WorldWideScience

Sample records for absorption rate mapping

  1. Interest rates mapping

    Science.gov (United States)

    Kanevski, M.; Maignan, M.; Pozdnoukhov, A.; Timonin, V.

    2008-06-01

    The present study deals with the analysis and mapping of Swiss franc interest rates. Interest rates depend on time and maturity, defining term structure of the interest rate curves (IRC). In the present study IRC are considered in a two-dimensional feature space-time and maturity. Exploratory data analysis includes a variety of tools widely used in econophysics and geostatistics. Geostatistical models and machine learning algorithms (multilayer perceptron and Support Vector Machines) were applied to produce interest rate maps. IR maps can be used for the visualisation and pattern perception purposes, to develop and to explore economical hypotheses, to produce dynamic asset-liability simulations and for financial risk assessments. The feasibility of an application of interest rates mapping approach for the IRC forecasting is considered as well.

  2. Exercise, Insulin Absorption Rates, and Artificial Pancreas Control

    Science.gov (United States)

    Frank, Spencer; Hinshaw, Ling; Basu, Rita; Basu, Ananda; Szeri, Andrew J.

    2016-11-01

    Type 1 Diabetes is characterized by an inability of a person to endogenously produce the hormone insulin. Because of this, insulin must be injected - usually subcutaneously. The size of the injected dose and the rate at which the dose reaches the circulatory system have a profound effect on the ability to control glucose excursions, and therefore control of diabetes. However, insulin absorption rates via subcutaneous injection are variable and depend on a number of factors including tissue perfusion, physical activity (vasodilation, increased capillary throughput), and other tissue geometric and physical properties. Exercise may also have a sizeable effect on the rate of insulin absorption, which can potentially lead to dangerous glucose levels. Insulin-dosing algorithms, as implemented in an artificial pancreas controller, should account accurately for absorption rate variability and exercise effects on insulin absorption. The aforementioned factors affecting insulin absorption will be discussed within the context of both fluid mechanics and data driven modeling approaches.

  3. The absorption of carbon monoxide in COSORB solutions: absorption rate and capacity

    NARCIS (Netherlands)

    Hogendoorn, Kees; van Swaaij, Willibrordus Petrus Maria; Versteeg, Geert

    1995-01-01

    Absorption rate experiments and equilibrium experiments were carried out for the COSORB reaction at 300 K. The equilibrium data at 300 K could reasonably well be described with the following relation: [...] Determination of the kinetics and mechanism of a chemical reaction by means of absorption

  4. Subcutaneous insulin infusion: change in basal infusion rate has no immediate effect on insulin absorption rate

    International Nuclear Information System (INIS)

    Hildebrandt, P.; Birch, K.; Jensen, B.M.; Kuehl, C.

    1986-01-01

    Eight insulin-dependent diabetic patients were simultaneously given subcutaneous infusions (1.12 IU/h each) of 125 I-labeled Actrapid insulin in each side of the abdominal wall. After 24 h of infusion, the size of the infused insulin depots was measured by external counting for 5 h. The basal infusion rate was then doubled in one side and halved in the other for the next 4 h. Finally, 1.12 IU/h of insulin was given in both sides of the abdominal wall for an additional 3 h. The changes in the size of the depots were measured, and the absorption rates for each hour were calculated. During the first 5 h of infusion, the depot size was almost constant (approximately 5 IU) with an absorption rate that equaled the infusion rate. Doubling the infusion rate led to a significant increase in depot size, but the absorption rate remained unchanged for the first 3 h, and only thereafter was a significant increase seen. When the infusion rate was reduced to the initial 1.12 IU/h, the absorption rate remained elevated during the next 3 h. Correspondingly, when the infusion rate was decreased, the depot size also decreased, but the absorption rate remained unchanged for the first 3 h. The results show that a change in the basal insulin infusion rate does not lead to any immediate change in the insulin absorption rate. This should be considered when planning an insulin-infusion program that includes alteration(s) in the basal-rate setting

  5. Skin dose rate conversion factors after contamination with radiopharmaceuticals: influence of contamination area, epidermal thickness and percutaneous absorption

    International Nuclear Information System (INIS)

    Covens, P; Berus, D; Caveliers, V; Struelens, L; Vanhavere, F; Verellen, D

    2013-01-01

    Skin contamination with radiopharmaceuticals can occur during biomedical research and daily nuclear medicine practice as a result of accidental spills, after contact with bodily fluids of patients or by inattentively touching contaminated materials. Skin dose assessment should be carried out by repeated quantification to map the course of the contamination together with the use of appropriate skin dose rate conversion factors. Contamination is generally characterised by local spots on the palmar surface of the hand and complete decontamination is difficult as a result of percutaneous absorption. This specific issue requires special consideration as to the skin dose rate conversion factors as a measure for the absorbed dose rate to the basal layer of the epidermis. In this work we used Monte Carlo simulations to study the influence of the contamination area, the epidermal thickness and the percutaneous absorption on the absorbed skin dose rate conversion factors for a set of 39 medical radionuclides. The results show that the absorbed dose to the basal layer of the epidermis can differ by up to two orders of magnitude from the operational quantity H p (0.07) when using an appropriate epidermal thickness in combination with the effect of percutaneous absorption. (paper)

  6. Soft black hole absorption rates as conservation laws

    Energy Technology Data Exchange (ETDEWEB)

    Avery, Steven G. [Brown University, Department of Physics,182 Hope St, Providence, RI, 02912 (United States); Michigan State University, Department of Physics and Astronomy,East Lansing, MI, 48824 (United States); Schwab, Burkhard UniversityW. [Harvard University, Center for Mathematical Science and Applications,1 Oxford St, Cambridge, MA, 02138 (United States)

    2017-04-10

    The absorption rate of low-energy, or soft, electromagnetic radiation by spherically symmetric black holes in arbitrary dimensions is shown to be fixed by conservation of energy and large gauge transformations. We interpret this result as the explicit realization of the Hawking-Perry-Strominger Ward identity for large gauge transformations in the background of a non-evaporating black hole. Along the way we rederive and extend previous analytic results regarding the absorption rate for the minimal scalar and the photon.

  7. Soft black hole absorption rates as conservation laws

    International Nuclear Information System (INIS)

    Avery, Steven G.; Schwab, Burkhard UniversityW.

    2017-01-01

    The absorption rate of low-energy, or soft, electromagnetic radiation by spherically symmetric black holes in arbitrary dimensions is shown to be fixed by conservation of energy and large gauge transformations. We interpret this result as the explicit realization of the Hawking-Perry-Strominger Ward identity for large gauge transformations in the background of a non-evaporating black hole. Along the way we rederive and extend previous analytic results regarding the absorption rate for the minimal scalar and the photon.

  8. Integration of Absorption Feature Information from Visible to Longwave Infrared Spectral Ranges for Mineral Mapping

    Directory of Open Access Journals (Sweden)

    Veronika Kopačková

    2017-09-01

    Full Text Available Merging hyperspectral data from optical and thermal ranges allows a wider variety of minerals to be mapped and thus allows lithology to be mapped in a more complex way. In contrast, in most of the studies that have taken advantage of the data from the visible (VIS, near-infrared (NIR, shortwave infrared (SWIR and longwave infrared (LWIR spectral ranges, these different spectral ranges were analysed and interpreted separately. This limits the complexity of the final interpretation. In this study a presentation is made of how multiple absorption features, which are directly linked to the mineral composition and are present throughout the VIS, NIR, SWIR and LWIR ranges, can be automatically derived and, moreover, how these new datasets can be successfully used for mineral/lithology mapping. The biggest advantage of this approach is that it overcomes the issue of prior definition of endmembers, which is a requested routine employed in all widely used spectral mapping techniques. In this study, two different airborne image datasets were analysed, HyMap (VIS/NIR/SWIR image data and Airborne Hyperspectral Scanner (AHS, LWIR image data. Both datasets were acquired over the Sokolov lignite open-cast mines in the Czech Republic. It is further demonstrated that even in this case, when the absorption feature information derived from multispectral LWIR data is integrated with the absorption feature information derived from hyperspectral VIS/NIR/SWIR data, an important improvement in terms of more complex mineral mapping is achieved.

  9. THE SLOAN DIGITAL SKY SURVEY REVERBERATION MAPPING PROJECT: RAPID C iv BROAD ABSORPTION LINE VARIABILITY

    International Nuclear Information System (INIS)

    Grier, C. J.; Brandt, W. N.; Trump, J. R.; Schneider, D. P.; Hall, P. B.; Shen, Yue; Vivek, M.; Dawson, K. S.; Ak, N. Filiz; Chen, Yuguang; Denney, K. D.; Kochanek, C. S.; Peterson, B. M.; Green, Paul J.; Jiang, Linhua; McGreer, Ian D.; Pâris, I.; Tao, Charling; Wood-Vasey, W. M.; Bizyaev, Dmitry

    2015-01-01

    We report the discovery of rapid variations of a high-velocity C iv broad absorption line trough in the quasar SDSS J141007.74+541203.3. This object was intensively observed in 2014 as a part of the Sloan Digital Sky Survey Reverberation Mapping Project, during which 32 epochs of spectroscopy were obtained with the Baryon Oscillation Spectroscopic Survey spectrograph. We observe significant (>4σ) variability in the equivalent width (EW) of the broad (∼4000 km s −1 wide) C iv trough on rest-frame timescales as short as 1.20 days (∼29 hr), the shortest broad absorption line variability timescale yet reported. The EW varied by ∼10% on these short timescales, and by about a factor of two over the duration of the campaign. We evaluate several potential causes of the variability, concluding that the most likely cause is a rapid response to changes in the incident ionizing continuum. If the outflow is at a radius where the recombination rate is higher than the ionization rate, the timescale of variability places a lower limit on the density of the absorbing gas of n e ≳ 3.9 × 10 5 cm −3 . The broad absorption line variability characteristics of this quasar are consistent with those observed in previous studies of quasars, indicating that such short-term variability may in fact be common and thus can be used to learn about outflow characteristics and contributions to quasar/host-galaxy feedback scenarios

  10. THE SLOAN DIGITAL SKY SURVEY REVERBERATION MAPPING PROJECT: RAPID C iv BROAD ABSORPTION LINE VARIABILITY

    Energy Technology Data Exchange (ETDEWEB)

    Grier, C. J.; Brandt, W. N.; Trump, J. R.; Schneider, D. P. [Department of Astronomy and Astrophysics and Institute for Gravitation and the Cosmos, The Pennsylvania State University, 525 Davey Laboratory, University Park, PA 16802 (United States); Hall, P. B. [Department of Physics and Astronomy, York University, Toronto, ON M3J 1P3 (Canada); Shen, Yue [Carnegie Observatories, 813 Santa Barbara Street, Pasadena, CA 91101 (United States); Vivek, M.; Dawson, K. S. [Department of Physics and Astronomy, University of Utah, Salt Lake City, UT 84112 (United States); Ak, N. Filiz [Faculty of Sciences, Department of Astronomy and Space Sciences, Erciyes University, 38039 Kayseri (Turkey); Chen, Yuguang [Department of Astronomy, School of Physics, Peking University, Beijing 100871 (China); Denney, K. D.; Kochanek, C. S.; Peterson, B. M. [Department of Astronomy, The Ohio State University, 140 West 18th Avenue, Columbus, OH 43210 (United States); Green, Paul J. [Harvard-Smithsonian Center for Astrophysics, 60 Garden Street, Cambridge, MA 02138 (United States); Jiang, Linhua [Kavli Institute for Astronomy and Astrophysics, Peking University, Beijing 100871 (China); McGreer, Ian D. [Steward Observatory, The University of Arizona, 933 North Cherry Avenue, Tucson, AZ 85721-0065 (United States); Pâris, I. [INAF-Osservatorio Astronomico di Trieste, Via G. B. Tiepolo 11, I-34131 Trieste (Italy); Tao, Charling [Centre de Physique des Particules de Marseille, Aix-Marseille Universite, CNRS /IN2P3, 163, avenue de Luminy, Case 902, F-13288 Marseille Cedex 09 (France); Wood-Vasey, W. M. [PITT PACC, Department of Physics and Astronomy, University of Pittsburgh, 3941 O’Hara Street, Pittsburgh, PA 15260 (United States); Bizyaev, Dmitry, E-mail: grier@psu.edu [Apache Point Observatory and New Mexico State University, P.O. Box 59, Sunspot, NM, 88349-0059 (United States); and others

    2015-06-10

    We report the discovery of rapid variations of a high-velocity C iv broad absorption line trough in the quasar SDSS J141007.74+541203.3. This object was intensively observed in 2014 as a part of the Sloan Digital Sky Survey Reverberation Mapping Project, during which 32 epochs of spectroscopy were obtained with the Baryon Oscillation Spectroscopic Survey spectrograph. We observe significant (>4σ) variability in the equivalent width (EW) of the broad (∼4000 km s{sup −1} wide) C iv trough on rest-frame timescales as short as 1.20 days (∼29 hr), the shortest broad absorption line variability timescale yet reported. The EW varied by ∼10% on these short timescales, and by about a factor of two over the duration of the campaign. We evaluate several potential causes of the variability, concluding that the most likely cause is a rapid response to changes in the incident ionizing continuum. If the outflow is at a radius where the recombination rate is higher than the ionization rate, the timescale of variability places a lower limit on the density of the absorbing gas of n{sub e} ≳ 3.9 × 10{sup 5} cm{sup −3}. The broad absorption line variability characteristics of this quasar are consistent with those observed in previous studies of quasars, indicating that such short-term variability may in fact be common and thus can be used to learn about outflow characteristics and contributions to quasar/host-galaxy feedback scenarios.

  11. New 3D gas density maps of NaI and CaII interstellar absorption within 300 pc

    Science.gov (United States)

    Welsh, B. Y.; Lallement, R.; Vergely, J.-L.; Raimond, S.

