability to acknowledge uncertainty in all parameters, have made them the prevailing method for analysing the spread of infectious diseases in human or animal populations. We introduce a Bayesian approach to experimental host-pathogen systems that shares these attractive features. Since uncertainty in all parameters is acknowledged, existing information can be accounted for through prior distributions, rather than through fixing some parameter values. The non-linear dynamics, multi-factorial design, multiple measurements of responses over time and sampling error that are typical features of experimental host-pathogen systems can also be naturally incorporated. We analyse the dynamics of the free-living protozoan Paramecium caudatum and its specialist bacterial parasite Holospora undulata. Our analysis provides strong evidence for a saturable infection function, and we were able to reproduce the two waves of infection apparent in the data by separating the initial inoculum from the parasites released after the first cycle of infection. In addition, the parameter estimates from the hierarchical model can be combined to infer variations in the parasite's basic reproductive ratio across experimental groups, enabling us to make predictions about the effect of resources and host genotype on the ability of the parasite to spread. Even though the high level of variability between replicates limited the resolution of the results, this Bayesian framework has strong potential to be used more widely in experimental ecology.
Directory of Open Access Journals (Sweden)
G. R. McMeeking
2011-09-01
Full Text Available Black carbon (BC aerosols absorb sunlight thereby leading to a positive radiative forcing and a warming of climate and can also impact human health through their impact on the respiratory system. The state of mixing of BC with other aerosol species, particularly the degree of internal/external mixing, has been highlighted as a major uncertainty in assessing its radiative forcing and hence its climate impact, but few in situ observations of mixing state exist. We present airborne single particle soot photometer (SP2 measurements of refractory BC (rBC mass concentrations and mixing state coupled with aerosol composition and optical properties measured in urban plumes and regional pollution over the United Kingdom. All data were obtained using instrumentation flown on the UK's BAe-146-301 large Atmospheric Research Aircraft (ARA operated by the Facility for Airborne Atmospheric Measurements (FAAM. We measured sub-micron aerosol composition using an aerosol mass spectrometer (AMS and used positive matrix factorization to separate hydrocarbon-like (HOA and oxygenated organic aerosols (OOA. We found a higher number fraction of thickly coated rBC particles in air masses with large OOA relative to HOA, higher ozone-to-nitrogen oxides (NO_{x} ratios and large concentrations of total sub-micron aerosol mass relative to rBC mass concentrations. The more ozone- and OOA-rich air masses were associated with transport from continental Europe, while plumes from UK cities had higher HOA and NO_{x} and fewer thickly coated rBC particles. We did not observe any significant change in the rBC mass absorption efficiency calculated from rBC mass and light absorption coefficients measured by a particle soot absorption photometer despite observing significant changes in aerosol composition and rBC mixing state. The contributions of light scattering and absorption to total extinction (quantified by the single scattering albedo; SSA did change for
Bayesian non- and semi-parametric methods and applications
Rossi, Peter
2014-01-01
This book reviews and develops Bayesian non-parametric and semi-parametric methods for applications in microeconometrics and quantitative marketing. Most econometric models used in microeconomics and marketing applications involve arbitrary distributional assumptions. As more data becomes available, a natural desire to provide methods that relax these assumptions arises. Peter Rossi advocates a Bayesian approach in which specific distributional assumptions are replaced with more flexible distributions based on mixtures of normals. The Bayesian approach can use either a large but fixed number
Bayesian inference with information content model check for Langevin equations
DEFF Research Database (Denmark)
Krog, Jens F. C.; Lomholt, Michael Andersen
2017-01-01
The Bayesian data analysis framework has been proven to be a systematic and effective method of parameter inference and model selection for stochastic processes. In this work we introduce an information content model check which may serve as a goodness-of-fit, like the chi-square procedure......, to complement conventional Bayesian analysis. We demonstrate this extended Bayesian framework on a system of Langevin equations, where coordinate dependent mobilities and measurement noise hinder the normal mean squared displacement approach....
Towards Bayesian Inference of the Fast-Ion Distribution Function
DEFF Research Database (Denmark)
Stagner, L.; Heidbrink, W.W.; Salewski, Mirko
2012-01-01
sensitivity of the measurements are incorporated into Bayesian likelihood probabilities, while prior probabilities enforce physical constraints. As an initial step, this poster uses Bayesian statistics to infer the DIII-D electron density profile from multiple diagnostic measurements. Likelihood functions....... However, when theory and experiment disagree (for one or more diagnostics), it is unclear how to proceed. Bayesian statistics provides a framework to infer the DF, quantify errors, and reconcile discrepant diagnostic measurements. Diagnostic errors and ``weight functions" that describe the phase space...
Factors Affecting Aerosol Radiative Forcing
Wang, J.; Lin, J.; Ni, R.
