Corium quench in deep pool mixing experiments

International Nuclear Information System (INIS)

Spencer, B.W.; McUmber, L.; Gregorash, D.; Aeschlimann, R.; Sienicki, J.J.

1985-01-01

The results of two recent corium-water thermal interaction (CWTI) tests are described in which a stream of molten corium was poured into a deep pool of water in order to determine the mixing behavior, the corium-to-water heat transfer rates, and the characteristic sizes of the quenched debris. The corium composition was 60% UO 2 , 16% ZrO 2 , and 24% stainless steel by weight; its initial temperature was 3080 K, approx.160 K above the oxide phase liquidus temperature. The corium pour stream was a single-phase 2.2 cm dia liquid column which entered the water pool in film boiling at approx.4 m/s. The water subcooling was 6 and 75C in the two tests. Test results showed that with low subcooling, rapid steam generation caused the pool to boil up into a high void fraction regime. In contrast, with large subcooling no net steam generation occurred, and the pool remained relatively quiescent. Breakup of the jet appeared to occur by surface stripping. In neither test was the breakup complete during transit through the 32 cm deep water pool, and molten corium channeled to the base where it formed a melt layer. The characteristic heat transfer rates measured 3.5 MJ/s and 2.7 MJ/s during the fall stage for small and large subcooling, respectively; during the initial stage of bed quench, the surface heat fluxes measured 2.4 MW/m 2 and 3.7 MW/m 2 , respectively. A small mass of particles was formed in each test, measuring typically 0.1 to 1 mm and 1 to 5 mm dia for the large and small subcooling conditions, respectively. 9 refs., 13 figs., 1 tab

Comment on "Deep mixing of 3He: reconciling Big Bang and stellar nucleosynthesis".

Science.gov (United States)

Balser, Dana S; Rood, Robert T; Bania, T M

2007-08-31

Eggleton et al. (Reports, 8 December 2006, p. 1580) reported on a deep-mixing mechanism in low-mass stars caused by a Rayleigh-Taylor instability that destroys all of the helium isotope 3He produced during the star's lifetime. Observations of 3He in planetary nebulae, however, indicate that some stars produce prodigious amounts of 3He. This is inconsistent with the claim that all low-mass stars should destroy 3He.

Final Technical Report Advanced Anchoring Technology DOE Award Number DE-EE0003632 Project Period 09/10 - 09/12

Energy Technology Data Exchange (ETDEWEB)

Meggitt, Dallas J

2012-11-09

It is generally conceded that the costs associated with current practices for the mooring, anchoring, or foundation systems of Marine HydroKinetic (MHK) and Deepwater Floating Wind systems are a disproportionate portion of the total cost of an installed system. Reducing the cost of the mooring and anchoring components for MHK systems can contribute substantially to reducing the levelized cost of electricity (LCOE). Micropile anchors can reduce the LCOE both directly, because the anchors, associated mooring hardware and installation costs are less than conventional anchor and mooring systems, but also because micropile anchors require less extensive geotechnical surveys for confident design and proper implementation of an anchor or foundation system. This report presents the results of the development of critical elements of grouted marine micropile anchor (MMA) technology for application to MHK energy conversion systems and other ocean engineering applications that require fixing equipment to the seafloor. Specifically, this project identified grout formulations and developed designs for grout dispensing systems suitable for use in a seawater environment as a critical development need for successful implementation of practical MMA systems. The project conducted a thorough review of available information on the use of cement-based grouts in seawater. Based on this review and data available from commercial sources, the project selected a range of grout formulations for testing as part of a micropile system. The project also reviewed instrumentation for measuring grout density, pressure and flow rate, and integrated an instrumentation system suitable for use with micropile installation. The grout formulations and instrumentation system were tested successfully and demonstrated the suitability of MMA technology for implementation into anchor systems for MHK and other marine renewable energy systems. In addition, this project developed conceptual designs for micropile

Prefabricated Vertical Drain (PVD) and Deep Cement Mixing (DCM)/Stiffened DCM (SDCM) techniques for soft ground improvement

Science.gov (United States)

Bergado, D. T.; Long, P. V.; Chaiyaput, S.; Balasubramaniam, A. S.

2018-04-01

Soft ground improvement techniques have become most practical and popular methods to increase soil strength, soil stiffness and reduce soil compressibility including the soft Bangkok clay. This paper focuses on comparative performances of prefabricated vertical drain (PVD) using surcharge, vacuum and heat preloading as well as the cement-admixed clay of Deep Cement Mixing (DCM) and Stiffened DCM (SDCM) methods. The Vacuum-PVD can increase the horizontal coefficient of consolidation, Ch, resulting in faster rate of settlement at the same magnitudes of settlement compared to Conventional PVD. Several field methods of applying vacuum preloading are also compared. Moreover, the Thermal PVD and Thermal Vacuum PVD can increase further the coefficient of horizontal consolidation, Ch, with the associated reduction of kh/ks values by reducing the drainage retardation effects in the smear zone around the PVD which resulted in faster rates of consolidation and higher magnitudes of settlements. Furthermore, the equivalent smear effect due to non-uniform consolidation is also discussed in addition to the smear due to the mechanical installation of PVDs. In addition, a new kind of reinforced deep mixing method, namely Stiffened Deep Cement Mixing (SDCM) pile is introduced to improve the flexural resistance, improve the field quality control, and prevent unexpected failures of the Deep Cement Mixing (DCM) pile. The SDCM pile consists of DCM pile reinforced with the insertion of precast reinforced concrete (RC) core. The full scale test embankment on soft clay improved by SDCM and DCM piles was also analysed. Numerical simulations using the 3D PLAXIS Foundation finite element software have been done to understand the behavior of SDCM and DCM piles. The simulation results indicated that the surface settlements decreased with increasing lengths of the RC cores, and, at lesser extent, increasing sectional areas of the RC cores in the SDCM piles. In addition, the lateral movements

Winter−spring transition in the subarctic Atlantic: microbial response to deep mixing and pre-bloom production

DEFF Research Database (Denmark)

Paulsen, Maria Lund; Riisgaard, Karen; Thingstad, T. Frede

2015-01-01

In temperate, subpolar and polar marine systems, the classical perception is that diatoms initiate the spring bloom and thereby mark the beginning of the productive season. Contrary to this view, we document an active microbial food web dominated by pico- and nanoplankton prior to the diatom bloom......, a period with excess nutrients and deep convection of the water column. During repeated visits to stations in the deep Iceland and Norwegian basins and the shallow Shetland Shelf (26 March to 29 April 2012), we investigated the succession and dynamics of photo - synthetic and heterotrophic microorganisms....... We observed that the early phytoplankton production was followed by a decrease in the carbon:nitrogen ratio of the dissolved organic matter in the deep mixed stations, an increase in heterotrophic prokaryote (bacteria) abundance and activity (indicated by the high nucleic acid:low nucleic acid...

Mixed layer depth calculation in deep convection regions in ocean numerical models

Science.gov (United States)

Courtois, Peggy; Hu, Xianmin; Pennelly, Clark; Spence, Paul; Myers, Paul G.

2017-12-01

Mixed Layer Depths (MLDs) diagnosed by conventional numerical models are generally based on a density difference with the surface (e.g., 0.01 kg.m-3). However, the temperature-salinity compensation and the lack of vertical resolution contribute to over-estimated MLD, especially in regions of deep convection. In the present work, we examined the diagnostic MLD, associated with the deep convection of the Labrador Sea Water (LSW), calculated with a simple density difference criterion. The over-estimated MLD led us to develop a new tool, based on an observational approach, to recalculate MLD from model output. We used an eddy-permitting, 1/12° regional configuration of the Nucleus for European Modelling of the Ocean (NEMO) to test and discuss our newly defined MLD. We compared our new MLD with that from observations, and we showed a major improvement with our new algorithm. To show the new MLD is not dependent on a single model and its horizontal resolution, we extended our analysis to include 1/4° eddy-permitting simulations, and simulations using the Modular Ocean Model (MOM) model.

Improving deep convolutional neural networks with mixed maxout units.

Directory of Open Access Journals (Sweden)

Hui-Zhen Zhao

Full Text Available Motivated by insights from the maxout-units-based deep Convolutional Neural Network (CNN that "non-maximal features are unable to deliver" and "feature mapping subspace pooling is insufficient," we present a novel mixed variant of the recently introduced maxout unit called a mixout unit. Specifically, we do so by calculating the exponential probabilities of feature mappings gained by applying different convolutional transformations over the same input and then calculating the expected values according to their exponential probabilities. Moreover, we introduce the Bernoulli distribution to balance the maximum values with the expected values of the feature mappings subspace. Finally, we design a simple model to verify the pooling ability of mixout units and a Mixout-units-based Network-in-Network (NiN model to analyze the feature learning ability of the mixout models. We argue that our proposed units improve the pooling ability and that mixout models can achieve better feature learning and classification performance.

Particle mixing processes of Chernobyl fallout in deep Norwegian Sea sediments: Evidence for seasonal effects

Science.gov (United States)

Balzer, W.

1996-09-01

A 1430 m deep station in the Norwegian Sea (Voering Plateau) was occupied five times between May 1986 and February 1987 to investigate the seasonal variation in sediment mixing rates. Cherbnbyl-derived radiocesium, identified by its high proportion of short-lived 134Cs, was used as a tracer for mixing. Most of the nuclide input arrived at the sediment within a narrow time span in June/early July during the beginning of the seasonal biogenic sedimentation pulse. Measured 137Cs profiles in the sediment over time were compared with modelled distributions calculated with a finite difference scheme. The input function of radiocesium to the sea floor was evaluated from the increase of the total inventory with time. Time-invariant mixing coefficients did not provide reasonable fits to either summer or winter distributions. The best fit was obtained with a rate of mixing proportional to the radiocesium input flux, with an average enhancement factor of 6.6 during the two summer months. It appears that the benthic macrofauna are more active during the food supply season and rapidly ingest/bury freshly sedimented materials.

SU-E-T-319: Dosimetric Evaluation of IMRT with Mix-Energy Beam for Deep Seated Targets

Energy Technology Data Exchange (ETDEWEB)

Sharma, S; Manigandan, D; Gandhi, A; Sharma, D; Subramani, V; Chander, S; Julkha, P [Fortis Hospital, Mohali, Punjab (India); Rath, G

2015-06-15

Purpose: IMRT is preferred in the range of 6–10MV X-rays. Partially adding high energy (>10MV) treatment fields, may provide advantage of both higher and lower energies. To study IMRT dose distribution obtained from treatment plans with single (6MV) and mixed-energy (6MV and 15MV) for deep seated targets (separation more than 30cm). Methods: Five patients of carcinoma of cervix were studied using eclipse planning system. Two different dynamic IMRT plans were generated for Varian CL2300C/D linear accelerator; one is by using 6MV X-ray with seven equally spaced coplanar beams. In second plan, 2 lateral oblique fields (gantry angle 102°, 255°) beam energy was modified to 15MV by keeping all other parameters and dose volume constraints constant. Dose prescription for the planning target volume (PTV) was (5040cGy/28f). For plan comparison, dose volume histogram (DVH) was used and PTV coverage index (CI=Target volume covered by prescription dose/Target volume), heterogeneity index (D5/D95), mean dose to organ at risk (OAR) and normal tissue integral dose (NTID, liter-Gray) was also noted. Total monitor unit (MU) required to deliver a plan was also noted. Results: Mixed-energy plan showed a better conformity and CI values were 0.942±0.032 and 0.960±0.040 for single-energy and mixed-energy plan, respectively. In addition, HI value of mixed energy beam is comparable to that of single energy and the values were within 1.084±0.034 and 1.082±0.032 for single energy and mixed-energy plan, respectively. Variation in mean dose to bladder, rectum and bowel were within 1.05%, 0.87% and 0.90%. NTID was lesser for mixed-energy beam due to use of two high-energy fields. NTID were 1573.40±214.60 and 1510.20±249.80 litre-Gray for single energy and mixed-energy plan. MU needed to deliver a plan was similar in both plans and MUs were 238±45 and 237±47. Conclusion: Partial use of 15MV treatment fields in IMRT plan for deep seated targets showed dosimetric advantage over 6MV

The development of a collapsing method for the mixed group and point cross sections and its application on multi-dimensional deep penetration calculations

International Nuclear Information System (INIS)

Bor-Jing Chang; Yen-Wan H. Liu

1992-01-01

The HYBRID, or mixed group and point, method was developed to solve the neutron transport equation deterministically using detailed treatment at cross section minima for deep penetration calculations. Its application so far is limited to one-dimensional calculations due to the enormous computing time involved in multi-dimensional calculations. In this article, a collapsing method is developed for the mixed group and point cross section sets to provide a more direct and practical way of using the HYBRID method in the multi-dimensional calculations. A testing problem is run. The method is then applied to the calculation of a deep penetration benchmark experiment. It is observed that half of the window effect is smeared in the collapsing treatment, but it still provide a better cross section set than the VITAMIN-C cross sections for the deep penetrating calculations

Fluid mixing and the deep biosphere of a fossil Lost City-type hydrothermal system at the Iberia Margin.

Science.gov (United States)

Klein, Frieder; Humphris, Susan E; Guo, Weifu; Schubotz, Florence; Schwarzenbach, Esther M; Orsi, William D

2015-09-29

Subseafloor mixing of reduced hydrothermal fluids with seawater is believed to provide the energy and substrates needed to support deep chemolithoautotrophic life in the hydrated oceanic mantle (i.e., serpentinite). However, geosphere-biosphere interactions in serpentinite-hosted subseafloor mixing zones remain poorly constrained. Here we examine fossil microbial communities and fluid mixing processes in the subseafloor of a Cretaceous Lost City-type hydrothermal system at the magma-poor passive Iberia Margin (Ocean Drilling Program Leg 149, Hole 897D). Brucite-calcite mineral assemblages precipitated from mixed fluids ca. 65 m below the Cretaceous paleo-seafloor at temperatures of 31.7 ± 4.3 °C within steep chemical gradients between weathered, carbonate-rich serpentinite breccia and serpentinite. Mixing of oxidized seawater and strongly reducing hydrothermal fluid at moderate temperatures created conditions capable of supporting microbial activity. Dense microbial colonies are fossilized in brucite-calcite veins that are strongly enriched in organic carbon (up to 0.5 wt.% of the total carbon) but depleted in (13)C (δ(13)C(TOC) = -19.4‰). We detected a combination of bacterial diether lipid biomarkers, archaeol, and archaeal tetraethers analogous to those found in carbonate chimneys at the active Lost City hydrothermal field. The exposure of mantle rocks to seawater during the breakup of Pangaea fueled chemolithoautotrophic microbial communities at the Iberia Margin, possibly before the onset of seafloor spreading. Lost City-type serpentinization systems have been discovered at midocean ridges, in forearc settings of subduction zones, and at continental margins. It appears that, wherever they occur, they can support microbial life, even in deep subseafloor environments.

Stability of embankments over cement deep soil mixing columns

International Nuclear Information System (INIS)

Morilla Moar, P.; Melentijevic, S.

2014-01-01

The deep soil mixing (DSM) is one of the ground improvement methods used for the construction of embankments over soft soils. DSM column-supported embankments are constructed over soft soils to accelerate its construction, improve embankment stability, increase bearing capacity and control of total and differential settlements. There are two traditional design methods, the Japanese (rigid columns) and the scandinavian (soft and semi-rigid columns). Based on Laboratory analysis and numerical analysis these traditional approaches have been questioned by several authors due to its overestimation of the embankment stability considering that the most common failures types are not assumed. This paper presents a brief review of traditional design methods for embankments on DSM columns constructed in soft soils, studies carried out determine the most likely failure types of DSM columns, methods to decrease the overestimation when using limit equilibrium methods and numerical analysis methods that permit detect appropriate failure modes in DSM columns. Finally a case study was assessed using both limited equilibrium and finite element methods which confirmed the overestimation in the factors of safety on embankment stability over DSM columns. (Author)

The Vertical Profile of Ocean Mixing

Science.gov (United States)

Ferrari, R. M.; Nikurashin, M.; McDougall, T. J.; Mashayek, A.

2014-12-01

The upwelling of bottom waters through density surfaces in the deep ocean is not possible unless the sloping nature of the sea floor is taken into account. The bottom--intensified mixing arising from interaction of internal tides and geostrophic motions with bottom topography implies that mixing is a decreasing function of height in the deep ocean. This would further imply that the diapycnal motion in the deep ocean is downward, not upwards as is required by continuity. This conundrum regarding ocean mixing and upwelling in the deep ocean will be resolved by appealing to the fact that the ocean does not have vertical side walls. Implications of the conundrum for the representation of ocean mixing in climate models will be discussed.

Mixing of shallow and deep groundwater as indicated by the chemistry and age of karstic springs

Science.gov (United States)

Toth, David J.; Katz, Brian G.

2006-06-01

Large karstic springs in east-central Florida, USA were studied using multi-tracer and geochemical modeling techniques to better understand groundwater flow paths and mixing of shallow and deep groundwater. Spring water types included Ca-HCO3 (six), Na-Cl (four), and mixed (one). The evolution of water chemistry for Ca-HCO3 spring waters was modeled by reactions of rainwater with soil organic matter, calcite, and dolomite under oxic conditions. The Na-Cl and mixed-type springs were modeled by reactions of either rainwater or Upper Floridan aquifer water with soil organic matter, calcite, and dolomite under oxic conditions and mixed with varying proportions of saline Lower Floridan aquifer water, which represented 4-53% of the total spring discharge. Multiple-tracer data—chlorofluorocarbon CFC-113, tritium (3H), helium-3 (3Hetrit), sulfur hexafluoride (SF6)—for four Ca-HCO3 spring waters were consistent with binary mixing curves representing water recharged during 1980 or 1990 mixing with an older (recharged before 1940) tracer-free component. Young-water mixing fractions ranged from 0.3 to 0.7. Tracer concentration data for two Na-Cl spring waters appear to be consistent with binary mixtures of 1990 water with older water recharged in 1965 or 1975. Nitrate-N concentrations are inversely related to apparent ages of spring waters, which indicated that elevated nitrate-N concentrations were likely contributed from recent recharge.

Use of deep soil mixing as an alternate verticle barrier to slurry walls

International Nuclear Information System (INIS)

Miller, A.D.

1997-01-01

Slurry walls have become an accepted subsurface remediation technique to contain contaminated zones. However, situations develop where conventional slurry wall excavation techniques are not suitable. The use of conventional containment wall construction methods may involve removal and disposal of contaminated soils, stability concerns and the risk of open excavations. For these reasons, other installation techniques have received further consideration. Deep Soil Mixing (DSM) has emerged as a viable alternative to conventional slurry wall techniques. In situations dictating limited soil removal for contamination or stability concerns, or where space is a limitation, DSM can be used for installation of the barrier. Proper installation of a DSM wall requires sufficient monitoring and sampling to evaluate the continuity, mixing effectiveness, permeability and key into the confining layer. This paper describes a case study where DSM was used to cross major highways to avoid open excavation, and along slopes to reduce stability concerns. The DSM barrier was tied to an existing conventional slurry wall that had been installed in more stable areas without highway traffic

Erratum: Mixing of shallow and deep groundwater as indicated by the chemistry and age of karstic springs

Science.gov (United States)

Toth, David J.; Katz, Brian G.

2006-09-01

Large karstic springs in east-central Florida, USA were studied using multi-tracer and geochemical modeling techniques to better understand groundwater flow paths and mixing of shallow and deep groundwater. Spring water types included Ca-HCO3 (six), Na-Cl (four), and mixed (one). The evolution of water chemistry for Ca-HCO3 spring waters was modeled by reactions of rainwater with soil organic matter, calcite, and dolomite under oxic conditions. The Na-Cl and mixed-type springs were modeled by reactions of either rainwater or Upper Floridan aquifer water with soil organic matter, calcite, and dolomite under oxic conditions and mixed with varying proportions of saline Lower Floridan aquifer water, which represented 4-53% of the total spring discharge. Multiple-tracer data—chlorofluorocarbon CFC-113, tritium (3H), helium-3 (3Hetrit), sulfur hexafluoride (SF6)—for four Ca-HCO3 spring waters were consistent with binary mixing curves representing water recharged during 1980 or 1990 mixing with an older (recharged before 1940) tracer-free component. Young-water mixing fractions ranged from 0.3 to 0.7. Tracer concentration data for two Na-Cl spring waters appear to be consistent with binary mixtures of 1990 water with older water recharged in 1965 or 1975. Nitrate-N concentrations are inversely related to apparent ages of spring waters, which indicated that elevated nitrate-N concentrations were likely contributed from recent recharge.

Fixed versus mixed RSA: Explaining visual representations by fixed and mixed feature sets from shallow and deep computational models.

Science.gov (United States)

Khaligh-Razavi, Seyed-Mahdi; Henriksson, Linda; Kay, Kendrick; Kriegeskorte, Nikolaus

2017-02-01

Studies of the primate visual system have begun to test a wide range of complex computational object-vision models. Realistic models have many parameters, which in practice cannot be fitted using the limited amounts of brain-activity data typically available. Task performance optimization (e.g. using backpropagation to train neural networks) provides major constraints for fitting parameters and discovering nonlinear representational features appropriate for the task (e.g. object classification). Model representations can be compared to brain representations in terms of the representational dissimilarities they predict for an image set. This method, called representational similarity analysis (RSA), enables us to test the representational feature space as is (fixed RSA) or to fit a linear transformation that mixes the nonlinear model features so as to best explain a cortical area's representational space (mixed RSA). Like voxel/population-receptive-field modelling, mixed RSA uses a training set (different stimuli) to fit one weight per model feature and response channel (voxels here), so as to best predict the response profile across images for each response channel. We analysed response patterns elicited by natural images, which were measured with functional magnetic resonance imaging (fMRI). We found that early visual areas were best accounted for by shallow models, such as a Gabor wavelet pyramid (GWP). The GWP model performed similarly with and without mixing, suggesting that the original features already approximated the representational space, obviating the need for mixing. However, a higher ventral-stream visual representation (lateral occipital region) was best explained by the higher layers of a deep convolutional network and mixing of its feature set was essential for this model to explain the representation. We suspect that mixing was essential because the convolutional network had been trained to discriminate a set of 1000 categories, whose frequencies

Tomographic and Geodynamic Constraints on Convection-Induced Mixing in Earth's Deep Mantle

Science.gov (United States)

Hafter, D. P.; Forte, A. M.; Bremner, P. M.; Glisovic, P.

2017-12-01

Seismological studies reveal two large low-shear-velocity provinces (LLSVPs) in the lowermost mantle (e.g., Su et al. 1994; Wang & Wen 2007; He & Wen 2012), which may represent accumulations of subducted slabs at the CMB (Tan & Gurnis 2005; Christensen & Hoffman 1994) or primordial material generated in the early differentiation of Earth (e.g. Li et al. 2014). The longevity or stability of these large-scale heterogeneities in the deep mantle depends on the vigor and spatial distribution of the convective circulation, which is in turn dependent on the distribution of mantle buoyancy and viscosity (e.g. Glisovic & Forte 2015). Here we explore the state of convective mixing in the mantle using the ASPECT convection code (Kronbichler et al. 2012). A series of experiments are conducted to consider the geochemical and dynamical contributions of LLSVPs to deep-mantle upwellings and corresponding plume-sourced volcanism. The principal feature of these experiments is the use of particle tracers to track geochemical changes in the LLSVPs and mantle plumes in addition to identifying those parts of the mantle that may remain unmixed. We employ 3-D mantle density anomalies derived from joint inversions of seismic, geodynamic and mineral physics constraints and geodynamically-constrained viscosity distributions (Glisovic et al. 2015) to ensure that the predicted flow fields yield a good match to key geophysical constraints (e.g. heat flow, global gravity anomalies and plate velocities).

Molar enthalpy of mixing and refractive indices of choline chloride-based deep eutectic solvents with water

International Nuclear Information System (INIS)

Ma, Chunyan; Guo, Yanhua; Li, Dongxue; Zong, Jianpeng; Ji, Xiaoyan; Liu, Chang

2017-01-01

Highlights: • Molar enthalpy of mixing and refractive indices for binary mixtures of different deep eutectic solvents with water. • The Redlich–Kister equation and the NRTL model was used to fit the experimental data. • The NRTL model with fitted parameters were used to predict the vapour pressure and compared with experimental data. - Abstract: The molar enthalpies of mixing were measured for binary systems of choline chloride-based deep eutectic solvents (glycerol, ethylene glycol and malonic acid) with water at 298.15 K and 308.15 K, and atmospheric pressure with an isothermal calorimeter. Refractive indices were also measured at 303.15 K and atmospheric pressure. The binary mixtures of {chcl/glycerol (1:2) + water, chcl/ethylene glycol (1:2) + water} showed exothermic behaviour over the entire range of composition, while the binary mixture of {chcl/malonic acid (1:1) + water} showed endothermic behaviour at first and then changed to be exothermic with the increasing content of chcl/malonic acid (1:1). Experimental refractive indices were fitted with the Redlich–Kister equation, and experimental molar enthalpies of mixing were correlated with the Redlich–Kister equation and the non-random two-liquid (NRTL) model. The NRTL model with the fitted parameters was used to predict the vapour pressures of these three mixtures. For mixtures of {chcl/glycerol (1:2) + water} and {chcl/ethylene glycol (1:2) + water}, the predicted vapour pressures agreed well with the experimental results from the literature. While for mixture of {chcl/malonic acid (1:1) + water}, the predicted vapour pressures showed deviation at the high concentration of chcl/malonic acid (1:1), and this was probably because of the complex molecular interaction between chcl/malonic acid (1:1) and water.

Deep learning for image classification

Science.gov (United States)

McCoppin, Ryan; Rizki, Mateen

2014-06-01

This paper provides an overview of deep learning and introduces the several subfields of deep learning including a specific tutorial of convolutional neural networks. Traditional methods for learning image features are compared to deep learning techniques. In addition, we present our preliminary classification results, our basic implementation of a convolutional restricted Boltzmann machine on the Mixed National Institute of Standards and Technology database (MNIST), and we explain how to use deep learning networks to assist in our development of a robust gender classification system.

Comportamento à compressão de solo estabilizado com cimento utilizado em colunas de DEEP Soil Mixing

OpenAIRE

Geraldo Vanzolini Moretti

2012-01-01

Resumo: Apresenta-se neste trabalho o estudo comportamento à compressão não confinada de um solo argiloso aluvionar estabilizado segundo a metodologia Deep Soil Mixing (DSM). Esta técnica consiste no tratamento de solos moles através da mistura deste com agentes químicos estabilizantes, podendo-se utilizar cal e/ou cimento. Para a condução deste trabalho foram executadas colunas de DSM sob um aterro rodoviário localizado no nordeste do Brasil, com aproximadamente 300m de extensão. O sítio de ...

Lagrangian Studies of Lateral Mixing

Science.gov (United States)

2017-09-19

Final Technical 3. DATES COVERED (From - To) 01/01/2009 – 12/31/2015 4. TITLE AND SUBTITLE Lagrangian Studies of Lateral Mixing 5a. CONTRACT NUMBER...public release; distribution is unlimited. 13. SUPPLEMENTARY NOTES 14. ABSTRACT The Lateral Mixing Experiment (LATMIX) focused on mixing and...anomalies. LATMIX2 targeted the wintertime Gulf Stream, where deep mixed layers, strong lateral density gradients (Gulf Stream north wall) and the

Search for sterile neutrinos with IceCube DeepCore

Energy Technology Data Exchange (ETDEWEB)

Terliuk, Andrii [DESY, Platanenallee 6, 15738 Zeuthen (Germany); Collaboration: IceCube-Collaboration

2016-07-01

The DeepCore detector is a sub-array of the IceCube Neutrino Observatory that lowers the energy threshold for neutrino detection down to approximately 10 GeV. DeepCore is used for a variety of studies including atmospheric neutrino oscillations. The standard three-neutrino oscillation paradigm is tested using the DeepCore detector by searching for an additional light, sterile neutrino with a mass on the order of 1 eV. Sterile neutrinos do not interact with the ordinary matter, however they can be mixed with the three active neutrino states. Such mixture changes the picture of standard neutrino oscillations for atmospheric neutrinos with energies below 100 GeV. The capabilities of DeepCore detector to measure such sterile neutrino mixing will be presented in this talk.

Climate, carbon cycling, and deep-ocean ecosystems.

Science.gov (United States)

Smith, K L; Ruhl, H A; Bett, B J; Billett, D S M; Lampitt, R S; Kaufmann, R S

2009-11-17

Climate variation affects surface ocean processes and the production of organic carbon, which ultimately comprises the primary food supply to the deep-sea ecosystems that occupy approximately 60% of the Earth's surface. Warming trends in atmospheric and upper ocean temperatures, attributed to anthropogenic influence, have occurred over the past four decades. Changes in upper ocean temperature influence stratification and can affect the availability of nutrients for phytoplankton production. Global warming has been predicted to intensify stratification and reduce vertical mixing. Research also suggests that such reduced mixing will enhance variability in primary production and carbon export flux to the deep sea. The dependence of deep-sea communities on surface water production has raised important questions about how climate change will affect carbon cycling and deep-ocean ecosystem function. Recently, unprecedented time-series studies conducted over the past two decades in the North Pacific and the North Atlantic at >4,000-m depth have revealed unexpectedly large changes in deep-ocean ecosystems significantly correlated to climate-driven changes in the surface ocean that can impact the global carbon cycle. Climate-driven variation affects oceanic communities from surface waters to the much-overlooked deep sea and will have impacts on the global carbon cycle. Data from these two widely separated areas of the deep ocean provide compelling evidence that changes in climate can readily influence deep-sea processes. However, the limited geographic coverage of these existing time-series studies stresses the importance of developing a more global effort to monitor deep-sea ecosystems under modern conditions of rapidly changing climate.

Some Remarks on Practical Aspects of Laboratory Testing of Deep Soil Mixing Composites Achieved in Organic Soils

Science.gov (United States)

Kanty, Piotr; Rybak, Jarosław; Stefaniuk, Damian

2017-10-01

This paper presents the results of laboratory testing of organic soil-cement samples are presented in the paper. The research program continues previously reported the authors’ experiences with cement-fly ash-soil sample testing. Over 100 of compression and a dozen of tension tests have been carried out altogether. Several samples were waiting for failure test for over one year after they were formed. Several factors, like: the large amount of the tested samples, a long observation time, carrying out the tests in complex cycles of loading and the possibility of registering the loads and deformation in the axial and lateral direction - have made it possible to take into consideration numerous interdependencies, three of which have been presented in this work: the increments of compression strength, the stiffness of soil-cement in relation to strength and the tensile strength. Compressive strength, elastic modulus and tensile resistance of cubic samples were examined. Samples were mixed and stored in the laboratory conditions. Further numerical analysis in the Finite Element Method numerical code Z_Soil, were performed on the basis of laboratory test results. Computations prove that cement-based stabilization of organic soil brings serious risks (in terms of material capacity and stiffness) and Deep Soil Mixing technology should not be recommended for achieving it. The numerical analysis presented in the study below includes only one type of organic and sandy soil and several possible geometric combinations. Despite that, it clearly points to the fact that designing the DSM columns in the organic soil may be linked with a considerable risk and the settlement may reach too high values. During in situ mixing, the organic material surrounded by sand layers surely mixes with one another in certain areas. However, it has not been examined and it is difficult to assume such mixing already at the designing stage. In case of designing the DSM columns which goes through a

Engineering characteristics of the improved soil by deep mixing method using coal ash; Sekitanbai wo riyoshita FGC shinso kongo shori koho ni yoru kairyodo no kogakuteki seishitsu to kongo no tenbo ni tsuite

Energy Technology Data Exchange (ETDEWEB)

Watanabe, T [Center for Coal Utilization, Japan, Tokyo (Japan); Azuma, K; Watanabe, M [Electric Power Development Co. Ltd., Tokyo (Japan)

1996-09-01

Japan currently produces about six million tons of coal ash annually, whose effective bulk utilization to earth engineering materials is an important issue of technological development. A slurry may be made by mixing the following three kinds of materials: fly ash discharged from power plants exclusively burning dust coal (F), gypsum generated as a by-product in a stack gas desulfurizing process (G), and commercially available cement (C). The slurry would be called an FGC slurry taking the first letters of the materials. This paper presents the results of laboratory tests, in-situ execution tests and centrifuge model testing on engineering characteristics of soils improved by the FGC slurry when the slurry is applied to implementing the deep mixing method. As a result, a large number of findings were obtained including the following matters: the FGC deep mixing method makes it possible to improve ground beds having small deformation coefficient with the same accuracy as in the cement-based deep mixing (CDM) method at strengths lower than 5 kg/cm {sup 2} which is difficult with the CDM method, not to speak of strengths equivalent to that is possible with the normal CDM method; and development of a ground bed with improved strength is possible without being governed by quality and kinds of the fly ash. 1 ref., 23 figs., 5 tabs.

Does deep ocean mixing drive upwelling or downwelling of abyssal waters?

Science.gov (United States)

Ferrari, R. M.; McDougall, T. J.; Mashayek, A.; Nikurashin, M.; Campin, J. M.

2016-02-01

It is generally understood that small-scale mixing, such as is caused by breaking internal waves, drives upwelling of the densest ocean waters that sink to the ocean bottom at high latitudes. However the observational evidence that the turbulent fluxes generated by small-scale mixing in the stratified ocean interior are more vigorous close to the ocean bottom than above implies that small-scale mixing converts light waters into denser ones, thus driving a net sinking of abyssal water. Using a combination of numerical models and observations, it will be shown that abyssal waters return to the surface along weakly stratified boundary layers, where the small-scale mixing of density decays to zero. The net ocean meridional overturning circulation is thus the small residual of a large sinking of waters, driven by small-scale mixing in the stratified interior, and a comparably large upwelling, driven by the reduced small-scale mixing along the ocean boundaries.

DEEP MIXING IN EVOLVED STARS. II. INTERPRETING Li ABUNDANCES IN RED GIANT BRANCH AND ASYMPTOTIC GIANT BRANCH STARS

International Nuclear Information System (INIS)

Palmerini, S.; Busso, M.; Maiorca, E.; Cristallo, S.; Abia, C.; Uttenthaler, S.; Gialanella, L.

2011-01-01

We reanalyze the problem of Li abundances in red giants of nearly solar metallicity. After outlining the problems affecting our knowledge of the Li content in low-mass stars (M ≤ 3 M sun ), we discuss deep-mixing models for the red giant branch stages suitable to account for the observed trends and for the correlated variations of the carbon isotope ratio; we find that Li destruction in these phases is limited to masses below about 2.3 M sun . Subsequently, we concentrate on the final stages of evolution for both O-rich and C-rich asymptotic giant branch (AGB) stars. Here, the constraints on extra-mixing phenomena previously derived from heavier nuclei (from C to Al), coupled to recent updates in stellar structure models (including both the input physics and the set of reaction rates used), are suitable to account for the observations of Li abundances below A(Li) ≡ log ε(Li) ≅ 1.5 (and sometimes more). Also, their relations with other nucleosynthesis signatures of AGB phases (like the abundance of F, and the C/O and 12 C/ 13 C ratios) can be explained. This requires generally moderate efficiencies (M-dot -6 M sun yr -1 ) for non-convective mass transport. At such rates, slow extra mixing does not remarkably modify Li abundances in early AGB phases; on the other hand, faster mixing encounters a physical limit in destroying Li, set by the mixing velocity. Beyond this limit, Li starts to be produced; therefore, its destruction on the AGB is modest. Li is then significantly produced by the third dredge up. We also show that effective circulation episodes, while not destroying Li, would easily bring the 12 C/ 13 C ratios to equilibrium, contrary to the evidence in most AGB stars, and would burn F beyond the limits shown by C(N) giants. Hence, we do not confirm the common idea that efficient extra mixing drastically reduces the Li content of C stars with respect to K-M giants. This misleading appearance is induced by biases in the data, namely: (1) the difficulty

Tracers confirm downward mixing of Tyrrhenian Sea upper waters associated with the Eastern Mediterranean Transient

Directory of Open Access Journals (Sweden)

W. Roether

2011-01-01

Full Text Available Observations of tritium and ³He in the Tyrrhenian Sea, 1987–2009, confirm the enhanced vertical mixing of intermediate waters into the deep waters that has been noted and associated with the Eastern Mediterranean Transient in previous studies. Our evidence for the mixing rests on increasing tracer concentrations in the Tyrrhenian deep waters, accompanied by decreases in the upper waters, which are supplied from the Eastern Mediterranean. The downward transfer is particularly evident between 1987 and 1997. Later on, information partly rests on increasing tritium-³He ages; here we correct the observed ³He for contributions released from the ocean floor. The Tyrrhenian tracer distributions are fully compatible with data upstream of the Sicily Strait and in the Western Mediterranean. The tracer data show that mixing reached to the bottom and confirm a cyclonic nature of the deep water circulation in the Tyrrhenian. They furthermore indicate that horizontal homogenization of the deep waters occurs on a time scale of roughly 5 years. Various features point to a reduced impact of Western Mediterranean Deep Water (WMDW in the Tyrrhenian during the enhanced-mixing period. This is an important finding because it implies less upward mixing of WMDW, which has been named a major process to enable the WMDW to leave the Mediterranean via the Gibraltar Strait. On the other hand, the TDW outflow for several years represented a major influx of enhanced salinity and density waters into the deep-water range of the Western Mediterranean.

Groundwater mixing at fracture intersections triggers massive iron-rich microbial mats

Science.gov (United States)

Bochet, O.; Le Borgne, T.; Bethencourt, L.; Aquilina, L.; Dufresne, A.; Pédrot, M.; Farasin, J.; Abbott, B. W.; Labasque, T.; Chatton, E.; Lavenant, N.; Petton, C.

2017-12-01

While most freshwater on Earth resides and flows in groundwater systems, these deep subsurface environments are often assumed to have little biogeochemical activity compared to surface environments. Here we report a massive microbial mat of iron-oxidizing bacteria, flourishing 60 meters below the surface, far below the mixing zone where most microbial activity is believed to occur. The abundance of microtubular structures in the mat hinted at the prevalence of of Leptothrix ochracea, but metagenomic analysis revealed a diverse consortium of iron-oxidizing bacteria dominated by unknown members of the Gallionellaceae family. This deep biogeochemical hot spot formed at the intersection of bedrock fractures, which maintain redox gradients by mixing water with different residence times and chemical compositions. Using measured fracture properties and hydrological conditions we developed a quantitative model to simulate the reactive zone where such deep hot spots could occur. While seasonal fluctuations are generally thought to decrease with depth, we found that meter-scale changes in water table level moved the depth of the reactive zone hundreds of meters because the microaerophilic threshold for ironoxidizers is highly sensitive to changes in mixing rates at fracture intersections. These results demonstrate that dynamic microbial communities can be sustained deep below the surface in bedrock fractures. Given the ubiquity of fractures at multiple scales in Earth's subsurface, such deep hot spots may strongly influence global biogeochemical cycles.

Shifts in the bacterial community composition along deep soil profiles in monospecific and mixed stands of Eucalyptus grandis and Acacia mangium

Science.gov (United States)

de Andrade, Pedro Avelino Maia; Bini, Daniel; Durrer, Ademir; Robin, Agnès; Bouillet, Jean Pierre; Andreote, Fernando Dini; Cardoso, Elke Jurandy Bran Nogueira

2017-01-01

Our knowledge of the rhizosphere bacterial communities in deep soils and the role of Eucalyptus and Acacia on the structure of these communities remains very limited. In this study, we targeted the bacterial community along a depth profile (0 to 800 cm) and compared community structure in monospecific or mixed plantations of Acacia mangium and Eucalyptus grandis. We applied quantitative PCR (qPCR) and sequence the V6 region of the 16S rRNA gene to characterize composition of bacterial communities. We identified a decrease in bacterial abundance with soil depth, and differences in community patterns between monospecific and mixed cultivations. Sequence analysis indicated a prevalent effect of soil depth on bacterial communities in the mixed plant cultivation system, and a remarkable differentiation of bacterial communities in areas solely cultivated with Eucalyptus. The groups most influenced by soil depth were Proteobacteria and Acidobacteria (more frequent in samples between 0 and 300 cm). The predominant bacterial groups differentially displayed in the monospecific stands of Eucalyptus were Firmicutes and Proteobacteria. Our results suggest that the addition of an N2-fixing tree in a monospecific cultivation system modulates bacterial community composition even at a great depth. We conclude that co-cultivation systems may represent a key strategy to improve soil resources and to establish more sustainable cultivation of Eucalyptus in Brazil. PMID:28686690

The role of Southern Ocean mixing and upwelling in glacial-interglacial atmospheric CO2 change

International Nuclear Information System (INIS)

Watson, Andrew J.; Naveira Garabato, Alberto C.