    2010-02-01

    Aims: We present new high resolution (R > 50 000) absorption measurements of the NaI doublet (5889-5895 Å) along 482 nearby sight-lines, in addition to 807 new measurements of the CaII K (3933 Å) absorption line. We have combined these new data with previously reported measurements to produce a catalog of absorptions towards a total of 1857 early-type stars located within 800 pc of the Sun. Using these data we have determined the approximate 3-dimensional spatial distribution of neutral and partly ionized interstellar gas density within a distance-cube of 300 pc from the Sun. Methods: All newly recorded spectra were analyzed by means of a multi-component line profile-fitting program, in most cases using simultaneous fits to the line doublets. Normalized absorption profiles were fitted by varying the velocity, doppler width and column density for all intervening interstellar clouds. The resulting total column densities were then used in conjunction with the Hipparcos distances of the target stars to construct inversion maps of the 3D spatial density distribution of the NaI and CaII bearing gas. Results: A plot of the equivalent width of NaI versus distance reveals a wall of neutral gas at ~80 pc that can be associated with the boundary wall to the central rarefied Local Cavity region. In contrast, a similar plot for the equivalent width of CaII shows no sharply increasing absorption at 80 pc, but instead we observe a slowly increasing value of CaII equivalent width with increasing sight-line distance sampled. Low values for the volume density of NaI (nNaI values in the range 10-8 >nNaI > 10-10 cm-3 are found for sight-lines with distance >300 pc. Both high and low values of the volume density of CaII (nCaII) are found for sight-lines 100 pc a value of nCaII ~ 10-9 cm-3 is typical for most sight-lines, indicating that the distribution of CaII bearing gas is fairly uniform throughout the general ISM. Our three maps of the 3D spatial distribution of local neutral Na

  12. Determination of sedimentation rates and absorption coefficient of ...

    African Journals Online (AJOL)

    DR. MIKE HORSFALL

    particles have pores that can absorb radiation. Gamma rays have been used to study the absorption coefficients of cobalt(II) insoluble compounds (Essien and Ekpe, 1998), densities of marine sediments. (Gerland and Villinger, 1995) and soil particle-size distribution (Vaz et al., 1992). In this study, sedimentation rates of ...

  13. Energy absorption at high strain rate of glass fiber reinforced mortars

    Directory of Open Access Journals (Sweden)

    Fenu Luigi

    2015-01-01

    Full Text Available In this paper, the dynamic behaviour of cement mortars reinforced with glass fibers was studied. The influence of the addition of glass fibers on energy absorption and tensile strength at high strain-rate was investigated. Static tests in compression, in tension and in bending were first performed. Dynamic tests by means of a Modified Hopkinson Bar were then carried out in order to investigate how glass fibers affected energy absorption and tensile strength at high strain-rate of the fiber reinforced mortar. The Dynamic Increase Factor (DIF was finally evaluated.

  14. Enhanced specific absorption rate of bi-magnetic nanoparticles for heating applications

    Energy Technology Data Exchange (ETDEWEB)

    Hammad, Mohaned; Hempelmann, Rolf, E-mail: r.hempelmann@mx.uni-saarland.de

    2017-02-15

    Truncated octahedron bi-magnetic core/shell nanoparticles of Zn{sub 0.4}Co{sub 0.6}Fe{sub 2}O{sub 4}@Zn{sub 0.4}Mn{sub 0.6}Fe{sub 2}O{sub 4} with different size distributions have been synthesized, and their structural and magnetic properties have been studied. The structure and morphology of the core/shell nanostructures were established by using X-ray diffraction, and transmission electron microscopy. Dark field-TEM and X-ray photoelectron spectroscopy results confirmed the formation of bi-magnetic core/shell nanoparticles. The synthesized nanoparticles are superparamagnetic at room temperature. The Curie temperature increases with the increase of particle size from 360 K to 394 K. The experimental results showed that core/shell nanoparticles have a higher specific absorption rate compared to the core ones. These nanoparticles are interfacial exchange coupled between hard and soft magnetic phases. We demonstrated that the specific absorption rate could be tuned by the concentration of precursor and the synthesis time. - Highlights: • Zn{sub 0.4}Co{sub 0.6}Fe{sub 2}O{sub 4}@Zn{sub 0.4}Mn{sub 0.6}Fe{sub 2}O{sub 4} nanoparticles were synthesized by seed-mediated growth method. • Exchange-coupling between magnetic hard and soft phase of the magnetic nanoparticles affects the specific absorption rate. • The specific absorption rate could be tuned by the concentration of precursor and the synthesis time. • An increase of the core/shell magnetic nanoparticles size resulted in the increase of Curie temperature.

  15. Absolute absorption cross-section and photolysis rate of I2

    Directory of Open Access Journals (Sweden)

    A. Saiz-Lopez

    2004-01-01

    Full Text Available Following recent observations of molecular iodine (I2 in the coastal marine boundary layer (MBL (Saiz-Lopez and Plane, 2004, it has become important to determine the absolute absorption cross-section of I2 at reasonably high resolution, and also to evaluate the rate of photolysis of the molecule in the lower atmosphere. The absolute absorption cross-section (σ of gaseous I2 at room temperature and pressure (295K, 760Torr was therefore measured between 182 and 750nm using a Fourier Transform spectrometer at a resolution of 4cm-1 (0.1nm at λ=500nm. The maximum absorption cross-section in the visible region was observed at λ=533.0nm to be σ=(4.24±0.50x10-18cm2molecule-1. The spectrum is available as supplementary material accompanying this paper. The photo-dissociation rate constant (J of gaseous I2 was also measured directly in a solar simulator, yielding J(I2=0.12±0.03s-1 for the lower troposphere. This is in excellent agreement with the value of 0.12±0.015s-1 calculated using the measured absorption cross-section, terrestrial solar flux for clear sky conditions and assuming a photo-dissociation yield of unity. A two-stream radiation transfer model was then used to determine the variation in photolysis rate with solar zenith angle (SZA, from which an analytic expression is derived for use in atmospheric models. Photolysis appears to be the dominant loss process for I2 during daytime, and hence an important source of iodine atoms in the lower atmosphere.

  16. Imaging Plasmon Hybridization of Fano Resonances via Hot-Electron-Mediated Absorption Mapping.

    Science.gov (United States)

    Simoncelli, Sabrina; Li, Yi; Cortés, Emiliano; Maier, Stefan A

    2018-05-04

    The inhibition of radiative losses in dark plasmon modes allows storing electromagnetic energy more efficiently than in far-field excitable bright-plasmon modes. As such, processes benefiting from the enhanced absorption of light in plasmonic materials could also take profit of dark plasmon modes to boost and control nanoscale energy collection, storage, and transfer. We experimentally probe this process by imaging with nanoscale precision the hot-electron driven desorption of thiolated molecules from the surface of gold Fano nanostructures, investigating the effect of wavelength and polarization of the incident light. Spatially resolved absorption maps allow us to show the contribution of each element of the nanoantenna in the hot-electron driven process and their interplay in exciting a dark plasmon mode. Plasmon-mode engineering allows control of nanoscale reactivity and offers a route to further enhance and manipulate hot-electron driven chemical reactions and energy-conversion and transfer at the nanoscale.

  17. Rate of absorption and interfacial area of chlorine into aqueous ...

    African Journals Online (AJOL)

    aghomotsegin

    Due to excellent mass transfer characteristics with energy efficiency jet ejectors can be used in place of ... developed. The rate of absorption predicted from developed model is compared with experimental results. .... Numerical implementation.

  18. Effects of animal activity on the absorption rate of soils in the ...

    African Journals Online (AJOL)

    The rates of absorption into various microsites in Karoo soils were compared. The absorption of water by hard, bare intershrub soils was significantly increased by the presence of emergence holes of adult cicadas and near nest-mounds of the harvester ant Messor capensis. Both these insects play an important role in ...

  19. The SAURON project - XI. Stellar populations from absorption-line strength maps of 24 early-type spirals

    NARCIS (Netherlands)

    Peletier, Reynier F.; Falcon-Barroso, Jesus; Bacon, Roland; Cappellari, Michele; Davies, Roger L.; de Zeeuw, P. T.; Emsellem, Eric; Ganda, Katia; Krajnovic, Davor; Kuntschner, Harald; McDermid, Richard M.; Sarzi, Marc; van de Ven, Glenn

    2007-01-01

    We present absorption-line strength maps of a sample of 24 representative early-type spiral galaxies, mostly of type Sa, obtained as part of the SAURON (Spectrographic Areal Unit for Research on Optical Nebulae) survey of nearby galaxies using our custom-built integral-field spectrograph. Using

  20. Measurement of erosion rate by absorption spectroscopy in a Hall thruster

    International Nuclear Information System (INIS)

    Yamamoto, Naoji; Yokota, Shigeru; Matsui, Makoto; Komurasaki, Kimiya; Arakawa, Yoshihiro

    2005-01-01

    The erosion rate of a Hall thruster was estimated with the objective of building a real-time erosion rate monitoring system using a 1 kW class anode layer type Hall thruster. This system aids the understanding of the tradeoff between lifetime and performance. To estimate the flux of the sputtered wall material, the number density of the sputtered iron was measured by laser absorption spectroscopy using an absorption line from ground atomic iron at 371.9935 nm. An ultravioletAl x In y Ga (1-x-y) N diode laser was used as the probe. The estimated number density of iron was 1.1x10 16 m -3 , which is reasonable when compared with that measured by duration erosion tests. The relation between estimated erosion rate and magnetic flux density also agreed with that measured by duration erosion tests

  1. DIGITAL FLOOD INSURANCE RATE MAP DATABASE,

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk Information And supporting data used to develop the risk data. The primary risk;...

  2. Distribution of photon absorption rates across the rat retina.

    Science.gov (United States)

    Williams, T P; Webbers, J P; Giordano, L; Henderson, R P

    1998-04-15

    1. An investigation into the distribution of light intensity across the rat retina was carried out on excised, intact rat eyes exposed to Ganzfeld illumination from a helium-neon laser (543 nm). 2. Some of the light entering the eyes exits through the sclera where its intensity can be monitored with an optical 'pick-up' that samples the intensity coming from a small region of external sclera and underlying retina. The spatial resolution of the pick-up is such that it samples light that has passed through ca 2 % of the rods in the rat eye. 3. Some of the laser light is absorbed by the rod pigment, rhodopsin, which gradually bleaches. Bleaching in the retina, in turn, causes an exponential increase in intensity emanating from the sclera. By monitoring this intensity increase, we are able to measure two important parameters in a single bleaching run: the local rhodopsin concentration and the local intensity falling on the rods. 4. With an ocular transmission photometer, we have measured both the local intensity and the local rhodopsin concentration across wide regions of rat retina. Both pigmented and albino rats were studied. 5. The distributions of rhodopsin and intensity were both nearly uniform; consequently, the product, (rhodopsin concentration) x (intensity), was similarly nearly equal across the retina. This means that the initial rate of photon absorption is about the same at all retinal locations. 6. Interpreted in terms of photostasis (the regulation of daily photon catch), this means that the rate of photon absorption is about the same in each rod, viz. 14 400 photons absorbed per rod per second. Since this rate of absorption is sufficient to saturate the rod, one possible purpose of photostasis is to maintain the rod system in a saturated state during daylight hours.

  3. Mapping Surface Water DOC in the Northern Gulf of Mexico Using CDOM Absorption Coefficients and Remote Sensing Imagery

    Science.gov (United States)

    Kelly, B.; Chelsky, A.; Bulygina, E.; Roberts, B. J.

    2017-12-01

    Remote sensing techniques have become valuable tools to researchers, providing the capability to measure and visualize important parameters without the need for time or resource intensive sampling trips. Relationships between dissolved organic carbon (DOC), colored dissolved organic matter (CDOM) and spectral data have been used to remotely sense DOC concentrations in riverine systems, however, this approach has not been applied to the northern Gulf of Mexico (GoM) and needs to be tested to determine how accurate these relationships are in riverine-dominated shelf systems. In April, July, and October 2017 we sampled surface water from 80+ sites over an area of 100,000 km2 along the Louisiana-Texas shelf in the northern GoM. DOC concentrations were measured on filtered water samples using a Shimadzu TOC-VCSH analyzer using standard techniques. Additionally, DOC concentrations were estimated from CDOM absorption coefficients of filtered water samples on a UV-Vis spectrophotometer using a modification of the methods of Fichot and Benner (2011). These values were regressed against Landsat visible band spectral data for those same locations to establish a relationship between the spectral data, CDOM absorption coefficients. This allowed us to spatially map CDOM absorption coefficients in the Gulf of Mexico using the Landsat spectral data in GIS. We then used a multiple linear regressions model to derive DOC concentrations from the CDOM absorption coefficients and applied those to our map. This study provides an evaluation of the viability of scaling up CDOM absorption coefficient and remote-sensing derived estimates of DOC concentrations to the scale of the LA-TX shelf ecosystem.

  4. Specific absorption rate analysis of broadband mobile antenna with negative index metamaterial

    Science.gov (United States)

    Alam, Touhidul; Faruque, Mohammad Rashed Iqbal; Islam, Mohammad Tariqul

    2016-03-01

    This paper presents a negative index metamaterial-inspired printed mobile wireless antenna that can support most mobile applications such as GSM, UMTS, Bluetooth and WLAN frequency bands. The antenna consists of a semi-circular patch, a 50Ω microstrip feed line and metamaterial ground plane. The antenna occupies a very small space of 37 × 47 × 0.508 mm3, making it suitable for mobile wireless application. The perceptible novelty shown in this proposed antenna is that reduction of specific absorption rate using the negative index metamaterial ground plane. The proposed antenna reduced 72.11 and 75.53 % of specific absorption rate at 1.8 and 2.4 GHz, respectively.

  5. Effect of carbon dioxide on the rate of iodine vapor absorption by aqueous solution of sodium hydroxide

    International Nuclear Information System (INIS)

    Eguchi, Wataru; Adachi, Motonari; Miyake, Yoshikazu

    1978-01-01

    There is always carbon dioxide in the atmosphere as an impurity. Since this is an acid gas similar to iodine, each absorption rate seems to be affected by the other due to the coexistence of these two. Experiments have been conducted to clarify the absorption rate and absorption mechanism of iodine in the simultaneous absorption of iodine and carbon dioxide. Carbon dioxide coexisting with gas phases as an impurity decreases the absorption rate of iodine in the removal by washing with water of iodine mixed in the air. The first cause of this is that the diffusion coefficient of iodine in gas phase decreases with the carbon dioxide content in the gas phase. The second cause is that coexistent carbon dioxide is an acid gas, dissociates by dissolving into the absorbing solution, increases hydrogen ion concentration together with the formation of negative ions of bicarbonate and carbonate, and reduces hydroxyl ion concentration as a result. It is more important that existence of iodine has a catalytic effect to the rate of basic catalytic hydrolysis of carbon dioxide simultaneously dissolved in water phase, and accelerates this reaction rate. The mechanism of catalytic effect of iodine for the hydrolysis of carbon dioxide can not be clarified in detail only by this experiment, but the simultaneous absorption rate of iodine and carbon dioxide can be explained satisfactorily. (Wakatsuki, Y

  6. A review of lung-to-blood absorption rates for radon progeny

    International Nuclear Information System (INIS)

    Marsh, J. W.; Bailey, M. R.