2016-12-01
Rapid industrial and economic growth has meant large amount of aerosols in the atmosphere with strong radiative forcing (RF) upon the climate system. Over parts of the globe, the negative forcing of aerosols has overcompensated for the positive forcing of greenhouse gases. Aerosol RF is determined by emissions and various chemical-transport-radiative processes in the atmosphere, a multi-factor problem whose individual contributors have not been well quantified. In this study, we analyze the major factors affecting RF of secondary inorganic aerosols (SIOAs, including sulfate, nitrate and ammonium), primary organic aerosol (POA), and black carbon (BC). We analyze the RFof aerosols produced by 11 major regions across the globe, including but not limited to East Asia, Southeast Asia, South Asia, North America, and Western Europe. Factors analyzed include population size, per capita gross domestic production (GDP), emission intensity (i.e., emissionsper unit GDP), chemical efficiency (i.e., mass per unit emissions) and radiative efficiency (i.e., RF per unit mass). We find that among the 11 regions, East Asia produces the largest emissions and aerosol RF, due to relatively high emission intensity and a tremendous population size.South Asia produce the second largest RF of SIOA and BC and the highest RF of POA, in part due to its highest chemical efficiency among all regions. Although Southeast Asia also has large emissions,its aerosol RF is alleviated by its lowest chemical efficiency.The chemical efficiency and radiative efficiency of BC produced by the Middle East-North Africa are the highest across the regions, whereas its RF is loweredbyasmall per capita GDP.Both North America and Western Europe have low emission intensity, compensating for the effects on RF of large population sizes and per capita GDP. There has been a momentum to transfer industries to Southeast Asia and South Asia, and such transition is expected to continue in the coming years. The resulting
Computational fluid dynamics analysis of aerosol deposition in pebble beds
Mkhosi, Margaret Msongi
2007-12-01
The Pebble Bed Modular Reactor is a high temperature gas cooled reactor which uses helium gas as a coolant. The reactor uses spherical graphite pebbles as fuel. The fuel design is inherently resistant to the release of the radioactive material up to high temperatures; therefore, the plant can withstand a broad spectrum of accidents with limited release of radionuclides to the environment. Despite safety features of the concepts, these reactors still contain large inventories of radioactive materials. The transport of most of the radioactive materials in an accident occurs in the form of aerosol particles. In this dissertation, the limits of applicability of existing computational fluid dynamics code FLUENT to the prediction of aerosol transport have been explored. The code was run using the Reynolds Averaged Navier-Stokes turbulence models to determine the effects of different turbulence models on the prediction of aerosol particle deposition. Analyses were performed for up to three unit cells in the orthorhombic configuration. For low flow conditions representing natural circulation driven flow, the laminar flow model was used and the results were compared with existing experimental data for packed beds. The results compares well with experimental data in the low flow regime. For conditions corresponding to normal operating of the reactor, analyses were performed using the standard k-ɛ turbulence model. From the inertial deposition results, a correlation that can be used to estimate the deposition of aerosol particles within pebble beds given inlet flow conditions has been developed. These results were converted into a dimensionless form as a function of a modified Stokes number. Based on results obtained in the laminar regime and for individual pebbles, the correlation developed for the inertial impaction component of deposition is believed to be credible. The form of the correlation developed also allows these results to be applied to pebble beds of different
Fission product core release model evaluation in MELCOR code
International Nuclear Information System (INIS)
Song, Y. M.; Kim, D. H.; Kim, H. D.
2003-01-01
The fission product core release in the MELCOR code is based on the CORSOR models developed by Battelle Memorial Institute. Release of radionuclides can occur from the fuel-cladding gap when a failure temperature criterion exceeds or intact geometry is lost, and various CORSOR empirical release correlations based on fuel temperatures are used for the release. Released masses into the core may exist as aerosols and/or vapors, depending on the vapor pressure of the radionuclide class and the surrounding temperature. This paper shows a release analysis for selected representative volatile and non-volatile radionuclides during conservative high and low pressure sequences in the APR1400 plant. Three core release models (CORSOR, CORSOR-M, CORSOR-Booth) in the latest MELCOR 1.8.5 version are used. In the analysis, the option of the fuel component surface-to-volume ratio in the CORSOR and CORSOR-M models and the option of the high and low burn-up in the CORSOR-Booth model are considered together. As the results, the CORSOR-M release rate is high for volatile radionuclides, and the CORSOR release rate is high for non-volatile radionuclides with insufficient consistency. As the uncertainty range for the release rate expands from several times (volatile radionuclides) to more than maximum 10,000 times (non-volatile radionuclides), user's careful choice for core release models is needed
Study of uranium mine aerosols
International Nuclear Information System (INIS)
Barzic, J.-Y.