2006-01-01

Decreased ventilation of the Southern Ocean in glacial time is implicated in most explanations of lower glacial atmospheric CO 2 . Today, the deep (>2000 m) ocean south of the Polar Front is rapidly ventilated from below, with the interaction of deep currents with topography driving high mixing rates well up into the water column. We show from a buoyancy budget that mixing rates are high in all the deep waters of the Southern Ocean. Between the surface and 2000 m depth, water is upwelled by a residual meridional overturning that is directly linked to buoyancy fluxes through the ocean surface. Combined with the rapid deep mixing, this upwelling serves to return deep water to the surface on a short time scale. We propose two new mechanisms by which, in glacial time, the deep Southern Ocean may have been more isolated from the surface. Firstly, the deep ocean appears to have been more stratified because of denser bottom water resulting from intense sea ice formation near Antarctica. The greater stratification would have slowed the deep mixing. Secondly, subzero atmospheric temperatures may have meant that the present-day buoyancy flux from the atmosphere to the ocean surface was reduced or reversed. This in turn would have reduced or eliminated the upwelling (contrary to a common assumption, upwelling is not solely a function of the wind stress but is coupled to the air/sea buoyancy flux too). The observed very close link between Antarctic temperatures and atmospheric CO 2 could then be explained as a natural consequence of the connection between the air/sea buoyancy flux and upwelling in the Southern Ocean, if slower ventilation of the Southern Ocean led to lower atmospheric CO 2 . Here we use a box model, similar to those of previous authors, to show that weaker mixing and reduced upwelling in the Southern Ocean can explain the low glacial atmospheric CO 2 in such a formulation

Highly efficient deep ultraviolet generation by sum-frequency mixing ...

Indian Academy of Sciences (India)

Generation of deep ultraviolet radiation at 210 nm by Type-I third harmonic generation is achieved in a pair of BBO crystals with conversion efficiency as high as 36%. The fundamental source is the dye laser radiation pumped by the second harmonic of a Q-switched Nd : YAG laser. A walk-off compensated configuration ...

Deep shaft high rate aerobic digestion: laboratory and pilot plant performance

Energy Technology Data Exchange (ETDEWEB)

Tran, F; Gannon, D

1981-01-01

The Deep Shaft is essentially an air-lift reactor, sunk deep in the ground (100-160 m); the resulting high hydrostatic pressure together with very efficient mixing in the shaft provide extremely high O transfer efficiencies (O.T.E.) of less than or equal to 90% vs. 4-20% in other aerators. This high O.T.E. suggests real potential for Deep-Shaft technology in the aerobic digestion of sludges and animal wastes: with conventional aerobic digesters an O.T.E. over 8% is extremely difficult to achieve. Laboratory and pilot plant Deep-Shaft aerobic digester studies carried out at Eco-Research's Pointe Claire, Quebec laboratories, and at the Paris, Ontario pilot Deep-Shaft digester are described.

North Jamaican Deep Fore-Reef Sponges

NARCIS (Netherlands)

Lehnert, Helmut; Soest, van R.W.M.

1996-01-01

An unexpectedly high amount of new species, revealed within only one hour of summarized bottom time, leads to the conclusion that the sponge fauna of the steep slopes of the deep fore-reef is still largely unknown. Four mixed gas dives at depths between 70 and 90 m, performed in May and June, 1993,

Two case studies on the origin of aqueous sulphate in deep crystalline rocks

International Nuclear Information System (INIS)

Michelot, J.L.; Fontes, J.C.

1987-01-01

The paper reports preliminary results obtained from studies in Central Sweden (Stripa) and in Northern Switzerland (Boettstein). The isotopic compositions ( 34 S, 18 O) of dissolved sulphates in shallow and deep groundwaters from the Stripa test site show that (1) the origins of the salinity in the shallow and in the deep groundwaters are probably different, (2) the low sulphate content of the waters collected from the upper part of the deep aquifer system could be derived from the shallow aqueous sulphate through bacterial reduction, (3) a deeper bulk of sulphate can be identified. After examining several hypotheses, a Permian or Triassic origin is attributed to this deep sulphate. Boettstein is the first drilled borehole of the NAGRA (Swiss National Co-operative for the Storage of Radioactive Wastes programme). In the 34 S versus 18 O diagram, most of the representative points of samples collected at different depths (from apparently different water bodies), lie along a straight line. It seems that this line cannot be a reduction line, nor a precipitation line (gypsum or anhydrite). It is thus interpreted as a mixing line. The end members of this mixing line are still unknown. However, a deep brine is present at the bottom of the system, probably related to brines circulating in the Permian channel found at the same depth, a few kilometres away. A working hypothesis involving this deep brine as a source for both end members of the mixing, through two different processes, is presented, with the problem of possible connections between the different water bodies. (author). 16 refs, 5 figs, 2 tabs

Mixing of solids

CERN Document Server

Weinekötter, Ralf

2000-01-01

This book is a welcome edition to the Particle Technology Series, formerly Powder Technology Series. It is the second book in the series which describes powder mixing and we make no excuses for that. The topic of powder mixing is fundamental to powder technology and is one which always aroses interest. That will not change. As powder products become more complex they will pose new mixing problems. The solutions lie in the intelligent use of equipment, an understanding of powder properties and a good knowledge of basic statistics. The authors of this book have presented those three ingredients with great clarity. The book is based on long experience and deep thought, I have enjoyed reading it and am pleased to recommend it. Delft University of Technology, NL-Delft, July 1999 Brian Scarlett, Series Editor IX VII Foreword to the English Edition In response to many enquiries from industrial organisations and institutes involved with the technology of processing bulk materials, we are pleased to present the Englis...

Stability of embankments over cement deep soil mixing columns; Estabilidad de terraplenes sobre columnas de suelo-cemento

Energy Technology Data Exchange (ETDEWEB)

Morilla Moar, P.; Melentijevic, S.

2014-07-01

The deep soil mixing (DSM) is one of the ground improvement methods used for the construction of embankments over soft soils. DSM column-supported embankments are constructed over soft soils to accelerate its construction, improve embankment stability, increase bearing capacity and control of total and differential settlements. There are two traditional design methods, the Japanese (rigid columns) and the scandinavian (soft and semi-rigid columns). Based on Laboratory analysis and numerical analysis these traditional approaches have been questioned by several authors due to its overestimation of the embankment stability considering that the most common failures types are not assumed. This paper presents a brief review of traditional design methods for embankments on DSM columns constructed in soft soils, studies carried out determine the most likely failure types of DSM columns, methods to decrease the overestimation when using limit equilibrium methods and numerical analysis methods that permit detect appropriate failure modes in DSM columns. Finally a case study was assessed using both limited equilibrium and finite element methods which confirmed the overestimation in the factors of safety on embankment stability over DSM columns. (Author)

Deep amplicon sequencing reveals mixed phytoplasma infection within single grapevine plants

DEFF Research Database (Denmark)

Nicolaisen, Mogens; Contaldo, Nicoletta; Makarova, Olga

2011-01-01

The diversity of phytoplasmas within single plants has not yet been fully investigated. In this project, deep amplicon sequencing was used to generate 50,926 phytoplasma sequences from 11 phytoplasma-infected grapevine samples from a PCR amplicon in the 5' end of the 16S region. After clustering ...

Importance of deep mixing for initiating the North Atlantic spring bloom

DEFF Research Database (Denmark)

Riisgaard, Karen; Paulsen, Maria Lund; Thingstad, T. Frede

The phytoplankton spring bloom is one of the most important recurrent events in the sup-polar part of the Atlantic Ocean. The classical idea is that the bloom is controlled by nutrients and light, but recent observations challenge this hypothesis. During repeated visits to stations in the deep...

Applied model for the growth of the daytime mixed layer

DEFF Research Database (Denmark)

Batchvarova, E.; Gryning, Sven-Erik

1991-01-01

numerically. When the mixed layer is shallow or the atmosphere nearly neutrally stratified, the growth is controlled mainly by mechanical turbulence. When the layer is deep, its growth is controlled mainly by convective turbulence. The model is applied on a data set of the evolution of the height of the mixed...... layer in the morning hours, when both mechanical and convective turbulence contribute to the growth process. Realistic mixed-layer developments are obtained....

Deep Mixing of 3He: Reconciling Big Bang and Stellar Nucleosynthesis

International Nuclear Information System (INIS)

Eggleton, P P; Dearborn, D P; Lattanzio, J

2006-01-01

Low-mass stars, ∼ 1-2 solar masses, near the Main Sequence are efficient at producing 3 He, which they mix into the convective envelope on the giant branch and should distribute into the Galaxy by way of envelope loss. This process is so efficient that it is difficult to reconcile the low observed cosmic abundance of 3 He with the predictions of both stellar and Big Bang nucleosynthesis. In this paper we find, by modeling a red giant with a fully three-dimensional hydrodynamic code and a full nucleosynthetic network, that mixing arises in the supposedly stable and radiative zone between the hydrogen-burning shell and the base of the convective envelope. This mixing is due to Rayleigh-Taylor instability within a zone just above the hydrogen-burning shell, where a nuclear reaction lowers the mean molecular weight slightly. Thus we are able to remove the threat that 3 He production in low-mass stars poses to the Big Bang nucleosynthesis of 3 He

Deep mixing of 3He: reconciling Big Bang and stellar nucleosynthesis.

Science.gov (United States)

Eggleton, Peter P; Dearborn, David S P; Lattanzio, John C

2006-12-08

Low-mass stars, approximately 1 to 2 solar masses, near the Main Sequence are efficient at producing the helium isotope 3He, which they mix into the convective envelope on the giant branch and should distribute into the Galaxy by way of envelope loss. This process is so efficient that it is difficult to reconcile the low observed cosmic abundance of 3He with the predictions of both stellar and Big Bang nucleosynthesis. Here we find, by modeling a red giant with a fully three-dimensional hydrodynamic code and a full nucleosynthetic network, that mixing arises in the supposedly stable and radiative zone between the hydrogen-burning shell and the base of the convective envelope. This mixing is due to Rayleigh-Taylor instability within a zone just above the hydrogen-burning shell, where a nuclear reaction lowers the mean molecular weight slightly. Thus, we are able to remove the threat that 3He production in low-mass stars poses to the Big Bang nucleosynthesis of 3He.

Interannual control of plankton communities by deep winter mixing and prey/predator interactions in the NW Mediterranean: Results from a 30-year 3D modeling study

Science.gov (United States)

Auger, P. A.; Ulses, C.; Estournel, C.; Stemmann, L.; Somot, S.; Diaz, F.

2014-05-01

A realistic modeling approach is designed to address the role of winter mixing on the interannual variability of plankton dynamics in the north-western (NW) Mediterranean basin. For the first time, a high-resolution coupled hydrodynamic-biogeochemical model (Eco3m-S) covering a 30-year period (1976-2005) is validated on available in situ and satellite data for the NW Mediterranean. In this region, cold, dry winds in winter often lead to deep convection and strong upwelling of nutrients into the euphotic layer. High nutrient contents at the end of winter then support the development of a strong spring bloom of phytoplankton. Model results indicate that annual primary production is not affected by winter mixing due to seasonal balance (minimum in winter and maximum in spring). However, the total annual water column-integrated phytoplankton biomass appears to be favored by winter mixing because zooplankton grazing activity is low in winter and early spring. This reduced grazing is explained here by the rarefaction of prey due to both light limitation and the effect of mixing-induced dilution on prey/predator interactions. A negative impact of winter mixing on winter zooplankton biomass is generally simulated except for mesozooplankton. This difference is assumed to stem from the lower parameterized mortality, top trophic position and detritivorous diet of mesozooplankton in the model. Moreover, model suggests that the variability of annual mesozooplankton biomass is principally modulated by the effects of winter mixing on winter biomass. Thus, interannual variability of winter nutrient contents in the euphotic layer, resulting from winter mixing, would control spring primary production and thus annual mesozooplankton biomass. Our results show a bottom-up control of mesozooplankton communities, as observed at a coastal location of the Ligurian Sea.

Mixing processes at the subsurface layer in the Amundsen Sea shelf region

Science.gov (United States)

Mojica, J.; Djoumna, G.; Francis, D. K.; Holland, D.

2017-12-01

In the Amundsen Sea shelf region, mixing processes promote an upward transport of diapycnal fluxes of heat and salt from the subsurface to the surface mixing layer. Here we estimate the diapycnal mixing rates on the Amundsen shelf from a multi-year mooring cluster and five research cruises. By applying fine-scale parameterizations, the mixing rates obtained were higher near the southern end of Pine Island glacier front and exceeded 10-2 m2s-1. The eddy diffusivity increased near the critical latitude (74o 28' S) for semi-diurnal M2 tides, which coincided with near-critical topography on the shelf. This condition favored the generation of internal waves of M2 frequency. The semi-diurnal dynamic enhanced the mixing that potentially affected the heat budget and the circulation of the modified Circumpolar Deep Water. This can be observed in the characteristics of water exchange both below the ice shelves and between the continental shelf and the ice shelf cavities. The location of the critical latitude and critical topography provided favorable conditions for the generation of internal waves. KEYWORDS: Mixing processes, diapycnal fluxes, critical latitude, Circumpolar Deep Water.

Simulation of deep ventilation in Crater Lake, Oregon, 1951–2099

Science.gov (United States)

Wood, Tamara M.; Wherry, Susan A.; Piccolroaz, Sebastiano; Girdner, Scott F

2016-05-04

The frequency of deep ventilation events in Crater Lake, a caldera lake in the Oregon Cascade Mountains, was simulated in six future climate scenarios, using a 1-dimensional deep ventilation model (1DDV) that was developed to simulate the ventilation of deep water initiated by reverse stratification and subsequent thermobaric instability. The model was calibrated and validated with lake temperature data collected from 1994 to 2011. Wind and air temperature data from three general circulation models and two representative concentration pathways were used to simulate the change in lake temperature and the frequency of deep ventilation events in possible future climates. The lumped model air2water was used to project lake surface temperature, a required boundary condition for the lake model, based on air temperature in the future climates.The 1DDV model was used to simulate daily water temperature profiles through 2099. All future climate scenarios projected increased water temperature throughout the water column and a substantive reduction in the frequency of deep ventilation events. The least extreme scenario projected the frequency of deep ventilation events to decrease from about 1 in 2 years in current conditions to about 1 in 3 years by 2100. The most extreme scenario considered projected the frequency of deep ventilation events to be about 1 in 7.7 years by 2100. All scenarios predicted that the temperature of the entire water column will be greater than 4 °C for increasing lengths of time in the future and that the conditions required for thermobaric instability induced mixing will become rare or non-existent.The disruption of deep ventilation by itself does not provide a complete picture of the potential ecological and water quality consequences of warming climate to Crater Lake. Estimating the effect of warming climate on deep water oxygen depletion and water clarity will require careful modeling studies to combine the physical mixing processes affected by

Seismic tomography and mixing in the deep earth

Directory of Open Access Journals (Sweden)

W. R. Peltier

1995-01-01

Full Text Available Recently constructed tomographic models of the lateral heterogeneity of elastic properties in the Earth's mantle are contrasted in terms of their implications concerning the extent to which the endothermic phase transformation at 660 km depth is influencing the radial style of mixing. Previously published whole mantle and split mantle tomographic reconstructions, SH8/WMI3 and SH8/U4L8 respectively, fit the seismic observations equally well but disagree on the extent to which radial mixing may be impeded across this depth horizon. We show that inferences from seismic tomographic images based on the application of diagnostic functions (global and regional variance spectra and the radial correlation function lead to the conclusion that the mantle circulation is whole mantle in style if model SH8/WM13 is employed. The split mantle tomographic inversion SHS/U4L8 leads to the contradictory conclusion that the mantle circulation is significantly impeded across the 660 km depth horizon. This latter interpretation is reinforced when we employ the new higher resolution split mantle model SH12/U7L5 in our calculations. We demonstrate that the depth-dependent radial heat flow delivered by both of the split models implies the existence of a thermal boundary layer at 660 km depth, and therefore significant layering, whereas that delivered by the whole mantle model does not. By insisting that the depth-dependent viscosity profile of the mantle be compatible with the thermal structure if the flow were layered, we argue that the split mantle tomographic inversions lead to a self-consistent description of geodynamic constraints (geoid and postglacial rebound data.

Facile one-pot transformation using structure-guided combustion waves of micro-nanostructured β-Bi2O3 to α-Bi2O3@C and analysis of electrochemical capacitance

Science.gov (United States)

Hwang, Hayoung; Shin, Jung-ho; Lee, Kang Yeol; Choi, Wonjoon

2018-01-01

Precise phase-transformation can facilitate control of the properties of various materials, while an organic coating surrounding inorganic materials can yield useful characteristics. Herein, we demonstrate facile, selective manipulation of micro-nanostructured bismuth oxide (Bi2O3) for phase transformation from microflower-like β-Bi2O3 to micropill-like α-Bi2O3, with carbon-coating layer deposition, using structure-guided combustion waves (SGCWs). Microflower-like β-Bi2O3 are synthesized as core materials and nitrocellulose is coated on their surfaces for the formation of core-shell hybrid structures of Bi2O3 and chemical fuel. The SGCWs, which propagate along the core-material and fuel interfaces, apply high thermal energy (550-600 °C) and deposit incompletely combusted carbonaceous fuel on the microflower-like β-Bi2O3 to enable transformation to α-phase and carbon-coating-layer synthesis. SGCW-induced improvements to the electrochemical characteristics of the developed micropill-like α-Bi2O3@C, compared with the microflower-like β-Bi2O3, are investigated. The enhanced stability from the α-phase Bi2O3 and micropill-like structures during charge-discharge cycling improves the specific capacitance, while the carbon-coating layers facilitate increased electrical conductivity. SGCW-based methods exhibit high potential for selective phase manipulation and synthesis of carbon coatings surrounding micro-nanomaterials. They constitute a low-cost, fast, large-scale process for metal oxides, ceramics, and hybrid materials, implemented through control of the processing parameters by tuning the temperature, chemical fuel, and ambient conditions.

Surface wind mixing in the Regional Ocean Modeling System (ROMS)

Science.gov (United States)

Robertson, Robin; Hartlipp, Paul

2017-12-01

Mixing at the ocean surface is key for atmosphere-ocean interactions and the distribution of heat, energy, and gases in the upper ocean. Winds are the primary force for surface mixing. To properly simulate upper ocean dynamics and the flux of these quantities within the upper ocean, models must reproduce mixing in the upper ocean. To evaluate the performance of the Regional Ocean Modeling System (ROMS) in replicating the surface mixing, the results of four different vertical mixing parameterizations were compared against observations, using the surface mixed layer depth, the temperature fields, and observed diffusivities for comparisons. The vertical mixing parameterizations investigated were Mellor- Yamada 2.5 level turbulent closure (MY), Large- McWilliams- Doney Kpp (LMD), Nakanishi- Niino (NN), and the generic length scale (GLS) schemes. This was done for one temperate site in deep water in the Eastern Pacific and three shallow water sites in the Baltic Sea. The model reproduced the surface mixed layer depth reasonably well for all sites; however, the temperature fields were reproduced well for the deep site, but not for the shallow Baltic Sea sites. In the Baltic Sea, the models overmixed the water column after a few days. Vertical temperature diffusivities were higher than those observed and did not show the temporal fluctuations present in the observations. The best performance was by NN and MY; however, MY became unstable in two of the shallow simulations with high winds. The performance of GLS nearly as good as NN and MY. LMD had the poorest performance as it generated temperature diffusivities that were too high and induced too much mixing. Further observational comparisons are needed to evaluate the effects of different stratification and wind conditions and the limitations on the vertical mixing parameterizations.

Mechanical behavior of embankments overlying on loose subgrade stabilized by deep mixed columns

Directory of Open Access Journals (Sweden)

Morteza Esmaeili

2016-10-01

Full Text Available Deep mixed column (DMC is known as one of the effective methods for stabilizing the natural earth beneath road or railway embankments to control stability and settlements under traffic loads. The load distribution mechanism of embankment overlying on loose subgrades stabilized with DMCs considerably depends on the columns' mechanical and geometrical specifications. The present study uses the laboratory investigation to understand the behavior of embankments lying on loose sandy subgrade in three different conditions: (1 subgrade without reinforcement, (2 subgrade reinforced with DMCs in a triangular pattern and horizontal plan, and (3 subgrade reinforced with DMCs in a square pattern and horizontal plan. For this purpose, by adopting the scale factor of 1:10, a reference embankment with 20 cm height, 250 cm length, and 93% maximum dry density achieved in standard Proctor compaction test was constructed over a 70 cm thick loose sandy bed with the relative density of 50% in a loading chamber, and its load-displacement behavior was evaluated until the failure occurred. In the next two tests, DMCs (with 10 cm diameter, 40 cm length, and 25 cm center-to-center spacing were placed in groups in two different patterns (square and triangular in the same sandy bed beneath the embankment and, consequently, the embankments were constructed over the reinforced subgrades and gradually loaded until the failure happened. In all the three tests, the load-displacement behaviors of the embankment and the selected DMCs were instrumented for monitoring purpose. The obtained results implied 64% increase in failure load and 40% decrease in embankment crest settlement when using the square pattern of DMCs compared with those of the reference embankment, while these values were 63% and 12%, respectively, for DMCs in triangular pattern. This confirmed generally better performance of DMCs with a triangular pattern.

Experimental observation of strong mixing due to internal wave focusing over sloping terrain

NARCIS (Netherlands)

Swart, A.; Manders, A.; Harlander, U.; Maas, L.R.M.

2010-01-01

This paper reports on experimental observation of internal waves that are focused due to a sloping topography. A remarkable mixing of the density field was observed. This result is of importance for the deep ocean, where internal waves are believed to play a role in mixing. The experiments were

Gateways and Water Mass Mixing in the Late Cretaceous North Atlantic

Science.gov (United States)

Asgharian Rostami, M.; Martin, E. E.; MacLeod, K. G.; Poulsen, C. J.; Vande Guchte, A.; Haynes, S.

2017-12-01

Regions of intermediate/deep water formation and water-mass mixing in the North Atlantic are poorly defined for the Late Cretaceous, a time of gateway evolution and cooler conditions following the Mid Cretaceous greenhouse. Improved proxy data combined with modeling efforts are required to effectively evaluate the relationship between CO2, paleogeography, and circulation during this cooler interval. We analyzed and compiled latest Cretaceous (79 - 66 Ma) ɛNd and δ13C records from seven bathyal (paleodepths 0.2 - 2 km) and eight abyssal (paleodepths > 2 km) sites in the North Atlantic. Data suggest local downwelling of Northern Component Water (NCW; ɛNd -9.5 and δ13C 1.7 ‰) is the primary source of intermediate/deep water masses in the basin. As this water flows southward and ages, δ13C values decrease and ɛNd values increase; however, additional chemical changes at several sites require mixing with contributions from several additional water masses. Lower ɛNd ( -10) and higher δ13C ( 1.9 ‰) values in the deep NW part of the basin indicate proximal contributions from a region draining old continental crust, potentially representing deep convection following opening of the Labrador Sea. In the deep NE Iberian Basin, higher ɛNd ( -7) and lower δ13C ( 0.8 ‰) during the Campanian suggest mixing with a Tethyan source (ɛNd -7 and δ13C 0.1 ‰) whose importance decreased with restriction of that gateway in the Maastrichtian. Data from bathyal sites suggest additional mixing. In the SE Cape Verde region, observed ɛNd variations from -10 in the Campanian to -13 and -12 in the early and late Maastrichtian, respectively, may record variations in output rates of Tethyan and/or NCW sources and Demerara Bottom Water (ɛNd -16), a proposed warm saline intermediate water mass formed in shallow, equatorial seas. Pacific inflow through the Caribbean gateway impacts intermediate sites at Blake Nose (ɛNd values -8), particularly the shallowest site during the late

Near-bottom pelagic bacteria at a deep-water sewage sludge disposal site

Energy Technology Data Exchange (ETDEWEB)

Takizawa, M.; Straube, W.L.; Hill, R.T.; Colwell, R.R.

1994-01-01

The epibenthic bacterial community at deep-ocean sewage sludge disposal site DWD-106, located approximately 106 miles (ca. 196 km) off the coast of New Jersey, was assessed for changes associated with the introduction of large amounts of sewage sludge. Mixed cultures and bacterial isolates obtained from water overlying sediment core samples collected at the deep-water (2,500 m) municipal sewage disposal site were tested for the ability to grow under in situ conditions of temperature and pressure. The responses of cultures collected at a DWD-106 station heavily impacted by sewage sludge were compared with those of samples collected from a station at the same depth which was not contaminated by sewage sludge. Significant differences were observed in the ability of mixed bacterial cultures and isolates from the two sites to grow under deep-sea pressure and temperature conditions. The levels of sludge contamination were established by enumerating Clostridium perfringens, a sewage indicator bacterium, in sediment samples from the two sites. (Copyright (c) 1993, American Society for Microbiology.)

Deep learning enables reduced gadolinium dose for contrast-enhanced brain MRI.

Science.gov (United States)

Gong, Enhao; Pauly, John M; Wintermark, Max; Zaharchuk, Greg

2018-02-13

There are concerns over gadolinium deposition from gadolinium-based contrast agents (GBCA) administration. To reduce gadolinium dose in contrast-enhanced brain MRI using a deep learning method. Retrospective, crossover. Sixty patients receiving clinically indicated contrast-enhanced brain MRI. 3D T 1 -weighted inversion-recovery prepped fast-spoiled-gradient-echo (IR-FSPGR) imaging was acquired at both 1.5T and 3T. In 60 brain MRI exams, the IR-FSPGR sequence was obtained under three conditions: precontrast, postcontrast images with 10% low-dose (0.01mmol/kg) and 100% full-dose (0.1 mmol/kg) of gadobenate dimeglumine. We trained a deep learning model using the first 10 cases (with mixed indications) to approximate full-dose images from the precontrast and low-dose images. Synthesized full-dose images were created using the trained model in two test sets: 20 patients with mixed indications and 30 patients with glioma. For both test sets, low-dose, true full-dose, and the synthesized full-dose postcontrast image sets were compared quantitatively using peak-signal-to-noise-ratios (PSNR) and structural-similarity-index (SSIM). For the test set comprised of 20 patients with mixed indications, two neuroradiologists scored blindly and independently for the three postcontrast image sets, evaluating image quality, motion-artifact suppression, and contrast enhancement compared with precontrast images. Results were assessed using paired t-tests and noninferiority tests. The proposed deep learning method yielded significant (n = 50, P 5 dB PSNR gains and >11.0% SSIM). Ratings on image quality (n = 20, P = 0.003) and contrast enhancement (n = 20, P deep learning method, gadolinium dose can be reduced 10-fold while preserving contrast information and avoiding significant image quality degradation. 3 Technical Efficacy: Stage 5 J. Magn. Reson. Imaging 2018. © 2018 International Society for Magnetic Resonance in Medicine.

A new approach to irreversibility in deep inelastic collisions

International Nuclear Information System (INIS)

Nemes, M.C.

1982-01-01

We use concepts of statistical mechanics to discuss the irreversible character of the experimental data in deep inelastic collisions. A definition of irreversibility proposed by Ruch permits a unified overview on current theories which describe these reactions. An information theoretical analysis of the data leads to a Fokker-Planck equation for the collective variables (excitation energy, charge and mass). The concept of mixing distance can serve as a quantitative measure to characterize the 'approach to equilibrium'. We apply it to the brownian motion as an illustration and also to the phenomenological analysis of deep inelastic scattering data with interesting results. (orig.)

Resolving both entrainment-mixing and number of activated CCN in deep convective clouds

Directory of Open Access Journals (Sweden)

E. Freud

2011-12-01

Full Text Available The number concentration of activated CCN (N_a is the most fundamental microphysical property of a convective cloud. It determines the rate of droplet growth with cloud depth and conversion into precipitation-sized particles and affects the radiative properties of the clouds. However, measuring N_a is not always possible, even in the cores of the convective clouds, because entrainment of sub-saturated ambient air deeper into the cloud lowers the concentrations by dilution and may cause partial or total droplet evaporation, depending on whether the mixing is homogeneous or extreme inhomogeneous, respectively.

Here we describe a methodology to derive N_a based on the rate of cloud droplet effective radius (R_e growth with cloud depth and with respect to the cloud mixing with the entrained ambient air. We use the slope of the tight linear relationship between the adiabatic liquid water mixing ratio and R_e³ (or R_v³ to derive an upper limit for N_a assuming extreme inhomogeneous mixing. Then we tune N_a down to find the theoretical relative humidity that the entrained ambient air would have for each horizontal cloud penetration, in case of homogeneous mixing. This allows us to evaluate both the entrainment and mixing process in the vertical dimension in addition to getting a better estimation for N_a.

We found that the derived N_a from the entire profile data is highly correlated with the independent CCN measurements from below cloud base. Moreover, it was found that mixing of sub-saturated ambient air into the cloud at scales of ~100 m and above is inclined towards the extreme inhomogeneous limit, i.e. that the time scale of droplet evaporation is significantly smaller than that for turbulent mixing. This means that ambient air that entrains

Intercomparison of model simulations of mixed-phase clouds observed during the ARM Mixed-Phase Arctic Cloud Experiment. Part II: Multi-layered cloud

Energy Technology Data Exchange (ETDEWEB)

Morrison, H; McCoy, R B; Klein, S A; Xie, S; Luo, Y; Avramov, A; Chen, M; Cole, J; Falk, M; Foster, M; Genio, A D; Harrington, J; Hoose, C; Khairoutdinov, M; Larson, V; Liu, X; McFarquhar, G; Poellot, M; Shipway, B; Shupe, M; Sud, Y; Turner, D; Veron, D; Walker, G; Wang, Z; Wolf, A; Xu, K; Yang, F; Zhang, G

2008-02-27

Results are presented from an intercomparison of single-column and cloud-resolving model simulations of a deep, multi-layered, mixed-phase cloud system observed during the ARM Mixed-Phase Arctic Cloud Experiment. This cloud system was associated with strong surface turbulent sensible and latent heat fluxes as cold air flowed over the open Arctic Ocean, combined with a low pressure system that supplied moisture at mid-level. The simulations, performed by 13 single-column and 4 cloud-resolving models, generally overestimate the liquid water path and strongly underestimate the ice water path, although there is a large spread among the models. This finding is in contrast with results for the single-layer, low-level mixed-phase stratocumulus case in Part I of this study, as well as previous studies of shallow mixed-phase Arctic clouds, that showed an underprediction of liquid water path. The overestimate of liquid water path and underestimate of ice water path occur primarily when deeper mixed-phase clouds extending into the mid-troposphere were observed. These results suggest important differences in the ability of models to simulate Arctic mixed-phase clouds that are deep and multi-layered versus shallow and single-layered. In general, models with a more sophisticated, two-moment treatment of the cloud microphysics produce a somewhat smaller liquid water path that is closer to observations. The cloud-resolving models tend to produce a larger cloud fraction than the single-column models. The liquid water path and especially the cloud fraction have a large impact on the cloud radiative forcing at the surface, which is dominated by the longwave flux for this case.

Characterization of mixed mode crack opening in concrete

DEFF Research Database (Denmark)

Jacobsen, Jonas Sejersbøl; Poulsen, Peter Noe; Olesen, John Forbes

2012-01-01

components of the mixed mode displacement are measured using a custom made orthogonal gauge, and the measurements are used directly as the closed loop control signals. A double notch, concrete specimen is used for the crack investigation. The tests are divided into two steps, a pure Mode I opening step......In real concrete structures cracks often open in mixed mode after their initiation. To capture the direct material behavior of a mixed mode crack opening a stiff biaxial testing machine, capable of imposing both normal and shear loads on a given crack area, has been applied. The opening and sliding......, where a macro crack is initiated in the specimen followed by the mixed mode opening step. The high stiffness of the set-up together with the closed control loop ensures a stable crack initiation followed by a controllable mixed mode opening. The deep notches result in a plane crack, only influenced...

Using environmental isotopes along with major hydro-geochemical compositions to assess deep groundwater formation and evolution in eastern coastal China

Science.gov (United States)

Xu, Naizheng; Gong, Jianshi; Yang, Guoqiang

2018-01-01

Hydrochemical analysis and environmental isotopic tracing are successfully applied to study groundwater evolution processes. Located in eastern China, the Jiangsu Coastal Plain is characterized by an extensively exploited deep groundwater system, and groundwater salinization has become the primary water environmental problem. This paper provides a case study on the use of a hydrochemical and environmental isotopic approach to assess possible mixing and evolution processes at Yoco Port, Jiangsu Province, China. Hydrochemical and isotopic patterns of deep groundwater allow one to distinguish different origins in deep water systems. HCO3- is the dominant anion in the freshwater samples, whereas Na+ and Cl- are the dominant major ions in the saline samples. According to δ18O, δ2H and 14C dating, the fresh water is derived from precipitation under a colder climate during the Glacial Maximum (Dali Glacial), while the saline groundwater is influenced by glacial-interglacial cycles during the Holocene Hypsithermal. The δ18O, δ2H and 3H data confirm that deep groundwater in some boreholes is mixed with overlying saline water. The deep groundwater reservoir can be divided into a saline water sector and a fresh water sector, and each show distinct hydrochemical and isotopic compositions. The saline groundwater found in the deep aquifer cannot be associated with present seawater intrusion. Since the Last Glacial Maximum in the Late Pleistocene, the deep groundwater flow system has evolved to its current status with the decrease in ice cover and the rising of sea level. However, the hydraulic connection is strengthened by continuous overexploitation, and deep groundwater is mixed with shallow groundwater at some points.

DeepPy: Pythonic deep learning

DEFF Research Database (Denmark)

Larsen, Anders Boesen Lindbo

This technical report introduces DeepPy – a deep learning framework built on top of NumPy with GPU acceleration. DeepPy bridges the gap between highperformance neural networks and the ease of development from Python/NumPy. Users with a background in scientific computing in Python will quickly...... be able to understand and change the DeepPy codebase as it is mainly implemented using high-level NumPy primitives. Moreover, DeepPy supports complex network architectures by letting the user compose mathematical expressions as directed graphs. The latest version is available at http...

Deep water overflow in the Faroe Bank Channel; modelling, processes, and impact

DEFF Research Database (Denmark)

Rullyanto, Arief

, creating new water masses with distinct temperature, salinity and density characteristics. The change of water mass characteristics not only affects the local environment, but also far distant regions. The Faroe Bank Channel, which is located in the southern part of Faroe Islands, is one of the most...... under different circumstances. The focus is on the Faroe Bank Channel, a relatively small region, which has a significant impact on the global ocean circulation and marine organisms that live in its environment....... or tides, but also deep beneath the surface, where deep-water currents circulate waters throughout the world’s oceans. In certain very-localized regions, the flow of the deep-water has to travel over a sill in a narrow submarine channel. This overflow process mixes the deep water with overlying waters...

Hot-spot mix in ignition-scale inertial confinement fusion targets.

Science.gov (United States)

Regan, S P; Epstein, R; Hammel, B A; Suter, L J; Scott, H A; Barrios, M A; Bradley, D K; Callahan, D A; Cerjan, C; Collins, G W; Dixit, S N; Döppner, T; Edwards, M J; Farley, D R; Fournier, K B; Glenn, S; Glenzer, S H; Golovkin, I E; Haan, S W; Hamza, A; Hicks, D G; Izumi, N; Jones, O S; Kilkenny, J D; Kline, J L; Kyrala, G A; Landen, O L; Ma, T; MacFarlane, J J; MacKinnon, A J; Mancini, R C; McCrory, R L; Meezan, N B; Meyerhofer, D D; Nikroo, A; Park, H-S; Ralph, J; Remington, B A; Sangster, T C; Smalyuk, V A; Springer, P T; Town, R P J

2013-07-26

Mixing of plastic ablator material, doped with Cu and Ge dopants, deep into the hot spot of ignition-scale inertial confinement fusion implosions by hydrodynamic instabilities is diagnosed with x-ray spectroscopy on the National Ignition Facility. The amount of hot-spot mix mass is determined from the absolute brightness of the emergent Cu and Ge K-shell emission. The Cu and Ge dopants placed at different radial locations in the plastic ablator show the ablation-front hydrodynamic instability is primarily responsible for hot-spot mix. Low neutron yields and hot-spot mix mass between 34(-13,+50)  ng and 4000(-2970,+17 160)  ng are observed.

Moisture Vertical Structure, Deep Convective Organization, and Convective Transition in the Amazon

Science.gov (United States)

Schiro, K. A.; Neelin, J. D.

2017-12-01

Constraining precipitation processes in climate models with observations is crucial to accurately simulating current climate and reducing uncertainties in future projections. Results from the Green Ocean Amazon (GOAmazon) field campaign (2014-2015) provide evidence that deep convection is strongly controlled by the availability of moisture in the free troposphere over the Amazon, much like over tropical oceans. Entraining plume buoyancy calculations confirm that CWV is a good proxy for the conditional instability of the environment, yet differences in convective onset as a function of CWV exist over land and ocean, as well as seasonally and diurnally over land. This is largely due to variability in the contribution of lower tropospheric humidity to the total column moisture. Boundary layer moisture shows a strong relationship to the onset during the day, which largely disappears during nighttime. Using S-Band radar, these transition statistics are examined separately for unorganized and mesoscale-organized convection, which exhibit sharp increases in probability of occurrence with increasing moisture throughout the column, particularly in the lower free troposphere. Retrievals of vertical velocity from a radar wind profiler indicate updraft velocity and mass flux increasing with height through the lower troposphere. A deep-inflow mixing scheme motivated by this — corresponding to deep inflow of environmental air into a plume that grows with height — provides a weighting of boundary layer and free tropospheric air that yields buoyancies consistent with the observed onset of deep convection across seasons and times of day, across land and ocean sites, and for all convection types. This provides a substantial improvement relative to more traditional constant mixing assumptions, and a dramatic improvement relative to no mixing. Furthermore, it provides relationships that are as strong or stronger for mesoscale-organized convection as for unorganized convection.

Processes governing transient responses of the deep ocean buoyancy budget to a doubling of CO2

Science.gov (United States)

Palter, J. B.; Griffies, S. M.; Hunter Samuels, B. L.; Galbraith, E. D.; Gnanadesikan, A.

2012-12-01

Recent observational analyses suggest there is a temporal trend and high-frequency variability in deep ocean buoyancy in the last twenty years, a phenomenon reproduced even in low-mixing models. Here we use an earth system model (GFDL's ESM2M) to evaluate physical processes that influence buoyancy (and thus steric sea level) budget of the deep ocean in quasi-steady state and under a doubling of CO2. A new suite of model diagnostics allows us to quantitatively assess every process that influences the buoyancy budget and its temporal evolution, revealing surprising dynamics governing both the equilibrium budget and its transient response to climate change. The results suggest that the temporal evolution of the deep ocean contribution to sea level rise is due to a diversity of processes at high latitudes, whose net effect is then advected in the Eulerian mean flow to mid and low latitudes. In the Southern Ocean, a slowdown in convection and spin up of the residual mean advection are approximately equal players in the deep steric sea level rise. In the North Atlantic, the region of greatest deep steric sea level variability in our simulations, a decrease in mixing of cold, dense waters from the marginal seas and a reduction in open ocean convection causes an accumulation of buoyancy in the deep subpolar gyre, which is then advected equatorward.

Chemical stratigraphy of the Apollo 17 deep drill cores 70009-70007

Science.gov (United States)

Ehmann, W. D.; Ali, M. Z.

1977-01-01

A description is presented of an analysis of a total of 26 samples from three core segments (70009, 70008, 70007) of the Apollo 17 deep drill string. The deep drill string was taken about 700 m east of the Camelot Crater in the Taurus-Littrow region of the moon. Three core segments have been chemically characterized from the mainly coarse-grained upper portion of the deep drill string. The chemical data suggest that the entire 70007-70009 portion of the deep drill string examined was not deposited as a single unit, but was formed by several events sampling slightly different source materials which may have occurred over a relatively short period of time. According to the data from drill stem 70007, there were at least two phases of deposition. Core segment 70009 is probably derived from somewhat different source material than 70008. It seems to be a very well mixed material.

A Methodology for Conducting Integrative Mixed Methods Research and Data Analyses

Science.gov (United States)

Castro, Felipe González; Kellison, Joshua G.; Boyd, Stephen J.; Kopak, Albert

2011-01-01

Mixed methods research has gained visibility within the last few years, although limitations persist regarding the scientific caliber of certain mixed methods research designs and methods. The need exists for rigorous mixed methods designs that integrate various data analytic procedures for a seamless transfer of evidence across qualitative and quantitative modalities. Such designs can offer the strength of confirmatory results drawn from quantitative multivariate analyses, along with “deep structure” explanatory descriptions as drawn from qualitative analyses. This article presents evidence generated from over a decade of pilot research in developing an integrative mixed methods methodology. It presents a conceptual framework and methodological and data analytic procedures for conducting mixed methods research studies, and it also presents illustrative examples from the authors' ongoing integrative mixed methods research studies. PMID:22167325

Mixing and its effects on biogeochemistry in the persistently stratified, deep, tropical Lake Matano, Indonesia

DEFF Research Database (Denmark)

Katsev, Sergei; Crowe, Sean; Mucci, Alfonso

2010-01-01

In the > 590-m deep, tropical Lake Matano (Indonesia), stratification is characterized by weak thermal gradients (... steady-state conditions, vertical eddy diffusion coefficients (K-z) cannot be estimated by conventional methods that rely on time derivatives of temperature distributions. We use and compare several alternative methods: one-dimensional k-epsilon modeling, three-dimensional hydrodynamic modeling...... composition in the deep waters is close to those of the ground and tributary waters. The vertical distribution of K-z is used in a biogeochemical reaction-transport model. We show that, outside of a narrow thermocline region, the vertical distributions of dissolved oxygen, iron, methane, and phosphorus...