    2013-01-01

    The International Commission on Radiological Protection (ICRP) Publication 66 Human Respiratory Tract Model (HRTM) treats clearance of materials from the respiratory tract as a competitive process between absorption into blood and particle transport to the alimentary tract and lymphatics. The ICRP recommended default absorption rates for lead and polonium (Type M) in ICRP Publication 71 but stated that the values were not appropriate for short-lived radon progeny. This paper reviews and evaluates published data from volunteer and laboratory animal experiments to estimate the HRTM absorption parameter values for short-lived radon progeny. Animal studies showed that lead ions have two phases of absorption: ∼10 % absorbed with a half-time of ∼15 min, the rest with a half-time of ∼10 h. The studies also indicated that some of the lead ions were bound to respiratory tract components. Bound fractions, f b , for lead were estimated from volunteer and animal studies and ranged from 0.2 to 0.8. Based on the evaluations of published data, the following HRTM absorption parameter values were derived for lead as a decay product of radon: f r = 0.1, s r = 100 d -1 , s s = 1.7 d -1 , f b = 0.5 and s b = 1.7 d -1 . Effective doses calculated assuming these absorption parameter values instead of a single absorption half-time of 10 h with no binding (as has generally been assumed) are only a few per cent higher. However, as there is some conflicting evidence on the absorption kinetics for radon progeny, dose calculations have been carried out for different sets of absorption parameter values derived from different studies. The results of these calculations are discussed. (authors)

  7. Absorption of subcutaneously infused insulin: influence of the basal rate pulse interval.

    Science.gov (United States)

    Hildebrandt, P; Birch, K; Jensen, B M; Kühl, C; Brange, J

    1985-01-01

    Eight insulin-dependent diabetic patients were given two constant infusions (each 1 IU/h) of 125I-labeled insulin into the abdominal subcutaneous tissue for about 12 h. Insulin was infused in pulses into one side of the abdomen in 6-min intervals (by means of an Auto-Syringe pump) and in the other side of the abdomen, insulin was infused in 1-h intervals (by means of a Medix pump). The size of the subcutaneous depots was continuously measured by counting the radioactivity at the infusion sites. After starting the infusions, the two depots were built up to steady-state levels at the same time and of the same size (approximately 3 IU) and with similar absorption rates. Thus, during basal rate insulin infusion, identical insulin absorption kinetics was achieved, irrespective of a 10-fold difference in the pulse rate.

  8. Specific absorption rate determination of magnetic nanoparticles through hyperthermia measurements in non-adiabatic conditions

    Energy Technology Data Exchange (ETDEWEB)

    Coïsson, M. [INRIM, strada delle Cacce 91, 10135 Torino (Italy); Barrera, G. [INRIM, strada delle Cacce 91, 10135 Torino (Italy); University of Torino, Chemistry Department, via P. Giuria 7, 10125 Torino (Italy); Celegato, F.; Martino, L.; Vinai, F. [INRIM, strada delle Cacce 91, 10135 Torino (Italy); Martino, P. [Politronica srl, via Livorno 60, 10144 Torino (Italy); Ferraro, G. [Center for Space Human Robotics, Istituto Italiano di Tecnologia - IIT, corso Trento 21, 10129 Torino (Italy); Tiberto, P. [INRIM, strada delle Cacce 91, 10135 Torino (Italy)

    2016-10-01

    An experimental setup for magnetic hyperthermia operating in non-adiabatic conditions is described. A thermodynamic model that takes into account the heat exchanged by the sample with the surrounding environment is developed. A suitable calibration procedure is proposed that allows the experimental validation of the model. Specific absorption rate can then be accurately determined just from the measurement of the sample temperature at the equilibrium steady state. The setup and the measurement procedure represent a simplification with respect to other systems requiring calorimeters or crucial corrections for heat flow. Two families of magnetic nanoparticles, one superparamagnetic and one characterised by larger sizes and static hysteresis, have been characterised as a function of field intensity, and specific absorption rate and intrinsic loss power have been obtained. - Highlights: • Development and thermodynamic modelling of a hyperthermia setup operating in non-adiabatic conditions. • Calibration of the experimental setup and validation of the model. • Accurate measurement of specific absorption rate and intrinsic loss power in non-adiabatic conditions.

  9. Flood Insurance Rate Map, Scott County, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  10. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, , USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk Information And supporting data used to develop the risk data. The primary risk;...

  11. Definition of a parameter for a typical specific absorption rate under real boundary conditions of cellular phones in a GSM networkd

    Science.gov (United States)

    Gerhardt, D.

    2003-05-01

    Using cellular phones the specific absorption rate (SAR) as a physical value must observe established and internationally defined levels to guarantee human protection. To assess human protection it is necessary to guarantee safety under worst-case conditions (especially maximum transmitting power) using cellular phones. To evaluate the exposure to electromagnetic fields under normal terms of use of cellular phones the limitations of the specific absorption rate must be pointed out. In a mobile radio network normal terms of use of cellular phones, i.e. in interconnection with a fixed radio transmitter of a mobile radio network, power control of the cellular phone as well as the antenna diagram regarding a head phantom are also significant for the real exposure. Based on the specific absorption rate, the antenna diagram regarding a head phantom and taking into consideration the power control a new parameter, the typical absorption rate (SARtyp), is defined in this contribution. This parameter indicates the specific absorption rate under average normal conditions of use. Constant radio link attenuation between a cellular phone and a fixed radio transmitter for all mobile models tested was assumed in order to achieve constant field strength at the receiving antenna of the fixed radio transmitter as a result of power control. The typical specific absorption rate is a characteristic physical value of every mobile model. The typical absorption rate was calculated for 16 different mobile models and compared with the absorption rate at maximum transmitting power. The results confirm the relevance of the definition of this parameter (SARtyp) as opposed to the specific absorption rate as a competent and applicable method to establish the real mean exposure from a cellular phone in a mobile radio network. The typical absorption rate provides a parameter to assess electromagnetic fields of a cellular phone that is more relevant to the consumer.

  12. Time-resolved absorption and hemoglobin concentration difference maps: a method to retrieve depth-related information on cerebral hemodynamics.

    Science.gov (United States)

    Montcel, Bruno; Chabrier, Renée; Poulet, Patrick

    2006-12-01

    Time-resolved diffuse optical methods have been applied to detect hemodynamic changes induced by cerebral activity. We describe a near infrared spectroscopic (NIRS) reconstruction free method which allows retrieving depth-related information on absorption variations. Variations in the absorption coefficient of tissues have been computed over the duration of the whole experiment, but also over each temporal step of the time-resolved optical signal, using the microscopic Beer-Lambert law.Finite element simulations show that time-resolved computation of the absorption difference as a function of the propagation time of detected photons is sensitive to the depth profile of optical absorption variations. Differences in deoxyhemoglobin and oxyhemoglobin concentrations can also be calculated from multi-wavelength measurements. Experimental validations of the simulated results have been obtained for resin phantoms. They confirm that time-resolved computation of the absorption differences exhibited completely different behaviours, depending on whether these variations occurred deeply or superficially. The hemodynamic response to a short finger tapping stimulus was measured over the motor cortex and compared to experiments involving Valsalva manoeuvres. Functional maps were also calculated for the hemodynamic response induced by finger tapping movements.

  13. MareyMap Online: A User-Friendly Web Application and Database Service for Estimating Recombination Rates Using Physical and Genetic Maps.

    Science.gov (United States)

    Siberchicot, Aurélie; Bessy, Adrien; Guéguen, Laurent; Marais, Gabriel A B

    2017-10-01

    Given the importance of meiotic recombination in biology, there is a need to develop robust methods to estimate meiotic recombination rates. A popular approach, called the Marey map approach, relies on comparing genetic and physical maps of a chromosome to estimate local recombination rates. In the past, we have implemented this approach in an R package called MareyMap, which includes many functionalities useful to get reliable recombination rate estimates in a semi-automated way. MareyMap has been used repeatedly in studies looking at the effect of recombination on genome evolution. Here, we propose a simpler user-friendly web service version of MareyMap, called MareyMap Online, which allows a user to get recombination rates from her/his own data or from a publicly available database that we offer in a few clicks. When the analysis is done, the user is asked whether her/his curated data can be placed in the database and shared with other users, which we hope will make meta-analysis on recombination rates including many species easy in the future. © The Author 2017. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution.

  14. Measurement of specific heat and specific absorption rate by nuclear magnetic resonance

    Energy Technology Data Exchange (ETDEWEB)

    Gultekin, David H., E-mail: david.gultekin@aya.yale.edu [Department of Electrical Engineering, Yale University, New Haven, CT 06520 (United States); Department of Medical Physics, Memorial Sloan-Kettering Cancer Center, New York, NY 10065 (United States); Department of Radiology, Memorial Sloan-Kettering Cancer Center, New York, NY 10065 (United States); Institute of Imaging Science, Vanderbilt University, Nashville, TN 37232 (United States); Gore, John C. [Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37232 (United States); Department of Radiology and Radiological Sciences, Vanderbilt University, Nashville, TN 37232 (United States); Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, TN 37232 (United States); Department of Physics and Astronomy, Vanderbilt University, Nashville, TN 37232 (United States); Institute of Imaging Science, Vanderbilt University, Nashville, TN 37232 (United States)

    2010-05-20

    We evaluate a nuclear magnetic resonance (NMR) method of calorimetry for the measurement of specific heat (c{sub p}) and specific absorption rate (SAR) in liquids. The feasibility of NMR calorimetry is demonstrated by experimental measurements of water, ethylene glycol and glycerol using any of three different NMR parameters (chemical shift, spin-spin relaxation rate and equilibrium nuclear magnetization). The method involves heating the sample using a continuous wave laser beam and measuring the temporal variation of the spatially averaged NMR parameter by non-invasive means. The temporal variation of the spatially averaged NMR parameter as a function of thermal power yields the ratio of the heat capacity to the respective nuclear thermal coefficient, from which the specific heat can be determined for the substance. The specific absorption rate is obtained by subjecting the liquid to heating by two types of radiation, radiofrequency (RF) and near-infrared (NIR), and by measuring the change in the nuclear spin phase shift by a gradient echo imaging sequence. These studies suggest NMR may be a useful tool for measurements of the thermal properties of liquids.

  15. Optimization between heating load and entropy-production rate for endoreversible absorption heat-transformers

    International Nuclear Information System (INIS)

    Sun Fengrui; Qin Xiaoyong; Chen Lingen; Wu Chih

    2005-01-01

    For an endoreversible four-heat-reservoir absorption heat-transformer cycle, for which a linear (Newtonian) heat-transfer law applies, an ecological optimization criterion is proposed for the best mode of operation of the cycle. This involves maximizing a function representing the compromise between the heating load and the entropy-production rate. The optimal relation between the ecological criterion and the COP (coefficient of performance), the maximum ecological criterion and the corresponding COP, heating load and entropy production rate, as well as the ecological criterion and entropy-production rate at the maximum heating load are derived using finite-time thermodynamics. Moreover, compared with the heating-load criterion, the effects of the cycle parameters on the ecological performance are studied by numerical examples. These show that achieving the maximum ecological criterion makes the entropy-production rate decrease by 77.0% and the COP increase by 55.4% with only 27.3% heating-load losses compared with the maximum heating-load objective. The results reflect that the ecological criterion has long-term significance for optimal design of absorption heat-transformers

  16. Chromatographic determination of the rate and extent of absorption of air pollutants by sea water

    International Nuclear Information System (INIS)

    Nikolakaki, S.; Vassilakos, C.; Katsanos, N.A.

    1994-01-01

    A simple chromatographic method is developed to determine the rate constant for expulsion of an air pollutant from water or its diffusion parameter in the liquid, the rate constant for chemical reaction of the pollutant with water, its mass transfer coefficient in the liquid, and the partition coefficient between liquid water and air. From these physicochemical parameters, the absorption rate by sea water and, therefore, the depletion rate of a polluting substance from the air can be calculated, together with the equilibrium state of this absorption. The method has been applied to nitrogen dioxide being absorbed by triple-distilled water and by sea water, at various temperatures. From the temperature variation of the reaction rate constant and of the partition coefficient, the activation energy for the reaction and the differential heat of solution were determined. (orig.)

  17. Determination of water absorption rate of palm kernel shells as an ...

    African Journals Online (AJOL)

    Sawdust, for instance, which is about the most commonly used organic pore agent is known to have high water absorption rates. This is in addition to the inability of the user to have it from one grade of wood-hard wood which is preferred. These factors amount to various drawbacks in the insulating refractory bricks produced ...

  18. Mapping the outdoor gamma dose rate in Indonesia

    International Nuclear Information System (INIS)

    Iskandar, Dadong; Syarbaini, Sutarman; Bunawas, Kusdiana

    2008-01-01

    Full text: Indonesia is the largest archipelago in the world, comprising five main islands - Java, Sumatra, Sulawesi, Kalimantan and Papua - as well as 30 archipelagoes totaling 17,508 islands with about 6000 of those inhabited. Mapping the outdoor gamma dose rate in Indonesia is a research project conducted by National Nuclear Energy Agency since 2005 aiming to produce a baseline data map as an overview for planning purposes. In these three years 4 main islands has been measured. The grid system has been used in the research. In Sumatra Island the grid is 50 x 50 km 2 , while in Java 40 x 40 km 2 , in Kalimantan 60 x 60 km 2 , and in Sulawesi 40 x 40 km 2 . The gamma dose rates have been measured by Mini Gamma Ray Spectrometer Model GR-130 made by Exploranium-Canada. Figure 1 shows the map of outdoor gamma dose rate in Indonesia. Range of dose rate are in Sumatra from 22,96 ± 0,46 n Sv/h to 186,08 ± 3,72 n Sv/h, in Java 11,32 ± 0,72 n Sv/h to 127,54 ± 6,14 n Sv/h, in Kalimantan 10.72 ± 8.32 n Sv/h to 349,48 ± 57,21 n Sv/h, and in Sulawesi 17.7 ± 11,5 n Sv/h to 467 ± 102 n Sv/h. The arithmetic and geometric mean of dose rate in Indonesia are 68 n Sv/h and 53 n Sv/h, respectively. In general, outdoor gamma dose rate in Indonesia is in a normal range. There are some regions have anomaly of gamma dose rate, for examples at North Sumatra 186.08 ± 3,72 n Sv/h (N 2.12727, E 99.80909), at West Kalimantan 349,48 ± 57,21 n Sv/h (S 1.39507, E 110.57584), at West Sulawesi 487 ± 103 n Sv/h (S 2.95781, E 118.86995), etc. These data is very useful as a radiation baseline in Indonesia. (author)

  19. The absorption and utilization rates for different types of 3H-vitamin A by broiler

    International Nuclear Information System (INIS)

    Cai Huiyi; Zhang Shu

    1992-01-01

    165 newly hatched Arbor Acres broiler chickens were divided into three groups for studying the absorption speed and the utilization rate of different types and doses of 3 H-vitamin A through feeding and metabolizing experiments. The results obtained are as follows: 1. All types of vitamin A could be absorbed by first-week chicken, and water-dispersible vitamin A is the best one. 2. Utilization rates for three types of 3 H-vitamin A were: oil type 80.67%, power type 82.91%, water-dispersible type 89.43%. 3. Chikens absorbed 3 H-vitamin A more quickly when they were 1-3 days old. Moreover, the absorption was mainly performed at the 2-4 hours after the intake of vitamin A. 4. The absorption of vitamin A in intestine was a continuing process lasted about 72 hours, but most of it was absorbed within 24-28 hours

  20. [Study on lead absorption in pumpkin by atomic absorption spectrophotometry].