1976-05-01
With a view to radiation protection of uranium-miners a study was made of the behaviour of radioactive and non-radioactive aerosols in the atmosphere of an experimental mine where temperature, pressure, relative himidity and ventilation are kept constant and in the air of a working area where the nature of the aerosol is dependent on the stage of work. Measurements of radon and daughter products carried out in various points of working areas showed that the gas was quickly diluted, equilibrium between radon and its daughter products (RaA, RaB, RaC) was never reached and the radon-aerosol contact was of short duration (a few minutes). Using a seven-stage Andersen impactor particle size distribution of the mine aerosol (particle diameter >0.3μm) was studied. The characteristic diameters were determined for each stage of the Andersen impactor and statistical analysis verified that aerosol distributions on the lower stages of the impactor were log-normal in most cases. Finally, determination of size distribution of α-radioactivity showed it was retained on fine particles. The percentage of free α-activity was evaluated using a diffusion battery [fr
Aerosol Chemical Speciation Monitor (ACSM) Instrument Handbook
Energy Technology Data Exchange (ETDEWEB)
Watson, Thomas B. [Brookhaven National Lab. (BNL), Upton, NY (United States)
2017-08-15
The Aerodyne Aerosol Chemical Speciation Monitor (ACSM) measures particle mass loading and chemical composition in real time for non-refractory sub-micron aerosol particles. The ACSM is designed for long-term unattended deployment and routine monitoring applications.
MISR Aerosol Climatology Product V001
National Aeronautics and Space Administration — MISR Aerosol Climatology Product is 1) the microphysical and scattering characteristics of pure aerosol upon which routine retrievals are based; 2) mixtures of pure...
Miniature Sensor for Aerosol Mass Measurements Project
National Aeronautics and Space Administration — This SBIR project seeks to develop a miniature sensor for mass measurement of size-classified aerosols. A cascade impactor will be used to classify aerosol sample...
Dawson, Kyle William
The study of climate and the associated impacts imposed by human activity has garnered the attention of scientists and policy makers since the 1950s. Research into the various atmospheric constituents that interact with solar radiation thus modulating Earth's radiative budget has been largely focused on the contributions from greenhouse gases and later focused on the role of atmospheric aerosol. The role of atmospheric aerosol, i.e. a solid or aqueous phase particulate, is complex and presents an opportunity for bettering the assessments of climate radiative forcing (i.e. the fraction of climate change due to anthropogenic, rather than natural, activities) in several ways. First, motivated to better understand the radiative effects of the Earth's background aerosol state to improve the assessment of anthropogenic effects, an experimental study on the water uptake ability of xanthan gum as a proxy for marine hydrogel, a component of natural primary emitted seaspray aerosol, is presented. Marine hydrogel comprises an organic component of the ocean surface microlayer that is released to the atmosphere via the bursting of bubbles generated by entrainment of air through crashing waves. This study investigates the water uptake ability (i.e. hygroscopicity) of these particles when exposed to a range of relative humidity (RH). The hydration characteristics of aerosolized pure xanthan gum as well as xanthan gum/salt mixtures were studied using a hygroscopic tandem differential mobility analyzer (HTDMA) and cloud condensation nuclei counter (CCNc). The hygroscopicity of the various solutions were compared to theoretical thermodynamic calculations accounting for the component volume fractions as a function of relative humidity. The data show that pure xanthan gum aerosol hygroscopicity behaves as other organic polysaccharides and, when combined with salts, is reasonably approximated by the volume fraction mixing rules above 90% RH. Deviations occur below 90% RH as well as for
Bayesian Test of Significance for Conditional Independence: The Multinomial Model
Directory of Open Access Journals (Sweden)
Pablo de Morais Andrade
2014-03-01
Full Text Available Conditional independence tests have received special attention lately in machine learning and computational intelligence related literature as an important indicator of the relationship among the variables used by their models. In the field of probabilistic graphical models, which includes Bayesian network models, conditional independence tests are especially important for the task of learning the probabilistic graphical model structure from data. In this paper, we propose the full Bayesian significance test for tests of conditional independence for discrete datasets. The full Bayesian significance test is a powerful Bayesian test for precise hypothesis, as an alternative to the frequentist’s significance tests (characterized by the calculation of the p-value.
Bayesian missing data problems EM, data augmentation and noniterative computation
Tan, Ming T; Ng, Kai Wang
2009-01-01
Bayesian Missing Data Problems: EM, Data Augmentation and Noniterative Computation presents solutions to missing data problems through explicit or noniterative sampling calculation of Bayesian posteriors. The methods are based on the inverse Bayes formulae discovered by one of the author in 1995. Applying the Bayesian approach to important real-world problems, the authors focus on exact numerical solutions, a conditional sampling approach via data augmentation, and a noniterative sampling approach via EM-type algorithms. After introducing the missing data problems, Bayesian approach, and poste