ON THE NEED FOR DEEP-MIXING IN ASYMPTOTIC GIANT BRANCH STARS OF LOW MASS

International Nuclear Information System (INIS)

Busso, M.; Palmerini, S.; Maiorca, E.; Cristallo, S.; Abia, C.; Straniero, O.; Gallino, R.; Cognata, M. La

2010-01-01

The photospheres of low-mass red giants show CNO isotopic abundances that are not satisfactorily accounted for by canonical stellar models. The same is true for the measurements of these isotopes and of the 26 Al/ 27 Al ratio in presolar grains of circumstellar origin. Non-convective mixing, occurring during both red giant branch (RGB) and asymptotic giant branch (AGB) stages, is the explanation commonly invoked to account for the above evidence. Recently, the need for such mixing phenomena on the AGB was questioned, and chemical anomalies usually attributed to them were suggested to be formed in earlier phases. We have therefore re-calculated extra-mixing effects in low-mass stars for both the RGB and AGB stages, in order to verify the above claims. Our results contradict them; we actually confirm that slow transport below the convective envelope occurs also on the AGB. This is required primarily by the oxygen isotopic mix and the 26 Al content of presolar oxide grains. Other pieces of evidence exist, in particular from the isotopic ratios of carbon stars of type N, or C(N), in the Galaxy and in the LMC, as well as of SiC grains of AGB origin. We further show that, when extra-mixing occurs in the RGB phases of Population I stars above about 1.2 M sun , this consumes 3 He in the envelope, probably preventing the occurrence of thermohaline diffusion on the AGB. Therefore, we argue that other extra-mixing mechanisms should be active in those final evolutionary phases.

Injection grout for deep repositories. Subproject 1: LowpH cementitious grout for larger fractures, leach testing of grout mixes and evaluation of the long-term safety

International Nuclear Information System (INIS)

Vuorinen, U.; Lehikoinen, J.; Imoto, Harutake; Yamamoto, Takeshi; Cruz Alonso, M.

2005-10-01

Constructing an underground disposal facility for spent nuclear fuel deep in bedrock requires lowpH cement-based injection grout, because assured data of the extent of a possible high-pH plume in saturated bedrock conditions is lacking. In this work low-pH grout mixes of new design were subjected to leach testing. Before chosen to leach testing the grout mixes had to fulfil certain technical requirements. Leach testing was performed in order to establish that the pH requirement (≤11) set for the leachates was met. For comparison reasons also one conventionally used cement based grout material was included in the tests. Two kinds of lowpH grout cement mixes were tested; mixes with added blast furnace slag (4 mixes) or added silica (6 mixes). All the mixes were not completely tested according to the test plan, because for some mixes during leach testing factors detrimental to the long-term safety of a repository were observed, e.g. too high pH or leached sulphide, which is harmful for copper. Leach testing of the grout mixes was performed in a glove-box (N 2 atmosphere) in order to avoid the interference of atmospheric CO 2 on the alkaline leachates. Two simulated groundwater solutions, saline OL-SO and fresh ALL-MR, were used as leachates. Two leach tests were applied; equilibrium and diffusion tests. In the equilibrium test at each measuring point only a part of the leachate was exchanged, whereas in the diffusion test the entire leachate was exchanged. The pH value of each leachate sample was measured, but total alkalinity was determined only for some leachates. Na, K, Ca, Mg, Al, Fe, Si, SO 4 2- , S TOT , and Cl were analysed in the leach solutions collected in the diffusion test of four grout mixes chosen. Also the corresponding solid specimens were analysed (SEM, XRD, EPMA, MIP, TG) in Japan. A few grout pore fluid pH values were measured in Spain, as well. The simplified thermodynamic model calculations were successful in qualitatively reproducing the

Monitoring of Deep Foundation Pit Support and Construction Process in Soft Soil Area of Pearl River Delta

Science.gov (United States)

Weiyi, Xie; Pengcheng

2018-03-01

The deep foundation pit supporting technology in the soft soil area of the Pearl River Delta is more complicated, and many factors influence and restrict it. In this project as an example, according to the geological conditions and the surrounding circumstances, the main foundation using bored piles and pre-stressed anchor cable supporting structure + five axis cement mixing pile curtain supporting form; partial use of double row piles supporting structure + five axis cement mixing pile curtain support type. Through the monitoring results of construction show that the foundation pit, the indicators of environmental changes are in the design range, the supporting scheme of deep foundation pit technology is feasible and reliable.

Modification of deep waters in Marguerite Bay, western Antarctic Peninsula, caused by topographic overflows

Science.gov (United States)

Venables, Hugh J.; Meredith, Michael P.; Brearley, J. Alexander

2017-05-01

Circumpolar Deep Water (CDW) intrudes from the mid-layers of the Antarctic Circumpolar Current onto the shelf of the western Antarctic Peninsula, providing a source of heat and nutrients to the regional ocean. It is well known that CDW is modified as it flows across the shelf, but the mechanisms responsible for this are not fully known. Here, data from underwater gliders with high spatial resolution are used to demonstrate the importance of detailed bathymetry in inducing multiple local mixing events. Clear evidence for overflows is observed in the glider data as water flows along a deep channel with multiple transverse ridges. The ridges block the densest waters, with overflowing water descending several hundred metres to fill subsequent basins. This vertical flow leads to entrainment of overlying colder and fresher water in localised mixing events. Initially this process leads to an increase in bottom temperatures due to the temperature maximum waters descending to greater depths. After several ridges, however, the mixing is sufficient to remove the temperature maximum completely and the entrainment of colder thermocline waters to depth reduces the bottom temperature, to approximately the same as in the source region of Marguerite Trough. Similarly, it is shown that deep waters of Palmer Deep are warmer than at the same depth at the shelf break. The exact details of the transformations observed are heavily dependent on the local bathymetry and water column structure, but glacially-carved troughs and shallow sills are a common feature of the bathymetry of polar shelves, and these types of processes may be a factor in determining the hydrographic conditions close to the coast across a wider area.

The abyssal and deep circulation of the Northeast Pacific Basin

Science.gov (United States)

Hautala, Susan L.

2018-01-01

Three-dimensional abyssal and deep circulation of the region to the east and north of the Emperor Seamount Chain/Hawaiian Ridge is determined from a compilation of CTD and Argo float data, using a new overdetermined inverse technique for the geostrophic reference velocity and diapycnal/lateral mixing coefficients. The Northeast Pacific Basin is primarily sourced from its northern boundary, at a rate of 3.5 Sv across 47°N below 3000 m. Bottom water in the western subarctic gyre recirculates cyclonically between the Emperor Seamount Chain and 155°W. Bottom water east of 155°W takes a more direct path southward along the flank of a broad topographic slope. In the deep water, a ridge of potential vorticity lying along the Mendocino Fracture Zone separates circulation systems north and south of ∼40°N. The region has very weak diapycnal and lateral mixing, and an aspect ratio for the overturning circulation that is correspondingly flat, with bottom water parcels rising less than 1 km during their long transit from the Aleutian Trench to the latitude of Hawaii.

Photoemission studies of mixed valent systems

International Nuclear Information System (INIS)

Parks, R.D.; Raaen, S.; denBoer, M.L.; Williams, G.P.

1984-01-01

Photoemission spectroscopy has been used to study a number of aspects of the mixed valent state (corresponding to non-integral 4f occupation) in rare earth systems. Deep core photoemission (e.g., from 3d or 4d levels) allows the measurement of the 4f occupancy and surface valence shifts, and, as well, the indirect measurement of the effect of solid state environment on the energy of hybridization between 4f electrons and conduction electrons. 4f-Derived photoemission has been used to study surface valance and chemical shifts and to infer the nature of the mixed valent ground state. A combination of 4f-derived photoemission and add-electron spectroscopy provides a measurement of the rf Coulomb correlation energy, an important parameter in the mixed valent problem. A review of these approaches will be presented, with emphasis on Ce-based systems, whose behavior falls outside the usual description of 4f-unstable systems

Climatology and evolution of the mixing height over water

Energy Technology Data Exchange (ETDEWEB)

Sempreviva, A.M. [Istituto di Fisica dell`Atmosfera, CNR, Rome (Italy); Grynig, S.E. [Risoe National Lab., Roskilde (Denmark)

1997-10-01

In this paper we present results from an experimental investigation on the height of the mixed layer h, using a meteorological station located on the Danish island of Anholt. The station was operational for two years from September 1990 to October 1992. We present the analysis of two years of radio-sounding showing the average daily evolution of h. Furthermore observations of the mixed layer growth under near-neutral and unstable atmospheric conditions during six consecutive days has been modelled using a simple zero-order mixed-layer height model. Finally we have compared the evolution of the mixing height from the model with the evolution of the correlation coefficient between temperature and humidity to study the influence of the deepness of the convective layer on the mechanism of the correlation between temperature and humidity in the surface layer. (au)

Mixed-Reality Prototypes to Support Early Creative Design

Science.gov (United States)

Safin, Stéphane; Delfosse, Vincent; Leclercq, Pierre

The domain we address is creative design, mainly architecture. Rooted in a multidisciplinary approach as well as a deep understanding of architecture and design, our method aims at proposing adapted mixed-reality solutions to support two crucial activities: sketch-based preliminary design and distant synchronous collaboration in design. This chapter provides a summary of our work on a mixed-reality device, based on a drawing table (the Virtual Desktop), designed specifically to address real-life/business-focused issues. We explain our methodology, describe the two supported activities and the related users’ needs, detail the technological solution we have developed, and present the main results of multiple evaluation sessions. We conclude with a discussion of the usefulness of a profession-centered methodology and the relevance of mixed reality to support creative design activities.

Forecasting the evolution in the mixing regime of a deep subalpine lake under climate change scenarios through numerical modelling (Lake Maggiore, Northern Italy/Southern Switzerland)

Science.gov (United States)

Fenocchi, Andrea; Rogora, Michela; Sibilla, Stefano; Ciampittiello, Marzia; Dresti, Claudia

2018-01-01

The impact of air temperature rise is eminent for the large deep lakes in the Italian subalpine district, climate change being caused there by both natural phenomena and anthropogenic greenhouse-gases (GHG) emissions. These oligomictic lakes are experiencing a decrease in the frequency of winter full turnover and an intensification of stability. As a result, hypolimnetic oxygen concentrations are decreasing and nutrients are accumulating in bottom water, with effects on the whole ecosystem functioning. Forecasting the future evolution of the mixing pattern is relevant to assess if a reduction in GHG releases would be able to revert such processes. The study focuses on Lake Maggiore, for which the thermal structure evolution under climate change in the 2016-2085 period was assessed through numerical simulations, performed with the General Lake Model (GLM). Different prospects of regional air temperature rise were considered, given by the Swiss Climate Change Scenarios CH2011. Multiple realisations were performed for each scenario to obtain robust statistical predictions, adopting random series of meteorological data produced with the Vector-Autoregressive Weather Generator (VG). Results show that a reversion in the increasing thermal stability would be possible only if global GHG emissions started to be reduced by 2020, allowing an equilibrium mixing regime to be restored by the end of the twenty-first century. Otherwise, persistent lack of complete-mixing, severe water warming and extensive effects on water quality are to be expected for the centuries to come. These projections can be extended to the other lakes in the subalpine district.

Nondestructive evaluation of differently doped InP wafers by time-resolved four-wave mixing technique

International Nuclear Information System (INIS)

Kadys, A.; Sudzius, M.; Jarasiunas, K.; Mao Luhong; Sun Niefeng

2006-01-01

Photoelectric properties of semi-insulating, differently doped, and undoped indium phosphide wafers, grown by the liquid encapsulation Czochralski method, have been investigated by time-resolved picosecond four-wave mixing technique. Deep defect related carrier generation, recombination, and transport properties were investigated experimentally by measuring four-wave mixing kinetics and exposure characteristics. The presence of deep donor states in undoped InP was confirmed by a pronounced effect of a space charge electric field to carrier transport. On the other hand, the recharging dynamics of electrically active residual impurities was observed in undoped and Fe-doped InP through the process of efficient trapping of excess carriers. The bipolar diffusion coefficients and mobilities were determined for the all wafers

Event-Driven Random Back-Propagation: Enabling Neuromorphic Deep Learning Machines.

Science.gov (United States)

Neftci, Emre O; Augustine, Charles; Paul, Somnath; Detorakis, Georgios

2017-01-01

An ongoing challenge in neuromorphic computing is to devise general and computationally efficient models of inference and learning which are compatible with the spatial and temporal constraints of the brain. One increasingly popular and successful approach is to take inspiration from inference and learning algorithms used in deep neural networks. However, the workhorse of deep learning, the gradient descent Gradient Back Propagation (BP) rule, often relies on the immediate availability of network-wide information stored with high-precision memory during learning, and precise operations that are difficult to realize in neuromorphic hardware. Remarkably, recent work showed that exact backpropagated gradients are not essential for learning deep representations. Building on these results, we demonstrate an event-driven random BP (eRBP) rule that uses an error-modulated synaptic plasticity for learning deep representations. Using a two-compartment Leaky Integrate & Fire (I&F) neuron, the rule requires only one addition and two comparisons for each synaptic weight, making it very suitable for implementation in digital or mixed-signal neuromorphic hardware. Our results show that using eRBP, deep representations are rapidly learned, achieving classification accuracies on permutation invariant datasets comparable to those obtained in artificial neural network simulations on GPUs, while being robust to neural and synaptic state quantizations during learning.

Event-Driven Random Back-Propagation: Enabling Neuromorphic Deep Learning Machines

Directory of Open Access Journals (Sweden)

Emre O. Neftci

2017-06-01

Full Text Available An ongoing challenge in neuromorphic computing is to devise general and computationally efficient models of inference and learning which are compatible with the spatial and temporal constraints of the brain. One increasingly popular and successful approach is to take inspiration from inference and learning algorithms used in deep neural networks. However, the workhorse of deep learning, the gradient descent Gradient Back Propagation (BP rule, often relies on the immediate availability of network-wide information stored with high-precision memory during learning, and precise operations that are difficult to realize in neuromorphic hardware. Remarkably, recent work showed that exact backpropagated gradients are not essential for learning deep representations. Building on these results, we demonstrate an event-driven random BP (eRBP rule that uses an error-modulated synaptic plasticity for learning deep representations. Using a two-compartment Leaky Integrate & Fire (I&F neuron, the rule requires only one addition and two comparisons for each synaptic weight, making it very suitable for implementation in digital or mixed-signal neuromorphic hardware. Our results show that using eRBP, deep representations are rapidly learned, achieving classification accuracies on permutation invariant datasets comparable to those obtained in artificial neural network simulations on GPUs, while being robust to neural and synaptic state quantizations during learning.

Waste disposal in the deep ocean: An overview

International Nuclear Information System (INIS)

O'Connor, T.P.; Kester, D.R.; Burt, W.V.; Capuzzo, J.M.; Park, P.K.; Duedall, I.W.

1985-01-01

Incineration at sea, industrial and sewage waste disposal in the surface mixing zone, and disposal of low-level nuclear wastes, obsolete munitions, and nerve gas onto the seafloor have been the main uses of the deep sea for waste management. In 1981 the wastes disposed of in the deep sea consisted of 48 X 10/sup 4/ t of liquid industrial wastes and 2 X 10/sup 4/ t of sewage sludge by the United States; 1.5 X 10/sup 4/ t (solids) of sewage sludge by the Federal Republic of German; 5300 t of liquid industrial wastes by Denmark; 99 t of solid industrial wastes by the United Kingdom; and 9400 t of low-level radioactive wastes by several European countries. Also in 1981 at-sea incineration of slightly more than 10/sup 5/ t of organic wastes from Belgium, France, the Federal Republic of Germany, the Netherlands, Norway, Sweden, and the United Kingdom was carried out in the North Sea. Unique oceanographic features of the deep sea include its large dilution capacity; the long residence time of deep-sea water (on the order of 10/sup 2/ y); low biological productivity in the surface water of the open ocean (≅50 g m/sup -2/ of carbon per year); the existence of an oxygen minimum zone at several hundred meters deep in the mid-latitudes; and the abyssal-clay regions showing sedimentary records of tens of millions of years of slow, uninterrupted deposition of fine-grained clay. Any deep-sea waste disposal strategy must take into account oceanic processes and current scientific knowledge in order to attain a safe solution that will last for centuries

Mechanisms of hypolimnion erosion in a deep lake (Lago Maggiore, N. Italy

Directory of Open Access Journals (Sweden)

Elisabetta A. CARRARA

2010-02-01

Full Text Available Holo-oligomixis is one of the most important hydrodynamic characteristics of deep lakes in temperate regions, especially those of the Southern Alps. It influences such important lake chemical and biological processes as the oxygenation of deep layers, recycling of nutrients, vertical migration of plankton, and reproduction. Analysis of physico-chemical data from Lago Maggiore over the years 1951 – 2008 has shown that in addition to ever active but relatively inefficient convective mixing, three other mechanisms act to oxygenate this lake’s deep waters in winter. These are conveyor belt currents, cold and well-oxygenated tributary inflows that sink down to depths of equal density, and differential cooling of littoral waters that subsequently slide down the lake flanks. Their common outcome is to cause deep erosion of the hypolimnion. Heat content and thermal stability also are affected and are analyzed here in relation to external driving forces, examining in particular how dynamics may be altered by climate change.

Geochemical evidence for groundwater mixing in the western Great Artesian Basin and recognition of deep inputs in continental-scale flow systems

Science.gov (United States)

Crossey, L. J.; Karlstrom, K. E.; Love, A.; Priestley, S.; Shand, P.

2010-12-01

Hot Springs record different degrees of fluid-rock interactions in granitic crust and small volume, but geochemically potent, crustal contributions to the endogenic fluids. U-Series dates indicate persistent deposition of travertine mound springs (conceptualized as “chemical volcanoes”) at discrete vent sites for millions of years. The geochemistry of the active travertine-depositing mound springs, coupled with geochemistry of the associated mound and platform travertine rock record, thus collectively provide a rich record that can be used to link the present hydrologic system to paleohydrology of the GAB over the last several million years at the continental scale. The overall goal is to test a model for interactions between mantle and deep crustal fluid inputs, neotectonic pathways, groundwater mixing, groundwater quantity and quality, and unique microbiology in the near-surface hydrologic systems.

Temperature, heat content, mixing and stability in Lake Orta: a pluriannual investigation

Directory of Open Access Journals (Sweden)

Luigi BARBANTI

2001-02-01

Full Text Available This paper describes the overall state of some physical phenomena occurring in Lake Orta, such as thermal stratification and destratification, accumulation and release of heat, vertical winter mixing, and stability of the water mass. The historical series of temperature distribution along the water column in the period 1984-1999, from which the holo-oligomictic character of Lake Orta emerges, is analysed. The monthly evaluation of the heat contents metre by metre from 0 to 143 m depth reveals how the complete winter mixing occurs only when the energy present within the whole column is less than 1,675 MJ m-2; above this value the circulation is only partial, as in the other deep subalpine lakes. A water layer in the deep hypolimnion has been shown to contain a climatic memory, which has generally increased since 1981. Walker’s stability analysis has revealed that when at a depth below 90 metres there is a level where 0.07 J m-2 are exceeded, total mixing cannot take place. In contrast, the Birgean work identifies, during the heating phase, the layers of the lake where energy is stored or lost.

Mixing, Hydrography, and Flow in the eastern Channel of the Lucky Strike Segment

OpenAIRE

Tippenhauer, Sandra

2015-01-01

Diapycnal mixing in the deep ocean is known to be much stronger in the vicinity of rough topography of mid-ocean ridges than over abyssal plains. In this thesis a microstructure probe attached to an autonomous underwater vehicle (AUV) was used to infer the spatial distribution of the dissipation rate of turbulent kinetic energy in the central valley of the Mid-Atlantic Ridge. This represents the first successful realization of a horizontal, AUV-based, deep-ocean microstructure survey. The stu...

Volume fracturing of deep shale gas horizontal wells

Directory of Open Access Journals (Sweden)

Tingxue Jiang

2017-03-01

Full Text Available Deep shale gas reservoirs buried underground with depth being more than 3500 m are characterized by high in-situ stress, large horizontal stress difference, complex distribution of bedding and natural cracks, and strong rock plasticity. Thus, during hydraulic fracturing, these reservoirs often reveal difficult fracture extension, low fracture complexity, low stimulated reservoir volume (SRV, low conductivity and fast decline, which hinder greatly the economic and effective development of deep shale gas. In this paper, a specific and feasible technique of volume fracturing of deep shale gas horizontal wells is presented. In addition to planar perforation, multi-scale fracturing, full-scale fracture filling, and control over extension of high-angle natural fractures, some supporting techniques are proposed, including multi-stage alternate injection (of acid fluid, slick water and gel and the mixed- and small-grained proppant to be injected with variable viscosity and displacement. These techniques help to increase the effective stimulated reservoir volume (ESRV for deep gas production. Some of the techniques have been successfully used in the fracturing of deep shale gas horizontal wells in Yongchuan, Weiyuan and southern Jiaoshiba blocks in the Sichuan Basin. As a result, Wells YY1HF and WY1HF yielded initially 14.1 × 104 m3/d and 17.5 × 104 m3/d after fracturing. The volume fracturing of deep shale gas horizontal well is meaningful in achieving the productivity of 50 × 108 m3 gas from the interval of 3500–4000 m in Phase II development of Fuling and also in commercial production of huge shale gas resources at a vertical depth of less than 6000 m.

Deep Echo State Network (DeepESN): A Brief Survey

OpenAIRE

Gallicchio, Claudio; Micheli, Alessio

2017-01-01

The study of deep recurrent neural networks (RNNs) and, in particular, of deep Reservoir Computing (RC) is gaining an increasing research attention in the neural networks community. The recently introduced deep Echo State Network (deepESN) model opened the way to an extremely efficient approach for designing deep neural networks for temporal data. At the same time, the study of deepESNs allowed to shed light on the intrinsic properties of state dynamics developed by hierarchical compositions ...

Ultra High Mode Mix in NIF NIC Implosions

Science.gov (United States)

Scott, Robbie; Garbett, Warren

2017-10-01

This work re-examines a sub-set of the low adiabat implosions from the National Ignition Campaign in an effort to better understand potential phenomenological sources of `excess' mix observed experimentally. An extensive effort has been made to match both shock-timing and backlit radiography (Con-A) implosion data in an effort to reproduce the experimental conditions as accurately as possible. Notably a 30% reduction in ablation pressure at peak drive is required to match the experimental data. The reduced ablation pressure required to match the experimental data allows the ablator to decompress, in turn causing the DT ice-ablator interface to go Rayleigh-Taylor unstable early in the implosion acceleration phase. Post-processing the runs with various mix models indicates high-mode mix from the DT ice-ablator interface may penetrate deep into the hotspot. This work offers a potential explanation of why these low-adiabat implosions exhibited significantly higher levels of mix than expected from high-fidelity multi-dimensional simulations. Through this new understanding, a possible route forward for low-adiabat implosions on NIF is suggested.

Spatial extent and dissipation of the deep chlorophyll layer in Lake Ontario during the Lake Ontario lower foodweb assessment, 2003 and 2008

Science.gov (United States)

Watkins, J. M.; Weidel, Brian M.; Rudstam, L. G.; Holek, K. T.

2014-01-01

Increasing water clarity in Lake Ontario has led to a vertical redistribution of phytoplankton and an increased importance of the deep chlorophyll layer in overall primary productivity. We used in situ fluorometer profiles collected in lakewide surveys of Lake Ontario in 2008 to assess the spatial extent and intensity of the deep chlorophyll layer. In situ fluorometer data were corrected with extracted chlorophyll data using paired samples from Lake Ontario collected in August 2008. The deep chlorophyll layer was present offshore during the stratified conditions of late July 2008 with maximum values from 4-13 μg l-1 corrected chlorophyll a at 10 to 17 m depth within the metalimnion. Deep chlorophyll layer was closely associated with the base of the thermocline and a subsurface maximum of dissolved oxygen, indicating the feature's importance as a growth and productivity maximum. Crucial to the deep chlorophyll layer formation, the photic zone extended deeper than the surface mixed layer in mid-summer. The layer extended through most of the offshore in July 2008, but was not present in the easternmost transect that had a deeper surface mixed layer. By early September 2008, the lakewide deep chlorophyll layer had dissipated. A similar formation and dissipation was observed in the lakewide survey of Lake Ontario in 2003.

Effects of turbulence on mixed-phase deep convective clouds under different basic-state winds and aerosol concentrations

Science.gov (United States)

Lee, Hyunho; Baik, Jong-Jin; Han, Ji-Young

2014-12-01

The effects of turbulence-induced collision enhancement (TICE) on mixed-phase deep convective clouds are numerically investigated using a 2-D cloud model with bin microphysics for uniform and sheared basic-state wind profiles and different aerosol concentrations. Graupel particles account for the most of the cloud mass in all simulation cases. In the uniform basic-state wind cases, graupel particles with moderate sizes account for some of the total graupel mass in the cases with TICE, whereas graupel particles with large sizes account for almost all the total graupel mass in the cases without TICE. This is because the growth of ice crystals into small graupel particles is enhanced due to TICE. The changes in the size distributions of graupel particles due to TICE result in a decrease in the mass-averaged mean terminal velocity of graupel particles. Therefore, the downward flux of graupel mass, and thus the melting of graupel particles, is reduced due to TICE, leading to a decrease in the amount of surface precipitation. Moreover, under the low aerosol concentration, TICE increases the sublimation of ice particles, consequently playing a partial role in reducing the amount of surface precipitation. The effects of TICE are less pronounced in the sheared basic-state wind cases than in the uniform basic-state wind cases because the number of ice crystals is much smaller in the sheared basic-state wind cases than in the uniform basic-state wind cases. Thus, the size distributions of graupel particles in the cases with and without TICE show little difference.

Mixing on the Heard Island Plateau during HEOBI

Science.gov (United States)

Robertson, R.

2016-12-01

On the plateau near Heard and McDonald Islands, the water column was nearly always well mixed. Typically, temperature differences between the surface and the bottom, 100-200 m, were less than 0.2oC and often less that 0.1oC. Surface stratification developed through insolation and deep primarily through a combination of upwelling from canyons and over the edge of the plateau and tidal advection. This stratification was primarily removed by a combination of wind and tidal mixing. Persistent winds of 30 knots mixed the upper 20-50 m. Strong wind events, 40-60 knots, mixed the water column to 100-200 m depth, which over the plateau, was often the entire water column. Benthic tidal friction mixed the bottom 30-50 m. Although the water column was unstratified at the two plume sites intensively investigated, tidal velocities were baroclinic, probably due to topographic controls. Tidal advection changed the bottom temperatures by 0.5oC within 8 hours, more than doubling the prior stratification. Wind mixing quickly homogenized the water column, resulting in the surface often showing the deeper upwelling and advective events. Although acoustic plumes with bubbles were observed in the water column, there was no evidence of geothermal vents or geothermal influence on temperatures. Mixing by bubbles rising in the water column was indistinguishable from the wind and tidal mixing, although the strongest upward vertical velocities were observed at the sites of these acoustic/bubble plumes.

Markov chains and mixing times

CERN Document Server

Levin, David A

2017-01-01

Markov Chains and Mixing Times is a magical book, managing to be both friendly and deep. It gently introduces probabilistic techniques so that an outsider can follow. At the same time, it is the first book covering the geometric theory of Markov chains and has much that will be new to experts. It is certainly THE book that I will use to teach from. I recommend it to all comers, an amazing achievement. -Persi Diaconis, Mary V. Sunseri Professor of Statistics and Mathematics, Stanford University Mixing times are an active research topic within many fields from statistical physics to the theory of algorithms, as well as having intrinsic interest within mathematical probability and exploiting discrete analogs of important geometry concepts. The first edition became an instant classic, being accessible to advanced undergraduates and yet bringing readers close to current research frontiers. This second edition adds chapters on monotone chains, the exclusion process and hitting time parameters. Having both exercises...

Tree species effects on calcium cycling: The role of calcium uptake in deep soils

NARCIS (Netherlands)

Dijkstra, F.A.; Smits, M.M.

2002-01-01

Soil acidity and calcium (Ca) availability in the surface soil differ substantially beneath sugar maple (Acer saccharum) and eastern hemlock (Tsuga canadensis) trees in a mixed forest in northwestern Connecticut. We determined the effect of pumping of Ca from deep soil (rooting zone below 20-cm

RPA spin-isospin nuclear response in the deep inelastic region

International Nuclear Information System (INIS)

Alberico, W.M.; Molinari, A.; De Pace, A.; Johnson, M.B.; Ericson, M.

1985-11-01

The spin-isospin volume responses of a finite nucleus are evaluated in the RPA frame, utilizing a harmonic oscillator basis. Particular emphasis is given to the mixing between the longitudinal and transverse couplings, which arise at the nuclear surface. We show that it reduces somewhat the contrast between the two spin responses. We compare the calculated transverse response with the experimental one extracted from deep inelastic electron scattering

Why & When Deep Learning Works: Looking Inside Deep Learnings

OpenAIRE

Ronen, Ronny

2017-01-01

The Intel Collaborative Research Institute for Computational Intelligence (ICRI-CI) has been heavily supporting Machine Learning and Deep Learning research from its foundation in 2012. We have asked six leading ICRI-CI Deep Learning researchers to address the challenge of "Why & When Deep Learning works", with the goal of looking inside Deep Learning, providing insights on how deep networks function, and uncovering key observations on their expressiveness, limitations, and potential. The outp...

Dynamic stresses in a Francis model turbine at deep part load

Science.gov (United States)

Weber, Wilhelm; von Locquenghien, Florian; Conrad, Philipp; Koutnik, Jiri

2017-04-01

A comparison between numerically obtained dynamic stresses in a Francis model turbine at deep part load with experimental ones is presented. Due to the change in the electrical power mix to more content of new renewable energy sources, Francis turbines are forced to operate at deep part load in order to compensate stochastic nature of wind and solar power and to ensure grid stability. For the extension of the operating range towards deep part load improved understanding of the harsh flow conditions and their impact on material fatigue of hydraulic components is required in order to ensure long life time of the power unit. In this paper pressure loads on a model turbine runner from unsteady two-phase computational fluid dynamics simulation at deep part load are used for calculation of mechanical stresses by finite element analysis. Therewith, stress distribution over time is determined. Since only few runner rotations are simulated due to enormous numerical cost, more effort has to be spent to evaluation procedure in order to obtain objective results. By comparing the numerical results with measured strains accuracy of the whole simulation procedure is verified.

Research on simulated infrared image utility evaluation using deep representation

Science.gov (United States)

Zhang, Ruiheng; Mu, Chengpo; Yang, Yu; Xu, Lixin

2018-01-01

Infrared (IR) image simulation is an important data source for various target recognition systems. However, whether simulated IR images could be used as training data for classifiers depends on the features of fidelity and authenticity of simulated IR images. For evaluation of IR image features, a deep-representation-based algorithm is proposed. Being different from conventional methods, which usually adopt a priori knowledge or manually designed feature, the proposed method can extract essential features and quantitatively evaluate the utility of simulated IR images. First, for data preparation, we employ our IR image simulation system to generate large amounts of IR images. Then, we present the evaluation model of simulated IR image, for which an end-to-end IR feature extraction and target detection model based on deep convolutional neural network is designed. At last, the experiments illustrate that our proposed method outperforms other verification algorithms in evaluating simulated IR images. Cross-validation, variable proportion mixed data validation, and simulation process contrast experiments are carried out to evaluate the utility and objectivity of the images generated by our simulation system. The optimum mixing ratio between simulated and real data is 0.2≤γ≤0.3, which is an effective data augmentation method for real IR images.

Pollen, water, and wind: Chaotic mixing in a puddle of water

DEFF Research Database (Denmark)

Jensen, Kaare Hartvig

2016-01-01

and nutrient distribution in puddles and small ponds.The flow patterns are generated by wind blowing across the puddle surface. This causes a shear stress at the atmospheric interface, which drives a flow in the liquid below. Chaotic mixing can occur if the wind direction changes over time. A fluid patch......This paper talks about how pine pollen grains dispersedin an approximately 1 m wide and 1 cm deep water puddle. The pollen has mixed due to wind blowing across the liquid surface, revealing a strikingly complex flow pattern. The flows revealed by nature’s tracer particles may influence circulation...

The Deep Atmospheric Boundary Layer and Its Significance to the Stratosphere and Troposphere Exchange over the Tibetan Plateau

Science.gov (United States)

Chen, Xuelong; Añel, Juan A.; Su, Zhongbo; de la Torre, Laura; Kelder, Hennie; van Peet, Jacob; Ma, Yaoming

2013-01-01

In this study the depth of the atmospheric boundary layer (ABL) over the Tibetan Plateau was measured during a regional radiosonde observation campaign in 2008 and found to be deeper than indicated by previously measurements. Results indicate that during fair weather conditions on winter days, the top of the mixed layers can be up to 5 km above the ground (9.4 km above sea level). Measurements also show that the depth of the ABL is quite distinct for three different periods (winter, monsoon-onset, and monsoon seasons). Turbulence at the top of a deep mixing layer can rise up to the upper troposphere. As a consequence, as confirmed by trajectory analysis, interaction occurs between deep ABLs and the low tropopause during winter over the Tibetan Plateau. PMID:23451108

Modification of the deep salinity-maximum in the Southern Ocean by circulation in the Antarctic Circumpolar Current and the Weddell Gyre

Science.gov (United States)

Donnelly, Matthew; Leach, Harry; Strass, Volker

2017-07-01

The evolution of the deep salinity-maximum associated with the Lower Circumpolar Deep Water (LCDW) is assessed using a set of 37 hydrographic sections collected over a 20-year period in the Southern Ocean as part of the WOCE/CLIVAR programme. A circumpolar decrease in the value of the salinity-maximum is observed eastwards from the North Atlantic Deep Water (NADW) in the Atlantic sector of the Southern Ocean through the Indian and Pacific sectors to Drake Passage. Isopycnal mixing processes are limited by circumpolar fronts, and in the Atlantic sector, this acts to limit the direct poleward propagation of the salinity signal. Limited entrainment occurs into the Weddell Gyre, with LCDW entering primarily through the eddy-dominated eastern limb. A vertical mixing coefficient, κV of (2.86 ± 1.06) × 10-4 m2 s-1 and an isopycnal mixing coefficient, κI of (8.97 ± 1.67) × 102 m2 s-1 are calculated for the eastern Indian and Pacific sectors of the Antarctic Circumpolar Current (ACC). A κV of (2.39 ± 2.83) × 10-5 m2 s-1, an order of magnitude smaller, and a κI of (2.47 ± 0.63) × 102 m2 s-1, three times smaller, are calculated for the southern and eastern Weddell Gyre reflecting a more turbulent regime in the ACC and a less turbulent regime in the Weddell Gyre. In agreement with other studies, we conclude that the ACC acts as a barrier to direct meridional transport and mixing in the Atlantic sector evidenced by the eastward propagation of the deep salinity-maximum signal, insulating the Weddell Gyre from short-term changes in NADW characteristics.

Enhanced Deep Blue Aerosol Retrieval Algorithm: The Second Generation

Science.gov (United States)

Hsu, N. C.; Jeong, M.-J.; Bettenhausen, C.; Sayer, A. M.; Hansell, R.; Seftor, C. S.; Huang, J.; Tsay, S.-C.

2013-01-01

The aerosol products retrieved using the MODIS collection 5.1 Deep Blue algorithm have provided useful information about aerosol properties over bright-reflecting land surfaces, such as desert, semi-arid, and urban regions. However, many components of the C5.1 retrieval algorithm needed to be improved; for example, the use of a static surface database to estimate surface reflectances. This is particularly important over regions of mixed vegetated and non- vegetated surfaces, which may undergo strong seasonal changes in land cover. In order to address this issue, we develop a hybrid approach, which takes advantage of the combination of pre-calculated surface reflectance database and normalized difference vegetation index in determining the surface reflectance for aerosol retrievals. As a result, the spatial coverage of aerosol data generated by the enhanced Deep Blue algorithm has been extended from the arid and semi-arid regions to the entire land areas.

Study on the mechanisms making the deep groundwater quality. Part 3

Energy Technology Data Exchange (ETDEWEB)

Ohara, Kin-ichi [CHISHITSU-KISO-KOGYO Co., Ltd. (Japan)

1997-03-01

We compiled geological data and chemical data of deep groundwater in the Joban Coal Field, and examined the qualities and the changes of groundwater by geochemical analysis and numerical simulation. On the chemical analysis, we classified the chemical type of the water which gathered in the coal mine tunnels, and clarified their distributions. Moreover we analyzed isotopes in the water which picked up from wells under running. As a consequence of these analysis, the origin of the groundwater character in the Joban Coal Field is inferred to be mostly mixed water with present sea water and fresh water. We detected some groundwater were mixed with fresh water in some ten years, while we recognized that some groundwater which were mixed clearly with fossilized sea water also exist. Concerning the numerical simulation, we set up the 3 dimensional model in this field which roughly represents the geological structures and physical conditions, and collected the data to inspect the analytical results. We simulated hydraulic conditions of this model for 100 years including three phases; those are the model with no tunnels, the model at mining, and abandoned mine model with re-submergence. In consequence, volume of influx water to the tunnels and restoration of water level after re-submergence are nearly represented, and we recognized the availability of this large-scale analysis. Moreover, we tried to simulate the very large 2 dimensional water system including the boundary of fresh water and sea water, and analyzed very long time change of the deep groundwater which was caused by sea level change. (author). 63 refs.

Lunar nitrogen: Secular variation or mixing?

International Nuclear Information System (INIS)

Norris, S.J.; Wright, I.P.; Pillinger, C.T.

1986-01-01

The two current models to explain the nearly 40% variation of the lunar nitrogen isotopic composition are: (1) secular variation of solar wind nitrogen; and (2) a two component mixing model having a constant, heavy solar wind admixed with varying amounts of indigenous light lunar N (LLN). Both models are needed to explain the step pyrolysis extraction profile. The secular variation model proposes that the low temperature release is modern day solar wind implanted into grain surfaces, the 900 C to 1100 C release is from grain surfaces which were once exposed to the ancient solar wind but which are now trapped inside agglutinates, and the >1100 C release as spallogenic N produced by cosmic rays. The mixing model ascribes the components to solar wind, indigenous lunar N and spallogenic N respectively. An extension of either interpretation is that the light N seen in lunar breccias or deep drill cores represent conditions when more N-14 was available to the lunar surface

Advanced Solid State Lighting for AES Deep Space Hab Project

Science.gov (United States)

Holbert, Eirik

2015-01-01

The advanced Solid State Lighting (SSL) assemblies augmented 2nd generation modules under development for the Advanced Exploration Systems Deep Space Habitat in using color therapy to synchronize crew circadian rhythms. Current RGB LED technology does not produce sufficient brightness to adequately address general lighting in addition to color therapy. The intent is to address both through a mix of white and RGB LEDs designing for fully addressable alertness/relaxation levels as well as more dramatic circadian shifts.