    Science.gov (United States)

    Li, Zhen-Xia; Sun, Yong-Dong; Chen, Bi-Hua; Li, Xin-Zheng

    2008-07-01

    A study was carried out on the characteristic of lead absorption in pumpkin via atomic absorption spectrophotometer. The results showed that lead absorption amount in pumpkin increased with time, but the absorption rate decreased with time; And the lead absorption amount reached the peak in pH 7. Lead and cadmium have similar characteristic of absorption in pumpkin.

  1. Absorption of carbon dioxide and isotope exchange rate of carbon in a reaction system between carbon dioxide and carbamic acid

    International Nuclear Information System (INIS)

    Takeshita, Kenji; Kitamoto, Asashi

    1985-01-01

    The performance of isotope separation of carbon-13 by chemical exchange between carbon dioxide and carbamic acid was studied. The working fluid used in the study was a solution of DNBA, (C 4 H 9 ) 2 NH and n-octane mixture. Factors related to the isotope exchange rate were measured, such as the absorption rate of carbon dioxide into the solution of DNBA and n-octane, the isotope exchange rate and the separation factor in the reaction between CO 2 and carbamic acid. The absorption of CO 2 into the working fluid was the sum of chemical absorption by DNBA and physical absorption by n-octane. The absorption of carbon dioxide into the working fluid was negligible at temperatures over 90 0 C, but increased gradually at lower temperatures. Carbon dioxide was absorbed into DNBA by chemical absorption, and DNBA was converted to carbamic acid by the reaction. The reaction for synthesis and decomposition of carbamic acid was reversible. The separation factor in equilibrium reached a large value at lower temperatures. The isotope exchange rate between gas and liquid was proportional to the product of the concentration of carbamic acid and the concentration of CO 2 by physical absorption. The isotope separation of carbon by chemical exchange reaction is better operated under the conditions of lower temperature and higher pressure. (author)

  2. Frequency-chirped readout of spatial-spectral absorption features

    International Nuclear Information System (INIS)

    Chang, Tiejun; Mohan, R. Krishna; Harris, Todd L.; Merkel, Kristian D.; Tian Mingzhen; Babbitt, Wm. Randall

    2004-01-01

    This paper examines the physical mechanisms of reading out spatial-spectral absorption features in an inhomogeneously broadened medium using linear frequency-chirped electric fields. A Maxwell-Bloch model using numerical calculation for angled beams with arbitrary phase modulation is used to simulate the chirped field readout process. The simulation results indicate that any spatial-spectral absorption feature can be read out with a chirped field with the appropriate bandwidth, duration, and intensity. Mapping spectral absorption features into temporal intensity modulations depends on the chirp rate of the field. However, when probing a spatial-spectral grating with a chirped field, a beat signal representing the grating period can be created by interfering the emitted photon echo chirped field with a reference chirped field, regardless of the chirp rate. Comparisons are made between collinear and angled readout configurations. Readout signal strength and spurious signal distortions are investigated as functions of the grating strength and the Rabi frequency of the readout pulse. Using a collinear readout geometry, distortions from optical nutation on the transmitted field and higher-order harmonics are observed, both of which are avoided in an angled beam geometry

  3. Rating environmental noise on the basis of noise maps

    NARCIS (Netherlands)

    Miedema, H.M.E.; Borst, H.C.

    2006-01-01

    A system that rates noise on the basis of noise maps has been developed which is based on empirical exposure-response relationships, so that effects in the community will be lower if the system gives a better rating. It is consistent with noise metrics and effect endpoint chosen in the EU, i.e., it

  4. Survey on Different Samsung with Nokia Smart Mobile Phones in the Specific Absorption Rate Electrical Field of Head.

    Science.gov (United States)

    Fakhri, Yadolah; Alinejad, Azim; Keramati, Hassan; Bay, Abotaleb; Avazpour, Moayed; Zandsalimi, Yahya; Moradi, Bigard; Rasouli Amirhajeloo, Leila; Mirzaei, Maryam

    2016-09-01

    The use of smart phones is increasing in the world. This excessive use, especially in the last two decades, has created too much concern on the effects of emitted electromagnetic fields and specific absorption rate on human health. In this descriptive-analytical study of the electric field resulting from smart phones of Samsung and Nokia by portable measuring device, electromagnetic field, Model HI-3603-VDT/VLF, were measured. Then, head absorption rate was calculated in these two mobiles by ICNIRP equation. Finally, the comparison of specific absorption rate, especially between Samsung and Nokia smart phones, was conducted by T-Test statistics analysis. The mean of electric field for Samsung and Nokia smart mobile phones was obtained 1.8 ±0.19 v/m  and 2.23±0.39 v/m , respectively, while the range of the electric field was obtained as 1.56-2.21 v/m and 1.69-2.89 v/m for them, respectively. The mean of specific absorption rate in Samsung and Nokia was obtained 0.002 ± 0.0005 W/Kg and 0.0041±0.0013 W/Kg at the frequency of 900 MHz and 0.004±0.001 W/Kg and 0.0062±0.0002 W/Kg at the frequency of 1800 MHz respectively. The ratio of mean electronic field to guidance in the Samsung mobile phone at the frequency of 900 MHz and 1800 MHz was 4.36% and 3.34%, while was 5.62% and 4.31% in the Nokia mobile phone, respectively. The ratio of mean head specific absorption rate in smart mobile phones of Samsung and Nokia in the guidance level at the frequency of 900 was 0.15% and 0.25%, respectively, while was 0.23 %and 0.38% at the frequency of 1800 MHz, respectively. The rate of specific absorption of Nokia smart  mobile phones at the frequencies of 900 and 1800 MHz  was significantly higher than Samsung (p value Samsung smart mobile phone.

  5. Experimental determination of the absorption rate of unattached radon progeny from respiratory tract to blood

    International Nuclear Information System (INIS)

    Butterweck, G.; Schuler, Ch.; Vessl, G.; Mueller, R.; Marsh, J.W.; Thrift, S.; Birchall, A.

    2002-01-01

    An exposure methodology was developed for the determination of the absorption rate of unattached radon progeny deposited in the human respiratory tract to blood. Twenty-one volunteers were exposed in a radon chamber during well-controlled aerosol and radon progeny conditions, with predominantly unattached radon daughters. Special efforts were made to restrict the dose to the volunteers to an absolute maximum of 0.08 mSv. Measurements of radon gas and radon progeny in blood samples of these volunteers indicated absorption half times of 20 min to 60 min. Former determinations, mainly performed with much larger aerosol particles of diameters between 100 nm and 1000 nm, implied absorption half times around 10 h. This indicates that the absorption of radon decay products from ciliated airways into blood is dependent upon particle size and particle composition. (author)

  6. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, OSCEOLA COUNTY, FL

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    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  7. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, HART COUNTY, KY

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    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  8. DIGITAL FLOOD INSURANCE RATE MAP DATABASE,GRAVES COUNTY, KY

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    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  9. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, LYON COUNTY, KY

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  10. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, WOLFE COUNTY, KY

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  11. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, WEBSTER COUNTY, KY

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  12. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, CHRISTIAN COUNTY, KY

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  13. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, DAVIESS COUNTY, KY

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  14. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, MARLBORO COUNTY, SC

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  15. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, MIDDLESEX, VA, USA

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  16. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, WARREN COUNTY, USA

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  17. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, SCOTT COUNTY, KY

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  18. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, NEWTON COUNTY, GEORGIA

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  19. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, LUCAS COUNTY, OHIO

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  20. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, HARDIN COUNTY, TX

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  1. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, SARPY COUNTY, USA

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  2. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, SUFFOLK COUNTY, MASSACHUSETTS

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  3. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, FAIRFIELD COUNTY, CONNECTICUT

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  4. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, BUTLER COUNTY, NE

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  5. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, WALKER COUNTY, TX

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  6. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, WALKER COUNTY, AL

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  7. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Wilcox COUNTY, AL

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  8. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, WASHINGTON COUNTY, USA

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  9. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, BALLARD COUNTY, KY

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  10. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, HARRISON COUNTY, KY

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  11. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, POWELL COUNTY, KY

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  12. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Eddy County, NM

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  13. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Winston COUNTY, AL

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  14. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Mitchell County, GA

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  15. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, ANGELINA COUNTY, TX

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  16. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, ATASCOSA COUNTY, TEXAS

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  17. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, DOUGLAS COUNTY, USA

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  18. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, FLEMING COUNTY, KY

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  19. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, SEWARD COUNTY, USA

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  20. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, MCCRACKEN COUNTY, KY

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  1. Flood Insurance Rate Map Database, Kent County, Delaware, USA

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  2. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, SAUNDERS COUNTY, NEBRASKA

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  3. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, CHEROKEE COUNTY, KANSAS

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  4. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, POLK COUNTY, NEBRASKA

    Data.gov (United States)

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  5. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, HARLAN COUNTY, NEBRASKA

    Data.gov (United States)

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  6. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, RENO COUNTY, KANSAS

    Data.gov (United States)

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  7. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, FURNAS COUNTY, NEBRASKA

    Data.gov (United States)

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  8. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, ALLAMAKEE COUNTY, IOWA

    Data.gov (United States)

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  9. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, CASS COUNTY, NEBRASKA

    Data.gov (United States)

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  10. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, MCPHERSON COUNTY, KANSAS

    Data.gov (United States)

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  11. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, DAWES COUNTY, NEBRASKA

    Data.gov (United States)

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  12. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, VALLEY COUNTY, NEBRASKA

    Data.gov (United States)

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  13. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, ELLSWORTH COUNTY, KANSAS

    Data.gov (United States)

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  14. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, SHERMAN COUNTY, NEBRASKA

    Data.gov (United States)

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  15. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, HARVEY COUNTY, KANSAS

    Data.gov (United States)

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  16. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, PLATTE COUNTY, NEBRASKA

    Data.gov (United States)

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  17. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, COLFAX COUNTY, NEBRASKA

    Data.gov (United States)

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  18. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, THURSTON COUNTY, NEBRASKA

    Data.gov (United States)

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  19. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, TAMA COUNTY, IOWA

    Data.gov (United States)

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  20. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, WEBSTER COUNTY, NEBRASKA

    Data.gov (United States)

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  1. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, BONNER COUNTY, IDAHO

    Data.gov (United States)

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  2. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, ROBERTSON COUNTY, KY

    Data.gov (United States)

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  3. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, CHEROKEE COUNTY, TX

    Data.gov (United States)

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  4. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, GRANT COUNTY, KY

    Data.gov (United States)

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  5. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, ESSEX COUNTY, MA

    Data.gov (United States)

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  6. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, SEBASTIAN COUNTY, AR

    Data.gov (United States)

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  7. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, SEBASTIAN COUNTY, ARKANSAS

    Data.gov (United States)

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  8. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, CHAMBERS COUNTY, TEXAS

    Data.gov (United States)

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  9. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, NELSON COUNTY, KY

    Data.gov (United States)

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  10. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, MCLEAN COUNTY, KY

    Data.gov (United States)

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  11. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, RUSK COUNTY, TX

    Data.gov (United States)

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  12. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, PEMBINA COUNTY, USA

    Data.gov (United States)

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  13. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, SHELBY COUNTY, AL

    Data.gov (United States)

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  14. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, JOHNSON COUNTY, KY

    Data.gov (United States)

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  15. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Harris COUNTY, TX

    Data.gov (United States)

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  16. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, PIKE COUNTY, AL

    Data.gov (United States)

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  17. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, MARION COUNTY, FLORIDA

    Data.gov (United States)

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  18. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, HOUSTON COUNTY, TX

    Data.gov (United States)

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  19. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Marshall COUNTY, AL

    Data.gov (United States)

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  20. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, GRIMES COUNTY, TX

    Data.gov (United States)

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  1. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, WALLER COUNTY, TX

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  2. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, CARBON COUNTY, UTAH

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  3. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Liberty County, TX

    Data.gov (United States)

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  4. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, TYLER COUNTY, TX

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  5. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Stafford County , VIRGINIA

    Data.gov (United States)

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  6. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, SUMNER COUNTY, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  7. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, HILLSBOROUGH COUNTY, FLORIDA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  8. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, LEE COUNTY, FLORIDA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  9. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, WAYNE COUNTY, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  10. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Jefferson COUNTY, AL

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  11. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Kenton COUNTY, Kentucky

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  12. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, RUSSELL COUNTY, KY

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  13. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, ST. LOUIS, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  14. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, ALPENA COUNTY, MI

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  15. Digital Flood Insurance Rate Map for Vermillion County, IN

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  16. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Dougherty County, GA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  17. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, POLK COUNTY, TX

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  18. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, MCDONALD COUNTY, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  19. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, MERCER COUNTY, KY

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  20. Digital Flood Insurance Rate Map Database, Crawford County, PA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  1. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, BATH COUNTY, KY

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  2. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Douglas COUNTY, Nevada

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  3. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Elbert County, Colorado

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  4. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, JASPER COUNTY, TX

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  5. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Cleburne COUNTY, AL

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  6. DIGITAL FLOOD INSURANCE RATE MAP DATABASE,CAMDEN COUNTY, GEORGIA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk Information And supporting data used to develop the risk data. The primary risk;...

  7. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, MENDOCINO COUNTY, CALIFORNIA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  8. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, ETOWAH COUNTY, AL

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  9. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, HUNTERDON CO., NJ

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  10. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, FINNEY COUNTY, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  11. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, CULLMAN COUNTY, AL

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  12. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, MARION COUNTY, KY

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  13. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, LEBANON COUNTY, PENNSYLVANIA

    Data.gov (United States)

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  14. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, ELLIOTT COUNTY, KY

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  15. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, GAGE COUNTY, NEBRASKA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  16. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, CLARK COUNTY, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  17. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, POLK COUNTY, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  18. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, MIDDLESEX COUNTY, MASSACHUSETTS

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  19. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, ESCAMBIA COUNTY, AL

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  20. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Baldwin COUNTY, AL

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  1. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, SONOMA COUNTY, CALIFORNIA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  2. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, BENTON COUNTY, ARKANSAS

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  3. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, GORDON COUNTY, GEORGIA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information And supporting data used to develop the risk data. The primary risk...

  4. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, MARIN COUNTY, CALIFORNIA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  5. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, BOYLE COUNTY, KY

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  6. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Bell COUNTY, Kentucky

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  7. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, BATH COUNTY, VIRGINIA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  8. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, SIMPSON COUNTY, KY

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  9. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, BANDERA COUNTY, TEXAS

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  10. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Washington COUNTY, NE

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  11. Digital Flood Insurance Rate Map Database, Mercer County, PA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  12. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, DUKES COUNTY, MA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  13. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Terrell County, GA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  14. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, GLOUCESTER, VA, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  15. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, WORCESTER COUNTY, MA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  16. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Cherokee COUNTY, AL

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  17. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, JACKSON COUNTY, AL

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  18. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, NORFOLK COUNTY, MASSACHUSETTS

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  19. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, COWLEY COUNTY, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  20. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, TALBOT, MARYLAND, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  1. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, NANTUCKET COUNTY, MASSACHUSETTS

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  2. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, CHESTERFIELD, VA, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  3. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, WORCESTER COUNTY, MASSACHUSETTS

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  4. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, OTTAWA COUNTY, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  5. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, GRAND COUNTY, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  6. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, DILLON COUNTY, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  7. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, ALLEN COUNTY, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  8. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, HOLMES COUNTY, OHIO

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  9. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, HOLMES COUNTY, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  10. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, KOOTENAI COUNTY, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  11. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Accomack County, VIRGINIA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  12. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, LYCOMING COUNTY, PENNSYLVANIA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  13. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, WHATCOM COUNTY, WASHINGTON

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  14. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, HARRISON COUNTY, TEXAS

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  15. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, MATHEWS COUNTY, VIRGINIA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  16. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, HIGHLAND COUNTY, VIRGINIA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  17. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, SUMTER COUNTY, AL

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  18. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, RALLS COUNTY, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  19. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, MARION COUNTY, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  20. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, HOUSTON COUNTY, Georgia

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  1. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, YOLO COUNTY, CALIFORNIA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  2. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Tuolumne County, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  3. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, MADERA COUNTY, CALIFORNIA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  4. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, TEHAMA COUNTY, CALIFORNIA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  5. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, SACRAMENTO COUNTY, CALIFORNIA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  6. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, HOPKINS COUNTY, KY

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  7. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Butts County, GA

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    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  8. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, MAYES COUNTY, OK

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  9. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, GRADY COUNTY, OKLAHOMA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  10. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, SHASTA COUNTY, CALIFORNIA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  11. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, ROSS COUNTY, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  12. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, BALTIMORE CITY, MARYLAND

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  13. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Chambers COUNTY, AL

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  14. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, ROGERS COUNTY, OKLAHOMA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  15. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, MEDINA COUNTY, TX

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  16. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, STARK COUNTY, OHIO

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  17. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, ALBEMARLE COUNTY, VIRGINIA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  18. Rate-distortion analysis of steganography for conveying stereovision disparity maps

    Science.gov (United States)

    Umeda, Toshiyuki; Batolomeu, Ana B. D. T.; Francob, Filipe A. L.; Delannay, Damien; Macq, Benoit M. M.

    2004-06-01

    3-D images transmission in a way which is compliant with traditional 2-D representations can be done through the embedding of disparity maps within the 2-D signal. This approach enables the transmission of stereoscopic video sequences or images on traditional analogue TV channels (PAL or NTSC) or printed photographic images. The aim of this work is to study the achievable performances of such a technique. The embedding of disparity maps has to be seen as a global rate-distortion problem. The embedding capacity through steganography is determined by the transmission channel noise and by the bearable distortion on the watermarked image. The distortion of the 3-D image displayed as two stereo views depends on the rate allocated to the complementary information required to build those two views from one reference 2-D image. Results from the works on the scalar Costa scheme are used to optimize the embedding of the disparity map compressed bit stream into the reference image. A method for computing the optimal trade off between the disparity map distortion and embedding distortion as a function of the channel impairments is proposed. The goal is to get a similar distortion on the left (the reference image) and the right (the disparity compensated image) images. We show that in typical situations the embedding of 2 bits/pixels in the left image, while the disparity map is compressed at 1 bit per pixel leads to a good trade-off. The disparity map is encoded with a strong error correcting code, including synchronisation bits.

  19. New constraints in absorptive capacity and the optimum rate of petroleum output

    Energy Technology Data Exchange (ETDEWEB)

    El Mallakh, R

    1980-01-01

    Economic policy in four oil-producing countries is analyzed within a framework that combines a qualitative assessment of the policy-making process with an empirical formulation based on historical and current trends in these countries. The concept of absorptive capacity is used to analyze the optimum rates of petroleum production in Iran, Iraq, Saudi Arabia, and Kuwait. A control solution with an econometric model is developed which is then modified for alternative development strategies based on analysis of factors influencing production decisions. The study shows the consistencies and inconsistencies between the goals of economic growth, oil production, and exports, and the constraints on economic development. Simulation experiments incorporated a number of the constraints on absorptive capacity. Impact of other constraints such as income distribution and political stability is considered qualitatively. (DLC)

  20. Influence of Subjectivity in Geological Mapping on the Net Penetration Rate Prediction for a Hard Rock TBM

    Science.gov (United States)

    Seo, Yongbeom; Macias, Francisco Javier; Jakobsen, Pål Drevland; Bruland, Amund

    2018-05-01

    The net penetration rate of hard rock tunnel boring machines (TBM) is influenced by rock mass degree of fracturing. This influence is taken into account in the NTNU prediction model by the rock mass fracturing factor ( k s). k s is evaluated by geological mapping, the measurement of the orientation of fractures and the spacing of fractures and fracture type. Geological mapping is a subjective procedure. Mapping results can therefore contain considerable uncertainty. The mapping data of a tunnel mapped by three researchers were compared, and the influence of the variation in geological mapping was estimated to assess the influence of subjectivity in geological mapping. This study compares predicted net penetration rates and actual net penetration rates for TBM tunneling (from field data) and suggests mapping methods that can reduce the error related to subjectivity. The main findings of this paper are as follows: (1) variation of mapping data between individuals; (2) effect of observed variation on uncertainty in predicted net penetration rates; (3) influence of mapping methods on the difference between predicted and actual net penetration rate.

  1. Construction of radioelement and dose rate baseline maps by combining ground and airborne radiometric data

    International Nuclear Information System (INIS)

    Rybach, L.; Medici, F.; Schwarz, G.F.

    1997-01-01

    For emergency situations like nuclear accidents, lost isotopic sources, debris of reactor-powered satellites etc. well-documented baseline information is indispensable. Maps of cosmic, terrestrial natural and artificial radiation can be constructed by assembling different datasets such as ground and airborne gamma spectrometry, direct dose rate measurements, and soil/rock samples. The in situ measurements were calibrated using the soil samples taken at/around the field measurement sites, the airborne measurements by a combination of in situ, and soil/rock sample data. The radioelement concentrations (Bq/kg) were in turn converted to dose-rate (nSv/h). First, the cosmic radiation map was constructed from a digital terrain model, averaging topographic heights within cells of 2 km X 2 km size. For the terrestrial radiation a total of 1615 ground data points were available, in addition to the airborne data. The artificial radiation map (Chernobyl and earlier fallout) has the smallest data base (184 data points from airborne and ground measurements). The dose rate map was constructed by summing up the above-mentioned contributions. It relies on a data base which corresponds to a density of about 1 point per 25 km 2 . The cosmic radiation map shows elevated dose rates in the high parts of the Swiss Alps. The cosmic dose rate ranges from 40 to 190 nSv/h, depending on altitude. The terrestrial dose rate maps show general agreement with lithology: elevated dose rates (100 to 200 nSv/h) characterize the Central Massifs of the Alps where crystalline rocks give a maximum of 370 nSv/h, whereas the sedimentary northern Alpine Foreland (Jura, Molasse basin) shows consistently lower dose rates (40-100 nSv/h). The artificial radiation map has its maximum value in the southern part of Switzerland (90 nSv/h). The map of total dose rate exhibits values from 55 to 570 nSv/h. These values are considerably higher than reported in the Radiation Atlas (''Natural Sources of Ionising

  2. INTERSTELLAR METASTABLE HELIUM ABSORPTION AS A PROBE OF THE COSMIC-RAY IONIZATION RATE

    International Nuclear Information System (INIS)

    Indriolo, Nick; McCall, Benjamin J.; Hobbs, L. M.; Hinkle, K. H.

    2009-01-01

    The ionization rate of interstellar material by cosmic rays has been a major source of controversy, with different estimates varying by three orders of magnitude. Observational constraints of this rate have all depended on analyzing the chemistry of various molecules that are produced following cosmic-ray ionization, and in many cases these analyses contain significant uncertainties. Even in the simplest case (H + 3 ), the derived ionization rate depends on an (uncertain) estimate of the absorption path length. In this paper, we examine the feasibility of inferring the cosmic-ray ionization rate using the 10830 A absorption line of metastable helium. Observations through the diffuse clouds toward HD 183143 are presented, but yield only an upper limit on the metastable helium column density. A thorough investigation of He + chemistry reveals that only a small fraction of He + will recombine into the triplet state and populate the metastable level. In addition, excitation to the triplet manifold of helium by secondary electrons must be accounted for as it is the dominant mechanism which produces He* in some environments. Incorporating these various formation and destruction pathways, we derive new equations for the steady state abundance of metastable helium. Using these equations in concert with our observations, we find ζ He -15 s -1 , an upper limit about 5 times larger than the ionization rate previously inferred for this sight line using H + 3 . While observations of interstellar He* are extremely difficult at present, and the background chemistry is not nearly as simple as previously thought, potential future observations of metastable helium would provide an independent check on the cosmic-ray ionization rate derived from H + 3 in diffuse molecular clouds, and, perhaps more importantly, allow the first direct measurements of the ionization rate in diffuse atomic clouds.

  3. A graphical review of radiogenic animal cancer data using the 'dose and dose-rate map'

    International Nuclear Information System (INIS)

    Yoshida, Kazuo; Hoshi, Yuko; Sakai, Kazuo

    2008-01-01

    We have been investigating the effects of low dose or low dose rate irradiation on mice, using our low dose-rate irradiation facilities. In these studies, we found that the effects were highly dependent on both total dose and dose rate. To show this visually, we proposed the 'dose/dose rate map', and plotted the results of our laboratory and our co-workers. The map demonstrated that dose/dose rate plane could be divided into three areas; 1) An area where harmful effects are observed, 2) An area where no harmful effects are observed, and 3) Another area, between previous two areas, where certain protective functions are enhanced. As this map would be a powerful tool to find some trend among the vast numbers of data relating the biological effects of ionizing radiation, we have developed a computer program which plots the collected data on the dose/dose rate map sorting by experimental conditions. In this study, we graphically reviewed and analyzed the data relating to the lifespan studies of animals with a view to determining the relationships between doses and dose rates of ionizing radiation and cancer incidence. The data contains about 800 sets of experiments, which concerns 187,000 animals exposed to gamma ray or X-ray and their 112,000 controls, and total of about 30,000 cancers in exposed animals and 14,000 cancers in controls. About 800 points of data were plotted on the dose/dose rate map. The plot showed that 1) The divided three areas in the dose/dose rate map were generally confirmed by these 800 points of data, and 2) In some particular conditions, e.g. sarcoma by X-rays, the biologically effective area is extended to relatively high dose/dose rate area. (author)

  4. Elementary reaction rate measurements at high temperatures by tunable-laser flash-absorption

    Energy Technology Data Exchange (ETDEWEB)

    Hessler, J.P. [Argonne National Laboratory, IL (United States)

    1993-12-01

    The major objective of this program is to measure thermal rate coefficients and branching ratios of elementary reactions. To perform these measurements, the authors constructed an ultrahigh-purity shock tube to generate temperatures between 1000 and 5500 K. The tunable-laser flash-absorption technique is used to measure the rate of change of the concentration of species which absorb below 50,000 cm{sup {minus}1} e.g.: OH, CH, and CH{sub 3}. This technique is being extended into the vacuum-ultraviolet spectral region where one can measure atomic species e.g.: H, D, C, O, and N; and diatomic species e.g.: O{sub 2}, CO, and OH.

  5. Determination of Lung-to-Blood Absorption Rates for Lead and Bismuth which are Appropriate for Radon Progeny

    International Nuclear Information System (INIS)

    Marsh, J.W.; Birchall, A.

    1999-01-01

    The ICRP Publication 66 Human Respiratory Tract Model (HRTM) treats clearance as a competitive process between absorption into blood and particle transport to the gastrointestinal tract and lymphatics. The ICRP recommends default absorption rates for lead and bismuth in ICRP Publication 71 but states that the values are not appropriate for short-lived radon progeny. This paper describes an evaluation of published data from volunteer experiments to estimate the absorption half-times of lead and bismuth that are appropriate for short-lived radon progeny. The absorption half-time for lead was determined to be 10±2 h, based on 212 Pb lung and blood retention data from several studies. The absorption half-time for bismuth was estimated to be about 13 h, based on 212 Bi urinary excretion data from one experiment and the ICRP biokinetic model for bismuth as a decay product of lead. (author)

  6. Effect of Feed Gas Flow Rate on CO2 Absorption through Super Hydrophobic Hollow Fiber membrane Contactor

    Science.gov (United States)

    Kartohardjono, Sutrasno; Alexander, Kevin; Larasati, Annisa; Sihombing, Ivander Christian

    2018-03-01

    Carbon dioxide is pollutant in natural gas that could reduce the heating value of the natural gas and cause problem in transportation due to corrosive to the pipeline. This study aims to evaluate the effects of feed gas flow rate on CO2 absorption through super hydrophobic hollow fiber contactor. Polyethyleneglycol-300 (PEG-300) solution was used as absorbent in this study, whilst the feed gas used in the experiment was a mixture of 30% CO2 and 70% CH4. There are three super hydrophobic hollow fiber contactors sized 6 cm and 25 cm in diameter and length used in this study, which consists of 1000, 3000 and 5000 fibers, respectively. The super hydrophobic fiber membrane used is polypropylene-based with outer and inner diameter of about 525 and 235 μm, respectively. In the experiments, the feed gas was sent through the shell side of the membrane contactor, whilst the absorbent solution was pumped through the lumen fibers. The experimental results showed that the mass transfer coefficient, flux, absorption efficiency for CO2-N2 system and CO2 loading increased with the feed gas flow rate, but the absorption efficiency for CO2-N2 system decreased. The mass transfer coefficient and the flux, at the same feed gas flow rate, decreased with the number of fibers in the membrane contactor, but the CO2 absorption efficiency and the CO2 loading increased.