Natural deep eutectic solvents as new potential media for green technology

International Nuclear Information System (INIS)

Dai, Yuntao; Spronsen, Jaap van; Witkamp, Geert-Jan; Verpoorte, Robert; Choi, Young Hae

2013-01-01

Highlights: ► Natural products were used as a source for deep eutectic solvents and ionic liquids. ► We define own chemical and physical properties of natural deep eutectic solvents. ► Interaction between natural deep eutectic solvents and solutes was confirmed by NMR. ► The developed natural deep eutectic solvents were applied as green media. - Abstract: Developing new green solvents is one of the key subjects in Green Chemistry. Ionic liquids (ILs) and deep eutectic solvents, thus, have been paid great attention to replace current harsh organic solvents and have been applied to many chemical processing such as extraction and synthesis. However, current ionic liquids and deep eutectic solvents have still limitations to be applied to a real chemical industry due to toxicity against human and environment and high cost of ILs and solid state of most deep eutectic solvents at room temperature. Recently we discovered that many plant abundant primary metabolites changed their state from solid to liquid when they were mixed in proper ratio. This finding made us hypothesize that natural deep eutectic solvents (NADES) play a role as alternative media to water in living organisms and tested a wide range of natural products, which resulted in discovery of over 100 NADES from nature. In order to prove deep eutectic feature the interaction between the molecules was investigated by nuclear magnetic resonance spectroscopy. All the tested NADES show clear hydrogen bonding between components. As next step physical properties of NADES such as water activity, density, viscosity, polarity and thermal properties were measured as well as the effect of water on the physical properties. In the last stage the novel NADES were applied to the solubilization of wide range of biomolecules such as non-water soluble bioactive natural products, gluten, starch, and DNA. In most cases the solubility of the biomolecules evaluated in this study was greatly higher than water. Based on the

Natural deep eutectic solvents as new potential media for green technology

Energy Technology Data Exchange (ETDEWEB)

Dai, Yuntao [Natural Products Laboratory, Institute of Biology, Leiden University, 2300 RA Leiden (Netherlands); Spronsen, Jaap van; Witkamp, Geert-Jan [Laboratory for Process Equipment, Delft University of Technology, Delft (Netherlands); Verpoorte, Robert [Natural Products Laboratory, Institute of Biology, Leiden University, 2300 RA Leiden (Netherlands); Choi, Young Hae, E-mail: y.choi@chem.leidenuniv.nl [Natural Products Laboratory, Institute of Biology, Leiden University, 2300 RA Leiden (Netherlands)

2013-03-05

Highlights: ► Natural products were used as a source for deep eutectic solvents and ionic liquids. ► We define own chemical and physical properties of natural deep eutectic solvents. ► Interaction between natural deep eutectic solvents and solutes was confirmed by NMR. ► The developed natural deep eutectic solvents were applied as green media. - Abstract: Developing new green solvents is one of the key subjects in Green Chemistry. Ionic liquids (ILs) and deep eutectic solvents, thus, have been paid great attention to replace current harsh organic solvents and have been applied to many chemical processing such as extraction and synthesis. However, current ionic liquids and deep eutectic solvents have still limitations to be applied to a real chemical industry due to toxicity against human and environment and high cost of ILs and solid state of most deep eutectic solvents at room temperature. Recently we discovered that many plant abundant primary metabolites changed their state from solid to liquid when they were mixed in proper ratio. This finding made us hypothesize that natural deep eutectic solvents (NADES) play a role as alternative media to water in living organisms and tested a wide range of natural products, which resulted in discovery of over 100 NADES from nature. In order to prove deep eutectic feature the interaction between the molecules was investigated by nuclear magnetic resonance spectroscopy. All the tested NADES show clear hydrogen bonding between components. As next step physical properties of NADES such as water activity, density, viscosity, polarity and thermal properties were measured as well as the effect of water on the physical properties. In the last stage the novel NADES were applied to the solubilization of wide range of biomolecules such as non-water soluble bioactive natural products, gluten, starch, and DNA. In most cases the solubility of the biomolecules evaluated in this study was greatly higher than water. Based on the

Deep iCrawl: An Intelligent Vision-Based Deep Web Crawler

OpenAIRE

R.Anita; V.Ganga Bharani; N.Nityanandam; Pradeep Kumar Sahoo

2011-01-01

The explosive growth of World Wide Web has posed a challenging problem in extracting relevant data. Traditional web crawlers focus only on the surface web while the deep web keeps expanding behind the scene. Deep web pages are created dynamically as a result of queries posed to specific web databases. The structure of the deep web pages makes it impossible for traditional web crawlers to access deep web contents. This paper, Deep iCrawl, gives a novel and vision-based app...

Spiraling pathways of global deep waters to the surface of the Southern Ocean.

Science.gov (United States)

Tamsitt, Veronica; Drake, Henri F; Morrison, Adele K; Talley, Lynne D; Dufour, Carolina O; Gray, Alison R; Griffies, Stephen M; Mazloff, Matthew R; Sarmiento, Jorge L; Wang, Jinbo; Weijer, Wilbert

2017-08-02

Upwelling of global deep waters to the sea surface in the Southern Ocean closes the global overturning circulation and is fundamentally important for oceanic uptake of carbon and heat, nutrient resupply for sustaining oceanic biological production, and the melt rate of ice shelves. However, the exact pathways and role of topography in Southern Ocean upwelling remain largely unknown. Here we show detailed upwelling pathways in three dimensions, using hydrographic observations and particle tracking in high-resolution models. The analysis reveals that the northern-sourced deep waters enter the Antarctic Circumpolar Current via southward flow along the boundaries of the three ocean basins, before spiraling southeastward and upward through the Antarctic Circumpolar Current. Upwelling is greatly enhanced at five major topographic features, associated with vigorous mesoscale eddy activity. Deep water reaches the upper ocean predominantly south of the Antarctic Circumpolar Current, with a spatially nonuniform distribution. The timescale for half of the deep water to upwell from 30° S to the mixed layer is ~60-90 years.Deep waters of the Atlantic, Pacific and Indian Oceans upwell in the Southern Oceanbut the exact pathways are not fully characterized. Here the authors present a three dimensional view showing a spiralling southward path, with enhanced upwelling by eddy-transport at topographic hotspots.

Deposits of minerals of the Kirovograd ore region of the Ukrainian shield: connection with the deep process

OpenAIRE

Usenko, O.V.

2017-01-01

Conditions of magmatic rocks complexes and mineral deposits formation of the central part of the Ingul block are determined by existence of two melting sources in the mantle and the crust as well as by the deep permeable «transform» zone - tectonic suture Kherson-Smolensk. Along the deep transform zone supply of the mantle source by melts and fluids is realized and the existence of the crust source provides mixing of earlier melts with new portions added from the mantle. Changing of compositi...

Pathways of upwelling deep waters to the surface of the Southern Ocean

Science.gov (United States)

Tamsitt, Veronica; Drake, Henri; Morrison, Adele; Talley, Lynne; Dufour, Carolina; Gray, Alison; Griffies, Stephen; Mazloff, Matthew; Sarmiento, Jorge; Wang, Jinbo; Weijer, Wilbert

2017-04-01

Upwelling of Atlantic, Indian and Pacific deep waters to the sea surface in the Southern Ocean closes the global overturning circulation and is fundamentally important for oceanic uptake of anthropogenic carbon and heat, nutrient resupply for sustaining oceanic biological production, and the melt rate of ice shelves. Here we go beyond the two-dimensional view of Southern Ocean upwelling, to show detailed Southern Ocean upwelling pathways in three dimensions, using hydrographic observations and particle tracking in high-resolution ocean and climate models. The northern deep waters enter the Antarctic Circumpolar Current (ACC) via narrow southward currents along the boundaries of the three ocean basins, before spiraling southeastward and upward through the ACC. Upwelling is greatly enhanced at five major topographic features, associated with vigorous mesoscale eddy activity. Deep water reaches the upper ocean predominantly south of the southern ACC boundary, with a spatially nonuniform distribution, regionalizing warm water supply to Antarctic ice shelves and the delivery of nutrient and carbon-rich water to the sea surface. The timescale for half of the deep water to upwell from 30°S to the mixed layer is on the order of 60-90 years, which has important implications for the timescale for signals to propagate through the deep ocean. In addition, we quantify the diabatic transformation along particle trajectories, to identify where diabatic processes are important along the upwelling pathways.

Effects of a Deep Mixed Shell on Solar g-Modes, p-Modes, and Neutrino Flux

Science.gov (United States)

Wolff, Charles L.

2009-08-01

A mixed-shell model that reflects g-modes away from the Sun's center is developed further by calibrating its parameters and evaluating a mixing mechanism: buoyancy. The shell roughly doubles g-mode oscillation periods and would explain why there is no definitive detection of their periods. But the shell has only minor effects on most p-modes. The model provides a mechanism for causing short-term fluctuations in neutrino flux and makes plausible the correlations between this flux and solar activity levels. Relations are derived for a shell heated asymmetrically by transient increases in nuclear burning in small "hot spots." The size of these spots and the timing of a heating event are governed by sets(ell) of standing asymptotic g-modes, coupled by a maximal principle that greatly enhances their excitation and concentrates power toward the equator, assisting the detection of higher-ell sets. Signals from all sets, except one, in the range 2 energy to mix the corresponding shell in a standard solar model in Lt107 yr.

Numerical assessment of the origin of deep salinity in a low permeability fractured medium

International Nuclear Information System (INIS)

Guimera, Jordi; Ruiz, Eduardo; Luna, Miguel; Arcos, David; Domenech, Cristina; Jordana, Salvador; Saegusa, Hiromitsu; Iwatsuki, Teruki

2007-01-01

Many possible origins have been proposed for the saline groundwater observed in many deep geological environments. In particular, samples obtained from deep boreholes located in granite at the Mizunami Underground Research Laboratory in Central Japan show total dissolved solids increasing to 50 mmol/L at depths below 800 m. Different hypothesis have been formulated to explain the observed fluid composition, among them, long-term water-rock interaction, mixing with residual fluids of magmatic origin and relict seawater dating from Miocene times. A review of the hydrochemical and isotopic data suggests that the three above hypotheses may be valid, at least to different degrees, or that processes acting over more recent geological times may be involved. The origin of the salinity was assessed by simulating land emersion by means of changing the upper recharge boundary. In this manner the Miocene seawater was modeled as being continually mixed with fresh water until the present time. The effects of different retardation processes were considered by varying factors such as matrix diffusion and fracture conductivity. Finally, geochemical reactions reproduced trends in major ions and master variables. This study shows that the salinity observed in the boreholes can be explained qualitatively as residual Miocene age seawater subjected to alteration due to long-term contact with the host material and continuous mixing with meteoric groundwater. (authors)

How stratospheric are deep stratospheric intrusions? LUAMI 2008

Directory of Open Access Journals (Sweden)

T. Trickl

2016-07-01

Full Text Available A large-scale comparison of water-vapour vertical-sounding instruments took place over central Europe on 17 October 2008, during a rather homogeneous deep stratospheric intrusion event (LUAMI, Lindenberg Upper-Air Methods Intercomparison. The measurements were carried out at four observational sites: Payerne (Switzerland, Bilthoven (the Netherlands, Lindenberg (north-eastern Germany, and the Zugspitze mountain (Garmisch-Partenkichen, German Alps, and by an airborne water-vapour lidar system creating a transect of humidity profiles between all four stations. A high data quality was verified that strongly underlines the scientific findings. The intrusion layer was very dry with a minimum mixing ratios of 0 to 35 ppm on its lower west side, but did not drop below 120 ppm on the higher-lying east side (Lindenberg. The dryness hardens the findings of a preceding study (“Part 1”, Trickl et al., 2014 that, e.g., 73 % of deep intrusions reaching the German Alps and travelling 6 days or less exhibit minimum mixing ratios of 50 ppm and less. These low values reflect values found in the lowermost stratosphere and indicate very slow mixing with tropospheric air during the downward transport to the lower troposphere. The peak ozone values were around 70 ppb, confirming the idea that intrusion layers depart from the lowermost edge of the stratosphere. The data suggest an increase of ozone from the lower to the higher edge of the intrusion layer. This behaviour is also confirmed by stratospheric aerosol caught in the layer. Both observations are in agreement with the idea that sections of the vertical distributions of these constituents in the source region were transferred to central Europe without major change. LAGRANTO trajectory calculations demonstrated a rather shallow outflow from the stratosphere just above the dynamical tropopause, for the first time confirming the conclusions in “Part 1” from the Zugspitze CO observations. The

Parameterization of Mixed Layer and Deep-Ocean Mesoscales Including Nonlinearity

Science.gov (United States)

Canuto, V. M.; Cheng, Y.; Dubovikov, M. S.; Howard, A. M.; Leboissetier, A.

2018-01-01

In 2011, Chelton et al. carried out a comprehensive census of mesoscales using altimetry data and reached the following conclusions: "essentially all of the observed mesoscale features are nonlinear" and "mesoscales do not move with the mean velocity but with their own drift velocity," which is "the most germane of all the nonlinear metrics."� Accounting for these results in a mesoscale parameterization presents conceptual and practical challenges since linear analysis is no longer usable and one needs a model of nonlinearity. A mesoscale parameterization is presented that has the following features: 1) it is based on the solutions of the nonlinear mesoscale dynamical equations, 2) it describes arbitrary tracers, 3) it includes adiabatic (A) and diabatic (D) regimes, 4) the eddy-induced velocity is the sum of a Gent and McWilliams (GM) term plus a new term representing the difference between drift and mean velocities, 5) the new term lowers the transfer of mean potential energy to mesoscales, 6) the isopycnal slopes are not as flat as in the GM case, 7) deep-ocean stratification is enhanced compared to previous parameterizations where being more weakly stratified allowed a large heat uptake that is not observed, 8) the strength of the Deacon cell is reduced. The numerical results are from a stand-alone ocean code with Coordinated Ocean-Ice Reference Experiment I (CORE-I) normal-year forcing.

Microbial ecology of deep-sea hypersaline anoxic basins

KAUST Repository

Merlino, Giuseppe

2018-05-09

Deep hypersaline anoxic basins (DHABs) are unique water bodies occurring within fractures at the bottom of the sea, where the dissolution of anciently buried evaporites created dense anoxic brines that are separated by a chemocline/pycnocline from the overlying oxygenated deep-seawater column. DHABs have been described in the Gulf of Mexico, the Mediterranean Sea, the Black Sea and the Red Sea. They are characterized by prolonged historical separation of the brines from the upper water column due to lack of mixing and by extreme conditions of salinity, anoxia, and relatively high hydrostatic pressure and temperatures. Due to these combined selection factors, unique microbial assemblages thrive in these polyextreme ecosystems. The topological localization of the different taxa in the brine-seawater transition zone coupled with the metabolic interactions and niche adaptations determine the metabolic functioning and biogeochemistry of DHABs. In particular, inherent metabolic strategies accompanied by genetic adaptations have provided insights on how prokaryotic communities can adapt to salt-saturated condition. Here, we review the current knowledge on the diversity, genomics, metabolisms and ecology of prokaryotes in DHABs.

Deep learning

CERN Document Server

Goodfellow, Ian; Courville, Aaron

2016-01-01

Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language proces...

Projection decomposition algorithm for dual-energy computed tomography via deep neural network.

Science.gov (United States)

Xu, Yifu; Yan, Bin; Chen, Jian; Zeng, Lei; Li, Lei

2018-03-15

Dual-energy computed tomography (DECT) has been widely used to improve identification of substances from different spectral information. Decomposition of the mixed test samples into two materials relies on a well-calibrated material decomposition function. This work aims to establish and validate a data-driven algorithm for estimation of the decomposition function. A deep neural network (DNN) consisting of two sub-nets is proposed to solve the projection decomposition problem. The compressing sub-net, substantially a stack auto-encoder (SAE), learns a compact representation of energy spectrum. The decomposing sub-net with a two-layer structure fits the nonlinear transform between energy projection and basic material thickness. The proposed DNN not only delivers image with lower standard deviation and higher quality in both simulated and real data, and also yields the best performance in cases mixed with photon noise. Moreover, DNN costs only 0.4 s to generate a decomposition solution of 360 × 512 size scale, which is about 200 times faster than the competing algorithms. The DNN model is applicable to the decomposition tasks with different dual energies. Experimental results demonstrated the strong function fitting ability of DNN. Thus, the Deep learning paradigm provides a promising approach to solve the nonlinear problem in DECT.

Deep-level optical spectroscopy investigation of N-doped TiO2 films

International Nuclear Information System (INIS)

Nakano, Yoshitaka; Morikawa, Takeshi; Ohwaki, Takeshi; Taga, Yasunori

2005-01-01

N-doped TiO 2 films were deposited on n + -GaN/Al 2 O 3 substrates by reactive magnetron sputtering and subsequently crystallized by annealing at 550 deg. C in flowing N 2 gas. The N-doping concentration was ∼8.8%, as determined from x-ray photoelectron spectroscopy measurements. Deep-level optical spectroscopy measurements revealed two characteristic deep levels located at ∼1.18 and ∼2.48 eV below the conduction band. The 1.18 eV level is probably attributable to the O vacancy state and can be active as an efficient generation-recombination center. Additionally, the 2.48 eV band is newly introduced by the N doping and contributes to band-gap narrowing by mixing with the O 2p valence band

An environmental model study of the deep layers of the North East Atlantic

International Nuclear Information System (INIS)

Bork, I.

1989-01-01

The field work of the north Atlantic monitoring Program (NOAMP) was supplemented by numerical simulations of the transport of radionuclides in the North Atlantic Ocean by annual mean flows and mixing processes. During the last year of NOAMP, a different attempt was made to compute the current field and three-dimensional trajectories of particles released in deep layers of the NOAMP area. It is the subject of this paper. The model used is of Bryan/Semtner type, but with smoothed topography and climatological (winter) temperature and salinity data. The results form a compromise between interpretation of climatological temperature and salinity data and the complete prediction of the current field by prognostic calculations, which yields a deep flow pattern that agrees with some ideas of the abyssal circulation

Deep Incremental Boosting

OpenAIRE

Mosca, Alan; Magoulas, George D

2017-01-01

This paper introduces Deep Incremental Boosting, a new technique derived from AdaBoost, specifically adapted to work with Deep Learning methods, that reduces the required training time and improves generalisation. We draw inspiration from Transfer of Learning approaches to reduce the start-up time to training each incremental Ensemble member. We show a set of experiments that outlines some preliminary results on some common Deep Learning datasets and discuss the potential improvements Deep In...

Science Potential of a Deep Ocean Antineutrino Observatory

International Nuclear Information System (INIS)

Dye, S.T.

2007-01-01

This paper presents science potential of a deep ocean antineutrino observatory being developed at Hawaii. The observatory design allows for relocation from one site to another. Positioning the observatory some 60 km distant from a nuclear reactor complex enables precision measurement of neutrino mixing parameters, leading to a determination of neutrino mass hierarchy and θ 13 . At a mid-Pacific location the observatory measures the flux and ratio of uranium and thorium decay neutrinos from earth's mantle and performs a sensitive search for a hypothetical natural fission reactor in earth's core. A subsequent deployment at another mid-ocean location would test lateral heterogeneity of uranium and thorium in earth's mantle

Deep Super Learner: A Deep Ensemble for Classification Problems

OpenAIRE

Young, Steven; Abdou, Tamer; Bener, Ayse

2018-01-01

Deep learning has become very popular for tasks such as predictive modeling and pattern recognition in handling big data. Deep learning is a powerful machine learning method that extracts lower level features and feeds them forward for the next layer to identify higher level features that improve performance. However, deep neural networks have drawbacks, which include many hyper-parameters and infinite architectures, opaqueness into results, and relatively slower convergence on smaller datase...

Effects of Precipitation on Ocean Mixed-Layer Temperature and Salinity as Simulated in a 2-D Coupled Ocean-Cloud Resolving Atmosphere Model

Science.gov (United States)

Li, Xiaofan; Sui, C.-H.; Lau, K-M.; Adamec, D.

1999-01-01

A two-dimensional coupled ocean-cloud resolving atmosphere model is used to investigate possible roles of convective scale ocean disturbances induced by atmospheric precipitation on ocean mixed-layer heat and salt budgets. The model couples a cloud resolving model with an embedded mixed layer-ocean circulation model. Five experiment are performed under imposed large-scale atmospheric forcing in terms of vertical velocity derived from the TOGA COARE observations during a selected seven-day period. The dominant variability of mixed-layer temperature and salinity are simulated by the coupled model with imposed large-scale forcing. The mixed-layer temperatures in the coupled experiments with 1-D and 2-D ocean models show similar variations when salinity effects are not included. When salinity effects are included, however, differences in the domain-mean mixed-layer salinity and temperature between coupled experiments with 1-D and 2-D ocean models could be as large as 0.3 PSU and 0.4 C respectively. Without fresh water effects, the nocturnal heat loss over ocean surface causes deep mixed layers and weak cooling rates so that the nocturnal mixed-layer temperatures tend to be horizontally-uniform. The fresh water flux, however, causes shallow mixed layers over convective areas while the nocturnal heat loss causes deep mixed layer over convection-free areas so that the mixed-layer temperatures have large horizontal fluctuations. Furthermore, fresh water flux exhibits larger spatial fluctuations than surface heat flux because heavy rainfall occurs over convective areas embedded in broad non-convective or clear areas, whereas diurnal signals over whole model areas yield high spatial correlation of surface heat flux. As a result, mixed-layer salinities contribute more to the density differences than do mixed-layer temperatures.

How ocean lateral mixing changes Southern Ocean variability in coupled climate models

Science.gov (United States)

Pradal, M. A. S.; Gnanadesikan, A.; Thomas, J. L.

2016-02-01

The lateral mixing of tracers represents a major uncertainty in the formulation of coupled climate models. The mixing of tracers along density surfaces in the interior and horizontally within the mixed layer is often parameterized using a mixing coefficient ARedi. The models used in the Coupled Model Intercomparison Project 5 exhibit more than an order of magnitude range in the values of this coefficient used within the Southern Ocean. The impacts of such uncertainty on Southern Ocean variability have remained unclear, even as recent work has shown that this variability differs between different models. In this poster, we change the lateral mixing coefficient within GFDL ESM2Mc, a coarse-resolution Earth System model that nonetheless has a reasonable circulation within the Southern Ocean. As the coefficient varies from 400 to 2400 m2/s the amplitude of the variability varies significantly. The low-mixing case shows strong decadal variability with an annual mean RMS temperature variability exceeding 1C in the Circumpolar Current. The highest-mixing case shows a very similar spatial pattern of variability, but with amplitudes only about 60% as large. The suppression of mixing is larger in the Atlantic Sector of the Southern Ocean relatively to the Pacific sector. We examine the salinity budgets of convective regions, paying particular attention to the extent to which high mixing prevents the buildup of low-saline waters that are capable of shutting off deep convection entirely.

DeepRT: deep learning for peptide retention time prediction in proteomics

OpenAIRE

Ma, Chunwei; Zhu, Zhiyong; Ye, Jun; Yang, Jiarui; Pei, Jianguo; Xu, Shaohang; Zhou, Ruo; Yu, Chang; Mo, Fan; Wen, Bo; Liu, Siqi

2017-01-01

Accurate predictions of peptide retention times (RT) in liquid chromatography have many applications in mass spectrometry-based proteomics. Herein, we present DeepRT, a deep learning based software for peptide retention time prediction. DeepRT automatically learns features directly from the peptide sequences using the deep convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) model, which eliminates the need to use hand-crafted features or rules. After the feature learning, pr...

Picocyanobacteria Dominance in Deep Biomass Layers: Relation to Diatom Presence and Episodic Events.

Science.gov (United States)

Aguilar, C.; Cuhel, R. L.

2016-02-01

In Offshore Marine and Large Lake Waters, most of the biomass and the productivity of phytoplankton occur below surface observation capabilities. Sub-mixed layer phytoplankton populations develop, increase, persist, and decay in relation to physical structure such as pycnocline density gradients interacting with progressively changing light fields. Basin-scale meteorological events and persistence of major invasive species have also left marks on biogeochemical cycling and ecosystem function in Lake Michigan. Among the former are precipitation and turbulence alterations brought on by unusual winter ice cover and a century-scale flood during 2008. Dampened seasonal silicate cycling indicated a basin-wide reduction of diatom production following mussel establishment. Communities in Lake Michigan shifted from diatom and big cell-dominated to small cell picocyanobacteria-dominated phytoplankton. Picocyanobacteria were beneficiaries of profound oligotrophication of the ecosystem starting in 2003. Photosynthetic parameters of pre-2003 Deep Biomass populations dominated by diatoms were systematically different from the cyanobacterial epoch following quagga mussel establishment and increase in depth of 1% incident light to 50-60m. Deep cyanobacterial production has now often been on the same scale as overlying waters. Photophysiology changes in a smooth depth gradient in this clear water as opposed to previous abrupt transition to shade adaptation. Among these many physicochemical permutations, community structure has consistently been a tradeoff between diatoms and picocyanobacteria. Opposing fluctuations of biomass favor one or the other on seasonal time frames of sequential years, with a complete system reset between each (winter mixing). For the Great Flood example, diatom surface blooms increased light extinction and drove the deep biomass maximum up - as populations settled into the pycnocline they had already outcompeted the picocyanobacteria. The opposite was true

Deep Modeling: Circuit Characterization Using Theory Based Models in a Data Driven Framework

Energy Technology Data Exchange (ETDEWEB)

Bolme, David S [ORNL; Mikkilineni, Aravind K [ORNL; Rose, Derek C [ORNL; Yoginath, Srikanth B [ORNL; Holleman, Jeremy [University of Tennessee, Knoxville (UTK); Judy, Mohsen [University of Tennessee, Knoxville (UTK), Department of Electrical Engineering and Computer Science

2017-01-01

Analog computational circuits have been demonstrated to provide substantial improvements in power and speed relative to digital circuits, especially for applications requiring extreme parallelism but only modest precision. Deep machine learning is one such area and stands to benefit greatly from analog and mixed-signal implementations. However, even at modest precisions, offsets and non-linearity can degrade system performance. Furthermore, in all but the simplest systems, it is impossible to directly measure the intermediate outputs of all sub-circuits. The result is that circuit designers are unable to accurately evaluate the non-idealities of computational circuits in-situ and are therefore unable to fully utilize measurement results to improve future designs. In this paper we present a technique to use deep learning frameworks to model physical systems. Recently developed libraries like TensorFlow make it possible to use back propagation to learn parameters in the context of modeling circuit behavior. Offsets and scaling errors can be discovered even for sub-circuits that are deeply embedded in a computational system and not directly observable. The learned parameters can be used to refine simulation methods or to identify appropriate compensation strategies. We demonstrate the framework using a mixed-signal convolution operator as an example circuit.

Impact of Parameterized Lateral Mixing on the Circulation of the Southern Ocean

Science.gov (United States)

Ragen, S.; Gnanadesikan, A.

2016-02-01

The Antarctic Circumpolar Current (ACC) is the strongest ocean current in the world, transporting approximately 130 Sv Eastward around Antarctica. This current is often poorly simulated in climate models. It is not clear why this is the case as the Circumpolar Current is affected by both wind and buoyancy. Changes in wind and buoyancy are not independent of each other, however, so determining the effects of both separately has proved difficult. This study was undertaken in order to examine the impact of changing the lateral diffusion coefficient A­redi on ACC transport. A­redi is poorly known and its value ranges across an order of magnitude in the current generation of climate models. To explore these dynamics, a coarse resolution, fully coupled model suite was run with A­redi mixing coefficients of 400 m2/s, 800 m2/s, 1200 m2/s, and 2400 m2/s. Additionally, two models were run with two-dimensional representations of the mixing coefficient based on altimetry. Our initial results indicate that higher values of the lateral mixing coefficient result in the following changes. We see weaker winds over the Southern Ocean as a whole. The high mixing case results in an 8.7% decrease in peak wind stress. We see a 2% weaker transport in the Drake Passage in the highest mixing case compared to the lowest, but an 11% decrease in transport for a zonal average. The change of temperature and salinity with depth with different Redi parameters also shows a significant difference between the Southern Ocean as a whole and the Drake Passage. Our findings seem to suggest that the Drake Passage is not an adequate diagnostic for explaining the differences between different climate models, as processes distant from the passage may play an important role. Observed changes in overturning with an increase in lateral mixing include an increase in northward transport of Antarctic Bottom Water fed by a small diversion of northern deep water inflows. This diversion means that less of the

Bay Ridge Gardens - Mixed Humid Affordable Multifamily Housing Deep Energy Retrofit

Energy Technology Data Exchange (ETDEWEB)

Lyons, James [Building America Partnership for Improved Residential Construction (BA-PIRC), Cocoa, FL (United States); Moore, Mike [Building America Partnership for Improved Residential Construction (BA-PIRC), Cocoa, FL (United States); Thompson, Margo [Building America Partnership for Improved Residential Construction (BA-PIRC), Cocoa, FL (United States)

2013-08-01

Under this project, Newport Partners (as part of the BA-PIRC research team) evaluated the installation, measured performance, and cost effectiveness of efficiency upgrade measures for a tenant-in-place deep energy retrofit (DER) at the Bay Ridge multifamily development in Annapolis, Maryland. This report summarizes system commissioning, short-term test results, utility bill data analysis, and analysis of real-time data collected over a one-year period after the retrofit was complete. The Bay Ridge project is comprised of a "base scope" retrofit which was estimated to achieve a 30%+ savings (relative to pre-retrofit) on 186 apartments, and a "DER scope" which was estimated to achieve 50% savings (relative to pre-retrofit) on a 12-unit building. A wide range of efficiency measures was applied to pursue this savings target for the DER building, including improvements/replacements of mechanical equipment and distribution systems, appliances, lighting and lighting controls, the building envelope, hot water conservation measures, and resident education. The results of this research build upon the current body of knowledge of multifamily retrofits. Towards this end, the research team has collected and generated data on the selection of measures, their estimated performance, their measured performance, and risk factors and their impact on potential measures.

Analyses of the deep borehole drilling status for a deep borehole disposal system

Energy Technology Data Exchange (ETDEWEB)

Lee, Jong Youl; Choi, Heui Joo; Lee, Min Soo; Kim, Geon Young; Kim, Kyung Su [KAERI, Daejeon (Korea, Republic of)

2016-05-15

The purpose of disposal for radioactive wastes is not only to isolate them from humans, but also to inhibit leakage of any radioactive materials into the accessible environment. Because of the extremely high level and long-time scale radioactivity of HLW(High-level radioactive waste), a mined deep geological disposal concept, the disposal depth is about 500 m below ground, is considered as the safest method to isolate the spent fuels or high-level radioactive waste from the human environment with the best available technology at present time. Therefore, as an alternative disposal concept, i.e., deep borehole disposal technology is under consideration in number of countries in terms of its outstanding safety and cost effectiveness. In this paper, the general status of deep drilling technologies was reviewed for deep borehole disposal of high level radioactive wastes. Based on the results of these review, very preliminary applicability of deep drilling technology for deep borehole disposal analyzed. In this paper, as one of key technologies of deep borehole disposal system, the general status of deep drilling technologies in oil industry, geothermal industry and geo scientific field was reviewed for deep borehole disposal of high level radioactive wastes. Based on the results of these review, the very preliminary applicability of deep drilling technology for deep borehole disposal such as relation between depth and diameter, drilling time and feasibility classification was analyzed.

Geothermal heating, diapycnal mixing and the abyssal circulation

Directory of Open Access Journals (Sweden)

J. Emile-Geay

2009-06-01

Full Text Available The dynamical role of geothermal heating in abyssal circulation is reconsidered using three independent arguments. First, we show that a uniform geothermal heat flux close to the observed average (86.4 mW m⁻² supplies as much heat to near-bottom water as a diapycnal mixing rate of ~10⁻⁴ m² s⁻¹ – the canonical value thought to be responsible for the magnitude of the present-day abyssal circulation. This parity raises the possibility that geothermal heating could have a dynamical impact of the same order. Second, we estimate the magnitude of geothermally-induced circulation with the density-binning method (Walin, 1982, applied to the observed thermohaline structure of Levitus (1998. The method also allows to investigate the effect of realistic spatial variations of the flux obtained from heatflow measurements and classical theories of lithospheric cooling. It is found that a uniform heatflow forces a transformation of ~6 Sv at σ₄=45.90, which is of the same order as current best estimates of AABW circulation. This transformation can be thought of as the geothermal circulation in the absence of mixing and is very similar for a realistic heatflow, albeit shifted towards slightly lighter density classes. Third, we use a general ocean circulation model in global configuration to perform three sets of experiments: (1 a thermally homogenous abyssal ocean with and without uniform geothermal heating; (2 a more stratified abyssal ocean subject to (i no geothermal heating, (ii a constant heat flux of 86.4 mW m⁻², (iii a realistic, spatially varying heat flux of identical global average; (3 experiments (i and (iii with enhanced vertical mixing at depth. Geothermal heating and diapycnal mixing are found to interact non-linearly through the density field, with geothermal heating eroding the deep stratification supporting a downward diffusive flux, while diapycnal mixing acts to map

Simulation of Deep Water Renewal in Crater Lake, Oregon, USA under Current and Future Climate Conditions

Science.gov (United States)

Piccolroaz, S.; Wood, T. M.; Wherry, S.; Girdner, S.

2015-12-01

We applied a 1-dimensional lake model developed to simulate deep mixing related to thermobaric instabilities in temperate lakes to Crater Lake, a 590-m deep caldera lake in Oregon's Cascade Range known for its stunning deep blue color and extremely clear water, in order to determine the frequency of deep water renewal in future climate conditions. The lake model was calibrated with 6 years of water temperature profiles, and then simulated 10 years of validation data with an RMSE ranging from 0.81°C at 50 m depth to 0.04°C at 350-460 m depth. The simulated time series of heat content in the deep lake accurately captured extreme years characterized by weak and strong deep water renewal. The lake model uses wind speed and lake surface temperature (LST) as boundary conditions. LST projections under six climate scenarios from the CMIP5 intermodel comparison project (2 representative concentration pathways X 3 general circulation models) were evaluated with air2water, a simple lumped model that only requires daily values of downscaled air temperature. air2water was calibrated with data from 1993-2011, resulting in a RMSE between simulated and observed daily LST values of 0.68°C. All future climate scenarios project increased water temperature throughout the water column and a substantive reduction in the frequency of deepwater renewal events. The least extreme scenario (CNRM-CM5, RCP4.5) projects the frequency of deepwater renewal events to decrease from about 1 in 2 years in the present to about 1 in 3 years by 2100. The most extreme scenario (HadGEM2-ES, RCP8.5) projects the frequency of deepwater renewal events to be less than 1 in 7 years by 2100 and lake surface temperatures never cooling to less than 4°C after 2050. In all RCP4.5 simulations the temperature of the entire water column is greater than 4°C for increasing periods of time. In the RCP8.5 simulations, the temperature of the entire water column is greater than 4°C year round by the year 2060 (HadGEM2

Deep Space Telecommunications

Science.gov (United States)

Kuiper, T. B. H.; Resch, G. M.

2000-01-01

The increasing load on NASA's deep Space Network, the new capabilities for deep space missions inherent in a next-generation radio telescope, and the potential of new telescope technology for reducing construction and operation costs suggest a natural marriage between radio astronomy and deep space telecommunications in developing advanced radio telescope concepts.

Greedy Deep Dictionary Learning

OpenAIRE

Tariyal, Snigdha; Majumdar, Angshul; Singh, Richa; Vatsa, Mayank

2016-01-01

In this work we propose a new deep learning tool called deep dictionary learning. Multi-level dictionaries are learnt in a greedy fashion, one layer at a time. This requires solving a simple (shallow) dictionary learning problem, the solution to this is well known. We apply the proposed technique on some benchmark deep learning datasets. We compare our results with other deep learning tools like stacked autoencoder and deep belief network; and state of the art supervised dictionary learning t...

DeepBipolar: Identifying genomic mutations for bipolar disorder via deep learning.

Science.gov (United States)

Laksshman, Sundaram; Bhat, Rajendra Rana; Viswanath, Vivek; Li, Xiaolin

2017-09-01

Bipolar disorder, also known as manic depression, is a brain disorder that affects the brain structure of a patient. It results in extreme mood swings, severe states of depression, and overexcitement simultaneously. It is estimated that roughly 3% of the population of the United States (about 5.3 million adults) suffers from bipolar disorder. Recent research efforts like the Twin studies have demonstrated a high heritability factor for the disorder, making genomics a viable alternative for detecting and treating bipolar disorder, in addition to the conventional lengthy and costly postsymptom clinical diagnosis. Motivated by this study, leveraging several emerging deep learning algorithms, we design an end-to-end deep learning architecture (called DeepBipolar) to predict bipolar disorder based on limited genomic data. DeepBipolar adopts the Deep Convolutional Neural Network (DCNN) architecture that automatically extracts features from genotype information to predict the bipolar phenotype. We participated in the Critical Assessment of Genome Interpretation (CAGI) bipolar disorder challenge and DeepBipolar was considered the most successful by the independent assessor. In this work, we thoroughly evaluate the performance of DeepBipolar and analyze the type of signals we believe could have affected the classifier in distinguishing the case samples from the control set. © 2017 Wiley Periodicals, Inc.

Deep learning? What deep learning? | Fourie | South African ...

African Journals Online (AJOL)

In teaching generally over the past twenty years, there has been a move towards teaching methods that encourage deep, rather than surface approaches to learning. The reason for this being that students, who adopt a deep approach to learning are considered to have learning outcomes of a better quality and desirability ...

The Mechanism of First Raindrops Formation in Deep Convective Clouds

Energy Technology Data Exchange (ETDEWEB)

Khain, Alexander; Prabha, Thara; Benmoshe, Nir; Pandithurai, G.; Ovchinnikov, Mikhail

2013-08-22

The formation of first raindrops in deep convective clouds is investigated. A combination of observational data analysis and 2-D and 3-D numerical bin microphysical simulations of deep convective clouds suggests that the first raindrops form at the top of undiluted or slightly diluted cores. It is shown that droplet size distributions in these regions are wider and contain more large droplets than in diluted volumes. The results of the study indicate that the initial raindrop formation is determined by the basic microphysical processes within ascending adiabatic volumes. It allows one to predict the height of the formation of first raindrops considering the processes of nucleation, diffusion growth and collisions. The results obtained in the study explain observational results reported by Freud and Rosenfeld (2012) according to which the height of first raindrop formation depends linearly on the droplet number concentration at cloud base. The results also explain why a simple adiabatic parcel model can reproduce this dependence. The present study provides a physical basis for retrieval algorithms of cloud microphysical properties and aerosol properties using satellites proposed by Rosenfeld et al. ( 2012). The study indicates that the role of mixing and entrainment in the formation of the first raindrops is not of crucial importance. It is also shown that low variability of effective and mean volume radii along horizontal traverses, as regularly observed by in situ measurements, can be simulated by high-resolution cloud models, in which mixing is parameterized by a traditional 1.5 order turbulence closure scheme.

DeepInfer: open-source deep learning deployment toolkit for image-guided therapy

Science.gov (United States)

Mehrtash, Alireza; Pesteie, Mehran; Hetherington, Jorden; Behringer, Peter A.; Kapur, Tina; Wells, William M.; Rohling, Robert; Fedorov, Andriy; Abolmaesumi, Purang

2017-03-01

Deep learning models have outperformed some of the previous state-of-the-art approaches in medical image analysis. Instead of using hand-engineered features, deep models attempt to automatically extract hierarchical representations at multiple levels of abstraction from the data. Therefore, deep models are usually considered to be more flexible and robust solutions for image analysis problems compared to conventional computer vision models. They have demonstrated significant improvements in computer-aided diagnosis and automatic medical image analysis applied to such tasks as image segmentation, classification and registration. However, deploying deep learning models often has a steep learning curve and requires detailed knowledge of various software packages. Thus, many deep models have not been integrated into the clinical research work ows causing a gap between the state-of-the-art machine learning in medical applications and evaluation in clinical research procedures. In this paper, we propose "DeepInfer" - an open-source toolkit for developing and deploying deep learning models within the 3D Slicer medical image analysis platform. Utilizing a repository of task-specific models, DeepInfer allows clinical researchers and biomedical engineers to deploy a trained model selected from the public registry, and apply it to new data without the need for software development or configuration. As two practical use cases, we demonstrate the application of DeepInfer in prostate segmentation for targeted MRI-guided biopsy and identification of the target plane in 3D ultrasound for spinal injections.

Science Potential of a Deep Ocean Antineutrino Observatory

Energy Technology Data Exchange (ETDEWEB)

Dye, S.T. [Department of Physics and Astronomy, University of Hawaii, 2505 Correa Road, Honolulu, Hawaii, 96822 (United States); College of Natural Sciences, Hawaii Pacific University, 45-045 Kamehameha Highway, Kaneohe, Hawaii 96744 (United States)

2007-06-15

This paper presents science potential of a deep ocean antineutrino observatory being developed at Hawaii. The observatory design allows for relocation from one site to another. Positioning the observatory some 60 km distant from a nuclear reactor complex enables precision measurement of neutrino mixing parameters, leading to a determination of neutrino mass hierarchy and {theta}{sub 13}. At a mid-Pacific location the observatory measures the flux and ratio of uranium and thorium decay neutrinos from earth's mantle and performs a sensitive search for a hypothetical natural fission reactor in earth's core. A subsequent deployment at another mid-ocean location would test lateral heterogeneity of uranium and thorium in earth's mantle.

Inter-comparison of stratospheric mean-meridional circulation and eddy mixing among six reanalysis data sets

Directory of Open Access Journals (Sweden)

K. Miyazaki

2016-05-01

Full Text Available The stratospheric mean-meridional circulation (MMC and eddy mixing are compared among six meteorological reanalysis data sets: NCEP-NCAR, NCEP-CFSR, ERA-40, ERA-Interim, JRA-25, and JRA-55 for the period 1979–2012. The reanalysis data sets produced using advanced systems (i.e., NCEP-CFSR, ERA-Interim, and JRA-55 generally reveal a weaker MMC in the Northern Hemisphere (NH compared with those produced using older systems (i.e., NCEP/NCAR, ERA-40, and JRA-25. The mean mixing strength differs largely among the data products. In the NH lower stratosphere, the contribution of planetary-scale mixing is larger in the new data sets than in the old data sets, whereas that of small-scale mixing is weaker in the new data sets. Conventional data assimilation techniques introduce analysis increments without maintaining physical balance, which may have caused an overly strong MMC and spurious small-scale eddies in the old data sets. At the NH mid-latitudes, only ERA-Interim reveals a weakening MMC trend in the deep branch of the Brewer–Dobson circulation (BDC. The relative importance of the eddy mixing compared with the mean-meridional transport in the subtropical lower stratosphere shows increasing trends in ERA-Interim and JRA-55; this together with the weakened MMC in the deep branch may imply an increasing age-of-air (AoA in the NH middle stratosphere in ERA-Interim. Overall, discrepancies between the different variables and trends therein as derived from the different reanalyses are still relatively large, suggesting that more investments in these products are needed in order to obtain a consolidated picture of observed changes in the BDC and the mechanisms that drive them.

Powering up the biogeochemical engine: The impact of exceptional ventilation of a deep meromictic lake on the lacustrine redox, nutrient, and methane balances

Directory of Open Access Journals (Sweden)

Moritz Felix Lehmann

2015-08-01

Full Text Available The Lake Lugano North Basin has been meromictic for several decades, with anoxic waters below 100m depth. Two consecutive cold winters in 2005 and 2006 induced exceptional deep mixing, leading to a transient oxygenation of the whole water column. With the ventilation of deep waters and the oxidation of large quantities of reduced solutes, the lake's total redox-balance turned positive, and the overall hypolimnetic oxygen demand of the lake strongly decreased. The disappearance of 150 t dissolved phosphorous (P during the first ventilation in March 2005 is attributed to the scavenging of water-column-borne P by newly formed metal oxyhydroxides and the temporary transfer to the sediments. The fixed nitrogen (N inventory was reduced by ~30% (~1000 t. The water-column turnover induced the nitratation of the previously NO3--free deep hypolimnion by oxidation of large amounts of legacy NH4+ and by mixing with NO3--rich subsurface water masses. Sediments with a strong denitrifying potential, but NO3--starved for decades, were brought in contact with NO3--replete waters, invigorating benthic denitrification and rapid fixed N loss from the lake in spite of the overall more oxygenated conditions. Similarly, a large microbial aerobic CH4 oxidation (MOx potential in the hypolimnion was capitalized with the ventilation of the deep basin. Almost all CH4, which had been built up over more than 40 years (~2800 t, was removed from the water column within 30 days. However, boosted MOx could only partly explain the disappearance of the CH4. The dominant fraction (75% of the CH4 evaded to the atmosphere, through storage flux upon exposure of anoxic CH4-rich water to the atmosphere. As of today, the North Basin seems far from homeostasis regarding its fixed N and CH4 budgets, and the deep basin's CH4 pool is recharging at a net production rate of ~66 t y-1. The size of impending CH4 outbursts will depend on the frequency and intensity of exceptional mixing events in

Evaluation of the radioactive wastes disposal into the deep ocean

International Nuclear Information System (INIS)

Aoyama, I.; Yamamoto, M.; Inoue, Y.

1977-01-01

A hazard assessment for deep sea disposal of low level radioactive solid wastes which originate from nuclear power reactors in Japan is presented. The model takes account of leaching characteristics of radionuclides from wastes solidified with cement, which has not been considered in other papers. Maximum and average concentrations of radionuclides in an upper mixed layer of the sea are estimated and maximum doses for individual and population doses for Japanese people are calculated. In order to evaluate an uncertainty of parameters in the model, a sensitivity analysis was performed. The discussions include: which parameter in an equation of the model affects most the average concentration of radionuclides in the upper mixed layer and, to what degree the fluctuation of parameters due to the variation of environmental factors affects the concentration. Generally, the most sensitive parameter is the depth of the seas where the solidified wastes would be deposited. The concentration of radionuclides in the surface water is not sensitively affected by the vertical diffusion coefficient. (author)

Albedo Neutron Dosimetry in a Deep Geological Disposal Repository for High-Level Nuclear Waste.

Science.gov (United States)

Pang, Bo; Becker, Frank

2017-04-28

Albedo neutron dosemeter is the German official personal neutron dosemeter in mixed radiation fields where neutrons contribute to personal dose. In deep geological repositories for high-level nuclear waste, where neutrons can dominate the radiation field, it is of interest to investigate the performance of albedo neutron dosemeter in such facilities. In this study, the deep geological repository is represented by a shielding cask loaded with spent nuclear fuel placed inside a rock salt emplacement drift. Due to the backscattering of neutrons in the drift, issues concerning calibration of the dosemeter arise. Field-specific calibration of the albedo neutron dosemeter was hence performed with Monte Carlo simulations. In order to assess the applicability of the albedo neutron dosemeter in a deep geological repository over a long time scale, spent nuclear fuel with different ages of 50, 100 and 500 years were investigated. It was found out, that the neutron radiation field in a deep geological repository can be assigned to the application area 'N1' of the albedo neutron dosemeter, which is typical in reactors and accelerators with heavy shielding. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Re-sampling of the KLX02 deep borehole at Laxemar

International Nuclear Information System (INIS)

Laaksoharju, M.; Andersson, Cecilia; Tullborg, E.L.; Wallin, B.; Ekwall, K.; Pedersen, K.

1999-01-01

The project focuses on the origin and changes of deep groundwaters, which are important for understanding the stability of the groundwater surrounding the final repository. The results from the sampling campaign in 1997 down to a depth of 1500m are compared with the results from 1993 sampled in the same borehole. The analytical results and some preliminary calculations are presented. The changes since the last sampling campaign 4 years ago indicate a high degree of mixing and dynamics in the system. The following conclusions are drawn: More changes in the water composition than expected compared with the results from the sampling campaign in 1993; Larger portions of meteoric water in the upper part of the borehole; Less glacial water in the intermediate part of the borehole; More brine water in the lower part of the borehole. The conclusion is that there has been a relatively large change in the groundwater system during the last 4 years in the Laxemar deep borehole. The disturbance removed the effect from the last glaciation and pulled in groundwater, which resulted in a mixture mainly consisting of meteoric and brine waters. The most probable reason is that the annual fluctuation and flow in the open borehole play an important role as a modificator especially for the isotopes. The results show the sensitivity of deep groundwater to changes in the prevailing hydrogeological situation

Geochemistry of coal-measure source rocks and natural gases in deep formations in Songliao Basin, NE China

Energy Technology Data Exchange (ETDEWEB)

Mi, Jingkui; Zhang, Shuichang; Hu, Guoyi; He, Kun [State Key Laboratory for Enhanced Oil Recovery, Beijing (China); Petroleum Geology Research and Laboratory Center, Research Institute of Petroleum Exploration and Development, PetroChina (China); Key Laboratory for Petroleum Geochemistry, China National Petroleum Corp. (China)

2010-12-01

The natural gases developed in deep volcanic rock reservoirs of the Songliao Basin, NE China are characterized by enriched {delta}{sup 13}C value for methane and frequently reversal carbon isotopic distribution pattern. Although many researchers consider such gas type as an abiogenic origin, we believe the natural gases have a biogenic origin mainly except little inorganic gases and the reversal carbon isotopic distribution pattern of gases is caused by mixing of different origin gases. Methane carbon isotopic values for majority samples fall in the range from - 24 permille to - 32 permille, which is heavier than typical coal-type gases in other Chinese basins. There are several reasons caused heavy carbon isotope of methane: (1) Carbon isotopic values of source kerogen are 3-5 permille heavier than these from other basins; (2) Source rocks are at extremely high maturity stage with vitrinite reflectance mostly above 3.0%; (3) Portion of gas is derived from basement mudrock or slate with higher maturity. The observation on the organic from deep formation reveals that there is a relatively high content for liptinite, which reaches approximately 8 to 10%. The macerals component of source rock shows that the source rocks have some ability to generate oil. Small portion of oil was generated from high hydrogen content macerals in coals and shales as proof by oil found in microcrack and in micropore of coal and oil-bearing fluid inclusions grown in volcanic reservoir. The occurrence of pyrobitumen in volcanic reservoir indicates preexisted oil had been cracked into wet gas, and this kind of gas had also been found in gas pools. Heavy isotopic methane is derived from coal at extremely high maturity stage. There may be little inorganic alkane gases in deep layers for their geochemistry and special geological setting of Songliao Basin. Artificial mixing experiments of different origins gases confirm that inorganic gas such as gas from well FS1 mixed with other end members

Deep learning with Python

CERN Document Server

Chollet, Francois

2018-01-01

DESCRIPTION Deep learning is applicable to a widening range of artificial intelligence problems, such as image classification, speech recognition, text classification, question answering, text-to-speech, and optical character recognition. Deep Learning with Python is structured around a series of practical code examples that illustrate each new concept introduced and demonstrate best practices. By the time you reach the end of this book, you will have become a Keras expert and will be able to apply deep learning in your own projects. KEY FEATURES • Practical code examples • In-depth introduction to Keras • Teaches the difference between Deep Learning and AI ABOUT THE TECHNOLOGY Deep learning is the technology behind photo tagging systems at Facebook and Google, self-driving cars, speech recognition systems on your smartphone, and much more. AUTHOR BIO Francois Chollet is the author of Keras, one of the most widely used libraries for deep learning in Python. He has been working with deep neural ...

Deep learning evaluation using deep linguistic processing

OpenAIRE

Kuhnle, Alexander; Copestake, Ann

2017-01-01

We discuss problems with the standard approaches to evaluation for tasks like visual question answering, and argue that artificial data can be used to address these as a complement to current practice. We demonstrate that with the help of existing 'deep' linguistic processing technology we are able to create challenging abstract datasets, which enable us to investigate the language understanding abilities of multimodal deep learning models in detail, as compared to a single performance value ...

In-situ stabilization of mixed waste contaminated soil

International Nuclear Information System (INIS)

Siegrist, R.L.; Cline, S.R.; Gilliam, T.M.; Conner, J.R.

1993-01-01

A full-scale field demonstration was conducted to evaluate in for stabilizing an inactive RCRA land treatment site at a DOE facility in Ohio. Subsurface silt and clay deposits were contaminated principally with up to 500 mg/kg of trichloroethylene and other halocarbons, but also trace to low levels of Pb, Cr, 235 U, and 99 Tc. In situ solidification was studied in three, 3.1 m diameter by 4.6 m deep columns. During mixing, a cement-based grout was injected and any missions from the mixed region were captured in a shroud and treated by filtration and carbon adsorption. During in situ processing, operation and performance parameters were measured, and soil cores were obtained from a solidified column 15 months later. Despite previous site-specific treatability experience, there were difficulties in selecting a grout with the requisite treatment agents amenable to subsurface injection and at a volume adequate for distribution throughout the mixed region while minimizing volume expansion. observations during the demonstration revealed that in situ solidification was rapidly accomplished (e.g., >90 m 3 /d) with limited emissions of volatile organics (i.e., -6 cm/s vs. 10 -8 cm/s). Leaching tests performed on the treated samples revealed non-detectable to acceptably low concentrations of all target contaminants

Three-Dimensional Effects of Artificial Mixing in a Shallow Drinking-Water Reservoir

Science.gov (United States)

Chen, Shengyang; Little, John C.; Carey, Cayelan C.; McClure, Ryan P.; Lofton, Mary E.; Lei, Chengwang

2018-01-01

Studies that examine the effects of artificial mixing for water-quality mitigation in lakes and reservoirs often view a water column with a one-dimensional (1-D) perspective (e.g., homogenized epilimnetic and hypolimnetic layers). Artificial mixing in natural water bodies, however, is inherently three dimensional (3-D). Using a 3-D approach experimentally and numerically, the present study visualizes thermal structure and analyzes constituent transport under the influence of artificial mixing in a shallow drinking-water reservoir. The purpose is to improve the understanding of artificial mixing, which may help to better design and operate mixing systems. In this reservoir, a side-stream supersaturation (SSS) hypolimnetic oxygenation system and an epilimnetic bubble-plume mixing (EM) system were concurrently deployed in the deep region. The present study found that, while the mixing induced by the SSS system does not have a distinct 3-D effect on the thermal structure, epilimnetic mixing by the EM system causes 3-D heterogeneity. In the experiments, epilimnetic mixing deepened the lower metalimnetic boundary near the diffuser by about 1 m, with 55% reduction of the deepening rate at 120 m upstream of the diffuser. In a tracer study using a 3-D hydrodynamic model, the operational flow rate of the EM system is found to be an important short-term driver of constituent transport in the reservoir, whereas the duration of the EM system operation is the dominant long-term driver. The results suggest that artificial mixing substantially alters both 3-D thermal structure and constituent transport, and thus needs to be taken into account for reservoir management.

The Sinking and Spreading of The Antarctic Deep Ice Shelf Water In The Ross Sea Studied By In Situ Observaions and Numerical Modeling

Science.gov (United States)

Rubino, A.; Budillon, G.; Pierini, S.; Spezie, G.

The sinking and spreading of the Deep Ice Shelf Water (DISW) in the Ross Sea are analyzed using in situ observations and the results of a nonlinear, reduced-gravity, frontal layered numerical "plume" model which is able to simulate the motion of a bottom-arrested current over realistic topography. The model is forced by prescribing the thickness of the DISW vein as well as its density structure at the southern model boundary. The ambient temperature and salinity are imposed using hydrographic data acquired by the Italian PNRA-CLIMA project. In the model water of the quiescent ambient ocean is allowed to entrain in the active deep layer due to a simple param- eterization of turbulent mixing. The importance of forcing the model with a realistic ambient density is demonstrated by carrying out a numerical simulation in which the bottom active layer is forced using an idealized ambient density. In a more realis- tic simulation the path and the density structure of the DISW vein flowing over the Challenger Basin are obtained and are found to be in good agreement with data. The evolution of the deep current beyond the continental shelf is also simulated. It provides useful information on the water flow and mixing in a region of the Ross Sea where the paucity of experimental data does not allow for a detailed description of the deep ocean dynamics.

Storm-driven Mixing and Potential Impact on the Arctic Ocean

Science.gov (United States)

Yang, Jiayan; Comiso, Josefino; Walsh, David; Krishfield, Richard; Honjo, Susumu; Koblinsky, Chester J. (Technical Monitor)

2001-01-01

Observations of the ocean, atmosphere, and ice made by Ice-Ocean Environmental Buoys (IOEBs) indicate that mixing events reaching the depth of the halocline have occurred in various regions in the Arctic Ocean. Our analysis suggests that these mixing events were mechanically forced by intense storms moving across the buoy sites. In this study, we analyzed these mixing events in the context of storm developments that occurred in the Beaufort Sea and in the general area just north of Fram Strait, two areas with quite different hydrographic structures. The Beaufort Sea is strongly influenced by inflow of Pacific water through Bering Strait, while the area north of Fram Strait is directly affected by the inflow of warm and salty North Atlantic water. Our analyses of the basin-wide evolution of the surface pressure and geostrophic wind fields indicate that the characteristics of the storms could be very different. The buoy-observed mixing occurred only in the spring and winter seasons when the stratification was relatively weak. This indicates the importance of stratification, although the mixing itself was mechanically driven. We also analyze the distribution of storms, both the long-term climatology as well as the patterns for each year in the last two decades. The frequency of storms is also shown to be correlated- (but not strongly) to Arctic Oscillation indices. This study indicates that the formation of new ice that leads to brine rejection is unlikely the mechanism that results in the type of mixing that could overturn the halocline. On the other hand, synoptic-scale storms can force mixing deep enough to the halocline and thermocline layer. Despite a very stable stratification associated with the Arctic halocline, the warm subsurface thermocline water is not always insulated from the mixed layer.

Deep sequencing analysis of HBV genotype shift and correlation with antiviral efficiency during adefovir dipivoxil therapy.

Directory of Open Access Journals (Sweden)

Yuwei Wang

Full Text Available Viral genotype shift in chronic hepatitis B (CHB patients during antiviral therapy has been reported, but the underlying mechanism remains elusive.38 CHB patients treated with ADV for one year were selected for studying genotype shift by both deep sequencing and Sanger sequencing method.Sanger sequencing method found that 7.9% patients showed mixed genotype before ADV therapy. In contrast, all 38 patients showed mixed genotype before ADV treatment by deep sequencing. 95.5% mixed genotype rate was also obtained from additional 200 treatment-naïve CHB patients. Of the 13 patients with genotype shift, the fraction of the minor genotype in 5 patients (38% increased gradually during the course of ADV treatment. Furthermore, responses to ADV and HBeAg seroconversion were associated with the high rate of genotype shift, suggesting drug and immune pressure may be key factors to induce genotype shift. Interestingly, patients with genotype C had a significantly higher rate of genotype shift than genotype B. In genotype shift group, ADV treatment induced a marked enhancement of genotype B ratio accompanied by a reduction of genotype C ratio, suggesting genotype C may be more sensitive to ADV than genotype B. Moreover, patients with dominant genotype C may have a better therapeutic effect. Finally, genotype shifts was correlated with clinical improvement in terms of ALT.Our findings provided a rational explanation for genotype shift among ADV-treated CHB patients. The genotype and genotype shift might be associated with antiviral efficiency.

Connectivity between surface and deep waters determines prokaryotic diversity in the North Atlantic Deep Water.

Science.gov (United States)

Frank, Alexander H; Garcia, Juan A L; Herndl, Gerhard J; Reinthaler, Thomas

2016-06-01

To decipher the influence of depth stratification and surface provincialism on the dark ocean prokaryotic community composition, we sampled the major deep-water masses in the eastern North Atlantic covering three biogeographic provinces. Their diversity was evaluated using ordination and canonical analysis of 454 pyrotag sequences. Variance partitioning suggested that 16% of the variation in the bacterial community composition was based on depth stratification while 9% of the variation was due to geographic location. General linear mixed effect models showed that the community of the subsurface waters was connected to the dark ocean prokaryotic communities in different biogeographic provinces. Cluster analysis indicated that some prokaryotic taxa are specific to distinct regions in bathypelagic water masses. Taken together, our data suggest that the dark ocean prokaryotic community composition of the eastern North Atlantic is primed by the formation and the horizontal transport of water masses. © 2016 Society for Applied Microbiology and John Wiley & Sons Ltd.

DeepMitosis: Mitosis detection via deep detection, verification and segmentation networks.

Science.gov (United States)

Li, Chao; Wang, Xinggang; Liu, Wenyu; Latecki, Longin Jan

2018-04-01

Mitotic count is a critical predictor of tumor aggressiveness in the breast cancer diagnosis. Nowadays mitosis counting is mainly performed by pathologists manually, which is extremely arduous and time-consuming. In this paper, we propose an accurate method for detecting the mitotic cells from histopathological slides using a novel multi-stage deep learning framework. Our method consists of a deep segmentation network for generating mitosis region when only a weak label is given (i.e., only the centroid pixel of mitosis is annotated), an elaborately designed deep detection network for localizing mitosis by using contextual region information, and a deep verification network for improving detection accuracy by removing false positives. We validate the proposed deep learning method on two widely used Mitosis Detection in Breast Cancer Histological Images (MITOSIS) datasets. Experimental results show that we can achieve the highest F-score on the MITOSIS dataset from ICPR 2012 grand challenge merely using the deep detection network. For the ICPR 2014 MITOSIS dataset that only provides the centroid location of mitosis, we employ the segmentation model to estimate the bounding box annotation for training the deep detection network. We also apply the verification model to eliminate some false positives produced from the detection model. By fusing scores of the detection and verification models, we achieve the state-of-the-art results. Moreover, our method is very fast with GPU computing, which makes it feasible for clinical practice. Copyright © 2018 Elsevier B.V. All rights reserved.

Deep frying

NARCIS (Netherlands)

Koerten, van K.N.

2016-01-01

Deep frying is one of the most used methods in the food processing industry. Though practically any food can be fried, French fries are probably the most well-known deep fried products. The popularity of French fries stems from their unique taste and texture, a crispy outside with a mealy soft

224Ra distribution in surface and deep water of Long Island Sound: sources and horizontal transport rates

International Nuclear Information System (INIS)

Torgersen, T.; O'Donnell, J.; DeAngelo, E.; Turekian, K.K.; Turekian, V.C.; Tanaka, N.

1997-01-01

Measurements of surface water and deep water 224 Ra(half-life 3.64 days) distributions in Long Island Sound (LIS) were conducted in July 1991. Because the pycnocline structure of LIS had been in place for about 50 days in July (long compared to the half-life of 224 Ra) in the surface water and the deep water operate as separate systems. In the surface water, the fine-grain sediments of nearshore and saltmarsh environments provide a strong source of 224 Ra, which is horizontally mixed away from the short to central LIS. A one-dimensional model of 224 Ra distribution suggests a cross-LIS horizontal eddy dispersivity of 5-50 m 2 s -1 . In the deep water, the mid-LIS sediment flux of 224 Ra is enhanced by ∼ 2x relative to the periphery, and the horizontal eddy flux is from central LIS to the periphery. A second one-dimensional model suggests a cross-LIS horizontal eddy dispersivity below the thermocline of 5-50 m 2 -1 . 224 Ra fluxes into the deep water of the central LIS are likely enhanced by (1) inhomogeneous sediment or (2) a reduced scavenging of 224 Ra in the sediments of central LIS brought about by low oxygen conditions (hypoxia) and the loss of the MnO 2 scavenging layer in the sediments. These rates of horizontal eddy dispersivity are significantly less than the estimate of 100-650 m 2 s -1 (Riley, 1967) but are consistent with the transport necessary to explain the dynamics of oxygen depletion in summer LIS. These results demonstrate the use of 224 Ra for quantifying the parameters needed to describe estuarine mixing and transport. (Author)

Sizable NSI from the SU(2){sub L} scalar doublet-singlet mixing and the implications in DUNE

Energy Technology Data Exchange (ETDEWEB)

Forero, David V. [Center for Neutrino Physics, Virginia Tech,Blacksburg, VA, 24061 (United States); Huang, Wei-Chih [Fakultät für Physik, Technische Universität Dortmund,Dortmund, 44221 (Germany)

2017-03-03

We propose a novel and simple mechanism where sizable effects of non-standard interactions (NSI) in neutrino propagation are induced from the mixing between an electrophilic second Higgs doublet and a charged singlet. The mixing arises from a dimensionful coupling of the scalar doublet and singlet to the standard model Higgs boson. In light of the small mass, the light mass eigenstate from the doublet-singlet mixing can generate much larger NSI than those induced by the heavy eigenstate. We show that a sizable NSI ε{sub eτ} (∼0.3) can be attained without being excluded by a variety of experimental constraints. Furthermore, we demonstrate that NSI can mimic effects of the Dirac CP phase in the neutrino mixing matrix but they can potentially be disentangled by future long-baseline neutrino experiments, such as the Deep Underground Neutrino Experiment (DUNE).

DeepPVP: phenotype-based prioritization of causative variants using deep learning

KAUST Repository

Boudellioua, Imene

2018-05-02

Background: Prioritization of variants in personal genomic data is a major challenge. Recently, computational methods that rely on comparing phenotype similarity have shown to be useful to identify causative variants. In these methods, pathogenicity prediction is combined with a semantic similarity measure to prioritize not only variants that are likely to be dysfunctional but those that are likely involved in the pathogenesis of a patient\\'s phenotype. Results: We have developed DeepPVP, a variant prioritization method that combined automated inference with deep neural networks to identify the likely causative variants in whole exome or whole genome sequence data. We demonstrate that DeepPVP performs significantly better than existing methods, including phenotype-based methods that use similar features. DeepPVP is freely available at https://github.com/bio-ontology-research-group/phenomenet-vp Conclusions: DeepPVP further improves on existing variant prioritization methods both in terms of speed as well as accuracy.

Design and construction of a deep slurry trench barrier

International Nuclear Information System (INIS)

Deming, P.W.

1997-01-01

A 24 m (80 ft) deep slurry trench surrounding a former chromium manufacturing facility on the Patapsco River in Baltimore, Maryland was constructed in 1995 to contain groundwater and site Soils, and to reduce the volume of groundwater extracted to maintain an inward gradient. In 1992, an embankment made of crushed stone was constructed in the Patapsco River to make land for barrier construction outboard of the bulkheads, and to protect the barrier. Stability of the slurry-supported trench excavation in the embankment required construction from an elevated work platform. An extended reach backhoe was used to excavate the deep slurry trench and to clean the trench bottom. Soil-Bentonite backfill was prepared at a central mixing area and transported by truck to the perimeter barrier. A synthetic membrane was inserted partially into the backfill for connection to a multimedia cap, and for redundancy and erosion control in the tidal zone. Hydraulic testing of the aquitard contained by the barrier demonstrated excellent performance of the barrier and bottom closure. Detailed definition of subsurface conditions and the closure stratum was necessary for the design and successful construction of the barrier, and is recommended for comparable slurry trench construction projects

Hot, deep origin of petroleum: deep basin evidence and application

Science.gov (United States)

Price, Leigh C.

1978-01-01

Use of the model of a hot deep origin of oil places rigid constraints on the migration and entrapment of crude oil. Specifically, oil originating from depth migrates vertically up faults and is emplaced in traps at shallower depths. Review of petroleum-producing basins worldwide shows oil occurrence in these basins conforms to the restraints of and therefore supports the hypothesis. Most of the world's oil is found in the very deepest sedimentary basins, and production over or adjacent to the deep basin is cut by or directly updip from faults dipping into the basin deep. Generally the greater the fault throw the greater the reserves. Fault-block highs next to deep sedimentary troughs are the best target areas by the present concept. Traps along major basin-forming faults are quite prospective. The structural style of a basin governs the distribution, types, and amounts of hydrocarbons expected and hence the exploration strategy. Production in delta depocenters (Niger) is in structures cut by or updip from major growth faults, and structures not associated with such faults are barren. Production in block fault basins is on horsts next to deep sedimentary troughs (Sirte, North Sea). In basins whose sediment thickness, structure and geologic history are known to a moderate degree, the main oil occurrences can be specifically predicted by analysis of fault systems and possible hydrocarbon migration routes. Use of the concept permits the identification of significant targets which have either been downgraded or ignored in the past, such as production in or just updip from thrust belts, stratigraphic traps over the deep basin associated with major faulting, production over the basin deep, and regional stratigraphic trapping updip from established production along major fault zones.

MICROSTRUCTURING OF SU-8 RESIST FOR MEMS AND BIO-APPLICATIONS

OpenAIRE

Dey, P.K.; Pramanick, B.; RaviShankar, A.; Ganguly, P.; Das, S.

2017-01-01

Some studies on the fabrication of micro-needles, micro-pillers, and micro-channels using SU-8 negative photoresist for MEMS and bio-applications are reported. The SU-8 processing technology was standardized for the purpose. Micro-pillars were fabricated on SU-8 polymer by soft lithographic technique. Micro-needles were realized on SU-8 film utilizing lensing effect of the etched groove structure of the glass substrate. Micro-channel was fabricated by molding of PDMS polymer on patterned SU-8...

Deep learning in bioinformatics.

Science.gov (United States)

Min, Seonwoo; Lee, Byunghan; Yoon, Sungroh

2017-09-01

In the era of big data, transformation of biomedical big data into valuable knowledge has been one of the most important challenges in bioinformatics. Deep learning has advanced rapidly since the early 2000s and now demonstrates state-of-the-art performance in various fields. Accordingly, application of deep learning in bioinformatics to gain insight from data has been emphasized in both academia and industry. Here, we review deep learning in bioinformatics, presenting examples of current research. To provide a useful and comprehensive perspective, we categorize research both by the bioinformatics domain (i.e. omics, biomedical imaging, biomedical signal processing) and deep learning architecture (i.e. deep neural networks, convolutional neural networks, recurrent neural networks, emergent architectures) and present brief descriptions of each study. Additionally, we discuss theoretical and practical issues of deep learning in bioinformatics and suggest future research directions. We believe that this review will provide valuable insights and serve as a starting point for researchers to apply deep learning approaches in their bioinformatics studies. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Heterogeneous distribution of plankton within the mixed layer and its implications for bloom formation in tropical seas

KAUST Repository

Calbet, Albert; Agersted, Mette Dalgaard; Kaartvedt, Stein; Mø hl, Malene; Mø ller, Eva Friis; Enghoff-Poulsen, Sø ren; Paulsen, Maria Lund; Solberg, Ingrid; Tang, Kam W.; Tonnesson, Kajsa; Raitsos, Dionysios E.; Nielsen, Torkel Gissel

2015-01-01

Intensive sampling at the coastal waters of the central Red Sea during a period of thermal stratification, prior to the main seasonal bloom during winter, showed that vertical patches of prokaryotes and microplankton developed and persisted for several days within the apparently density uniform upper layer. These vertical structures were most likely the result of in situ growth and mortality (e.g., grazing) rather than physical or behavioural aggregation. Simulating a mixing event by adding nutrient-rich deep water abruptly triggered dense phytoplankton blooms in the nutrient-poor environment of the upper layer. These findings suggest that vertical structures within the mixed layer provide critical seeding stocks that can rapidly exploit nutrient influx during mixing, leading to winter bloom formation.

Heterogeneous distribution of plankton within the mixed layer and its implications for bloom formation in tropical seas

KAUST Repository

Calbet, Albert

2015-06-11

Intensive sampling at the coastal waters of the central Red Sea during a period of thermal stratification, prior to the main seasonal bloom during winter, showed that vertical patches of prokaryotes and microplankton developed and persisted for several days within the apparently density uniform upper layer. These vertical structures were most likely the result of in situ growth and mortality (e.g., grazing) rather than physical or behavioural aggregation. Simulating a mixing event by adding nutrient-rich deep water abruptly triggered dense phytoplankton blooms in the nutrient-poor environment of the upper layer. These findings suggest that vertical structures within the mixed layer provide critical seeding stocks that can rapidly exploit nutrient influx during mixing, leading to winter bloom formation.

Heterogeneous distribution of plankton within the mixed layer and its implications for bloom formation in tropical seas

DEFF Research Database (Denmark)

Calbet, Albert; Agersted, Mette Dalgaard; Kaartvedt, Stein

2015-01-01

Intensive sampling at the coastal waters of the central Red Sea during a period of thermal stratification, prior to the main seasonal bloom during winter, showed that vertical patches of prokaryotes and microplankton developed and persisted for several days within the apparently density uniform...... upper layer. These vertical structures were most likely the result of in situ growth and mortality (e.g., grazing) rather than physical or behavioural aggregation. Simulating a mixing event by adding nutrient-rich deep water abruptly triggered dense phytoplankton blooms in the nutrient-poor environment...... of the upper layer. These findings suggest that vertical structures within the mixed layer provide critical seeding stocks that can rapidly exploit nutrient influx during mixing, leading to winter bloom formation...

Importance of the variability of hydrographic preconditioning for deep convection in the Gulf of Lion, NW Mediterranean

Directory of Open Access Journals (Sweden)

L. Grignon

2010-06-01

Full Text Available We study the variability of hydrographic preconditioning defined as the heat and salt contents in the Ligurian Sea before convection. The stratification is found to reach a maximum in the intermediate layer in December, whose causes and consequences for the interannual variability of convection are investigated. Further study of the interannual variability and correlation tests between the properties of the deep water formed and the winter surface fluxes support the description of convection as a process that transfers the heat and salt contents from the top and intermediate layers to the deep layer. A proxy for the rate of transfer is given by the final convective mixed layer depth, that is shown to depend equally on the surface fluxes and on the preconditioning. In particular, it is found that deep convection in winter 2004–2005 would have happened even with normal winter conditions, due to low pre-winter stratification.

DeepSimulator: a deep simulator for Nanopore sequencing

KAUST Repository

Li, Yu

2017-12-23

Motivation: Oxford Nanopore sequencing is a rapidly developed sequencing technology in recent years. To keep pace with the explosion of the downstream data analytical tools, a versatile Nanopore sequencing simulator is needed to complement the experimental data as well as to benchmark those newly developed tools. However, all the currently available simulators are based on simple statistics of the produced reads, which have difficulty in capturing the complex nature of the Nanopore sequencing procedure, the main task of which is the generation of raw electrical current signals. Results: Here we propose a deep learning based simulator, DeepSimulator, to mimic the entire pipeline of Nanopore sequencing. Starting from a given reference genome or assembled contigs, we simulate the electrical current signals by a context-dependent deep learning model, followed by a base-calling procedure to yield simulated reads. This workflow mimics the sequencing procedure more naturally. The thorough experiments performed across four species show that the signals generated by our context-dependent model are more similar to the experimentally obtained signals than the ones generated by the official context-independent pore model. In terms of the simulated reads, we provide a parameter interface to users so that they can obtain the reads with different accuracies ranging from 83% to 97%. The reads generated by the default parameter have almost the same properties as the real data. Two case studies demonstrate the application of DeepSimulator to benefit the development of tools in de novo assembly and in low coverage SNP detection. Availability: The software can be accessed freely at: https://github.com/lykaust15/DeepSimulator.

Deep learning relevance

DEFF Research Database (Denmark)

Lioma, Christina; Larsen, Birger; Petersen, Casper

2016-01-01

train a Recurrent Neural Network (RNN) on existing relevant information to that query. We then use the RNN to "deep learn" a single, synthetic, and we assume, relevant document for that query. We design a crowdsourcing experiment to assess how relevant the "deep learned" document is, compared...... to existing relevant documents. Users are shown a query and four wordclouds (of three existing relevant documents and our deep learned synthetic document). The synthetic document is ranked on average most relevant of all....

Mesoscale mixing of the Denmark Strait Overflow in the Irminger Basin

Science.gov (United States)

Koszalka, Inga M.; Haine, Thomas W. N.; Magaldi, Marcello G.

2017-04-01

The Denmark Strait Overflow (DSO) is a major export route for dense waters from the Nordic Seas forming the lower limb of the Atlantic Meridional Overturning Circulation, an important element of the climate system. Mixing processes along the DSO pathway influence its volume transport and properties contributing to the variability of the deep overturning circulation. They are poorly sampled by observations, however, which hinders development of a proper DSO representation in global circulation models. We employ a high resolution regional ocean model of the Irminger Basin to quantify impact of the mesoscale flows on DSO mixing focusing on geographical localization and the time-modulation of water property changes. The model reproduces the observed bulk warming of the DSO plume 100-200 km downstream of the Denmark Strait sill. It also reveals that mesoscale variability of the overflow ('DSO-eddies', of 20-30 km extent and a time scale of 2-5 day) modulates water property changes and turbulent mixing, diagnosed with the vertical shear of horizontal velocity and the eddy heat flux divergence. The space-time localization of the DSO mixing and warming and the role of coherent mesoscale structures should be explored by turbulence measurements and factored into the coarse circulation models.

STIMULATION TECHNOLOGIES FOR DEEP WELL COMPLETIONS

Energy Technology Data Exchange (ETDEWEB)

Stephen Wolhart

2003-06-01

The Department of Energy (DOE) is sponsoring a Deep Trek Program targeted at improving the economics of drilling and completing deep gas wells. Under the DOE program, Pinnacle Technologies is conducting a project to evaluate the stimulation of deep wells. The objective of the project is to assess U.S. deep well drilling & stimulation activity, review rock mechanics & fracture growth in deep, high pressure/temperature wells and evaluate stimulation technology in several key deep plays. Phase 1 was recently completed and consisted of assessing deep gas well drilling activity (1995-2007) and an industry survey on deep gas well stimulation practices by region. Of the 29,000 oil, gas and dry holes drilled in 2002, about 300 were drilled in the deep well; 25% were dry, 50% were high temperature/high pressure completions and 25% were simply deep completions. South Texas has about 30% of these wells, Oklahoma 20%, Gulf of Mexico Shelf 15% and the Gulf Coast about 15%. The Rockies represent only 2% of deep drilling. Of the 60 operators who drill deep and HTHP wells, the top 20 drill almost 80% of the wells. Six operators drill half the U.S. deep wells. Deep drilling peaked at 425 wells in 1998 and fell to 250 in 1999. Drilling is expected to rise through 2004 after which drilling should cycle down as overall drilling declines.

Toward the Characterization of Mixed-Phase Clouds Using Remote Sensing

Science.gov (United States)

Andronache, C.

2015-12-01

Mixed-phase clouds consist of a mixture of ice particles and liquid droplets at temperatures below 0 deg C. They are present in all seasons in many regions of the world, account for about 30% of the global cloud coverage, and are linked to cloud electrification and aircraft icing. The mix of ice particles, liquid droplets, and water vapor is unstable, and such clouds are thought to have a short lifetime. A characteristic parameter is the phase composition of mixed-phase clouds. It affects the cloud life cycle and the rate of precipitation. This parameter is important for cloud parameters retrievals by radar, lidar, and satellite and is relevant for climate modeling. The phase transformation includes the remarkable Wegener-Bergeron-Findeisen (WBF) process. The direction and the rate of the phase transformations depend on the local thermodynamic and microphysical properties. Cloud condensation nuclei (CCN) and ice nuclei (IN) particles determine to a large extent cloud microstructure and the dynamic response of clouds to aerosols. The complexity of dynamics and microphysics involved in mixed-phase clouds requires a set of observational and modeling tools that continue to be refined. Among these techniques, the remote sensing methods provide an increasing number of parameters, covering large regions of the world. Thus, a series of studies were dedicated to stratiform mixed-phase clouds revealing longer lifetime than previously thought. Satellite data and aircraft in situ measurements in deep convective clouds suggest that highly supercooled water often occurs in vigorous continental convective storms. In this study, we use cases of convective clouds to discuss the feasibility of mixed-phase clouds characterization and potential advantages of remote sensing.

Deep learning in TMVA Benchmarking Benchmarking TMVA DNN Integration of a Deep Autoencoder

CERN Document Server

Huwiler, Marc

2017-01-01

The TMVA library in ROOT is dedicated to multivariate analysis, and in partic- ular oers numerous machine learning algorithms in a standardized framework. It is widely used in High Energy Physics for data analysis, mainly to perform regression and classication. To keep up to date with the state of the art in deep learning, a new deep learning module was being developed this summer, oering deep neural net- work, convolutional neural network, and autoencoder. TMVA did not have yet any autoencoder method, and the present project consists in implementing the TMVA autoencoder class based on the deep learning module. It also includes some bench- marking performed on the actual deep neural network implementation, in comparison to the Keras framework with Tensorflow and Theano backend.

Localised mixing and heterogeneity in the plankton food web in a frontal region of the Sargasso Sea

DEFF Research Database (Denmark)

Richardson, Katherine; Bendtsen, Joøgen; Christensen, Jens Tang

2014-01-01

the diatom communities at 10 m and > 100 m (in the deep chlorophyll maximum, DCM) than in other parts of the frontal region. Thorpe displacements supported the hypothesis of elevated mixing intensities around these stations, as did vertical mixing rates inferred from stratification and vertical current shear...... influence the plankton food web, as indicated by elevated values/concentrations of (1) primary production, (2) variable fluorescence (F-v/F-m) and (3) total seston. In addition, the fraction of the total biomass of both copepods and nauplii found closest to the DCM in the frontal region correlated...

Localised mixing and heterogeneity in the plankton food web in a frontal region of the Sargasso Sea

DEFF Research Database (Denmark)

Richardson, Katherine; Bendtsen, Joøgen; Christensen, Jens Tang

2014-01-01

the diatom communities at 10 m and > 100 m (in the deep chlorophyll maximum, DCM) than in other parts of the frontal region. Thorpe displacements supported the hypothesis of elevated mixing intensities around these stations, as did vertical mixing rates inferred from stratification and vertical current shear...... with the stratification (Brunt-V is l frequency), with the greatest fractions found below 75 m at the most weakly stratified stations. While this study cannot directly link these observations to eel larvae ecology, Munk et al. (2010; Proc R Soc B 277: 3593-3599) reported that eel larvae were most abundant at locations...

Tradeoffs in Chemical and Thermal Variations in the Post-perovskite Phase Transition: Mixed Phase Regions in the Deep Lower Mantle?

Science.gov (United States)

Giles, G. F.; Spera, F. J.; Yuen, D. A.

2005-12-01

The recent discovery of a phase-transition in Mg-rich perovskite (Pv) to a post-perovskite (pPv) phase at lower mantle depths and its relationship to D", lower mantle heterogeneity and iron content prompted an investigation of the relative importance of lower mantle (LM) compositional and temperature fluctuations in creating topographic undulations on mixed phase regions. Above the transition, Mg-rich Pv makes up ~70 percent by mass of the LM. Using results from experimental phase equilibria, first-principles computations and thermodynamic relations for Fe2+-Mg mixing in silicates, a preliminary thermodynamic model for the perovskite to post-perovskite phase transition in the divariant system MgSiO3-FeSiO3 is developed. Complexities associated with components Fe2O3 and Al2O3 and other phases (Ca-Pv, magnesiowustite) are neglected. The model predicts phase transition pressures are sensitive to the FeSiO3 content of perovskite (~-1.5 GPa per one mole percent FeSiO3). This leads to considerable topography along the top boundary of the mixed phase region. The Clapeyron slope for the Pv to pPv transition at XFeSiO3=0.1 is +11 MPa/K about 20% higher than for pure Mg-Pv. Increasing bulk concentration of iron elevates the mixed (two-phase) layer above the core-mantle boundary (CMB); increasing temperature acts to push the mixed layer deeper into the LM into the D" thermal boundary layer resting upon the (CMB). For various LM geotherms and CMB temperatures, a single mixed layer of thickness ~300 km lies within the bottom 40% of the lower mantle. For low iron contents (XFeSiO3 ~5 mole percent or less), two perched layers are found. This is the divariant analog to the univariant double-crosser. The hotter the mantle, the deeper the mixed phase layer; the more iron-rich the LM, the higher the mixed phase layer. In a hotter Hadean Earth with interior temperatures everywhere 200-500 K warmer pPv is not stable unless the LM bulk composition is Fe-enriched compared to the present

Deep subsurface microbial processes

Science.gov (United States)

Lovley, D.R.; Chapelle, F.H.