  7. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, MCINTOSH COUNTY, GEORGIA, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk Information And supporting data used to develop the risk data. The primary risk;...

  8. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, HAWAII COUNTY, HAWAII, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  9. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, SHELBY COUNTY, KENTUCKY, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  10. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, CHRISTIAN COUNTY, ILLINOIS USA

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    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  11. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, BENTON COUNTY, MINNESOTA, USA

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    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk Information And supporting data used to develop the risk data. The primary risk;...

  12. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, MONROE COUNTY, GEORGIA, USA

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    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk Information And supporting data used to develop the risk data. The primary risk;...

  13. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, HENRY COUNTY, GEORGIA, USA

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    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk Information And supporting data used to develop the risk data. The primary risk;...

  14. FINAL DIGITAL FLOOD INSURANCE RATE MAP DATABASE, GREENWOOD COUNTY, SC

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    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  15. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, RICE COUNTY, MINNESOTA, USA

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    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk Information And supporting data used to develop the risk data. The primary risk;...

  16. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, KARNES COUNTY, TEXAS, USA

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    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  17. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, VOLUSIA COUNTY, FL, USA

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    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  18. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, SHELBY COUNTY, IA, USA

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    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  19. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, POTTAWATTAMIE COUNTY, IOWA, USA

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    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk Information And supporting data used to develop the risk data. The primary risk;...

  20. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, MITCHELL COUNTY, IOWA, USA

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    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk Information And supporting data used to develop the risk data. The primary risk;...

  1. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, CLAYTON COUNTY, IOWA, USA

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    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  2. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, HOWARD COUNTY, IOWA, USA

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    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk Information And supporting data used to develop the risk data. The primary risk;...

  3. DIGITAL FLOOD INSURANCE RATE MAP DATABAES, LA PAZ COUNTY, AZ

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    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  4. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, NEWTON COUNTY, GEORGIA, USA

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    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk Information And supporting data used to develop the risk data. The primary risk;...

  5. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, NASSAU COUNTY, NEW YORK

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    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  6. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, SULLIVAN COUNTY, NEW YORK

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    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  7. Digital Flood Insurance Rate Map Database, PRINCE GEORGE, VA, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  8. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, SHELBY COUNTY, OHIO, USA

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    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  9. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, WAGONER COUNTY, OKLAHOMA, USA

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    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  10. DRAFT DIGITAL FLOOD INSURANCE RATE MAP DATABASE, CHEROKEE COUNTY, SC

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    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  11. Digital Flood Insurance Rate Map Database, Buchanan County, Iowa, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  12. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, SACRAMENTO COUNTY, CALIFORNIA, USA

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    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  13. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, CITY OF SACRAMENTO, CALIFORNIA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  14. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, HONOLULU COUNTY, HI, USA

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    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  15. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, KAUAI COUNTY, HAWAII, USA

    Data.gov (United States)

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  16. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, PASCO COUNTY, FLORIDA, USA

    Data.gov (United States)

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  17. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, GILMER COUNTY, GEORGIA, USA

    Data.gov (United States)

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  18. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, MIAMI - DADE COUNTY, FLORIDA

    Data.gov (United States)

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  19. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, WALKER COUNTY, GEORGIA, USA

    Data.gov (United States)

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  20. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, APPLING COUNTY, GEORGIA, USA

    Data.gov (United States)

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  1. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, LINCOLN COUNTY, ARKANSAS, USA

    Data.gov (United States)

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  2. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, CARROLL COUNTY, GEORGIA, USA

    Data.gov (United States)

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  3. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, CONVERSE COUNTY, WYOMING, USA.

    Data.gov (United States)

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  4. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, DOUGLAS COUNTY, ILLINOIS USA

    Data.gov (United States)

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  5. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Douglas County, Oregon, USA

    Data.gov (United States)

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  6. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, COLFAX COUNTY, New Mexico

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  7. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, SOLANO COUNTY, CALIFORNIA, USA

    Data.gov (United States)

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  8. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, TAYLOR COUNTY, FL, USA

    Data.gov (United States)

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  9. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, ALLEN COUNTY, INDIANA, USA

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  10. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, DOUGLAS COUNTY, NEBRASKA, USA

    Data.gov (United States)

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  11. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, NEWPORT COUNTY, RHODE ISLAND

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  12. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Charles COUNTY, MD, USA

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  13. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, OTTAWA COUNTY, OHIO, USA

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  14. DIGITAL FLOOD INSURANCE RATE MAP DATABASE ROCKLAND COUNTY, NY, USA

    Data.gov (United States)

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  15. Digital Flood Insurance Rate Map Database, Richmond County, Virginia, USA

    Data.gov (United States)

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  16. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, WASATCH COUNTY, UTAH, USA

    Data.gov (United States)

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  17. Digital Flood Insurance Rate Map Database, Westmoreland County, Virginia, USA

    Data.gov (United States)

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  18. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, TOM GREEN COUNTY, TEXAS

    Data.gov (United States)

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  19. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Berks County, Pennsylvania, USA

    Data.gov (United States)

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  20. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, STONE COUNTY, MISSOURI, USA

    Data.gov (United States)

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  1. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, VAL VERDE COUNTY, TEXAS

    Data.gov (United States)

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  2. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, RED WILLOW COUNTY, NEBRASKA

    Data.gov (United States)

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  3. DIGITAL FLOOD INSURANCE RATE MAP DATABASE FOR HOWARD COUNTY, NEBRASKA

    Data.gov (United States)

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  4. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, LANCASTER COUNTY, NE, USA

    Data.gov (United States)

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  5. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, FERGUS COUNTY, MONTANA, USA

    Data.gov (United States)

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  6. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, SAN JOAQUIN COUNTY, CALIFORNIA

    Data.gov (United States)

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  7. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, LORAIN COUNTY, OHIO USA

    Data.gov (United States)

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  8. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, COSHOCTON COUNTY, OHIO, USA

    Data.gov (United States)

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  9. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, NEWTON COUNTY, MISSOURI, USA

    Data.gov (United States)

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  10. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, HAMILTON COUNTY, OHIO, USA

    Data.gov (United States)

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  11. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, RAY COUNTY, MISSOURI, USA

    Data.gov (United States)

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  12. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, JEFFERSON COUNTY, IDAHO, USA

    Data.gov (United States)

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  13. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, LAWRENCE COUNTY, OHIO, USA

    Data.gov (United States)

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  14. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, GRANT COUNTY, WISCONSIN, USA

    Data.gov (United States)

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  15. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Nelson County, VA, USA

    Data.gov (United States)

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  16. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, CHEROKEE COUNTY, GEORGIA, USA

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  17. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, ST. FRANCOIS COUNTY, USA

    Data.gov (United States)

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  18. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, LAFAYETTE COUNTY, MISSOURI, USA

    Data.gov (United States)

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  19. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, RANDALL COUNTY, TX, USA

    Data.gov (United States)

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  20. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, GREENVILLE COUNTY, SOUTH CAROLINA

    Data.gov (United States)

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  1. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, McCormick County, SC

    Data.gov (United States)

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  2. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, McCURTAIN COUNTY, OK

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  3. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, DICKENSON COUNTY, VA, USA

    Data.gov (United States)

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  4. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, MARIPOSA_CO_CA, CALIFORNIA

    Data.gov (United States)

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  5. Digital Flood Insurance Rate Map Database, Charles County, Maryland, USA

    Data.gov (United States)

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  6. Digital Flood Insurance Rate Map Database, Essex County, Virginia, USA

    Data.gov (United States)

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  7. Digital Flood Insurance Rate Map Database, Calvert County, Maryland, USA

    Data.gov (United States)

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  8. Digital Flood Insurance Rate Map Database, Bradford County, Pennsylvania, USA

    Data.gov (United States)

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  9. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, DESOTO COUNTY, FL, USA

    Data.gov (United States)

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  10. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, FANNIN COUNTY, GEORGIA, USA

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  11. DRAFT DIGITAL FLOOD INSURANCE RATE MAP DATABASE, HEMPSTEAD COUNTY, AR

    Data.gov (United States)

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  12. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, CHATHAM COUNTY, GEORGIA, USA

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  13. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, SAN JACINTO COUNTY, TX

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  14. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, New London County, CT

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  15. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Westmoreland County, PA, USA

    Data.gov (United States)

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  16. Digital Flood Insurance Rate Map Database, Sussex County, Delaware, USA

    Data.gov (United States)

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  17. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, DELAWARE COUNTY, OK, USA

    Data.gov (United States)

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  18. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, UNION COUNTY, FLORIDA, USA

    Data.gov (United States)

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  19. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, HAMILTON COUNTY, FLORIDA, USA

    Data.gov (United States)

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  20. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, WALTON COUNTY, FL, USA

    Data.gov (United States)

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  1. DRAFT DIGITAL FLOOD INSURANCE RATE MAP DATABASE, LANCASTER COUNTY, SC

    Data.gov (United States)

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  2. Digital Flood Insurance Rate Map Database, Allegheny County, Pennsylvania, USA

    Data.gov (United States)

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  3. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, BARTOW COUNTY, GEORGIA, USA

    Data.gov (United States)

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  4. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, CALDWELL PARISH, LOUISIANA, USA

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  5. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, SHIAWASSEE COUNTY, MICHIGAN, USA

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  6. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, FRANKLIN COUNTY, OHIO,USA

    Data.gov (United States)

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  7. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, EL DORADO COUNTY, CALIFORNIA

    Data.gov (United States)

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  8. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, GADSDEN COUNTY, FL, USA

    Data.gov (United States)

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  9. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, LACLEDE COUNTY, MISSOURI, USA

    Data.gov (United States)

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  10. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, CLINTON COUNTY, MISSOURI, USA

    Data.gov (United States)

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  11. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, AUGUSTA COUNTY, VA, USA

    Data.gov (United States)

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  12. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, WARREN COUNTY, OH, USA

    Data.gov (United States)

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  13. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Spartanburg County, South Carolina

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  14. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Rio Grande County, Colorado

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  15. DIGITAL FLOOD INSURANCE RATE MAP DATABASE,FREDERICK COUNTY, VA, USA

    Data.gov (United States)

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  16. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Roosevelt COUNTY, New Mexico

    Data.gov (United States)

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  17. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Linn County, Oregon, USA

    Data.gov (United States)

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  18. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, TEHAMA COUNTY, CALIFORNIA, USA

    Data.gov (United States)

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  19. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Wythe County, VA, USA

    Data.gov (United States)

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  20. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, SULLIVAN COUNTY, PA, USA

    Data.gov (United States)

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  1. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, KITSAP COUNTY, WASHINGTON, USA

    Data.gov (United States)

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  2. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Northumberland County, VA, USA

    Data.gov (United States)

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  3. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, CADDO PARISH, LOUISIANA, USA

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  4. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, FORT BEND COUNTY, TEXAS

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  5. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, SEDGWICK COUNTY, KANSAS, USA

    Data.gov (United States)

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  6. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, JOHNSON COUNTY, GEORGIA, USA

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  7. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, CRAWFORD COUNTY, AR ,USA

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  8. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, MONMOUTH COUNTY, NEW JERSEY

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  9. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, YORK COUNTY, PA, USA

    Data.gov (United States)

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  10. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, SUSSEX COUNTY, NEW JERSEY

    Data.gov (United States)

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  11. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, CLEARFIELD COUNTY, PA, USA

    Data.gov (United States)

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  12. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, COOPER COUNTY, MISSOURI, USA

    Data.gov (United States)

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  13. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, CAMERON COUNTY, PA, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  14. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, WARREN COUNTY, NEW JERSEY

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    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  15. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, FAYETTE COUNTY, GEORGIA, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk Information And supporting data used to develop the risk data. The primary risk;...

  16. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, SUFFOLK COUNTY, NEW YORK

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  17. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, INDIAN RIVER COUNTY, FL

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    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  18. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, CAROLINE COUNTY, VA, USA

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    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  19. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, ST JOSEPH COUNTY, MI

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    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  20. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, SHERBURNE COUNTY, MINNESOTA, USA

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    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk Information And supporting data used to develop the risk data. The primary risk;...

  1. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Delaware County, Pennsylvania, USA

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    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  2. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, HALL COUNTY, NE, USA

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    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  3. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, PATRICK COUNTY, VA, USA

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    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  4. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, RANDOLPH COUNTY, WV, USA

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    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  5. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, GRAYSON COUNTY, VA, USA

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    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  6. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, SURRY COUNTY, VA, USA

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    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  7. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Buckingham County, VA, USA

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    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  8. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, GARRETT COUNTY, Maryland, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  9. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, RALEIGH COUNTY, WV, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  10. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Essex County, VA, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  11. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Caroline COUNTY, Maryland, USA

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    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  12. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, TUCKER COUNTY, WV, USA

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    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  13. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Sussex County, VA, USA

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    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  14. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, WESTMORELAND COUNTY, VA, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  15. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, FLUVANNA COUNTY, VA, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  16. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Richmond County, VA, USA

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    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  17. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Pulaski County, VA, USA

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    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  18. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Scott County, VA, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  19. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Upshur County, WV, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  20. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, LINN COUNTY, IA, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  1. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, CLARKE COUNTY, GEORGIA, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk Information And supporting data used to develop the risk data. The primary risk;...

  2. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, DELTA COUNTY, COLORADO, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk Information And supporting data used to develop the risk data. The primary risk;...

  3. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, COBB COUNTY, GA, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  4. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, GULF COUNTY, FLORIDA, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  5. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, LUNA COUNTY, New Mexico

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  6. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, FRANKLIN COUNTY, VA, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  7. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, GREENE COUNTY, GEORGIA, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  8. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, CLATSOP COUNTY, OR, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  9. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, HENRY COUNTY, VA, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  10. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, MIDLAND COUNTY, MICHIGAN, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk Information And supporting data used to develop the risk data. The primary risk;...

  11. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, SISKIYOU COUNTY, CALIFORNIA, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  12. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, PLUMAS COUNTY, CALIFORNIA, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  13. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, ORANGE COUNTY, CALIFORNIA, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  14. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, RIVERSIDE COUNTY, CALIFORNIA, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  15. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, PIMA COUNTY, ARIZONA, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  16. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, COCHISE COUNTY, ARIZONA, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  17. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, YUMA COUNTY, ARIZONA, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  18. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, BUTTE COUNTY, CALIFORNIA, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  19. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, EATON COUNTY, MICHIGAN, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk Information And supporting data used to develop the risk data. The primary risk;...

  20. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Oswego COUNTY, New York

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  1. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, BULLOCH COUNTY, GEORGIA, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk Information And supporting data used to develop the risk data. The primary risk;...

  2. DIGITAL FLOOD INSURACE RATE MAP DATABASE, LEON COUNTY, FL, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  3. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Lancaster County, VA, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  4. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, BEDFORD COUNTY, VA, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  5. DRAFT DIGITAL FLOOD INSURANCE RATE MAP DATABASE, HONOLULU COUNTY, HI

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  6. DRAFT DIGITAL FLOOD INSURANCE RATE MAP DATABASE, NEWBERRY COUNTY, SC

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  7. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, HOWELL COUNTY, MISSOURI, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  8. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, JONES COUNTY, GEORGIA, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk Information And supporting data used to develop the risk data. The primary risk;...

  9. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, CALHOUN COUNTY, FL, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  10. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Bucks COUNTY, PA, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  11. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, WALTON COUNTY, GEORGIA, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk Information And supporting data used to develop the risk data. The primary risk;...

  12. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, HURON COUNTY, MICHIGAN USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  13. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, LEVY COUNTY, FL, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  14. Digital Flood Insurance Rate Map Database, Middlesex County, Virginia, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  15. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, LAKE COUNTY, ILLINOIS USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  16. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, DAUPHIN COUNTY, PENNSYLVANIA, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk Information And supporting data used to develop the risk data. The primary risk;...

  17. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, City of Poquoson, Virginia

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  18. Variable food absorption by Antarctic krill: Relationships between diet, egestion rate and the composition and sinking rates of their fecal pellets

    Science.gov (United States)

    Atkinson, A.; Schmidt, K.; Fielding, S.; Kawaguchi, S.; Geissler, P. A.

    2012-01-01

    The kinetics of food processing by zooplankton affects both their energy budgets and the biogeochemical fate of their fecal pellets. We sampled 40 schools of krill across the Scotia Sea during spring, summer and autumn and found that in all 3 seasons, every aspect of their absorption and defecation varied greatly. The C content of fecal pellets varied from 0.85% to 29% of their dry mass (median 9.8%) and C egestion rates varied 75-fold. C:N mass ratios of pellets ranged from 4.9 to 13.2 (median 7.8), higher than values of 3.9 in the krill and 5.4 in their food, pointing to enhanced uptake of N. Pellet sinking rates equated to 27-1218 m d -1 (median 304 m d -1), being governed mainly by pellet diameter (80-600 μm, mean 183 μm) and density (1.038-1.391 g cm -3, mean 1.121 g cm -3). Pellets showed little loss of C or N in filtered seawater over the first 2 days and were physically robust. When feeding rates were low, slow gut passage time and high absorption efficiency resulted in low egestion rates of pellets that were low in C and N content. These pellets were compact, dense and fast-sinking. Conversely, in good feeding conditions much food tended to pass quickly through the gut and was not efficiently absorbed, producing C and N-rich, slow-sinking pellets. Such "superfluous feeding" probably maximises the absolute rates of nutrient absorption. Food composition was also important: diatom-rich diets depressed the C content of the pellets but increased their sinking rates, likely due to silica ballasting. So depending on how krill process food, their pellets could represent both vehicles for rapid export and slow sinking, C and N-rich food sources for pelagic scavengers. C egestion rates by krill averaged 3.4% of summer primary production (and ingestion rates would be 2-10-fold higher than this) so whatever the fate of the pellets, krill are an important re-packager within the food web. While salp pellets tend to sink faster than those of krill, it is the latter

  19. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, DODGE COUNTY, WISCONSIN (AND INCORPORATED AREAS) - Fox Lake Physical Map Revision

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk;...

  20. EVALUATION OF THE STRUCTURAL FUNDS ABSORPTION RATE BY MEANS OF THE HERMIN MODEL

    Directory of Open Access Journals (Sweden)

    Opritescu Elena Madalina

    2012-07-01

    Full Text Available The main objective of this article is to highlight the main method that could quantify the impact of the structural funds on the Gross Domestic Product. I also presented the regional disparities situation and the European funds absorption rate. The HERMIN model has been designed considering the evolution of macro-variables throughout transition and pre-accession process, as well as out of the need to analyze the gradual alignment of Romania’s economic policies to those of EU. The fact that, initially, the HERMIN model was designed for the European Union’s less developed economies represented the cornerstone in choosing it, as it was the case for Romania, too. However, the quantitative evaluation must always be accompanied by a qualitative evaluation, in order to comprise factors which cannot be measured by the econometrical modeling. For this purpose, when the results of econometrical model based evaluation are used, it is important to be aware of the fact that models simplify reality, no matter the impressive mathematical calculations they employ. Also, we must not omit the fact that Romania’s major development needs and the current economic context imperatively demand a high as possible level of structural funds absorption, as well as their efficient use, meant to generate a significant impact at a national, regional and local level. One of the main instruments employed to sustain economic growth, while also reducing disparities between regions is represented by the structural funds. These funds, consisting in financial contributions of the member states, according to their level of development, are redistributed in compliance with an extremely complex regulating and procedural frame, to those EU states of regions which are fallen behind from a social and economical development perspective Nevertheless, when absorption capacity of a member state is evaluated, the used percentage from the allocated funds is not the only

  1. The effect of sewage sludge application on the growth and absorption rates of Pb and As in water spinach

    Directory of Open Access Journals (Sweden)

    Rong Wang

    2016-01-01

    Full Text Available This paper investigated the effect of the application of sewage sludge on the growth rates and absorption rates of Pb and As in potted water spinach. Our results indicated that application of sewage sludge promoted vegetable growth, and the dry weight of water spinach reached a maximal value (4.38 ± 0.82 g upon 8% sludge application. We also found that the dry weights of water spinach after treatment were all greater than those of the control systems (CK. Treatment with sludge promoted the absorption of Pb and As in water spinach, with a significant (p < 0.05 increase of absorbed Pb following treatment concentrations above 10%, and a peak absorption of As at 8%. Finally, we found that concentrations of Pb and As were higher in rhizosphere-attached soil than in free pot.

  2. GLAM: Glycogen-derived Lactate Absorption Map for visual analysis of dense and sparse surface reconstructions of rodent brain structures on desktop systems and virtual environments

    KAUST Repository

    Agus, Marco; Boges, Daniya; Gagnon, Nicolas; Magistretti, Pierre J.; Hadwiger, Markus; Cali, Corrado

    2018-01-01

    Human brain accounts for about one hundred billion neurons, but they cannot work properly without ultrastructural and metabolic support. For this reason, mammalian brains host another type of cells called “glial cells”, whose role is to maintain proper conditions for efficient neuronal function. One type of glial cell, astrocytes, are involved in particular in the metabolic support of neurons, by feeding them with lactate, one byproduct of glucose metabolism that they can take up from blood vessels, and store it under another form, glycogen granules. These energy-storage molecules, whose morphology resembles to spheres with a diameter ranging 10–80 nanometers roughly, can be easily recognized using electron microscopy, the only technique whose resolution is high enough to resolve them. Understanding and quantifying their distribution is of particular relevance for neuroscientists, in order to understand where and when neurons use energy under this form. To answer this question, we developed a visualization technique, dubbed GLAM (Glycogen-derived Lactate Absorption Map), and customized for the analysis of the interaction of astrocytic glycogen on surrounding neurites in order to formulate hypotheses on the energy absorption mechanisms. The method integrates high-resolution surface reconstruction of neurites, astrocytes, and the energy sources in form of glycogen granules from different automated serial electron microscopy methods, like focused ion beam scanning electron microscopy (FIB-SEM) or serial block face electron microscopy (SBEM), together with an absorption map computed as a radiance transfer mechanism. The resulting visual representation provides an immediate and comprehensible illustration of the areas in which the probability of lactate shuttling is higher. The computed dataset can be then explored and quantified in a 3D space, either using 3D modeling software or virtual reality environments. Domain scientists have evaluated the technique by

  3. GLAM: Glycogen-derived Lactate Absorption Map for visual analysis of dense and sparse surface reconstructions of rodent brain structures on desktop systems and virtual environments

    KAUST Repository

    Agus, Marco

    2018-05-21

    Human brain accounts for about one hundred billion neurons, but they cannot work properly without ultrastructural and metabolic support. For this reason, mammalian brains host another type of cells called “glial cells”, whose role is to maintain proper conditions for efficient neuronal function. One type of glial cell, astrocytes, are involved in particular in the metabolic support of neurons, by feeding them with lactate, one byproduct of glucose metabolism that they can take up from blood vessels, and store it under another form, glycogen granules. These energy-storage molecules, whose morphology resembles to spheres with a diameter ranging 10–80 nanometers roughly, can be easily recognized using electron microscopy, the only technique whose resolution is high enough to resolve them. Understanding and quantifying their distribution is of particular relevance for neuroscientists, in order to understand where and when neurons use energy under this form. To answer this question, we developed a visualization technique, dubbed GLAM (Glycogen-derived Lactate Absorption Map), and customized for the analysis of the interaction of astrocytic glycogen on surrounding neurites in order to formulate hypotheses on the energy absorption mechanisms. The method integrates high-resolution surface reconstruction of neurites, astrocytes, and the energy sources in form of glycogen granules from different automated serial electron microscopy methods, like focused ion beam scanning electron microscopy (FIB-SEM) or serial block face electron microscopy (SBEM), together with an absorption map computed as a radiance transfer mechanism. The resulting visual representation provides an immediate and comprehensible illustration of the areas in which the probability of lactate shuttling is higher. The computed dataset can be then explored and quantified in a 3D space, either using 3D modeling software or virtual reality environments. Domain scientists have evaluated the technique by

  4. Digital Flood Insurance Rate Map Database for Hunt County, TX, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  5. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, MIDDLESEX COUNTY, CONNECTICUT (ALL JURISDICTIONS)

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  6. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, TWIN FALLS COUNTY, IDAHO, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  7. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, WEST BATON ROUGE PARISH, LA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  8. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, DES MOINES COUNTY, IOWA, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  9. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, CERRO GORDO COUNTY, IOWA, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  10. DRAFT DIGITAL FLOOD INSURANCE RATE MAP DATABASE, LA PAZ COUNTY, AZ

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  11. Digital Flood Insurance Rate Map Database for Brazos County, TX, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  12. DIGITAL FlOOD INSURANCE RATE MAP DATABASE, CHESTER COUNTY, SC, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts Flood risk information and supporting data used to develop the risk data. The primary risk...

  13. DIGITAL FLOOD INSURANCE RATE MAP, FLATHEAD COUNTY, MONTANA (AND INCORPORATED AREAS)

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  14. DIGITAL FLOOD INSURANCE RATE MAP, SMYTH COUNTY, VIRGINIA (AND INCORPORATED AREAS)

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  15. Final DIGITAL FLOOD INSURANCE RATE MAP DATABASE, RANDOLPH COUNTY, ILLINOIS USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  16. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, BRISTOL COUNTY, MASSACHUSETTS (ALL JURISDICTIONS)

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  17. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, CHARLES CITY COUNTY, VA, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  18. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, CONTRA COSTA COUNTY, CALIFORNIA, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  19. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, SANTA FE COUNTY, NEW MEXICO

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  20. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, SANTA CLARA COUNTY, CALIFORNIA, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  1. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, NELSON COUNTY, NORTH DAKOTA, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk Information And supporting data used to develop the risk data. The primary risk;...

  2. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, BAMBERG COUNTY, SOUTH CAROLINA, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  3. Digital Flood Insurance Rate Map Database, St.Mary's County, Maryland, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  4. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, PRINCE EDWARD COUNTY, VA, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  5. DIGITAL FLOOD INSURANCE RATE MAP, CROOK COUNTY, OREGON (AND INCORPORATED AREAS)

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  6. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, NANTUCKET COUNTY, MASSACHUSETTS (ALL JURISDICTIONS)

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  7. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, JEFFERSON COUNTY, NEW YORK, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk Information And supporting data used to develop the risk data. The primary risk;...

  8. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, ULSTER COUNTY, NEW YORK, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  9. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, San Juan COUNTY, New Mexico

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  10. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, San Bernardino COUNTY, CALIFORNIA, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  11. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, VALENCIA COUNTY, NEW MEXICO, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  12. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, BROWN COUNTY, SOUTH DAKOTA, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  13. Digital Flood Insurance Rate Map Database, Anne Arundel County, Maryland, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  14. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, MILWAUKEE, WISCONSIN (AND INCORPORATED AREAS)

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  15. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, CITY OF NORFOLK, VA, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  16. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, CITY OF ALEXANDRIA, VA, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  17. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Queen Anne's COUNTY, Maryland, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  18. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, CITY OF Colonial Heights, VIRGINIA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  19. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, CITY OF GALAX, VA, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  20. Digital Flood Insurance Rate Map Database for Cass County, TX, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  1. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, CARROLL COUNTY, NEW HAMPSHIRE, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk Information And supporting data used to develop the risk data. The primary risk;...

  2. Final Digital Flood Insurance Rate Map Database, Lubbock County, TX, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  3. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, STEWART COUNTY (AND INCORPORATED AREAS)

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  4. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, ANDERSON COUNTY, SOUTH CAROLINA, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  5. Preliminary DIGITAL FLOOD INSURANCE RATE MAP DATABASE, COOK COUNTY, ILLINOIS USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  6. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, EAST BATON ROUGE PARISH, LOUISIANA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  7. Digital Flood Insurance Rate Map Database, New Castle County, Delaware, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  8. Frenkel defect absorption on dislocations and dislocation discharge rate. Modeling determination of the absorption zone

    International Nuclear Information System (INIS)

    Mikhlin, Eh.Ya.