1995-01-01

Information on the microbiology of the deep subsurface is necessary in order to understand the factors controlling the rate and extent of the microbially catalyzed redox reactions that influence the geophysical properties of these environments. Furthermore, there is an increasing threat that deep aquifers, an important drinking water resource, may be contaminated by man's activities, and there is a need to predict the extent to which microbial activity may remediate such contamination. Metabolically active microorganisms can be recovered from a diversity of deep subsurface environments. The available evidence suggests that these microorganisms are responsible for catalyzing the oxidation of organic matter coupled to a variety of electron acceptors just as microorganisms do in surface sediments, but at much slower rates. The technical difficulties in aseptically sampling deep subsurface sediments and the fact that microbial processes in laboratory incubations of deep subsurface material often do not mimic in situ processes frequently necessitate that microbial activity in the deep subsurface be inferred through nonmicrobiological analyses of ground water. These approaches include measurements of dissolved H2, which can predict the predominant microbially catalyzed redox reactions in aquifers, as well as geochemical and groundwater flow modeling, which can be used to estimate the rates of microbial processes. Microorganisms recovered from the deep subsurface have the potential to affect the fate of toxic organics and inorganic contaminants in groundwater. Microbial activity also greatly influences 1 the chemistry of many pristine groundwaters and contributes to such phenomena as porosity development in carbonate aquifers, accumulation of undesirably high concentrations of dissolved iron, and production of methane and hydrogen sulfide. Although the last decade has seen a dramatic increase in interest in deep subsurface microbiology, in comparison with the study of

DeepSurv: personalized treatment recommender system using a Cox proportional hazards deep neural network.

Science.gov (United States)

Katzman, Jared L; Shaham, Uri; Cloninger, Alexander; Bates, Jonathan; Jiang, Tingting; Kluger, Yuval

2018-02-26

Medical practitioners use survival models to explore and understand the relationships between patients' covariates (e.g. clinical and genetic features) and the effectiveness of various treatment options. Standard survival models like the linear Cox proportional hazards model require extensive feature engineering or prior medical knowledge to model treatment interaction at an individual level. While nonlinear survival methods, such as neural networks and survival forests, can inherently model these high-level interaction terms, they have yet to be shown as effective treatment recommender systems. We introduce DeepSurv, a Cox proportional hazards deep neural network and state-of-the-art survival method for modeling interactions between a patient's covariates and treatment effectiveness in order to provide personalized treatment recommendations. We perform a number of experiments training DeepSurv on simulated and real survival data. We demonstrate that DeepSurv performs as well as or better than other state-of-the-art survival models and validate that DeepSurv successfully models increasingly complex relationships between a patient's covariates and their risk of failure. We then show how DeepSurv models the relationship between a patient's features and effectiveness of different treatment options to show how DeepSurv can be used to provide individual treatment recommendations. Finally, we train DeepSurv on real clinical studies to demonstrate how it's personalized treatment recommendations would increase the survival time of a set of patients. The predictive and modeling capabilities of DeepSurv will enable medical researchers to use deep neural networks as a tool in their exploration, understanding, and prediction of the effects of a patient's characteristics on their risk of failure.

Pathogenesis of deep endometriosis.

Science.gov (United States)

Gordts, Stephan; Koninckx, Philippe; Brosens, Ivo

2017-12-01

The pathophysiology of (deep) endometriosis is still unclear. As originally suggested by Cullen, change the definition "deeper than 5 mm" to "adenomyosis externa." With the discovery of the old European literature on uterine bleeding in 5%-10% of the neonates and histologic evidence that the bleeding represents decidual shedding, it is postulated/hypothesized that endometrial stem/progenitor cells, implanted in the pelvic cavity after birth, may be at the origin of adolescent and even the occasionally premenarcheal pelvic endometriosis. Endometriosis in the adolescent is characterized by angiogenic and hemorrhagic peritoneal and ovarian lesions. The development of deep endometriosis at a later age suggests that deep infiltrating endometriosis is a delayed stage of endometriosis. Another hypothesis is that the endometriotic cell has undergone genetic or epigenetic changes and those specific changes determine the development into deep endometriosis. This is compatible with the hereditary aspects, and with the clonality of deep and cystic ovarian endometriosis. It explains the predisposition and an eventual causal effect by dioxin or radiation. Specific genetic/epigenetic changes could explain the various expressions and thus typical, cystic, and deep endometriosis become three different diseases. Subtle lesions are not a disease until epi(genetic) changes occur. A classification should reflect that deep endometriosis is a specific disease. In conclusion the pathophysiology of deep endometriosis remains debated and the mechanisms of disease progression, as well as the role of genetics and epigenetics in the process, still needs to be unraveled. Copyright © 2017 American Society for Reproductive Medicine. Published by Elsevier Inc. All rights reserved.

DeepSpark: A Spark-Based Distributed Deep Learning Framework for Commodity Clusters

OpenAIRE

Kim, Hanjoo; Park, Jaehong; Jang, Jaehee; Yoon, Sungroh

2016-01-01

The increasing complexity of deep neural networks (DNNs) has made it challenging to exploit existing large-scale data processing pipelines for handling massive data and parameters involved in DNN training. Distributed computing platforms and GPGPU-based acceleration provide a mainstream solution to this computational challenge. In this paper, we propose DeepSpark, a distributed and parallel deep learning framework that exploits Apache Spark on commodity clusters. To support parallel operation...

DeepPVP: phenotype-based prioritization of causative variants using deep learning

KAUST Repository

Boudellioua, Imene; Kulmanov, Maxat; Schofield, Paul N; Gkoutos, Georgios V; Hoehndorf, Robert

2018-01-01

phenotype-based methods that use similar features. DeepPVP is freely available at https://github.com/bio-ontology-research-group/phenomenet-vp Conclusions: DeepPVP further improves on existing variant prioritization methods both in terms of speed as well

DeepARG: a deep learning approach for predicting antibiotic resistance genes from metagenomic data.

Science.gov (United States)

Arango-Argoty, Gustavo; Garner, Emily; Pruden, Amy; Heath, Lenwood S; Vikesland, Peter; Zhang, Liqing

2018-02-01

Growing concerns about increasing rates of antibiotic resistance call for expanded and comprehensive global monitoring. Advancing methods for monitoring of environmental media (e.g., wastewater, agricultural waste, food, and water) is especially needed for identifying potential resources of novel antibiotic resistance genes (ARGs), hot spots for gene exchange, and as pathways for the spread of ARGs and human exposure. Next-generation sequencing now enables direct access and profiling of the total metagenomic DNA pool, where ARGs are typically identified or predicted based on the "best hits" of sequence searches against existing databases. Unfortunately, this approach produces a high rate of false negatives. To address such limitations, we propose here a deep learning approach, taking into account a dissimilarity matrix created using all known categories of ARGs. Two deep learning models, DeepARG-SS and DeepARG-LS, were constructed for short read sequences and full gene length sequences, respectively. Evaluation of the deep learning models over 30 antibiotic resistance categories demonstrates that the DeepARG models can predict ARGs with both high precision (> 0.97) and recall (> 0.90). The models displayed an advantage over the typical best hit approach, yielding consistently lower false negative rates and thus higher overall recall (> 0.9). As more data become available for under-represented ARG categories, the DeepARG models' performance can be expected to be further enhanced due to the nature of the underlying neural networks. Our newly developed ARG database, DeepARG-DB, encompasses ARGs predicted with a high degree of confidence and extensive manual inspection, greatly expanding current ARG repositories. The deep learning models developed here offer more accurate antimicrobial resistance annotation relative to current bioinformatics practice. DeepARG does not require strict cutoffs, which enables identification of a much broader diversity of ARGs. The

Stimulation Technologies for Deep Well Completions

Energy Technology Data Exchange (ETDEWEB)

None

2003-09-30

The Department of Energy (DOE) is sponsoring the Deep Trek Program targeted at improving the economics of drilling and completing deep gas wells. Under the DOE program, Pinnacle Technologies is conducting a study to evaluate the stimulation of deep wells. The objective of the project is to assess U.S. deep well drilling & stimulation activity, review rock mechanics & fracture growth in deep, high pressure/temperature wells and evaluate stimulation technology in several key deep plays. An assessment of historical deep gas well drilling activity and forecast of future trends was completed during the first six months of the project; this segment of the project was covered in Technical Project Report No. 1. The second progress report covers the next six months of the project during which efforts were primarily split between summarizing rock mechanics and fracture growth in deep reservoirs and contacting operators about case studies of deep gas well stimulation.

DeepQA: improving the estimation of single protein model quality with deep belief networks.

Science.gov (United States)

Cao, Renzhi; Bhattacharya, Debswapna; Hou, Jie; Cheng, Jianlin

2016-12-05

Protein quality assessment (QA) useful for ranking and selecting protein models has long been viewed as one of the major challenges for protein tertiary structure prediction. Especially, estimating the quality of a single protein model, which is important for selecting a few good models out of a large model pool consisting of mostly low-quality models, is still a largely unsolved problem. We introduce a novel single-model quality assessment method DeepQA based on deep belief network that utilizes a number of selected features describing the quality of a model from different perspectives, such as energy, physio-chemical characteristics, and structural information. The deep belief network is trained on several large datasets consisting of models from the Critical Assessment of Protein Structure Prediction (CASP) experiments, several publicly available datasets, and models generated by our in-house ab initio method. Our experiments demonstrate that deep belief network has better performance compared to Support Vector Machines and Neural Networks on the protein model quality assessment problem, and our method DeepQA achieves the state-of-the-art performance on CASP11 dataset. It also outperformed two well-established methods in selecting good outlier models from a large set of models of mostly low quality generated by ab initio modeling methods. DeepQA is a useful deep learning tool for protein single model quality assessment and protein structure prediction. The source code, executable, document and training/test datasets of DeepQA for Linux is freely available to non-commercial users at http://cactus.rnet.missouri.edu/DeepQA/ .

Magma Mixing: Why Picrites are Not So Hot

Science.gov (United States)

Natland, J. H.

2010-12-01

porosity in regions where crustal-level magma chambers and flanking rift zones do not have a chance to form. Low-magma supply is favored. In the ocean basins, such upper mantle mainlining occurs only at certain fracture zones, deep propagating rifts at microplates, or ultra-slow spreading ridges, but no liquids (glasses) with >10% MgO occur at any of these places. On continents, rift structures through cratons might allow this, but so far no picrite, ferropicrite, or meimichite that has been adequately described from these places lacks evidence for end-member mixing. Low-temperature iron-rich magmas can accumulate in the deep lower crust and later rise to form substantial intrusions (e.g. Skaergaard) or erupt as flood basalts (Columbia River). Some komatiites might represent high-temperature liquids, but many are so altered that original liquid compositions cannot be deduced (e.g., Gorgona). The hottest intraplate volcano is Kilauea, Hawaii, where rare picrite glass with 15% MgO has an estimated eruptive temperature (1) of ~1350C and a potential temperature at 1 GPa of ~1420C. Lavas at all other linear island chains, Iceland and even west Greenland where picrites are abundant, are cooler than this. (1) Beattie, P., 1993. CMP 115: 103-111.

A case of mixed type laryngocele presented with deep neck infection and review of the literature

Directory of Open Access Journals (Sweden)

Salih Bakır

2012-09-01

Full Text Available Laryngocele is an abnormal dilatation of the laryngeal ventricular saccule that may extend into the subcutaneous tissues of the neck through the thyrohyoid membrane or confined to the endolarynx. The etiology is still unclear. Many laryngoceles are asymptomatic. An asymptomatic laryngocele appears and produces symptoms only as it enlarges or when it becomes infected. In this report, we present a 40-year-old female patient, which had an asymptomatic neck swelling for 20 years, referred for deep neck infection, dysphonia and dyspnea. J Clin Exp Invest 2012; 3 (3: 415-419Key words: Larynx, laryngocele, laryngopyocele, neck mass

Stimulation Technologies for Deep Well Completions

Energy Technology Data Exchange (ETDEWEB)

Stephen Wolhart

2005-06-30

The Department of Energy (DOE) is sponsoring the Deep Trek Program targeted at improving the economics of drilling and completing deep gas wells. Under the DOE program, Pinnacle Technologies conducted a study to evaluate the stimulation of deep wells. The objective of the project was to review U.S. deep well drilling and stimulation activity, review rock mechanics and fracture growth in deep, high-pressure/temperature wells and evaluate stimulation technology in several key deep plays. This report documents results from this project.

DeepPicker: A deep learning approach for fully automated particle picking in cryo-EM.

Science.gov (United States)

Wang, Feng; Gong, Huichao; Liu, Gaochao; Li, Meijing; Yan, Chuangye; Xia, Tian; Li, Xueming; Zeng, Jianyang

2016-09-01

Particle picking is a time-consuming step in single-particle analysis and often requires significant interventions from users, which has become a bottleneck for future automated electron cryo-microscopy (cryo-EM). Here we report a deep learning framework, called DeepPicker, to address this problem and fill the current gaps toward a fully automated cryo-EM pipeline. DeepPicker employs a novel cross-molecule training strategy to capture common features of particles from previously-analyzed micrographs, and thus does not require any human intervention during particle picking. Tests on the recently-published cryo-EM data of three complexes have demonstrated that our deep learning based scheme can successfully accomplish the human-level particle picking process and identify a sufficient number of particles that are comparable to those picked manually by human experts. These results indicate that DeepPicker can provide a practically useful tool to significantly reduce the time and manual effort spent in single-particle analysis and thus greatly facilitate high-resolution cryo-EM structure determination. DeepPicker is released as an open-source program, which can be downloaded from https://github.com/nejyeah/DeepPicker-python. Copyright © 2016 Elsevier Inc. All rights reserved.

DeepBase: annotation and discovery of microRNAs and other noncoding RNAs from deep-sequencing data.

Science.gov (United States)

Yang, Jian-Hua; Qu, Liang-Hu

2012-01-01

Recent advances in high-throughput deep-sequencing technology have produced large numbers of short and long RNA sequences and enabled the detection and profiling of known and novel microRNAs (miRNAs) and other noncoding RNAs (ncRNAs) at unprecedented sensitivity and depth. In this chapter, we describe the use of deepBase, a database that we have developed to integrate all public deep-sequencing data and to facilitate the comprehensive annotation and discovery of miRNAs and other ncRNAs from these data. deepBase provides an integrative, interactive, and versatile web graphical interface to evaluate miRBase-annotated miRNA genes and other known ncRNAs, explores the expression patterns of miRNAs and other ncRNAs, and discovers novel miRNAs and other ncRNAs from deep-sequencing data. deepBase also provides a deepView genome browser to comparatively analyze these data at multiple levels. deepBase is available at http://deepbase.sysu.edu.cn/.

Deep Borehole Disposal as an Alternative Concept to Deep Geological Disposal

Energy Technology Data Exchange (ETDEWEB)

Lee, Jongyoul; Lee, Minsoo; Choi, Heuijoo; Kim, Kyungsu [Korea Atomic Energy Research Institute, Daejeon (Korea, Republic of)

2016-10-15

In this paper, the general concept and key technologies for deep borehole disposal of spent fuels or HLW, as an alternative method to the mined geological disposal method, were reviewed. After then an analysis on the distance between boreholes for the disposal of HLW was carried out. Based on the results, a disposal area were calculated approximately and compared with that of mined geological disposal. These results will be used as an input for the analyses of applicability for DBD in Korea. The disposal safety of this system has been demonstrated with underground research laboratory and some advanced countries such as Finland and Sweden are implementing their disposal project on commercial stage. However, if the spent fuels or the high-level radioactive wastes can be disposed of in the depth of 3-5 km and more stable rock formation, it has several advantages. Therefore, as an alternative disposal concept to the mined deep geological disposal concept (DGD), very deep borehole disposal (DBD) technology is under consideration in number of countries in terms of its outstanding safety and cost effectiveness. In this paper, the general concept of deep borehole disposal for spent fuels or high level radioactive wastes was reviewed. And the key technologies, such as drilling technology of large diameter borehole, packaging and emplacement technology, sealing technology and performance/safety analyses technologies, and their challenges in development of deep borehole disposal system were analyzed. Also, very preliminary deep borehole disposal concept including disposal canister concept was developed according to the nuclear environment in Korea.

Deep Borehole Disposal as an Alternative Concept to Deep Geological Disposal

International Nuclear Information System (INIS)

Lee, Jongyoul; Lee, Minsoo; Choi, Heuijoo; Kim, Kyungsu

2016-01-01

In this paper, the general concept and key technologies for deep borehole disposal of spent fuels or HLW, as an alternative method to the mined geological disposal method, were reviewed. After then an analysis on the distance between boreholes for the disposal of HLW was carried out. Based on the results, a disposal area were calculated approximately and compared with that of mined geological disposal. These results will be used as an input for the analyses of applicability for DBD in Korea. The disposal safety of this system has been demonstrated with underground research laboratory and some advanced countries such as Finland and Sweden are implementing their disposal project on commercial stage. However, if the spent fuels or the high-level radioactive wastes can be disposed of in the depth of 3-5 km and more stable rock formation, it has several advantages. Therefore, as an alternative disposal concept to the mined deep geological disposal concept (DGD), very deep borehole disposal (DBD) technology is under consideration in number of countries in terms of its outstanding safety and cost effectiveness. In this paper, the general concept of deep borehole disposal for spent fuels or high level radioactive wastes was reviewed. And the key technologies, such as drilling technology of large diameter borehole, packaging and emplacement technology, sealing technology and performance/safety analyses technologies, and their challenges in development of deep borehole disposal system were analyzed. Also, very preliminary deep borehole disposal concept including disposal canister concept was developed according to the nuclear environment in Korea

Mixing and Progressive Melting of Deep and Shallow Mantle Sources in the NE Atlantic and Arctic

DEFF Research Database (Denmark)

Trønnes, Reidar; Debaille, Vincianne; Erambert, M.

2013-01-01

). The SCLM-component was mixed with the local asthenosphere during and shortly after the continental rifting and ocean basin opening. Using combined Sr-Nd-Pb- Os-He-isotope systematics, the Iceland plume can be modelled as a mixture of 70% refractory/primordial lower mantle (LM) and 30% recycled oceanic...

Evaluation of vertical coordinate and vertical mixing algorithms in the HYbrid-Coordinate Ocean Model (HYCOM)

Science.gov (United States)

Halliwell, George R.

Vertical coordinate and vertical mixing algorithms included in the HYbrid Coordinate Ocean Model (HYCOM) are evaluated in low-resolution climatological simulations of the Atlantic Ocean. The hybrid vertical coordinates are isopycnic in the deep ocean interior, but smoothly transition to level (pressure) coordinates near the ocean surface, to sigma coordinates in shallow water regions, and back again to level coordinates in very shallow water. By comparing simulations to climatology, the best model performance is realized using hybrid coordinates in conjunction with one of the three available differential vertical mixing models: the nonlocal K-Profile Parameterization, the NASA GISS level 2 turbulence closure, and the Mellor-Yamada level 2.5 turbulence closure. Good performance is also achieved using the quasi-slab Price-Weller-Pinkel dynamical instability model. Differences among these simulations are too small relative to other errors and biases to identify the "best" vertical mixing model for low-resolution climate simulations. Model performance deteriorates slightly when the Kraus-Turner slab mixed layer model is used with hybrid coordinates. This deterioration is smallest when solar radiation penetrates beneath the mixed layer and when shear instability mixing is included. A simulation performed using isopycnic coordinates to emulate the Miami Isopycnic Coordinate Ocean Model (MICOM), which uses Kraus-Turner mixing without penetrating shortwave radiation and shear instability mixing, demonstrates that the advantages of switching from isopycnic to hybrid coordinates and including more sophisticated turbulence closures outweigh the negative numerical effects of maintaining hybrid vertical coordinates.

Deep Mapping and Spatial Anthropology

Directory of Open Access Journals (Sweden)

Les Roberts

2016-01-01

Full Text Available This paper provides an introduction to the Humanities Special Issue on “Deep Mapping”. It sets out the rationale for the collection and explores the broad-ranging nature of perspectives and practices that fall within the “undisciplined” interdisciplinary domain of spatial humanities. Sketching a cross-current of ideas that have begun to coalesce around the concept of “deep mapping”, the paper argues that rather than attempting to outline a set of defining characteristics and “deep” cartographic features, a more instructive approach is to pay closer attention to the multivalent ways deep mapping is performatively put to work. Casting a critical and reflexive gaze over the developing discourse of deep mapping, it is argued that what deep mapping “is” cannot be reduced to the otherwise a-spatial and a-temporal fixity of the “deep map”. In this respect, as an undisciplined survey of this increasing expansive field of study and practice, the paper explores the ways in which deep mapping can engage broader discussion around questions of spatial anthropology.

Deep Vein Thrombosis

African Journals Online (AJOL)

OWNER

Deep Vein Thrombosis: Risk Factors and Prevention in Surgical Patients. Deep Vein ... preventable morbidity and mortality in hospitalized surgical patients. ... the elderly.3,4 It is very rare before the age ... depends on the risk level; therefore an .... but also in the post-operative period. ... is continuing uncertainty regarding.

Southern Ocean Circulation: a High Resolution Examination of the Last Deglaciation from Deep-Sea Corals

Science.gov (United States)

Robinson, L. F.; Li, T.; Chen, T.; Burke, A.; Pegrum Haram, A.; Stewart, J.; Rae, J. W. B.; van de Flierdt, T.; Struve, T.; Wilson, D. J.

2017-12-01

Two decades ago it was first noted that the skeletal remains of deep-sea corals had the potential to provide absolutely dated archives of past ocean conditions. In the intervening twenty years this field has developed to the point where strategic collections and high throughput dating techniques now allow high resolution, well dated records of past deep sea behaviour to be produced. Likewise, efforts to improve understanding of biomineralisation and growth rates are leading to refinements in proxy tools useful for examining circulation, nutrient and carbon cycling, temperature and weathering processes. Deep-sea corals are particularly valuable archives in high latitude regions where radiocarbon-based age models are susceptible to large changes in surface reservoir ages. In this presentation we show new high resolution multiproxy records of the Southern Ocean (Drake Passage) made on U-Th dated corals spanning the last glacial cycle. With more than seventeen hundred reconnaissance ages, and around 200 precise isotope dilution U-Th ages, subtle changes in ocean behaviour can be identified during times of abrupt climate change. The geochemical signature of corals from the deepest sites, closest to modern day Lower Circumpolar Deep Waters, typically show a gradual shift from glacial to Holocene values during deglaciation, likely related to ventilation of the deep ocean. By contrast for the samples collected shallower in the water column (within sites currently bathed by Upper Circumpolar Deep Waters and Antarctic Intermediate and Mode Waters) the evidence points to a more complicated picture. Vertical zonation in the geochemical data suggests that periods of stratification are interspersed with mixing events within the upper 1500m of the water column. At the same time comparison to U-Th dated records from the low latitude Pacific and Atlantic points to an important role for the Southern Ocean in feeding the intermediate waters of both ocean basins throughout the

Mixing ratio sensor of alcohol mixed fuel

Energy Technology Data Exchange (ETDEWEB)

Miyata, Shigeru; Matsubara, Yoshihiro

1987-08-07

In order to improve combustion efficiency of an internal combustion engine using gasoline-alcohol mixed fuel and to reduce harmful substance in its exhaust gas, it is necessary to control strictly the air-fuel ratio to be supplied and the ignition timing and change the condition of control depending upon the mixing ratio of the mixed fuel. In order to detect the mixing ratio of the mixed fuel, the above mixing ratio has so far been detected by casting a ray of light to the mixed fuel and utilizing a change of critical angle associated with the change of the composition of the fluid of the mixed fuel. However, in case when a light emitting diode is used for the light source above, two kinds of sensors are further needed. Concerning the two kinds of sensors above, this invention offers a mixing ratio sensor for the alcohol mixed fuel which can abolish a temperature sensor to detect the environmental temperature by making a single compensatory light receiving element deal with the compensation of the amount of light emission of the light emitting element due to the temperature change and the compensation of the critical angle caused by the temperature change. (6 figs)

pDeep: Predicting MS/MS Spectra of Peptides with Deep Learning.

Science.gov (United States)

Zhou, Xie-Xuan; Zeng, Wen-Feng; Chi, Hao; Luo, Chunjie; Liu, Chao; Zhan, Jianfeng; He, Si-Min; Zhang, Zhifei

2017-12-05

In tandem mass spectrometry (MS/MS)-based proteomics, search engines rely on comparison between an experimental MS/MS spectrum and the theoretical spectra of the candidate peptides. Hence, accurate prediction of the theoretical spectra of peptides appears to be particularly important. Here, we present pDeep, a deep neural network-based model for the spectrum prediction of peptides. Using the bidirectional long short-term memory (BiLSTM), pDeep can predict higher-energy collisional dissociation, electron-transfer dissociation, and electron-transfer and higher-energy collision dissociation MS/MS spectra of peptides with >0.9 median Pearson correlation coefficients. Further, we showed that intermediate layer of the neural network could reveal physicochemical properties of amino acids, for example the similarities of fragmentation behaviors between amino acids. We also showed the potential of pDeep to distinguish extremely similar peptides (peptides that contain isobaric amino acids, for example, GG = N, AG = Q, or even I = L), which were very difficult to distinguish using traditional search engines.

Genomic variation in macrophage-cultured European porcine reproductive and respiratory syndrome virus Olot/91 revealed using ultra-deep next generation sequencing.

Science.gov (United States)

Lu, Zen H; Brown, Alexander; Wilson, Alison D; Calvert, Jay G; Balasch, Monica; Fuentes-Utrilla, Pablo; Loecherbach, Julia; Turner, Frances; Talbot, Richard; Archibald, Alan L; Ait-Ali, Tahar

2014-03-04

Porcine Reproductive and Respiratory Syndrome (PRRS) is a disease of major economic impact worldwide. The etiologic agent of this disease is the PRRS virus (PRRSV). Increasing evidence suggest that microevolution within a coexisting quasispecies population can give rise to high sequence heterogeneity in PRRSV. We developed a pipeline based on the ultra-deep next generation sequencing approach to first construct the complete genome of a European PRRSV, strain Olot/9, cultured on macrophages and then capture the rare variants representative of the mixed quasispecies population. Olot/91 differs from the reference Lelystad strain by about 5% and a total of 88 variants, with frequencies as low as 1%, were detected in the mixed population. These variants included 16 non-synonymous variants concentrated in the genes encoding structural and nonstructural proteins; including Glycoprotein 2a and 5. Using an ultra-deep sequencing methodology, the complete genome of Olot/91 was constructed without any prior knowledge of the sequence. Rare variants that constitute minor fractions of the heterogeneous PRRSV population could successfully be detected to allow further exploration of microevolutionary events.

Evolution of power plant foundations in Nova Scotia

Energy Technology Data Exchange (ETDEWEB)

Chen, C.T.; Larade, M.; Myette, R.; MacIvor, T. [Nova Scotia Power Inc., Halifax, NS (Canada)

2009-07-01

Many thermal steam plants in Nova Scotia, including the first 3 units Dartmouth's Tufts Cove Generating Station, were constructed on bedrock and near the sea for easy access to cooling water. Major equipment foundations such as turbine/condenser foundations, boiler feed pump foundations and fan foundations are concrete block foundations that are designed to withstand the heavy equipment and environmental loads imposed throughout the service-life of the plant. This paper discussed the rationale for choosing different types of foundations at the 5 power generating units at the Tufts Cove Generating Station. Due to the presence of boulders, pipe piles were installed in drilled holes in 1995 for the unit 2 precipitator foundation. In 2005, micro-piles were installed for unit 1 and 3 precipitators because they were relatively non-protrusive for the congested urban site that was full of buried structures and services. Similarly, for the unit 4 and 5 sound wall foundations, the high resistance capacities for both downward and uplift loads of the micro-piles embedded into the bedrock proved to be very valuable. These tall wall structures are subject to very high wind loads and were constructed at a geotechnically challenged site. Good geotechnical investigation and consultation was shown to be the basis for good design and construction of foundation works. It was concluded that the lessons learned at Tufts Cove can be applied to other similar foundation designs. 7 refs., 10 figs.

Controls on turbulent mixing on the West Antarctic Peninsula shelf

Science.gov (United States)

Brearley, J. Alexander; Meredith, Michael P.; Naveira Garabato, Alberto C.; Venables, Hugh J.; Inall, Mark E.

2017-05-01

The ocean-to-atmosphere heat budget of the West Antarctic Peninsula is controlled in part by the upward flux of heat from the warm Circumpolar Deep Water (CDW) layer that resides below 200 m to the Antarctic Surface Water (AASW), a water mass which varies strongly on a seasonal basis. Upwelling and mixing of CDW influence the formation of sea ice in the region and affect biological productivity and functioning of the ecosystem through their delivery of nutrients. In this study, 2.5-year time series of both Acoustic Doppler Current Profiler (ADCP) and conductivity-temperature-depth (CTD) data are used to quantify both the diapycnal diffusivity κ and the vertical heat flux Q at the interface between CDW and AASW. Over the period of the study, a mean upward heat flux of 1 W m-2 is estimated, with the largest heat fluxes occurring shortly after the loss of winter fast ice when the water column is first exposed to wind stress without being strongly stratified by salinity. Differences in mixing mechanisms between winter and summer seasons are investigated. Whilst tidally-driven mixing at the study site occurs year-round, but is likely to be relatively weak, a strong increase in counterclockwise-polarized near-inertial energy (and shear) is observed during the fast-ice-free season, suggesting that the direct impact of storms on the ocean surface is responsible for much of the observed mixing at the site. Given the rapid reduction in sea-ice duration in this region in the last 30 years, a shift towards an increasingly wind-dominated mixing regime may be taking place.

Mixed deep learning and natural language processing method for fake-food image recognition and standardization to help automated dietary assessment.

Science.gov (United States)

Mezgec, Simon; Eftimov, Tome; Bucher, Tamara; Koroušić Seljak, Barbara

2018-04-06

The present study tested the combination of an established and a validated food-choice research method (the 'fake food buffet') with a new food-matching technology to automate the data collection and analysis. The methodology combines fake-food image recognition using deep learning and food matching and standardization based on natural language processing. The former is specific because it uses a single deep learning network to perform both the segmentation and the classification at the pixel level of the image. To assess its performance, measures based on the standard pixel accuracy and Intersection over Union were applied. Food matching firstly describes each of the recognized food items in the image and then matches the food items with their compositional data, considering both their food names and their descriptors. The final accuracy of the deep learning model trained on fake-food images acquired by 124 study participants and providing fifty-five food classes was 92·18 %, while the food matching was performed with a classification accuracy of 93 %. The present findings are a step towards automating dietary assessment and food-choice research. The methodology outperforms other approaches in pixel accuracy, and since it is the first automatic solution for recognizing the images of fake foods, the results could be used as a baseline for possible future studies. As the approach enables a semi-automatic description of recognized food items (e.g. with respect to FoodEx2), these can be linked to any food composition database that applies the same classification and description system.

Enhanced removal of lead from contaminated soil by polyol-based deep eutectic solvents and saponin

Science.gov (United States)

Mukhopadhyay, Soumyadeep; Mukherjee, Sumona; Hayyan, Adeeb; Hayyan, Maan; Hashim, Mohd Ali; Sen Gupta, Bhaskar

2016-11-01

Deep eutectic solvents (DESs) are a class of green solvents analogous to ionic liquids, but less costly and easier to prepare. The objective of this study is to remove lead (Pb) from a contaminated soil by using polyol based DESs mixed with a natural surfactant saponin for the first time. The DESs used in this study were prepared by mixing a quaternary ammonium salt choline chloride with polyols e.g. glycerol and ethylene glycol. A natural surfactant saponin obtained from soapnut fruit pericarp, was mixed with DESs to boost their efficiency. The DESs on their own did not perform satisfactory due to higher pH; however, they improved the performance of soapnut by up to 100%. Pb removal from contaminated soil using mixture of 40% DES-Gly and 1% saponin and mixture of 10% DES-Gly and 2% saponin were above 72% XRD and SEM studies did not detect any major corrosion in the soil texture. The environmental friendliness of both DESs and saponin and their affordable costs merit thorough investigation of their potential as soil washing agents.

Searching for prostate cancer by fully automated magnetic resonance imaging classification: deep learning versus non-deep learning.

Science.gov (United States)

Wang, Xinggang; Yang, Wei; Weinreb, Jeffrey; Han, Juan; Li, Qiubai; Kong, Xiangchuang; Yan, Yongluan; Ke, Zan; Luo, Bo; Liu, Tao; Wang, Liang

2017-11-13

Prostate cancer (PCa) is a major cause of death since ancient time documented in Egyptian Ptolemaic mummy imaging. PCa detection is critical to personalized medicine and varies considerably under an MRI scan. 172 patients with 2,602 morphologic images (axial 2D T2-weighted imaging) of the prostate were obtained. A deep learning with deep convolutional neural network (DCNN) and a non-deep learning with SIFT image feature and bag-of-word (BoW), a representative method for image recognition and analysis, were used to distinguish pathologically confirmed PCa patients from prostate benign conditions (BCs) patients with prostatitis or prostate benign hyperplasia (BPH). In fully automated detection of PCa patients, deep learning had a statistically higher area under the receiver operating characteristics curve (AUC) than non-deep learning (P = 0.0007 deep learning method and 0.70 (95% CI 0.63-0.77) for non-deep learning method, respectively. Our results suggest that deep learning with DCNN is superior to non-deep learning with SIFT image feature and BoW model for fully automated PCa patients differentiation from prostate BCs patients. Our deep learning method is extensible to image modalities such as MR imaging, CT and PET of other organs.

MIX-MEDIA NARRATIVES WORKSHOP: MULTIDISCIPLINARY TEAMS’ PRODUCTION

Directory of Open Access Journals (Sweden)

Sonia Liliana da Silva Vieira

2017-11-01

Full Text Available The development of creation and content production teams, regarding design and digital publishing areas, their processes and transdisciplinary knowledge, are making deep changes in media production structures, and in its own professional sustainability model. The Mix-media Narratives Workshop that took place at Universidade da Beira Interior (Portugal intended to simulate the reality of production structures and the recent integrated digital writing paradigm, as well as to evaluate the several multi and transdisciplinary challenges that may be present in the creation of digital media contents. Design, journalism and film production students were not only confronted with real editorial development situations, but also with professional feedback, which was possible due to a partnership with the newspaper Expresso.pt. A fellow editor from this newspaper kept track of the sessions, with the help and presence of professors from many of the courses involved. Data gathered from observation notes, pictures and video during laboratorial work meetings, was compiled and crossed with research results, analyses and comments made by the participant professors and Expresso. pt editor’s observations. Our goal was to analyze in which way future workers will deal with the challenging task of collaborating with multidisciplinary teams and produce mix-media content with real publishing purpose.

Deep learning for computational chemistry

Energy Technology Data Exchange (ETDEWEB)

Goh, Garrett B. [Advanced Computing, Mathematics, and Data Division, Pacific Northwest National Laboratory, 902 Battelle Blvd Richland Washington 99354; Hodas, Nathan O. [Advanced Computing, Mathematics, and Data Division, Pacific Northwest National Laboratory, 902 Battelle Blvd Richland Washington 99354; Vishnu, Abhinav [Advanced Computing, Mathematics, and Data Division, Pacific Northwest National Laboratory, 902 Battelle Blvd Richland Washington 99354

2017-03-08

The rise and fall of artificial neural networks is well documented in the scientific literature of both the fields of computer science and computational chemistry. Yet almost two decades later, we are now seeing a resurgence of interest in deep learning, a machine learning algorithm based on “deep” neural networks. Within the last few years, we have seen the transformative impact of deep learning the computer science domain, notably in speech recognition and computer vision, to the extent that the majority of practitioners in those field are now regularly eschewing prior established models in favor of deep learning models. In this review, we provide an introductory overview into the theory of deep neural networks and their unique properties as compared to traditional machine learning algorithms used in cheminformatics. By providing an overview of the variety of emerging applications of deep neural networks, we highlight its ubiquity and broad applicability to a wide range of challenges in the field, including QSAR, virtual screening, protein structure modeling, QM calculations, materials synthesis and property prediction. In reviewing the performance of deep neural networks, we observed a consistent outperformance against non neural networks state-of-the-art models across disparate research topics, and deep neural network based models often exceeded the “glass ceiling” expectations of their respective tasks. Coupled with the maturity of GPU-accelerated computing for training deep neural networks and the exponential growth of chemical data on which to train these networks on, we anticipate that deep learning algorithms will be a useful tool and may grow into a pivotal role for various challenges in the computational chemistry field.

Deep learning for computational chemistry.

Science.gov (United States)

Goh, Garrett B; Hodas, Nathan O; Vishnu, Abhinav

2017-06-15

The rise and fall of artificial neural networks is well documented in the scientific literature of both computer science and computational chemistry. Yet almost two decades later, we are now seeing a resurgence of interest in deep learning, a machine learning algorithm based on multilayer neural networks. Within the last few years, we have seen the transformative impact of deep learning in many domains, particularly in speech recognition and computer vision, to the extent that the majority of expert practitioners in those field are now regularly eschewing prior established models in favor of deep learning models. In this review, we provide an introductory overview into the theory of deep neural networks and their unique properties that distinguish them from traditional machine learning algorithms used in cheminformatics. By providing an overview of the variety of emerging applications of deep neural networks, we highlight its ubiquity and broad applicability to a wide range of challenges in the field, including quantitative structure activity relationship, virtual screening, protein structure prediction, quantum chemistry, materials design, and property prediction. In reviewing the performance of deep neural networks, we observed a consistent outperformance against non-neural networks state-of-the-art models across disparate research topics, and deep neural network-based models often exceeded the "glass ceiling" expectations of their respective tasks. Coupled with the maturity of GPU-accelerated computing for training deep neural networks and the exponential growth of chemical data on which to train these networks on, we anticipate that deep learning algorithms will be a valuable tool for computational chemistry. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

What Really is Deep Learning Doing?

OpenAIRE

Xiong, Chuyu

2017-01-01

Deep learning has achieved a great success in many areas, from computer vision to natural language processing, to game playing, and much more. Yet, what deep learning is really doing is still an open question. There are a lot of works in this direction. For example, [5] tried to explain deep learning by group renormalization, and [6] tried to explain deep learning from the view of functional approximation. In order to address this very crucial question, here we see deep learning from perspect...

Taoism and Deep Ecology.

Science.gov (United States)

Sylvan, Richard; Bennett, David

1988-01-01

Contrasted are the philosophies of Deep Ecology and ancient Chinese. Discusses the cosmology, morality, lifestyle, views of power, politics, and environmental philosophies of each. Concludes that Deep Ecology could gain much from Taoism. (CW)

deepTools2: a next generation web server for deep-sequencing data analysis.

Science.gov (United States)

Ramírez, Fidel; Ryan, Devon P; Grüning, Björn; Bhardwaj, Vivek; Kilpert, Fabian; Richter, Andreas S; Heyne, Steffen; Dündar, Friederike; Manke, Thomas

2016-07-08

We present an update to our Galaxy-based web server for processing and visualizing deeply sequenced data. Its core tool set, deepTools, allows users to perform complete bioinformatic workflows ranging from quality controls and normalizations of aligned reads to integrative analyses, including clustering and visualization approaches. Since we first described our deepTools Galaxy server in 2014, we have implemented new solutions for many requests from the community and our users. Here, we introduce significant enhancements and new tools to further improve data visualization and interpretation. deepTools continue to be open to all users and freely available as a web service at deeptools.ie-freiburg.mpg.de The new deepTools2 suite can be easily deployed within any Galaxy framework via the toolshed repository, and we also provide source code for command line usage under Linux and Mac OS X. A public and documented API for access to deepTools functionality is also available. © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.