    1988-01-01

    A situation connected with the fact that evaluations of dislocation discharge strength which somehow or other are based on the elasticity theory in the dislocation nucleus or near it, do not lead to results complying with experimental data, is discussed. Bases of the alternative approach to this problem consisting in direct investigation into the process of Frenkel defect absorption on dislocation by its computerized simulation at the microscopic level are also presented. Methods of investigation and results are described using α dislocation in iron-alpha as an example. The concept of zones of vacancy and interstitial atom absorption on dislocation is discussed. It is shown that a spontaneous transition, performed by any of these defects near a dislocation is not always identical to absorption and usually appears to be only a part of a multistage process leading to the defect disappearance. Potential relief characteristics for vacancy movement near the dislocation are found. An area wide enough in a transverse direction is found around the dislocation. Vacncies reaching this area can be easily transported to places of their disappearance. Therefore the vacancy entry to this area is equivalent to the absorption. the procedure of simulating the atomic structure of a crystallite containing a dislocation with a step is described. Positions from which these defects perform spontaneous transitions, reaching the disappearance places are found on the dislocation near the step

  9. Development of a kinetic model of hydrogen absorption and desorption in magnesium and analysis of the rate-determining step

    Science.gov (United States)

    Kitagawa, Yuta; Tanabe, Katsuaki

    2018-05-01

    Mg is promising as a new light-weight and low-cost hydrogen-storage material. We construct a numerical model to represent the hydrogen dynamics on Mg, comprising dissociative adsorption, desorption, bulk diffusion, and chemical reaction. Our calculation shows a good agreement with experimental data for hydrogen absorption and desorption on Mg. Our model clarifies the evolution of the rate-determining processes as absorption and desorption proceed. Furthermore, we investigate the optimal condition and materials design for efficient hydrogen storage in Mg. By properly understanding the rate-determining processes using our model, one can determine the design principle for high-performance hydrogen-storage systems.

  10. Dose-rate mapping and search of radioactive sources in Estonia

    International Nuclear Information System (INIS)

    Ylaetalo, S.; Karvonen, J.; Ilander, T.; Honkamaa, T.; Toivonen, H.

    1996-12-01

    The Estonian Ministry of Environment and the Finnish Centre for Radiation and Nuclear Safety (STUK) agreed in 1995 on a radiation mapping project in Estonia. The country was searched to find potential man-made radioactive sources. Another goal of the project was to produce a background dose-rate map over the whole country. The measurements provided an excellent opportunity to test new in-field measuring systems that are useful in a nuclear disaster. The basic idea was to monitor road sides, cities, domestic waste storage places and former military or rocket bases from a moving vehicle by measuring gamma spectrum and dose rate. The measurements were carried out using vehicle installed systems consisting of a pressurised ionisation chamber (PIC) in 1995 and a combination of a scintillation spectrometer (NaI(TI)) and Geiger-Mueller-counter (GM) in 1996. All systems utilised GPS-satellite navigation signals to relate the measured dose rates and gamma-spectra to current geographical location. The data were recorded for further computer analysis. The dose rate varied usually between 0.03-0.17 μSv/h in the whole country, excluding a few nuclear material storage places (in Saku and in Sillamae). Enhanced dose rates of natural origin (0.17-0.5 μSv/h) were measured near granite statues, buildings and bridges. No radioactive sources were found on road sides or in towns or villages. (orig.) (14 refs.)

  11. Genome-wide recombination rate variation in a recombination map of cotton.

    Science.gov (United States)

    Shen, Chao; Li, Ximei; Zhang, Ruiting; Lin, Zhongxu

    2017-01-01

    Recombination is crucial for genetic evolution, which not only provides new allele combinations but also influences the biological evolution and efficacy of natural selection. However, recombination variation is not well understood outside of the complex species' genomes, and it is particularly unclear in Gossypium. Cotton is the most important natural fibre crop and the second largest oil-seed crop. Here, we found that the genetic and physical maps distances did not have a simple linear relationship. Recombination rates were unevenly distributed throughout the cotton genome, which showed marked changes along the chromosome lengths and recombination was completely suppressed in the centromeric regions. Recombination rates significantly varied between A-subgenome (At) (range = 1.60 to 3.26 centimorgan/megabase [cM/Mb]) and D-subgenome (Dt) (range = 2.17 to 4.97 cM/Mb), which explained why the genetic maps of At and Dt are similar but the physical map of Dt is only half that of At. The translocation regions between A02 and A03 and between A04 and A05, and the inversion regions on A10, D10, A07 and D07 indicated relatively high recombination rates in the distal regions of the chromosomes. Recombination rates were positively correlated with the densities of genes, markers and the distance from the centromere, and negatively correlated with transposable elements (TEs). The gene ontology (GO) categories showed that genes in high recombination regions may tend to response to environmental stimuli, and genes in low recombination regions are related to mitosis and meiosis, which suggested that they may provide the primary driving force in adaptive evolution and assure the stability of basic cell cycle in a rapidly changing environment. Global knowledge of recombination rates will facilitate genetics and breeding in cotton.

  12. Geodesy- and geology-based slip-rate models for the Western United States (excluding California) national seismic hazard maps

    Science.gov (United States)

    Petersen, Mark D.; Zeng, Yuehua; Haller, Kathleen M.; McCaffrey, Robert; Hammond, William C.; Bird, Peter; Moschetti, Morgan; Shen, Zhengkang; Bormann, Jayne; Thatcher, Wayne

    2014-01-01

    The 2014 National Seismic Hazard Maps for the conterminous United States incorporate additional uncertainty in fault slip-rate parameter that controls the earthquake-activity rates than was applied in previous versions of the hazard maps. This additional uncertainty is accounted for by new geodesy- and geology-based slip-rate models for the Western United States. Models that were considered include an updated geologic model based on expert opinion and four combined inversion models informed by both geologic and geodetic input. The two block models considered indicate significantly higher slip rates than the expert opinion and the two fault-based combined inversion models. For the hazard maps, we apply 20 percent weight with equal weighting for the two fault-based models. Off-fault geodetic-based models were not considered in this version of the maps. Resulting changes to the hazard maps are generally less than 0.05 g (acceleration of gravity). Future research will improve the maps and interpret differences between the new models.

  13. Near-IR Spectral Imaging of Semiconductor Absorption Sites in Integrated Circuits

    Directory of Open Access Journals (Sweden)

    E. C. Samson

    2004-12-01

    Full Text Available We derive spectral maps of absorption sites in integrated circuits (ICs by varying the wavelength of the optical probe within the near-IR range. This method has allowed us to improve the contrast of the acquired images by revealing structures that have a different optical absorption from neighboring sites. A false color composite image from those acquired at different wavelengths is generated from which the response of each semiconductor structure can be deduced. With the aid of the spectral maps, nonuniform absorption was also observed in a semiconductor structure located near an electrical overstress defect. This method may prove important in failure analysis of ICs by uncovering areas exhibiting anomalous absorption, which could improve localization of defective edifices in the semiconductor parts of the microchip

  14. Synchrotron x-ray studies of the keel of the short-spined sea urchin lytechinus variegatus: absorption microtomography (microCT) and small beam diffraction mapping

    International Nuclear Information System (INIS)

    Stock, S.R.; Barss, J.; Dahl, T.; Veis, A.; Almer, J.D.; De Carlo, F.

    2003-01-01

    In sea urchin teeth, the keel plays an important structural role, and this paper reports results of microstructural characterization of the keel of Lytechinus variegatus using two noninvasive synchrotron x-ray techniques: x-ray absorption microtomography (microCT) and x-ray diffraction mapping. MicroCT with 14 keV x-rays mapped the spatial distribution of mineral at the 1.3 microm level in a millimeter-sized fragment of a mature portion of the keel. Two rows of low absorption channels (i.e., primary channels) slightly less than 10 microm in diameter were found running linearly from the flange to the base of the keel and parallel to its sides. The primary channels paralleled the oral edge of the keel, and the microCT slices revealed a planar secondary channel leading from each primary channel to the side of the keel. The primary and secondary channels were more or less coplanar and may correspond to the soft tissue between plates of the carinar process. Transmission x-ray diffraction with 80.8 keV x-rays and a 0.1 mm beam mapped the distribution of calcite crystal orientations and the composition Ca(1-x)Mg(x)CO(3) of the calcite. Unlike the variable Mg concentration and highly curved prisms found in the keel of Paracentrotus lividus, a constant Mg content (x = 0.13) and relatively little prism curvature was found in the keel of Lytechinus variegatus.

  15. Retrieval interval mapping, a tool to optimize the spectral retrieval range in differential optical absorption spectroscopy

    Science.gov (United States)

    Vogel, L.; Sihler, H.; Lampel, J.; Wagner, T.; Platt, U.

    2012-06-01

    Remote sensing via differential optical absorption spectroscopy (DOAS) has become a standard technique to identify and quantify trace gases in the atmosphere. The technique is applied in a variety of configurations, commonly classified into active and passive instruments using artificial and natural light sources, respectively. Platforms range from ground based to satellite instruments and trace-gases are studied in all kinds of different environments. Due to the wide range of measurement conditions, atmospheric compositions and instruments used, a specific challenge of a DOAS retrieval is to optimize the parameters for each specific case and particular trace gas of interest. This becomes especially important when measuring close to the detection limit. A well chosen evaluation wavelength range is crucial to the DOAS technique. It should encompass strong absorption bands of the trace gas of interest in order to maximize the sensitivity of the retrieval, while at the same time minimizing absorption structures of other trace gases and thus potential interferences. Also, instrumental limitations and wavelength depending sources of errors (e.g. insufficient corrections for the Ring effect and cross correlations between trace gas cross sections) need to be taken into account. Most often, not all of these requirements can be fulfilled simultaneously and a compromise needs to be found depending on the conditions at hand. Although for many trace gases the overall dependence of common DOAS retrieval on the evaluation wavelength interval is known, a systematic approach to find the optimal retrieval wavelength range and qualitative assessment is missing. Here we present a novel tool to determine the optimal evaluation wavelength range. It is based on mapping retrieved values in the retrieval wavelength space and thus visualize the consequence of different choices of retrieval spectral ranges, e.g. caused by slightly erroneous absorption cross sections, cross correlations and

  16. Quantitative X-ray mapping, scatter diagrams and the generation of correction maps to obtain more information about your material

    Science.gov (United States)

    Wuhrer, R.; Moran, K.

    2014-03-01

    Quantitative X-ray mapping with silicon drift detectors and multi-EDS detector systems have become an invaluable analysis technique and one of the most useful methods of X-ray microanalysis today. The time to perform an X-ray map has reduced considerably with the ability to map minor and trace elements very accurately due to the larger detector area and higher count rate detectors. Live X-ray imaging can now be performed with a significant amount of data collected in a matter of minutes. A great deal of information can be obtained from X-ray maps. This includes; elemental relationship or scatter diagram creation, elemental ratio mapping, chemical phase mapping (CPM) and quantitative X-ray maps. In obtaining quantitative x-ray maps, we are able to easily generate atomic number (Z), absorption (A), fluorescence (F), theoretical back scatter coefficient (η), and quantitative total maps from each pixel in the image. This allows us to generate an image corresponding to each factor (for each element present). These images allow the user to predict and verify where they are likely to have problems in our images, and are especially helpful to look at possible interface artefacts. The post-processing techniques to improve the quantitation of X-ray map data and the development of post processing techniques for improved characterisation are covered in this paper.

  17. Quantitative X-ray mapping, scatter diagrams and the generation of correction maps to obtain more information about your material

    International Nuclear Information System (INIS)

    Wuhrer, R; Moran, K

    2014-01-01

    Quantitative X-ray mapping with silicon drift detectors and multi-EDS detector systems have become an invaluable analysis technique and one of the most useful methods of X-ray microanalysis today. The time to perform an X-ray map has reduced considerably with the ability to map minor and trace elements very accurately due to the larger detector area and higher count rate detectors. Live X-ray imaging can now be performed with a significant amount of data collected in a matter of minutes. A great deal of information can be obtained from X-ray maps. This includes; elemental relationship or scatter diagram creation, elemental ratio mapping, chemical phase mapping (CPM) and quantitative X-ray maps. In obtaining quantitative x-ray maps, we are able to easily generate atomic number (Z), absorption (A), fluorescence (F), theoretical back scatter coefficient (η), and quantitative total maps from each pixel in the image. This allows us to generate an image corresponding to each factor (for each element present). These images allow the user to predict and verify where they are likely to have problems in our images, and are especially helpful to look at possible interface artefacts. The post-processing techniques to improve the quantitation of X-ray map data and the development of post processing techniques for improved characterisation are covered in this paper

  18. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, TUNICA COUNTY, MISSISSIPPI AND INCORPORATED AREAS

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  19. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, CROCKETT COUNTY, TENNESSEE AND INCORPORATED AREAS

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  20. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, CLAY COUNTY, FLORIDA AND INCORPORATED AREAS

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  1. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, LAFAYETTE COUNTY, MISSISSIPPI AND INCORPORATED AREAS

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  2. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, DARKE COUNTY, OHIO (AND INCORPORATED AREAS)

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  3. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, HARRISON COUNTY, MISSISSIPPI AND INCORPORATED AREAS

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  4. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, CALHOUN COUNTY, MISSISSIPPI AND INCORPORATED AREAS

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  5. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, MUSKOGEE COUNTY, OKLAHOMA AND INCORPORATED AREAS

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  6. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, ATKINSON COUNTY, GEORGIA AND INCORPORATED AREAS

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  7. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, COFFEE COUNTY, ALABAMA AND INCORPORATED AREAS

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  8. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, COFFEE COUNTY, GEORGIA AND INCORPORATED AREAS

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  9. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, CLAIBORNE COUNTY, MISSISSIPPI AND INCORPORATED AREAS

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  10. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, VOLUSIA COUNTY, FLORIDA AND INCORPORATED AREAS

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  11. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, POWESHIEK COUNTY, IOWA AND INCORPORATED AREAS

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  12. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, HANCOCK COUNTY, MISSISSIPPI AND INCORPORATED AREAS

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  13. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, BENTON COUNTY, TENNESSEE AND INCORPORATED AREAS

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  14. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, JACKSON COUNTY, TEXAS (AND INCORPORATED AREAS)

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  15. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, ALBANY COUNTY, WYOMING (AND INCORPORATED AREAS)

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  16. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, EAU CLAIRE COUNTY PMR, WISCONSIN, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk;...

  17. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, MOHAVE COUNTY, ARIZONA (AND INCORPORATED AREAS)

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  18. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, NAVARRO COUNTY, TEXAS (AND INCORPORATED AREAS)

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  19. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, COMMONWEALTH OF PUERTO RICO, PUERTO RICO

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  20. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, RANKIN COUNTY, MISSISSIPPI AND INCORPORATED AREAS

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...