Into the deep: A coarse-grained carbonate turbidite thalweg generated by gigantic submarine chutes

Science.gov (United States)

Mulder, Thierry; Gillet, Hervé; Reijmer, John; Droxler, André; cavailhes, Thibault; Hanquiez, Vincent; Fauquembergue, Kelly; Bujan, Stéphane; Blanck, David; bashah, Sara; Guiastrennec, Léa; Fabregas, Natacha; Recouvreur, Audrey; Seibert, Chloé

2017-04-01

New high-resolution multibeam mapping, in the Southeastern Bahamas, images in exquisite details the southern part of Exuma Sound, and its unchartered transition area to the deep abyssal plain of the Western North Atlantic bounded by the Bahama Escarpment (BE) between San Salvador Island and Samana Cay, referred here to the San Salvador abyssal plain. The transition area is locally referred to as Crooked Island Passage, loosely delineated by Crooked, Long, and Conception Islands, Rum and Samana Cays. Surprisingly in such a pure carbonate landscape, the newly established map reveals the detailed and complex morphology of a giant valley formed by numerous gravity flows originated in Exuma Sound itself, in addition to many secondary slope gullies and smaller tributaries draining the surrounding upper slopes. The valley referred here as the Exuma canyon system starts with a perched valley with low sinuosity, characterized by several flow restrictions and knickpoints initiated by the presence of drowned isolated platforms and merging tributaries. The valley abruptly transforms itself into a deep incised canyon, rivaling the depth of the Colorado Grand Canyon, through two major knickpoints with outsized chutes exceeding several hundred of meters in height, a total of 1600-1800 m. The sudden transformation of the wide valley into a deep narrow canyon, occurring when the flows incised deep into an underlying lower Cretaceous drowned carbonate platform, generates a huge hydraulic jump and creates an enormous plunge pool and related deposits with mechanisms comparable to the ones operating along giant subaerial waterfalls. The high kinetic flow energy, constrained by this narrow and deeply incised canyon, formed, when it is released at its mouth in the abyssal plain, a wide deep-sea channel with well-developed levees and fan, made of coarse-grained carbonate defined layers separated by fine carbonate sediments mixed with fine siliciclastics transported along the BE by the

WHATS-3: An improved flow-through multi-bottle fluid sampler for deep-sea geofluid research

Science.gov (United States)

Miyazaki, Junichi; Makabe, Akiko; Matsui, Yohei; Ebina, Naoya; Tsutsumi, Saki; Ishibashi, Jun-ichiro; Chen, Chong; Kaneko, Sho; Takai, Ken; Kawagucci, Shinsuke

2017-06-01

Deep-sea geofluid systems, such as hydrothermal vents and cold seeps, are key to understanding subseafloor environments of Earth. Fluid chemistry, especially, provides crucial information towards elucidating the physical, chemical and biological processes that occur in these ecosystems. To accurately assess fluid and gas properties of deep-sea geofluids, well-designed pressure-tight fluid samplers are indispensable and as such they are important assets of deep-sea geofluid research. Here, the development of a new flow-through, pressure-tight fluid sampler capable of four independent sampling events (two subsamples for liquid and gas analyses from each) is reported. This new sampler, named WHATS-3, is a new addition to the WHATS-series samplers and a major upgrade from the previous WHATS-2 sampler with improvements in sample number, valve operational time, physical robustness, and ease of maintenance. Routine laboratory-based pressure tests proved that it is suitable for operation up to 35 MPa pressure. Successful field tests of the new sampler were also carried out in five hydrothermal fields, two in Indian Ocean and three in Okinawa Trough (max. depth 3,300 m). Relations of Mg and major ion species demonstrated bimodal mixing trends between a hydrothermal fluid and seawater, confirming the high-quality of fluids sampled. The newly developed WHATS-3 sampler is well-balanced in sampling capability, field usability, and maintenance feasibility, and can serve as one of the best geofluid samplers available at present to conduct efficient research of deep-sea geofluid systems.

Is Multitask Deep Learning Practical for Pharma?

Science.gov (United States)

Ramsundar, Bharath; Liu, Bowen; Wu, Zhenqin; Verras, Andreas; Tudor, Matthew; Sheridan, Robert P; Pande, Vijay

2017-08-28

Multitask deep learning has emerged as a powerful tool for computational drug discovery. However, despite a number of preliminary studies, multitask deep networks have yet to be widely deployed in the pharmaceutical and biotech industries. This lack of acceptance stems from both software difficulties and lack of understanding of the robustness of multitask deep networks. Our work aims to resolve both of these barriers to adoption. We introduce a high-quality open-source implementation of multitask deep networks as part of the DeepChem open-source platform. Our implementation enables simple python scripts to construct, fit, and evaluate sophisticated deep models. We use our implementation to analyze the performance of multitask deep networks and related deep models on four collections of pharmaceutical data (three of which have not previously been analyzed in the literature). We split these data sets into train/valid/test using time and neighbor splits to test multitask deep learning performance under challenging conditions. Our results demonstrate that multitask deep networks are surprisingly robust and can offer strong improvement over random forests. Our analysis and open-source implementation in DeepChem provide an argument that multitask deep networks are ready for widespread use in commercial drug discovery.

Turbidite Systems in Brazil: From Outcrops to Deep Waters

Science.gov (United States)

´Avila, R. S. F.; Arienti, L. M.; Vesely, F. F.; Santos, S. F.; Voelcker, H. E.

2012-04-01

Reliable depositional models depend on careful observation of rocks, to allow the correct description and interpretation of facies and facies associations and their formative processes. They are of paramount importance to characterize deep water depositional systems, which still are the most important siliciclastic reservoirs for the oil industry. Turbidite sandstone reservoirs are responsible for almost 80% of petroleum produced from Brazilian Basins. A comprehensive characterization of these systems, depicting the main differences in terms of their geometries and facies will be presented. In Brazilian basins most of the turbidites were originated from extremely catastrophic flows, essentially linked to fluvio-deltaic influx that generates very dense hyperpycnal flows. Based on outcrop and subsurface data, two main zones with characteristic geometries and facies associations are commonly identified in turbidite systems: the transference zone and the depositional zone. Erosion and bypass dominate in the transference zone, which frequently occur as submarine canyons and channels. Turbidite channels can contain residual conglomeratic facies and coarser sandstone facies. The depositional area comprises lobes that constitute a major exploratory target because of their greater lateral continuity and the concentration of clean reservoirs. Turbidite lobes can be tabular or lenticular deposits associated with channelized bodies. Taking into account outcrop and subsurface data we can distinguish five main turbidite systems: foredeep turbidite systems, prodelta turbidite systems, mixed turbidite systems, meandering channels turbidite systems and channel-levee turbidite systems. In the Brazilian margin, deep water turbidites and other gravity-flow deposits are commonly associated with bottom current deposits, largely in Tertiary strata. Such bottom current deposits, often called contourites, are also important petroleum reservoirs, commonly mistaken as turbidites. Integration

DeepGait: A Learning Deep Convolutional Representation for View-Invariant Gait Recognition Using Joint Bayesian

Directory of Open Access Journals (Sweden)

Chao Li

2017-02-01

Full Text Available Human gait, as a soft biometric, helps to recognize people through their walking. To further improve the recognition performance, we propose a novel video sensor-based gait representation, DeepGait, using deep convolutional features and introduce Joint Bayesian to model view variance. DeepGait is generated by using a pre-trained “very deep” network “D-Net” (VGG-D without any fine-tuning. For non-view setting, DeepGait outperforms hand-crafted representations (e.g., Gait Energy Image, Frequency-Domain Feature and Gait Flow Image, etc.. Furthermore, for cross-view setting, 256-dimensional DeepGait after PCA significantly outperforms the state-of-the-art methods on the OU-ISR large population (OULP dataset. The OULP dataset, which includes 4007 subjects, makes our result reliable in a statistically reliable way.

Immunosuppressive compounds from a deep water marine sponge, Agelas flabelliformis.

Science.gov (United States)

Gunasekera, S P; Cranick, S; Longley, R E

1989-01-01

Two immunosuppressive compounds, 4 alpha-methyl-5 alpha-cholest-8-en-3 beta-ol and 4,5-dibromo-2-pyrrolic acid were isolated from a deep water marine sponge, Agelas flabelliformis. Their structures were determined by comparison of their spectral data with those of samples isolated from other organisms. Both compounds were highly active in suppression of the response of murine splenocytes in the two-way mixed lymphocyte reaction (MLR) with little to no demonstrable cytotoxicity at lower doses. In addition, 4,5-dibromo-2-pyrrolic acid suppressed the proliferative response of splenocytes to suboptimal concentrations of the mitogen, concanavalin A (Con A). These results describe for the first time compounds isolated from the marine sponge A. flabelliformis that possess potent in vitro immunosuppressive activity.

Invited talk: Deep Learning Meets Physics

CERN Multimedia

CERN. Geneva

2018-01-01

Deep Learning has emerged as one of the most successful fields of machine learning and artificial intelligence with overwhelming success in industrial speech, text and vision benchmarks. Consequently it evolved into the central field of research for IT giants like Google, facebook, Microsoft, Baidu, and Amazon. Deep Learning is founded on novel neural network techniques, the recent availability of very fast computers, and massive data sets. In its core, Deep Learning discovers multiple levels of abstract representations of the input. The main obstacle to learning deep neural networks is the vanishing gradient problem. The vanishing gradient impedes credit assignment to the first layers of a deep network or to early elements of a sequence, therefore limits model selection. Major advances in Deep Learning can be related to avoiding the vanishing gradient like stacking, ReLUs, residual networks, highway networks, and LSTM. For Deep Learning, we suggested self-normalizing neural networks (SNNs) which automatica...

Deep geothermics

International Nuclear Information System (INIS)

Anon.

1995-01-01

The hot-dry-rocks located at 3-4 km of depth correspond to low permeable rocks carrying a large amount of heat. The extraction of this heat usually requires artificial hydraulic fracturing of the rock to increase its permeability before water injection. Hot-dry-rocks geothermics or deep geothermics is not today a commercial channel but only a scientific and technological research field. The Soultz-sous-Forets site (Northern Alsace, France) is characterized by a 6 degrees per meter geothermal gradient and is used as a natural laboratory for deep geothermal and geological studies in the framework of a European research program. Two boreholes have been drilled up to 3600 m of depth in the highly-fractured granite massif beneath the site. The aim is to create a deep heat exchanger using only the natural fracturing for water transfer. A consortium of german, french and italian industrial companies (Pfalzwerke, Badenwerk, EdF and Enel) has been created for a more active participation to the pilot phase. (J.S.). 1 fig., 2 photos

Stable architectures for deep neural networks

Science.gov (United States)

Haber, Eldad; Ruthotto, Lars

2018-01-01

Deep neural networks have become invaluable tools for supervised machine learning, e.g. classification of text or images. While often offering superior results over traditional techniques and successfully expressing complicated patterns in data, deep architectures are known to be challenging to design and train such that they generalize well to new data. Critical issues with deep architectures are numerical instabilities in derivative-based learning algorithms commonly called exploding or vanishing gradients. In this paper, we propose new forward propagation techniques inspired by systems of ordinary differential equations (ODE) that overcome this challenge and lead to well-posed learning problems for arbitrarily deep networks. The backbone of our approach is our interpretation of deep learning as a parameter estimation problem of nonlinear dynamical systems. Given this formulation, we analyze stability and well-posedness of deep learning and use this new understanding to develop new network architectures. We relate the exploding and vanishing gradient phenomenon to the stability of the discrete ODE and present several strategies for stabilizing deep learning for very deep networks. While our new architectures restrict the solution space, several numerical experiments show their competitiveness with state-of-the-art networks.

Mixing ratio sensor for alcohol mixed fuel

Energy Technology Data Exchange (ETDEWEB)

Miyata, Shigeru; Matsubara, Yoshihiro

1987-08-24

In order to improve the combustion efficiency of an internal combustion engine using gasoline-alcohol mixed fuel and to reduce harmful substance in its exhaust gas, it is necessary to control strictly the air-fuel ratio to be supplied and the ignition timing. In order to detect the mixing ratio of the mixed fuel, a mixing ratio sensor has so far been proposed to detect the above mixing ratio by casting a ray of light to the mixed fuel and utilizing a change of critical angle associated with the change of the composition of the fluid of the mixed fuel. However, because of the arrangement of its transparent substance in the fuel passage with the sealing material in between, this sensor invited the leakage of the fluid due to deterioration of the sealing material, etc. and its cost became high because of too many parts to be assembled. In view of the above, in order to reduce the number of parts, to lower the cost of parts and the assembling cost and to secure no fluid leakage from the fuel passage, this invention formed the above fuel passage and the above transparent substance both concerning the above mixing ratio sensor in an integrated manner using light transmitting resin. (3 figs)

Exploring the isopycnal mixing and helium-heat paradoxes in a suite of Earth system models

Science.gov (United States)

Gnanadesikan, A.; Pradal, M.-A.; Abernathey, R.

2015-07-01

This paper uses a suite of Earth system models which simulate the distribution of He isotopes and radiocarbon to examine two paradoxes in Earth science, each of which results from an inconsistency between theoretically motivated global energy balances and direct observations. The helium-heat paradox refers to the fact that helium emissions to the deep ocean are far lower than would be expected given the rate of geothermal heating, since both are thought to be the result of radioactive decay in Earth's interior. The isopycnal mixing paradox comes from the fact that many theoretical parameterizations of the isopycnal mixing coefficient ARedi that link it to baroclinic instability project it to be small (of order a few hundred m2 s-1) in the ocean interior away from boundary currents. However, direct observations using tracers and floats (largely in the upper ocean) suggest that values of this coefficient are an order of magnitude higher. Helium isotopes equilibrate rapidly with the atmosphere and thus exhibit large gradients along isopycnals while radiocarbon equilibrates slowly and thus exhibits smaller gradients along isopycnals. Thus it might be thought that resolving the isopycnal mixing paradox in favor of the higher observational estimates of ARedi might also solve the helium paradox, by increasing the transport of mantle helium to the surface more than it would radiocarbon. In this paper we show that this is not the case. In a suite of models with different spatially constant and spatially varying values of ARedi the distribution of radiocarbon and helium isotopes is sensitive to the value of ARedi. However, away from strong helium sources in the southeastern Pacific, the relationship between the two is not sensitive, indicating that large-scale advection is the limiting process for removing helium and radiocarbon from the deep ocean. The helium isotopes, in turn, suggest a higher value of ARedi below the thermocline than is seen in theoretical

Modeling the dispersal of Levantine Intermediate Water and its role in Mediterranean deep water formation

Science.gov (United States)

Wu, Peili; Haines, Keith

1996-03-01

This paper demonstrates the importance of Levantine Intermediate Water (LIW) in the deep water formation process in the Mediterranean using the modular ocean general circulation model at 0.25° resolution, 19 vertical levels, over the entire Mediterranean with an open Gibraltar strait. LIW formation is strongly prescribed in the Rhodes Gyre region by Haney [1971] relaxation, while in other regions, surface salinity relaxation is much reduced by applying the `mixed' thermohaline surface boundary conditions. Isopycnal diagnostics are used to trace water mass movements, and volume fluxes are monitored at straits. Low viscosity and diffusion are used to permit baroclinic eddies to play a role in water mass dispersal. The overall water budget is measured by an average flux at Gibraltar of 0.8 Sv, of which 0.7 Sv is exchanged with the eastern basin at Sicily. LIW (density around 28.95) spreads rapidly after formation throughout the entire Levantine due to baroclinic eddies. Toward the west, LIW accumulates in the northern and central Ionian, with some entering the Adriatic through Otranto and some mixing southward in eddies and exiting to the western Mediterranean through Sicily. LIW is converted to deep water in the south Adriatic at an average rate of 0.4 Sv. Water exchange through the Otranto strait appears to be buoyancy driven, with a strong bias to the end of winter (March-April), while at Sicily the exchange has a strong symmetric seasonal cycle, with maximum transport of 1.1 Sv in December indicating the effects of wind driving. LIW pathways in the west are complex and variable. In the Tyrrhenian, intermediate water becomes uniform on isopycnal surfaces due to eddy stirring. West of Sardinia, two LIW boundary currents are formed in the Balearic basin; one flows northward up the west coast of Sardinia and Corsica, and one westward along the northern African coast. The northward current is consistent with observations, while the westward current is intermittent for

Sustainable Poly(Ionic Liquids) for CO2 Capture Based on Deep Eutectic Monomers

KAUST Repository

Isik, Mehmet; Zulfiqar, Sonia; Edhaim, Fatimah; Ruiperez, Fernando; Rothenberger, Alexander; Mecerreyes, David

2016-01-01

The design of high performance solid sorbent materials for CO2 capture is a technology which has been employed to mitigate global warming. However, the covalent incorporation of functionalities into polymeric supports usually involves multistep energy-intensive chemical processes. This fact makes the net CO2 balance of the materials negative even though they possess good properties as CO2 sorbents. Here we show a new family of polymers which are based on amines, amidoximes, and natural carboxylic acids and can be obtained using sustainable low energy processes. Thus, deep eutectic monomers based on natural carboxylic acids, amidoximes, and amines have been prepared by just mixing with cholinium type methacrylic ammonium monomer. The formation of deep eutectic monomers was confirmed by differential scanning calorimetry measurements. In all cases, the monomers displayed glass transition temperatures well below room temperature. Computational studies revealed that the formation of eutectic complexes lengthens the distance between the cation and the anion causing charge delocalization. The liquid nature of the resulting deep eutectic monomers (DEMs) made it possible to conduct a fast photopolymerization process to obtain the corresponding poly(ionic liquids). Materials were characterized by means of nuclear magnetic resonance, differential scanning calorimetry, thermogravimetric analysis, and X-ray diffraction to evaluate the properties of the polymers. The polymers were then used as solid sorbents for CO2 capture. It has been shown that the polymers prepared with citric acid displayed better performance both experimentally and computationally. The current endeavor showed that sustainable poly(ionic liquids) based on deep eutectic monomers can be easily prepared to produce low-energy-cost alternatives to the materials currently being researched for CO2 capture. © 2016 American Chemical Society.

Sustainable Poly(Ionic Liquids) for CO2 Capture Based on Deep Eutectic Monomers

KAUST Repository

Isik, Mehmet

2016-10-05

The design of high performance solid sorbent materials for CO2 capture is a technology which has been employed to mitigate global warming. However, the covalent incorporation of functionalities into polymeric supports usually involves multistep energy-intensive chemical processes. This fact makes the net CO2 balance of the materials negative even though they possess good properties as CO2 sorbents. Here we show a new family of polymers which are based on amines, amidoximes, and natural carboxylic acids and can be obtained using sustainable low energy processes. Thus, deep eutectic monomers based on natural carboxylic acids, amidoximes, and amines have been prepared by just mixing with cholinium type methacrylic ammonium monomer. The formation of deep eutectic monomers was confirmed by differential scanning calorimetry measurements. In all cases, the monomers displayed glass transition temperatures well below room temperature. Computational studies revealed that the formation of eutectic complexes lengthens the distance between the cation and the anion causing charge delocalization. The liquid nature of the resulting deep eutectic monomers (DEMs) made it possible to conduct a fast photopolymerization process to obtain the corresponding poly(ionic liquids). Materials were characterized by means of nuclear magnetic resonance, differential scanning calorimetry, thermogravimetric analysis, and X-ray diffraction to evaluate the properties of the polymers. The polymers were then used as solid sorbents for CO2 capture. It has been shown that the polymers prepared with citric acid displayed better performance both experimentally and computationally. The current endeavor showed that sustainable poly(ionic liquids) based on deep eutectic monomers can be easily prepared to produce low-energy-cost alternatives to the materials currently being researched for CO2 capture. © 2016 American Chemical Society.

Learning speaker-specific characteristics with a deep neural architecture.

Science.gov (United States)

Chen, Ke; Salman, Ahmad

2011-11-01

Speech signals convey various yet mixed information ranging from linguistic to speaker-specific information. However, most of acoustic representations characterize all different kinds of information as whole, which could hinder either a speech or a speaker recognition (SR) system from producing a better performance. In this paper, we propose a novel deep neural architecture (DNA) especially for learning speaker-specific characteristics from mel-frequency cepstral coefficients, an acoustic representation commonly used in both speech recognition and SR, which results in a speaker-specific overcomplete representation. In order to learn intrinsic speaker-specific characteristics, we come up with an objective function consisting of contrastive losses in terms of speaker similarity/dissimilarity and data reconstruction losses used as regularization to normalize the interference of non-speaker-related information. Moreover, we employ a hybrid learning strategy for learning parameters of the deep neural networks: i.e., local yet greedy layerwise unsupervised pretraining for initialization and global supervised learning for the ultimate discriminative goal. With four Linguistic Data Consortium (LDC) benchmarks and two non-English corpora, we demonstrate that our overcomplete representation is robust in characterizing various speakers, no matter whether their utterances have been used in training our DNA, and highly insensitive to text and languages spoken. Extensive comparative studies suggest that our approach yields favorite results in speaker verification and segmentation. Finally, we discuss several issues concerning our proposed approach.

Dispersion of deep-sea hydrothermal vent effluents and larvae by submesoscale and tidal currents

Science.gov (United States)

Vic, Clément; Gula, Jonathan; Roullet, Guillaume; Pradillon, Florence

2018-03-01

Deep-sea hydrothermal vents provide sources of geochemical materials that impact the global ocean heat and chemical budgets, and support complex biological communities. Vent effluents and larvae are dispersed and transported long distances by deep ocean currents, but these currents are largely undersampled and little is known about their variability. Submesoscale (0.1-10 km) currents are known to play an important role for the dispersion of biogeochemical materials in the ocean surface layer, but their impact for the dispersion in the deep ocean is unknown. Here, we use a series of nested regional oceanic numerical simulations with increasing resolution (from δx = 6 km to δx = 0.75 km) to investigate the structure and variability of highly-resolved deep currents over the Mid-Atlantic Ridge (MAR) and their role on the dispersion of the Lucky Strike hydrothermal vent effluents and larvae. We shed light on a submesoscale regime of oceanic turbulence over the MAR at 1500 m depth, contrasting with open-ocean - i.e., far from topographic features - regimes of turbulence, dominated by mesoscales. Impacts of submesoscale and tidal currents on larval dispersion and connectivity among vent populations are investigated by releasing neutrally buoyant Lagrangian particles at the Lucky Strike hydrothermal vent. Although the absolute dispersion is overall not sensitive to the model resolution, submesoscale currents are found to significantly increase both the horizontal and vertical relative dispersion of particles at O(1-10) km and O(1-10) days, resulting in an increased mixing of the cloud of particles. A fraction of particles are trapped in submesoscale coherent vortices, which enable transport over long time and distances. Tidal currents and internal tides do not significantly impact the horizontal relative dispersion. However, they roughly double the vertical dispersion. Specifically, particles undergo strong tidally-induced mixing close to rough topographic features

A simple model for the dispersion of radioactive wastes dumped on the deep-sea bed

International Nuclear Information System (INIS)

Shepherd, J.G.

1976-01-01

A simple model has been developed for the dispersion of radioactive materials in a closed and finite ocean. It allows for the simultaneous action of both diffusion and horizontal (but not vertical) advection, and thus avoids the major limitations of previous models. It is sufficiently versatile to handle non-Fickian diffusion and radioactive decay, but requires numerical integration using some semi-empirical form for the Green function of diffusion from a point source. The model has been used to estimate equilibrium concentrations of radioactive materials in sea water arising from the continuous release of material from a dump on the bottom of the deep ocean, using parameters appropriate for the North Atlantic. It is found that except under rather extreme conditions the surface concentrations do not exceed the long-term average value which would be established in a perfectly mixed ocean. The concentrations are also rather insensitive to the values of the diffusion and advection parameters used, except for that for vertical diffusion, but depend strongly on the overall removal rate of material from the ocean, including processes other than radioactive decay. It is suggested that safety assessments of deep-sea dumping should utilize estimates of the environmental capacities of the oceans based on the long-term 'well-mixed' average concentrations (which are very easily calculated) using a safety factor of no more than ten to allow for the possible effects of pluming and upwelling. (author)

Deep Seawater Intrusion Enhanced by Geothermal Through Deep Faults in Xinzhou Geothermal Field in Guangdong, China

Science.gov (United States)

Lu, G.; Ou, H.; Hu, B. X.; Wang, X.

2017-12-01

This study investigates abnormal sea water intrusion from deep depth, riding an inland-ward deep groundwater flow, which is enhanced by deep faults and geothermal processes. The study site Xinzhou geothermal field is 20 km from the coast line. It is in southern China's Guangdong coast, a part of China's long coastal geothermal belt. The geothermal water is salty, having fueled an speculation that it was ancient sea water retained. However, the perpetual "pumping" of the self-flowing outflow of geothermal waters might alter the deep underground flow to favor large-scale or long distant sea water intrusion. We studied geochemical characteristics of the geothermal water and found it as a mixture of the sea water with rain water or pore water, with no indication of dilution involved. And we conducted numerical studies of the buoyancy-driven geothermal flow in the deep ground and find that deep down in thousand meters there is favorable hydraulic gradient favoring inland-ward groundwater flow, allowing seawater intrude inland for an unusually long tens of kilometers in a granitic groundwater flow system. This work formed the first in understanding geo-environment for deep ground water flow.

14C age reassessment of groundwater from the discharge zone due to cross-flow mixing in the deep confined aquifer

Science.gov (United States)

Mao, Xumei; Wang, Hua; Feng, Liang

2018-05-01

In a groundwater flow system, the age of groundwater should gradually increase from the recharge zone to the discharge zone within the same streamline. However, it is occasionally observed that the groundwater age becomes younger in the discharge zone in the piedmont alluvial plain, and the oldest age often appears in the middle of the plain. A new set of groundwater chemistry and isotopes was employed to reassess the groundwater 14C ages from the discharge zone in the North China Plain (NCP). Carbonate precipitation, organic matter oxidation and cross-flow mixing in the groundwater from the recharge zone to the discharge zone are recognized according to the corresponding changes of HCO3- (or DIC) and δ13C in the same streamline of the third aquifer of the NCP. The effects of carbonate precipitation and organic matter oxidation are calibrated with a 13C mixing model and DIC correction, but these corrected 14C ages seem unreasonable because they grow younger from the middle plain to the discharge zone in the NCP. The relationship of Cl- content and the recharge distance is used to estimate the expected Cl- content in the discharge zone, and ln(a14C)/Cl is proposed to correct the a14C in groundwater for the effect of cross-flow mixing. The 14C ages were reassessed with the corrected a14C due to the cross-flow mixing varying from 1.25 to 30.58 ka, and the groundwater becomes older gradually from the recharge zone to the discharge zone. The results suggest that the reassessed 14C ages are more reasonable for the groundwater from the discharge zone due to cross-flow mixing.

Deep Learning in Neuroradiology.

Science.gov (United States)

Zaharchuk, G; Gong, E; Wintermark, M; Rubin, D; Langlotz, C P

2018-02-01

Deep learning is a form of machine learning using a convolutional neural network architecture that shows tremendous promise for imaging applications. It is increasingly being adapted from its original demonstration in computer vision applications to medical imaging. Because of the high volume and wealth of multimodal imaging information acquired in typical studies, neuroradiology is poised to be an early adopter of deep learning. Compelling deep learning research applications have been demonstrated, and their use is likely to grow rapidly. This review article describes the reasons, outlines the basic methods used to train and test deep learning models, and presents a brief overview of current and potential clinical applications with an emphasis on how they are likely to change future neuroradiology practice. Facility with these methods among neuroimaging researchers and clinicians will be important to channel and harness the vast potential of this new method. © 2018 by American Journal of Neuroradiology.

Energy droughts in a 100% renewable electricity mix

Science.gov (United States)

Raynaud, Damien; Hingray, Benoît; François, Baptiste; Creutin, Jean-Dominique

2017-04-01

During the 21st conference of parties, 175 countries agreed on limiting the temperature increase due to global warming to 2°C above preindustrial levels. Such an ambitious goal necessitates a deep transformation of our society in order to reduce greenhouse gas (GHG) emissions. Europe has started its energy transition years ago by, for instance, increasing the share of renewables in the European electricity generation and should continue in this direction. Variable renewable energies (VRE) and especially those driven by weather conditions (namely wind, solar and hydro power from river flow), are expected to play a key role in achieving the GHG reduction target. However, these renewables are often criticized for their intermittency and for the resulting difficult integration in the power supply system, especially for large shares of VRE in the energy mix. Assessing the feasibility of electricity generation using large contributions of VRE requires a deep understanding and characterization of the VRE spatiotemporal variations. In the last decade, many studies have focused on the short-term intermittency of VRE generation, but the persistency and the characteristics of periods of low/high electricity generation have been rarely studied. Yet, these particular situations require some demanding adaptations of the power supply system in term of back-up sources or production curtailment respectively. This study focuses on what we call "energy droughts" which, by analogy with hydrological or meteorological droughts, are defined as periods of very low energy production. We consider in turn "energy droughts" associated to wind, solar and hydro power (run-of-the-river). Their characteristics are estimated for 12 European regions being subjected to different climatic regimes. For each region and energy source, "droughts" are evaluated from a 30-yr time series of power generation (1983-2012). These series are simulated by using a suite of weather-to-energy conversion models with

The Oceanic Flux Program: A three decade time-series of particle flux in the deep Sargasso Sea

Science.gov (United States)

Weber, J. C.; Conte, M. H.

2010-12-01

The Oceanic Flux Program (OFP), 75 km SE of Bermuda, is the longest running time-series of its kind. Initiated in 1978, the OFP has produced an unsurpassed, nearly continuous record of temporal variability in deep ocean fluxes, with a >90% temporal coverage at 3200m depth. The OFP, in conjunction with the co-located Bermuda-Atlantic Time Series (BATS) and the Bermuda Testbed Mooring (BTM) time-series, has provided key observations enabling detailed assessment of how seasonal and non-seasonal variability in the deep ocean is linked with the overlying physical and biogeochemical environment. This talk will focus on the short-term flux variability that overlies the seasonal flux pattern in the Sargasso Sea, emphasizing episodic extreme flux events. Extreme flux events are responsible for much of the year-to-year variability in mean annual flux and are most often observed during early winter and late spring when surface stratification is weak or transient. In addition to biological phenomena (e.g. salp blooms), passage of productive meso-scale features such as eddies, which alter surface water mixing characteristics and surface export fluxes, may initiate some extreme flux events. Yet other productive eddies show a minimal influence on the deep flux, underscoring the importance of upper ocean ecosystem structure and midwater processes on the coupling between the surface ocean environment and deep fluxes. Using key organic and inorganic tracers, causative processes that influence deep flux generation and the strength of the coupling with the surface ocean environment can be identified.

Suspended sediment dynamics in a large-scale turbidity current: Direct measurements from the deep-water Congo Canyon

Science.gov (United States)

Simmons, S.; Azpiroz, M.; Cartigny, M.; Clare, M. A.; Parsons, D. R.; Sumner, E.; Talling, P. J.

2016-12-01

Turbidity currents that transport sediment to the deep ocean deposit a greater volume of sediment than any other process on Earth. To date, only a handful of studies have directly measured turbidity currents, with flow durations ranging from a few minutes to a few hours. Our understanding of turbidity current dynamics is therefore largely derived from scaled laboratory experiments and numerical modelling. Recent years have seen the first field-scale measurements of depth-resolved velocity profiles, but sediment concentration (a key parameter for turbidity currents) remains elusive. Here, we present high resolution measurements of deep-water turbidity currents from the Congo Canyon; one of the world's largest submarine canyons. Direct measurements using acoustic Doppler current profilers (ADCPs) show that flows can last for many days, rather than hours as seen elsewhere, and provide the first quantification of concentration and grain size within deep-water turbidity currents.Velocity and backscatter were measured at 5 second intervals by an ADCP suspended 80 m above the canyon floor, at 2000 m water depth. A novel inversion method using multiple ADCP frequencies enabled quantification of sediment concentration and grain size within the flows. We identify high concentrations of coarse sediment within a thin frontal cell, which outruns a thicker, trailing body. Thus, the flows grow in length while propagating down-canyon. This is distinct from classical models and other field-scale measurements of turbidity currents. The slow-moving body is dominated by suspended fine-grained sediment. The body mixes with the surrounding fluid leaving diffuse clouds of sediment that persist for days after initial entrainment. Ambient tidal flow also controls the mixing within the body and the surrounding fluid. Our results provide a new quantification of suspended sediment within flows and the interaction with the surrounding fluid.

Temperature impacts on deep-sea biodiversity.

Science.gov (United States)

Yasuhara, Moriaki; Danovaro, Roberto

2016-05-01

Temperature is considered to be a fundamental factor controlling biodiversity in marine ecosystems, but precisely what role temperature plays in modulating diversity is still not clear. The deep ocean, lacking light and in situ photosynthetic primary production, is an ideal model system to test the effects of temperature changes on biodiversity. Here we synthesize current knowledge on temperature-diversity relationships in the deep sea. Our results from both present and past deep-sea assemblages suggest that, when a wide range of deep-sea bottom-water temperatures is considered, a unimodal relationship exists between temperature and diversity (that may be right skewed). It is possible that temperature is important only when at relatively high and low levels but does not play a major role in the intermediate temperature range. Possible mechanisms explaining the temperature-biodiversity relationship include the physiological-tolerance hypothesis, the metabolic hypothesis, island biogeography theory, or some combination of these. The possible unimodal relationship discussed here may allow us to identify tipping points at which on-going global change and deep-water warming may increase or decrease deep-sea biodiversity. Predicted changes in deep-sea temperatures due to human-induced climate change may have more adverse consequences than expected considering the sensitivity of deep-sea ecosystems to temperature changes. © 2014 Cambridge Philosophical Society.

Abundance of gap junctions at glutamatergic mixed synapses in adult Mosquitofish spinal cord neurons

Directory of Open Access Journals (Sweden)

Jose L Serrano-Velez

2014-06-01

Full Text Available Dye-coupling, whole-mount immunohistochemistry for gap junction channel protein connexin 35 (Cx35, and freeze-fracture replica immunogold labeling (FRIL reveal an abundance of electrical synapses/gap junctions at glutamatergic mixed synapses in the 14th spinal segment that innervates the adult male gonopodium of Western Mosquitofish, Gambusia affinis (Mosquitofish.To study gap junctions’ role in fast motor behavior, we used a minimally-invasive neural-tract-tracing technique to introduce gap junction-permeant or -impermeant dyes into deep muscles controlling the gonopodium of the adult male Mosquitofish, a teleost fish that rapidly transfers (complete in 50 of the 62 gap junctions at mixed synapses are in the 14th spinal segment.Our results support and extend studies showing gap junctions at mixed synapses in spinal cord segments involved in control of genital reflexes in rodents, and they suggest a link between mixed synapses and fast motor behavior. The findings provide a basis for studies of specific roles of spinal neurons in the generation/regulation of sex-specific behavior and for studies of gap junctions’ role in regulating fast motor behavior. Finally, the CoPA IN provides a novel candidate neuron for future studies of gap junctions and neural control of fast motor behaviors.

Extreme Longevity in Proteinaceous Deep-Sea Corals

Energy Technology Data Exchange (ETDEWEB)

Roark, E B; Guilderson, T P; Dunbar, R B; Fallon, S J; Mucciarone, D A

2009-02-09

Deep-sea corals are found on hard substrates on seamounts and continental margins world-wide at depths of 300 to {approx}3000 meters. Deep-sea coral communities are hotspots of deep ocean biomass and biodiversity, providing critical habitat for fish and invertebrates. Newly applied radiocarbon age date from the deep water proteinaceous corals Gerardia sp. and Leiopathes glaberrima show that radial growth rates are as low as 4 to 35 {micro}m yr{sup -1} and that individual colony longevities are on the order of thousands of years. The management and conservation of deep sea coral communities is challenged by their commercial harvest for the jewelry trade and damage caused by deep water fishing practices. In light of their unusual longevity, a better understanding of deep sea coral ecology and their interrelationships with associated benthic communities is needed to inform coherent international conservation strategies for these important deep-sea ecosystems.

New optimized drill pipe size for deep-water, extended reach and ultra-deep drilling

Energy Technology Data Exchange (ETDEWEB)

Jellison, Michael J.; Delgado, Ivanni [Grant Prideco, Inc., Hoston, TX (United States); Falcao, Jose Luiz; Sato, Ademar Takashi [PETROBRAS, Rio de Janeiro, RJ (Brazil); Moura, Carlos Amsler [Comercial Perfuradora Delba Baiana Ltda., Rio de Janeiro, RJ (Brazil)

2004-07-01

A new drill pipe size, 5-7/8 in. OD, represents enabling technology for Extended Reach Drilling (ERD), deep water and other deep well applications. Most world-class ERD and deep water wells have traditionally been drilled with 5-1/2 in. drill pipe or a combination of 6-5/8 in. and 5-1/2 in. drill pipe. The hydraulic performance of 5-1/2 in. drill pipe can be a major limitation in substantial ERD and deep water wells resulting in poor cuttings removal, slower penetration rates, diminished control over well trajectory and more tendency for drill pipe sticking. The 5-7/8 in. drill pipe provides a significant improvement in hydraulic efficiency compared to 5-1/2 in. drill pipe and does not suffer from the disadvantages associated with use of 6-5/8 in. drill pipe. It represents a drill pipe assembly that is optimized dimensionally and on a performance basis for casing and bit programs that are commonly used for ERD, deep water and ultra-deep wells. The paper discusses the engineering philosophy behind 5-7/8 in. drill pipe, the design challenges associated with development of the product and reviews the features and capabilities of the second-generation double-shoulder connection. The paper provides drilling case history information on significant projects where the pipe has been used and details results achieved with the pipe. (author)

Determination of deep water circulation in the East Atlantic Ocean by means of a box-model based evaluation of C-14 measurements and other tracer data

International Nuclear Information System (INIS)

Schlitzer, R.

1984-01-01

Radiocarbon (C-14) measurements proved to be an efficient means of determining the average, large-area deep water circulation in the Atlantic Ocean. The thesis under review explains and discusses measurements carried out in the equatorial West Atlantic and North Atlantic Ocean. The samples have been taken during mission 56 of the RS 'meteor' in spring 1981. The gas has been obtained by vacuum extraction and the measurements have been performed in proportional counter tubes, the error to be accounted for amounting to 2per mille. These measured data, together with measurements of the potential temperatures, the silicate and CO 2 concentrations, and measured data from the South-East Atlantic Ocean, have been used to calculate on the basis of a box model of the Atlantic Ocean the deep water flow from the West to the East Atlantic Ocean, the deep water circulation between the various East Atlantic basins, and the turbulent diffusion coefficients required to parameterize the deep water mixing processes. (orig./HP) [de

Deep Reinforcement Learning: An Overview

OpenAIRE

Li, Yuxi

2017-01-01

We give an overview of recent exciting achievements of deep reinforcement learning (RL). We discuss six core elements, six important mechanisms, and twelve applications. We start with background of machine learning, deep learning and reinforcement learning. Next we discuss core RL elements, including value function, in particular, Deep Q-Network (DQN), policy, reward, model, planning, and exploration. After that, we discuss important mechanisms for RL, including attention and memory, unsuperv...

Prokaryotic degradation of high molecular weight dissolved organic matter in the deep-sea waters of NW Mediterranean Sea under in situ temperature and pressure conditions during contrasted hydrological conditions

Science.gov (United States)

Tamburini, C.; Boutrif, M.; Garel, M.; Sempéré, R.; Repeta, D.; Charriere, B.; Nerini, D.; Panagiotopoulos, C.

2016-02-01

The contribution of the semi-labile dissolved organic carbon (DOC) to the global prokaryotic production has been assessed in very few previous studies. Some experiments show rapid utilization of semi-reactive DOC by prokaryotes, while other experiments show almost no utilization at all. However, all these studies did not take into account the role of hydrostatic pressure for the degradation of organic matter. In this study, we investigate (1) the degradation of "natural" high molecular weight DOM HMW-DOM (obtained after ultrafiltration) and (2) the uptake of labeled extracellular polymeric substances (3H-EPS) incubated with deep-sea water samples (2000 m-depth, NW Mediterranean Sea) under in situ pressure conditions (HP) and under atmospheric compression after decompression of the deep samples (ATM) during stratified and mixed water conditions (deep sea convection). Our results indicated that during HP incubations DOC exhibited the highest degradation rates (kHP DOC = 0.82 d-1) compared to the ATM conditions were no or few degradation was observed (kATM DOC= 0.007 d-1). An opposite trend was observed for the HP incubations from mixed deep water masses. HP incubation measurements displayed the lowest DOC degradation (kHP DOC=0.031 d-1) compared to the ATM conditions (kATM DOC=0.62 d-1). These results imply the presence of allochthonous prokaryotic cells in deep-sea samples after a winter water mass convection. Same trends were found using 3H-EPS uptake rates which were higher at HP than at ATM conditions during stratified period conditions whereas the opposite patterns were observed during deep-sea convection event. Moreover, we found than Euryarchaea were the main contributors to 3H-EPS assimilation at 2000m-depth, representing 58% of the total cells actively assimilating 3H-EPS. This study demonstrates that remineralization rates of semi-labile DOC in deep NW Med. Sea are controlled by the prokaryotic communities, which are influenced by the hydrological

Land Cover Classification via Multitemporal Spatial Data by Deep Recurrent Neural Networks

Science.gov (United States)

Ienco, Dino; Gaetano, Raffaele; Dupaquier, Claire; Maurel, Pierre

2017-10-01

Nowadays, modern earth observation programs produce huge volumes of satellite images time series (SITS) that can be useful to monitor geographical areas through time. How to efficiently analyze such kind of information is still an open question in the remote sensing field. Recently, deep learning methods proved suitable to deal with remote sensing data mainly for scene classification (i.e. Convolutional Neural Networks - CNNs - on single images) while only very few studies exist involving temporal deep learning approaches (i.e Recurrent Neural Networks - RNNs) to deal with remote sensing time series. In this letter we evaluate the ability of Recurrent Neural Networks, in particular the Long-Short Term Memory (LSTM) model, to perform land cover classification considering multi-temporal spatial data derived from a time series of satellite images. We carried out experiments on two different datasets considering both pixel-based and object-based classification. The obtained results show that Recurrent Neural Networks are competitive compared to state-of-the-art classifiers, and may outperform classical approaches in presence of low represented and/or highly mixed classes. We also show that using the alternative feature representation generated by LSTM can improve the performances of standard classifiers.

DeepMirTar: a deep-learning approach for predicting human miRNA targets.

Science.gov (United States)

Wen, Ming; Cong, Peisheng; Zhang, Zhimin; Lu, Hongmei; Li, Tonghua

2018-06-01

MicroRNAs (miRNAs) are small noncoding RNAs that function in RNA silencing and post-transcriptional regulation of gene expression by targeting messenger RNAs (mRNAs). Because the underlying mechanisms associated with miRNA binding to mRNA are not fully understood, a major challenge of miRNA studies involves the identification of miRNA-target sites on mRNA. In silico prediction of miRNA-target sites can expedite costly and time-consuming experimental work by providing the most promising miRNA-target-site candidates. In this study, we reported the design and implementation of DeepMirTar, a deep-learning-based approach for accurately predicting human miRNA targets at the site level. The predicted miRNA-target sites are those having canonical or non-canonical seed, and features, including high-level expert-designed, low-level expert-designed, and raw-data-level, were used to represent the miRNA-target site. Comparison with other state-of-the-art machine-learning methods and existing miRNA-target-prediction tools indicated that DeepMirTar improved overall predictive performance. DeepMirTar is freely available at https://github.com/Bjoux2/DeepMirTar_SdA. lith@tongji.edu.cn, hongmeilu@csu.edu.cn. Supplementary data are available at Bioinformatics online.

Extrusion-mixing compared with hand-mixing of polyether impression materials?

Science.gov (United States)

McMahon, Caroline; Kinsella, Daniel; Fleming, Garry J P

2010-12-01

The hypotheses tested were two-fold (a) whether altering the base:catalyst ratio influences working time, elastic recovery and strain in compression properties of a hand-mixed polyether impression material and (b) whether an extrusion-mixed polyether impression material would have a significant advantage over a hand-mixed polyether impression material mixed to the optimum base:catalyst ratio. The polyether was hand-mixed at the optimum (manufacturers recommended) base:catalyst ratios (7:1) and further groups were made by increasing or decreasing the catalyst length by 25%. Additionally specimens were also made from an extrusion-mixed polyether impression material and compared with the optimum hand-mixed base:catalyst ratio. A penetrometer assembly was used to measure the working time (n=5). Five cylindrical specimens for each hand-mixed and extrusion mixed group investigated were employed for elastic recovery and strain in compression testing. Hand-mixing polyether impression materials with 25% more catalyst than that recommended significantly decreased the working time while hand-mixing with 25% less catalyst than that recommended significantly increased the strain in compression. The extrusion-mixed polyether impression material provided similar working time, elastic recovery and strain in compression to the hand-mixed polyether mixed at the optimum base:catalyst ratio.

Fiscal 1999 research result report. Basic research on the evaluation method of deep water by fine algae; 1999 nendo bisai sorui wo mochiita shinsosui hyokaho ni kansuru kisoteki kenkyu hokokusho

Energy Technology Data Exchange (ETDEWEB)

NONE

2000-03-01

Basic research was made on establishment of a bioassay for testing the effect of deep water on surface biota. Mixing of surface water and deep water with high-concentration nutrient salts has effect on fine algae (phytoplankton) immediately. In this research, based on conventional AGP (algae growth potential) method as water quality evaluation method by fine algae, the multiplication potential of 13 strains of algae in Kochi's and Toyama's deep water was evaluated by using the increase rate of the number of cells. The research result showed that (1) deep water has the potential increasing cell concentrations of every fine algae to several times or over ten times as compared with surface water, (2) most of both nitrogen and phosphorus in deep water are consumed during the above process, (3) cell concentrations of both harmful and usable species increase, and (4) although no difference in mean potential is found between Kochi's and Toyama's deep water, the patterns of strains promoting multiplication are different between them. (NEDO)

Deep Unfolding for Topic Models.

Science.gov (United States)

Chien, Jen-Tzung; Lee, Chao-Hsi

2018-02-01

Deep unfolding provides an approach to integrate the probabilistic generative models and the deterministic neural networks. Such an approach is benefited by deep representation, easy interpretation, flexible learning and stochastic modeling. This study develops the unsupervised and supervised learning of deep unfolded topic models for document representation and classification. Conventionally, the unsupervised and supervised topic models are inferred via the variational inference algorithm where the model parameters are estimated by maximizing the lower bound of logarithm of marginal likelihood using input documents without and with class labels, respectively. The representation capability or classification accuracy is constrained by the variational lower bound and the tied model parameters across inference procedure. This paper aims to relax these constraints by directly maximizing the end performance criterion and continuously untying the parameters in learning process via deep unfolding inference (DUI). The inference procedure is treated as the layer-wise learning in a deep neural network. The end performance is iteratively improved by using the estimated topic parameters according to the exponentiated updates. Deep learning of topic models is therefore implemented through a back-propagation procedure. Experimental results show the merits of DUI with increasing number of layers compared with variational inference in unsupervised as well as supervised topic models.

Physical oceanographic characteristics influencing the dispersion of dissolved tracers released at the sea floor in selected deep ocean study areas

International Nuclear Information System (INIS)

Kupferman, S.L.; Moore, D.E.

1981-02-01

Scenarios which follow the development in space and time of the concentration field of a dissolved tracer released at the sea floor are presented for a Pacific and two Atlantic study areas. The scenarios are closely tied to available data by means of simple analytical models and proceed in stages from short time and space scales in the immediate vicinity of a release point to those scales characteristic of ocean basins. The concepts of internal mixing time and residence time in the benthic mixed layer, useful for developing an intuitive feeling for the behavior of a tracer in this feature, are introduced and discussed. We also introduce the concept of domain of occupation, which is useful in drawing distinctions between mixing and stirring in the ocean. From this study it is apparent that reliable estimation of mixing will require careful consideration of the dynamics of the eddy fields in the ocean. Another area in which more information is urgently needed is in the relation of deep isopycnal structure and bottom topography to local near-bottom circulation

Deep convective clouds at the tropopause

Directory of Open Access Journals (Sweden)

H. H. Aumann

2011-02-01

Full Text Available Data from the Atmospheric Infrared Sounder (AIRS on the EOS Aqua spacecraft each day show tens of thousands of Cold Clouds (CC in the tropical oceans with 10 μm window channel brightness temperatures colder than 225 K. These clouds represent a mix of cold anvil clouds and Deep Convective Clouds (DCC. This mix can be separated by computing the difference between two channels, a window channel and a channel with strong CO₂ absorption: for some cold clouds this difference is negative, i.e. the spectra for some cold clouds are inverted. We refer to cold clouds with spectra which are more than 2 K inverted as DCCi2. Associated with DCCi2 is a very high rain rate and a local upward displacement of the tropopause, a cold "bulge", which can be seen directly in the brightness temperatures of AIRS and Advanced Microwave Sounding Unit (AMSU temperature sounding channels in the lower stratosphere. The very high rain rate and the local distortion of the tropopause indicate that DCCi2 objects are associated with severe storms. Significant long-term trends in the statistical properties of DCCi2 could be interesting indicators of climate change. While the analysis of the nature and physical conditions related to DCCi2 requires hyperspectral infrared and microwave data, the identification of DCCi2 requires only one good window channel and one strong CO₂ sounding channel. This suggests that improved identification of severe storms with future advanced geostationary satellites could be accomplished with the addition of one or two narrow band channels.

WHATS-3: An Improved Flow-Through Multi-bottle Fluid Sampler for Deep-Sea Geofluid Research

Directory of Open Access Journals (Sweden)

Junichi Miyazaki

2017-06-01

Full Text Available Deep-sea geofluid systems, such as hydrothermal vents and cold seeps, are key to understanding subseafloor environments of Earth. Fluid chemistry, especially, provides crucial information toward elucidating the physical, chemical, and biological processes that occur in these ecosystems. To accurately assess fluid and gas properties of deep-sea geofluids, well-designed pressure-tight fluid samplers are indispensable and as such they are important assets of deep-sea geofluid research. Here, the development of a new flow-through, pressure-tight fluid sampler capable of four independent sampling events (two subsamples for liquid and gas analyses from each is reported. This new sampler, named WHATS-3, is a new addition to the WHATS-series samplers and a major upgrade from the previous WHATS-2 sampler with improvements in sample number, valve operational time, physical robustness, and ease of maintenance. Routine laboratory-based pressure tests proved that it is suitable for operation up to 35 MPa pressure. Successful field tests of the new sampler were also carried out in five hydrothermal fields, two in Indian Ocean, and three in Okinawa Trough (max. depth 3,300 m. Relations of Mg and major ion species demonstrated bimodal mixing trends between a hydrothermal fluid and seawater, confirming the high quality of fluids sampled. The newly developed WHATS-3 sampler is well-balanced in sampling capability, field usability, and maintenance feasibility, and can serve as one of the best geofluid samplers available at present to conduct efficient research of deep-sea geofluid systems.

Quasi-Geostrophic Diagnosis of Mixed-Layer Dynamics Embedded in a Mesoscale Turbulent Field

Science.gov (United States)

Chavanne, C. P.; Klein, P.

2016-02-01

A new quasi-geostrophic model has been developed to diagnose the three-dimensional circulation, including the vertical velocity, in the upper ocean from high-resolution observations of sea surface height and buoyancy. The formulation for the adiabatic component departs from the classical surface quasi-geostrophic framework considered before since it takes into account the stratification within the surface mixed-layer that is usually much weaker than that in the ocean interior. To achieve this, the model approximates the ocean with two constant-stratification layers : a finite-thickness surface layer (or the mixed-layer) and an infinitely-deep interior layer. It is shown that the leading-order adiabatic circulation is entirely determined if both the surface streamfunction and buoyancy anomalies are considered. The surface layer further includes a diabatic dynamical contribution. Parameterization of diabatic vertical velocities is based on their restoring impacts of the thermal-wind balance that is perturbed by turbulent vertical mixing of momentum and buoyancy. The model skill in reproducing the three-dimensional circulation in the upper ocean from surface data is checked against the output of a high-resolution primitive-equation numerical simulation. Correlation between simulated and diagnosed vertical velocities are significantly improved in the mixed-layer for the new model compared to the classical surface quasi-geostrophic model, reaching 0.9 near the surface.

Docker Containers for Deep Learning Experiments

OpenAIRE

Gerke, Paul K.

2017-01-01

Deep learning is a powerful tool to solve problems in the area of image analysis. The dominant compute platform for deep learning is Nvidia’s proprietary CUDA, which can only be used together with Nvidia graphics cards. The nivida-docker project allows exposing Nvidia graphics cards to docker containers and thus makes it possible to run deep learning experiments in docker containers.In our department, we use deep learning to solve problems in the area of medical image analysis and use docker ...

Auxiliary Deep Generative Models

DEFF Research Database (Denmark)

Maaløe, Lars; Sønderby, Casper Kaae; Sønderby, Søren Kaae

2016-01-01

Deep generative models parameterized by neural networks have recently achieved state-of-the-art performance in unsupervised and semi-supervised learning. We extend deep generative models with auxiliary variables which improves the variational approximation. The auxiliary variables leave...... the generative model unchanged but make the variational distribution more expressive. Inspired by the structure of the auxiliary variable we also propose a model with two stochastic layers and skip connections. Our findings suggest that more expressive and properly specified deep generative models converge...... faster with better results. We show state-of-the-art performance within semi-supervised learning on MNIST (0.96%), SVHN (16.61%) and NORB (9.40%) datasets....

Accelerating Deep Learning with Shrinkage and Recall

OpenAIRE

Zheng, Shuai; Vishnu, Abhinav; Ding, Chris

2016-01-01

Deep Learning is a very powerful machine learning model. Deep Learning trains a large number of parameters for multiple layers and is very slow when data is in large scale and the architecture size is large. Inspired from the shrinking technique used in accelerating computation of Support Vector Machines (SVM) algorithm and screening technique used in LASSO, we propose a shrinking Deep Learning with recall (sDLr) approach to speed up deep learning computation. We experiment shrinking Deep Lea...

Distribution of artificial radionuclides in deep sediments of the Mediterranean Sea

International Nuclear Information System (INIS)

Garcia-Orellana, J.; Pates, J.M.; Masque, P.; Bruach, J.M.; Sanchez-Cabeza, J.A.

2009-01-01

Artificial radionuclides enter the Mediterranean Sea mainly through atmospheric deposition following nuclear weapons tests and the Chernobyl accident, but also through the river discharge of nuclear facility effluents. Previous studies of artificial radionuclides impact of the Mediterranean Sea have focussed on shallow, coastal sediments. However, deep sea sediments have the potential to store and accumulate pollutants, including artificial radionuclides. Deep sea marine sediment cores were collected from Mediterranean Sea abyssal plains (depth > 2000 m) and analysed for 239,240 Pu and 137 Cs to elucidate the concentrations, inventories and sources of these radionuclides in the deepest areas of the Mediterranean. The activity - depth profiles of 210 Pb, together with 14 C dating, indicate that sediment mixing redistributes the artificial radionuclides within the first 2.5 cm of the sedimentary column. The excess 210 Pb inventory was used to normalize 239,240 Pu and 137 Cs inventories for variable sediment fluxes. The 239,240 Pu/ 210 Pb xs ratio was uniform across the entire sea, with a mean value of 1.24 x 10 -3 , indicating homogeneous fallout of 239,240 Pu. The 137 Cs/ 210 Pb xs ratio showed differences between the eastern (0.049) and western basins (0.030), clearly significant impact of deep sea sediments from the Chernobyl accident. The inventory ratios of 239,240 Pu/ 137 Cs were 0.041 and 0.025 in the western and eastern basins respectively, greater than the fallout ratio, 0.021, showing more efficient scavenging of 239,240 Pu in the water column and major sedimentation of 137 Cs in the eastern basin. Although areas with water depths of > 2000 m constitute around 40% of the entire Mediterranean basin, the sediments in these regions only contained 2.7% of the 239,240 Pu and 0.95% of the 137 Cs deposited across the Sea in 2000. These data show that the accumulation of artificial radionuclides in deep Mediterranean environments is much lower than predicted by

A Preliminary Assessment of a Deep Borehole disposal of Spent Fuels

International Nuclear Information System (INIS)

Lee, Younmyoung; Jeon, Jongtae

2014-01-01

Deep borehole disposal (DBD) of such radioactive waste as spent nuclear fuels (SFs) and other waste forms has been investigating mainly at Sandia National Labs for the US DOE as an alternative option. DBD can give advantages over less deep geological disposal since the disposal of wastes at a great depth where a low degree of permeability in the potentially steady rock condition will be beneficial for nuclide movement. Groundwater in the deep basement rock can even have salinity and less chance to mix with groundwater above. The DBD concept is quite straightforward and even simple: Waste canisters are simply emplaced in the lower 2 km part of the borehole down to 5 km deep. Through this study, a conceptual DBD is assessed for a similar case as the US DOE's approach, in which 400 SF canisters are to be emplaced at a deep bottom between 3km and 5km depths, upon which an additional 1km-thick compacted bentonite is overbuffered, and the remaining upper part of the borehole is backfilled again with a mixture of crushed rock and bentonite. Then, the total 5km-deep borehole has three zones: a disposal zone at the bottom 2km, a buffer zone at the next 1km, and backfill zone at the rest top 2km, as illustrated conceptually in Fig. 1. To demonstrate the feasibility in view of long-term radiological safety, a rough model for a safety assessment of this conceptual deep borehole repository system, providing detailed models for nuclide transport in and around the geosphere and biosphere under normal nuclide release scenarios that can occur after a closure of the repository, has been developed using GoldSim. A simple preliminary result in terms of the dose exposure rate from a safety assessment of the DBD is also presented and compared to the case of direct disposal of SFs in a KBS-3V vertical type repository, carried out in previous studies. For different types and shapes of repositories at each different depth, direct comparison between a DBD and a KBS-3 type disposal of

A Preliminary Assessment of a Deep Borehole disposal of Spent Fuels

Energy Technology Data Exchange (ETDEWEB)

Lee, Younmyoung; Jeon, Jongtae [Korea Atomic Energy Research Institute, Daejeon (Korea, Republic of)

2014-05-15

Deep borehole disposal (DBD) of such radioactive waste as spent nuclear fuels (SFs) and other waste forms has been investigating mainly at Sandia National Labs for the US DOE as an alternative option. DBD can give advantages over less deep geological disposal since the disposal of wastes at a great depth where a low degree of permeability in the potentially steady rock condition will be beneficial for nuclide movement. Groundwater in the deep basement rock can even have salinity and less chance to mix with groundwater above. The DBD concept is quite straightforward and even simple: Waste canisters are simply emplaced in the lower 2 km part of the borehole down to 5 km deep. Through this study, a conceptual DBD is assessed for a similar case as the US DOE's approach, in which 400 SF canisters are to be emplaced at a deep bottom between 3km and 5km depths, upon which an additional 1km-thick compacted bentonite is overbuffered, and the remaining upper part of the borehole is backfilled again with a mixture of crushed rock and bentonite. Then, the total 5km-deep borehole has three zones: a disposal zone at the bottom 2km, a buffer zone at the next 1km, and backfill zone at the rest top 2km, as illustrated conceptually in Fig. 1. To demonstrate the feasibility in view of long-term radiological safety, a rough model for a safety assessment of this conceptual deep borehole repository system, providing detailed models for nuclide transport in and around the geosphere and biosphere under normal nuclide release scenarios that can occur after a closure of the repository, has been developed using GoldSim. A simple preliminary result in terms of the dose exposure rate from a safety assessment of the DBD is also presented and compared to the case of direct disposal of SFs in a KBS-3V vertical type repository, carried out in previous studies. For different types and shapes of repositories at each different depth, direct comparison between a DBD and a KBS-3 type disposal of

Consolidated Deep Actor Critic Networks (DRAFT)

NARCIS (Netherlands)

Van der Laan, T.A.

2015-01-01

The works [Volodymyr et al. Playing atari with deep reinforcement learning. arXiv preprint arXiv:1312.5602, 2013.] and [Volodymyr et al. Human-level control through deep reinforcement learning. Nature, 518(7540):529–533, 2015.] have demonstrated the power of combining deep neural networks with

Deep Galaxy: Classification of Galaxies based on Deep Convolutional Neural Networks

OpenAIRE

Khalifa, Nour Eldeen M.; Taha, Mohamed Hamed N.; Hassanien, Aboul Ella; Selim, I. M.

2017-01-01

In this paper, a deep convolutional neural network architecture for galaxies classification is presented. The galaxy can be classified based on its features into main three categories Elliptical, Spiral, and Irregular. The proposed deep galaxies architecture consists of 8 layers, one main convolutional layer for features extraction with 96 filters, followed by two principles fully connected layers for classification. It is trained over 1356 images and achieved 97.272% in testing accuracy. A c...

Irradiation stratigraphy in the Apollo 16 deep drill section 60002

Science.gov (United States)

Blanford, G. E.; Wood, G. C.

1978-01-01

Particle track density frequency distributions, abundance of track rich grains and minimum track densities are reported for the upper 20 cm of the 60002 section of the Apollo 16 deep drill core. The principal stratigraphic feature is a boundary approximately 7 cm from the top of the section. Experimental evidence does not conclusively determine whether this contact is an ancient regolith surface or is simply a depositional boundary. If it is an ancient surface, it has a model exposure age of 3 to 7 million years and a reworking depth of about 0.5 cm. However, because track density frequency distributions indicate the mixing of soils of different maturities, we favor interpreting this contact as a depositional boundary. There may be a second depositional boundary approximately 19 cm below the top of 60002.

Uncovering molecular relaxation processes with nonlinear spectroscopies in the deep UV

International Nuclear Information System (INIS)

West, Brantley A.; Molesky, Brian P.; Giokas, Paul G.; Moran, Andrew M.

2013-01-01

Highlights: • We discuss the outlook for multidimensional spectroscopies in the deep UV. • Photophysics are examined in small DNA components at cryogenic temperatures. • Wavepacket motions are detected in ring-opening systems with 2DUV spectroscopy. • Measurements of electronic wavepacket motions in molecules are proposed. - Abstract: Nonlinear laser spectroscopies in the deep UV spectral range are motivated by studies of biological systems and elementary processes in small molecules. This perspective article discusses recent technical advances in this area with a particular emphasis on diffractive optic based approaches to four-wave mixing spectroscopies. Applications to two classes of systems illustrate present experimental capabilities. First, experiments on DNA components at cryogenic temperatures are used to uncover features of excited state potential energy surfaces and vibrational cooling mechanisms. Second, sub-200 fs internal conversion processes and coherent wavepacket motions are investigated in cyclohexadiene and α-terpinene. Finally, we propose new experimental directions that combine methods for producing few-cycle UV laser pulses in noble gases with incoherent detection methods (e.g., photoionization) in experiments with time resolution near a singlefemtosecond. These measurements are motivated by knowledge of extremely fast non-adiabatic dynamics and the resolution of electronic wavepacket motions in molecules

Deep Feature Consistent Variational Autoencoder

OpenAIRE

Hou, Xianxu; Shen, Linlin; Sun, Ke; Qiu, Guoping

2016-01-01

We present a novel method for constructing Variational Autoencoder (VAE). Instead of using pixel-by-pixel loss, we enforce deep feature consistency between the input and the output of a VAE, which ensures the VAE's output to preserve the spatial correlation characteristics of the input, thus leading the output to have a more natural visual appearance and better perceptual quality. Based on recent deep learning works such as style transfer, we employ a pre-trained deep convolutional neural net...

Bimaximal fermion mixing from the quark and leptonic mixing matrices

International Nuclear Information System (INIS)

Ohlsson, Tommy

2005-01-01

In this Letter, we show how the mixing angles of the standard parameterization add when multiplying the quark and leptonic mixing matrices, i.e., we derive explicit sum rules for the quark and leptonic mixing angles. In this connection, we also discuss other recently proposed sum rules for the mixing angles assuming bimaximal fermion mixing. In addition, we find that the present experimental and phenomenological data of the mixing angles naturally fulfill our sum rules, and thus, give rise to bilarge or bimaximal fermion mixing

A long history of equatorial deep-water upwelling in the Pacific Ocean

Science.gov (United States)

Zhang, Yi Ge; Pagani, Mark; Henderiks, Jorijntje; Ren, Haojia

2017-06-01

Cold, nutrient- and CO2-rich waters upwelling in the eastern equatorial Pacific (EEP) give rise to the Pacific cold tongue. Quasi-periodic subsidence of the thermocline and attenuation in wind strength expressed by El Niño conditions decrease upwelling rates, increase surface-water temperatures in the EEP, and lead to changes in regional climates both near and far from the equatorial Pacific. EEP surface waters have elevated CO2 concentrations during neutral (upwelling) or La Niña (strong upwelling) conditions. In contrast, approximate air-sea CO2 equilibrium characterizes El Niño events. One hypothesis proposes that changes in physical oceanography led to the establishment of a deep tropical thermocline and expanded mixed-layer prior to 3 million years ago. These effects are argued to have substantially reduced deep-water upwelling rates in the EEP and promoted a "permanent El Niño-like" climate state. For this study, we test this supposition by reconstructing EEP "excess CO2" and upwelling history for the past 6.5 million years using the alkenone-pCO2 methodology. Contrary to previous assertions, our results indicate that average temporal conditions in the EEP over the past ∼6.5 million years were characterized by substantial CO2 disequilibrium and high nutrient delivery to surface waters - characteristics that imply strong upwelling of deep waters. Upwelling appears most vigorous between ∼6.5 to 4.5 million years ago coinciding with high accumulation rates of biogenic material during the late Miocene - early Pliocene "biogenic bloom".

WRF nested large-eddy simulations of deep convection during SEAC4RS

Science.gov (United States)

Heath, Nicholas K.; Fuelberg, Henry E.; Tanelli, Simone; Turk, F. Joseph; Lawson, R. Paul; Woods, Sarah; Freeman, Sean

2017-04-01

Large-eddy simulations (LES) and observations are often combined to increase our understanding and improve the simulation of deep convection. This study evaluates a nested LES method that uses the Weather Research and Forecasting (WRF) model and, specifically, tests whether the nested LES approach is useful for studying deep convection during a real-world case. The method was applied on 2 September 2013, a day of continental convection that occurred during the Studies of Emissions and Atmospheric Composition, Clouds and Climate Coupling by Regional Surveys (SEAC4RS) campaign. Mesoscale WRF output (1.35 km grid length) was used to drive a nested LES with 450 m grid spacing, which then drove a 150 m domain. Results reveal that the 450 m nested LES reasonably simulates observed reflectivity distributions and aircraft-observed in-cloud vertical velocities during the study period. However, when examining convective updrafts, reducing the grid spacing to 150 m worsened results. We find that the simulated updrafts in the 150 m run become too diluted by entrainment, thereby generating updrafts that are weaker than observed. Lastly, the 450 m simulation is combined with observations to study the processes forcing strong midlevel cloud/updraft edge downdrafts that were observed on 2 September. Results suggest that these strong downdrafts are forced by evaporative cooling due to mixing and by perturbation pressure forces acting to restore mass continuity around neighboring updrafts. We conclude that the WRF nested LES approach, with further development and evaluation, could potentially provide an effective method for studying deep convection in real-world cases.

Recovery from deep-plane rhytidectomy following unilateral wound treatment with autologous platelet gel: a pilot study.

Science.gov (United States)

Powell, D M; Chang, E; Farrior, E H

2001-01-01

To determine the effects of treatment with autologous platelet-rich plasma mixed with thrombin and calcium chloride to form an autologous platelet gel (APG) on postoperative recovery from deep-plane rhytidectomy. A prospective, randomized, controlled pilot study. An accredited ambulatory facial plastic surgery center. Healthy volunteer women (N = 8) undergoing rhytidectomy. Unilateral autologous platelet-rich plasma wound treatment during standard deep-plane rhytidectomy. Staged postoperative facial photographs were graded in a blinded fashion by 3 facial plastic surgeon reviewers for postoperative ecchymosis and edema. Each facial side treated with APG that demonstrated less edema or ecchymosis than the non-APG-treated side was designated a positive response; otherwise, the response was equal (no difference) or negative (untreated side had less edema or ecchymosis). Twenty-one positive and 21 equal responses were observed compared with 8 negative ones. Of 20 unanimous observations, 15 were positive, only 3 equal, and 1 negative. Treatment with APG may prevent or improve edema or ecchymosis after deep-plane rhytidectomy. This trend is more apparent for ecchymosis than for edema, and is chiefly demonstrable in the early phases of recovery. These observations are consistent with previous reports of cell tissue culture and wound response to concentrated platelet product.

Property changes of deep and bottom waters in the Western Tropical Atlantic

Science.gov (United States)

Herrford, Josefine; Brandt, Peter; Zenk, Walter

2017-06-01

The flow of North Atlantic Deep Water (NADW) and Antarctic Bottom Water (AABW) contributes to the Atlantic meridional overturning circulation. Changes in the associated water mass formation might impact the deep ocean's capacity to take up anthropogenic CO2 while a warming of the deep ocean significantly contributes to global sea level rise. Here we compile historic and recent shipboard measurements of hydrography and velocity to provide a comprehensive view of water mass distribution, pathways, along-path transformation and long-term temperature changes of NADW and AABW in the western South and Equatorial Atlantic. We confirm previous results which show that the northwest corner of the Brazil Basin represents a splitting point for the southward/northward flow of NADW/AABW. The available measurements sample water mass transformation along the two major routes for deep and bottom waters in the tropical to South Atlantic - along the deep western boundary and eastward, parallel to the equator - as well as the hot-spots of extensive mixing. We find lower NADW and lighter AABW to form a highly interactive transition layer in the northern Brazil Basin. The AABW north of 5°S is relatively homogeneous with only lighter AABW being able to pass through the Equatorial Channel (EQCH) into the North Atlantic. Spanning a period of 26 years, our data also allow an estimation of long-term temperature trends in abyssal waters. We find a warming of 2.5±0.7•10-3 °C yr-1 of the waters in the northern Brazil Basin at temperatures colder than 0.6 °C throughout the period 1989-2014 and can relate this warming to a thinning of the dense AABW layer. Whereas isopycnal heave is the dominant effect which defines the vertical distribution of temperature trends on isobars, we also find temperature changes on isopycnals in the lower NADW and AABW layers. There temperatures on isopycnals exhibit decadal variations with warming in the 1990s and cooling in the 2000s - the contributions to the

How Stressful Is "Deep Bubbling"?

Science.gov (United States)

Tyrmi, Jaana; Laukkanen, Anne-Maria

2017-03-01

Water resistance therapy by phonating through a tube into the water is used to treat dysphonia. Deep submersion (≥10 cm in water, "deep bubbling") is used for hypofunctional voice disorders. Using it with caution is recommended to avoid vocal overloading. This experimental study aimed to investigate how strenuous "deep bubbling" is. Fourteen subjects, half of them with voice training, repeated the syllable [pa:] in comfortable speaking pitch and loudness, loudly, and in strained voice. Thereafter, they phonated a vowel-like sound both in comfortable loudness and loudly into a glass resonance tube immersed 10 cm into the water. Oral pressure, contact quotient (CQ, calculated from electroglottographic signal), and sound pressure level were studied. The peak oral pressure P(oral) during [p] and shuttering of the outer end of the tube was measured to estimate the subglottic pressure P(sub) and the mean P(oral) during vowel portions to enable calculation of transglottic pressure P(trans). Sensations during phonation were reported with an open-ended interview. P(sub) and P(oral) were higher in "deep bubbling" and P(trans) lower than in loud syllable phonation, but the CQ did not differ significantly. Similar results were obtained for the comparison between loud "deep bubbling" and strained phonation, although P(sub) did not differ significantly. Most of the subjects reported "deep bubbling" to be stressful only for respiratory and lip muscles. No big differences were found between trained and untrained subjects. The CQ values suggest that "deep bubbling" may increase vocal fold loading. Further studies should address impact stress during water resistance exercises. Copyright © 2017 The Voice Foundation. Published by Elsevier Inc. All rights reserved.

Evaluation of the DeepWind concept

DEFF Research Database (Denmark)

Schmidt Paulsen, Uwe; Borg, Michael; Gonzales Seabra, Luis Alberto

The report describes the DeepWind 5 MW conceptual design as a baseline for results obtained in the scientific and technical work packages of the DeepWind project. A comparison of DeepWi nd with existing VAWTs and paper projects are carried out and the evaluation of the concept in terms of cost...

Simulator Studies of the Deep Stall

Science.gov (United States)

White, Maurice D.; Cooper, George E.

1965-01-01

Simulator studies of the deep-stall problem encountered with modern airplanes are discussed. The results indicate that the basic deep-stall tendencies produced by aerodynamic characteristics are augmented by operational considerations. Because of control difficulties to be anticipated in the deep stall, it is desirable that adequate safeguards be provided against inadvertent penetrations.

Research on the neutron flux, secular equilibrium of chlorine-36 and groundwater age of the deep quaternary sediments, Hebei plain

International Nuclear Information System (INIS)

Dong Yuean; He Ming; Jiang Songsheng; Wu Shaoyong; Jiang Shan

2001-01-01

For the study of the neutron flux, secular equilibrium of chlorine-36 in the deep quaternary sediments of Hebei plain, the main chemical composition of water sand and confining bed was determined by neutron activation analysis. The mean neutron flux is 2.79 x 10 -5 cm -2 s -1 which was calculated by the chemical composition of the strata. The mean 36 Cl/Cl ratio in secular equilibrium is 1.27 x 10 -14 in the deep quaternary sediments, Hebei Plain. For the study of the groundwater age of the deep Quaternary sediments of Hebei Plain, the 36 Cl/Cl ratio of groundwater samples were determined by tandem accelerator mass spectrometry. The mixed groundwater 36 Cl/Cl ratio of the second and the third aquifer of Quaternary sediments in Baoding district is 247 x 10 -15 , that of the fourth aquifer in Baoding city is 224 x 10 -15 and the third aquifer in Cangzhou district is 40.5 x 10 -15 . The groundwater age of Baoding district was young and that of the third aquifer in Cangzhou was 229.2 ka

Deep Learning

DEFF Research Database (Denmark)

Jensen, Morten Bornø; Bahnsen, Chris Holmberg; Nasrollahi, Kamal

2018-01-01

I løbet af de sidste 10 år er kunstige neurale netværk gået fra at være en støvet, udstødt tekno-logi til at spille en hovedrolle i udviklingen af kunstig intelligens. Dette fænomen kaldes deep learning og er inspireret af hjernens opbygning.......I løbet af de sidste 10 år er kunstige neurale netværk gået fra at være en støvet, udstødt tekno-logi til at spille en hovedrolle i udviklingen af kunstig intelligens. Dette fænomen kaldes deep learning og er inspireret af hjernens opbygning....

Modification of Co-Mn-Al Mixed Oxide with Potassium and Its Effect on Deep Oxidation of VOC

Czech Academy of Sciences Publication Activity Database

Jirátová, Květa; Mikulová, Jana; Klempa, Jan; Grygar, Tomáš; Bastl, Zdeněk; Kovanda, F.

2009-01-01

Roč. 361, 1-2 (2009), s. 106-116 ISSN 0926-860X R&D Projects: GA ČR GA104/07/1400 Institutional research plan: CEZ:AV0Z40720504; CEZ:AV0Z40320502; CEZ:AV0Z40400503 Keywords : mixed oxide catalysts * VOC total oxidation * potassium promoter Subject RIV: CC - Organic Chemistry Impact factor: 3.564, year: 2009

Deep-Earth Equilibration between Molten Iron and Solid Silicates

Science.gov (United States)

Brennan, M.; Zurkowski, C. C.; Chidester, B.; Campbell, A.

2017-12-01

Elemental partitioning between iron-rich metals and silicate minerals influences the properties of Earth's deep interior, and is ultimately responsible for the nature of the core-mantle boundary. These interactions between molten iron and solid silicates were influential during planetary accretion, and persist today between the mantle and liquid outer core. Here we report the results of diamond anvil cell experiments at lower mantle conditions (40 GPa, >2500 K) aimed at examining systems containing a mixture of metals (iron or Fe-16Si alloy) and silicates (peridotite). The experiments were conducted at pressure-temperature conditions above the metallic liquidus but below the silicate solidus, and the recovered samples were analyzed by FIB/SEM with EDS to record the compositions of the coexisting phases. Each sample formed a three-phase equilibrium between bridgmanite, Fe-rich metallic melt, and an oxide. In one experiment, using pure Fe, the quenched metal contained 6 weight percent O, and the coexisting oxide was ferropericlase. The second experiment, using Fe-Si alloy, was highly reducing; its metal contained 10 wt% Si, and the coexisting mineral was stishovite. The distinct mineralogies of the two experiments derived from their different starting metals. These results imply that metallic composition is an important factor in determining the products of mixed phase iron-silicate reactions. The properties of deep-Earth interfaces such as the core-mantle boundary could be strongly affected by their metallic components.

Mixing regime of the residual water basins of the Aral Sea

Science.gov (United States)

Izhitskiy, Alexander; Zavialov, Peter; Kirillin, Georgiy

2017-04-01

The Aral Sea, a terminal salt lake in western Central Asia situated at the border between Uzbekistan and Kazakhstan, was ranked as the fourth largest inland water body in the mid-20th century. However, in the early 1960s, the lake's volume started to decrease rapidly due to severe changes in the Aral's water balance. Thus, the present-day Aral Sea can be considered as a system of separate water bodies with a common origin but very different physical, chemical and biological features. Our previous studies showed that the Large Aral Sea and Lake Tshchebas transformed into hyperhaline water bodies, while the Small Aral Sea was a brackish basin with rather similar to the pre-desiccation environment. On the other hand, the Small Aral Sea and Lake Tshchebas exhibited a mixed vertical structure, whereas the Western Large Aral Sea (especially the Chernyshev Bay) was strongly stratified. The presented study is focused on the seasonal mixing regimes of the residual basins. Isolation of deep waters from the atmosphere together with low rates of photosynthesis produce deep anoxia observed in the Chernyshev Bay and in the Large Aral. The high amount of organic matter provides a rich source of nutrients for anoxic microorganisms favoring methanogenesis in the bottom layer of the basins. In the Small Aral, the water column remains well-oxygenated down to the bottom throughout most of the year and development of anoxia is unlikely. The mixing regimes of the recently formed residual lakes of the former Aral Sea will provide manifold effect on the ongoing development of the aquatic system in the following decades. The study is based on a field data collected during two surveys of Shirshov Institute of Oceanology to the Aral Sea, which took place in October, 2015 and June, 2016. In situ measurements including CTD profiling and water sampling were carried out in the northern extremity of the western Large Aral (the Chernyshev Bay), in Lake Tshchebas, and in the Small Aral Sea

The effect of aerosol-derived changes in the warm phase on the properties of deep convective clouds

Science.gov (United States)

Chen, Qian; Koren, Ilan; Altaratz, Orit; Heiblum, Reuven; Dagan, Guy

2017-04-01

The aerosol impact on deep convective clouds starts in an increased number of cloud droplets in higher aerosol loading environment. This change drives many others, like enhanced condensational growth, delay in collision-coalescence and others. Since the warm processes serve as the initial and boundary conditions for the mixed and cold-phase processes in deep clouds, it is highly important to understand the aerosol effect on them. The weather research and forecasting model (WRF) with spectral bin microphysics was used to study a deep convective system over the Marshall Islands, during the Kwajalein Experiment (KWAJEX). Three simulations were conducted with aerosol concentrations of 100, 500 and 2000 cm-3, to reflect clean, semipolluted, and polluted conditions. The results of the clean run agreed well with the radar profiles and rain rate observations. The more polluted simulations resulted in larger total cloud mass, larger upper level cloud fraction and rain rates. There was an increased mass both below and above the zero temperature level. It indicates of more efficient growth processes both below and above the zero level. In addition the polluted runs showed an increased upward transport (across the zero level) of liquid water due to both stronger updrafts and larger droplet mobility. In this work we discuss the transport of cloud mass crossing the zero temperature level (in both directions) in order to gain a process level understanding of how aerosol effects on the warm processes affect the macro- and micro-properties of deep convective clouds.

Exploring the isopycnal mixing and helium–heat paradoxes in a suite of Earth system models

Directory of Open Access Journals (Sweden)

A. Gnanadesikan

2015-07-01

this paper we show that this is not the case. In a suite of models with different spatially constant and spatially varying values of ARedi the distribution of radiocarbon and helium isotopes is sensitive to the value of ARedi. However, away from strong helium sources in the southeastern Pacific, the relationship between the two is not sensitive, indicating that large-scale advection is the limiting process for removing helium and radiocarbon from the deep ocean. The helium isotopes, in turn, suggest a higher value of ARedi below the thermocline than is seen in theoretical parameterizations based on baroclinic growth rates. We argue that a key part of resolving the isopycnal mixing paradox is to abandon the idea that ARedi has a direct relationship to local baroclinic instability and to the so-called "thickness" mixing coefficient AGM.

European mixed forests

DEFF Research Database (Denmark)

Bravo-Oviedo, Andres; Pretzsch, Hans; Ammer, Christian

2014-01-01

Aim of study: We aim at (i) developing a reference definition of mixed forests in order to harmonize comparative research in mixed forests and (ii) review the research perspectives in mixed forests. Area of study: The definition is developed in Europe but can be tested worldwide. Material...... and Methods: Review of existent definitions of mixed forests based and literature review encompassing dynamics, management and economic valuation of mixed forests. Main results: A mixed forest is defined as a forest unit, excluding linear formations, where at least two tree species coexist at any...... density in mixed forests, (iii) conversion of monocultures to mixed-species forest and (iv) economic valuation of ecosystem services provided by mixed forests. Research highlights: The definition is considered a high-level one which encompasses previous attempts to define mixed forests. Current fields...

DeepNAT: Deep convolutional neural network for segmenting neuroanatomy.

Science.gov (United States)

Wachinger, Christian; Reuter, Martin; Klein, Tassilo

2018-04-15

We introduce DeepNAT, a 3D Deep convolutional neural network for the automatic segmentation of NeuroAnaTomy in T1-weighted magnetic resonance images. DeepNAT is an end-to-end learning-based approach to brain segmentation that jointly learns an abstract feature representation and a multi-class classification. We propose a 3D patch-based approach, where we do not only predict the center voxel of the patch but also neighbors, which is formulated as multi-task learning. To address a class imbalance problem, we arrange two networks hierarchically, where the first one separates foreground from background, and the second one identifies 25 brain structures on the foreground. Since patches lack spatial context, we augment them with coordinates. To this end, we introduce a novel intrinsic parameterization of the brain volume, formed by eigenfunctions of the Laplace-Beltrami operator. As network architecture, we use three convolutional layers with pooling, batch normalization, and non-linearities, followed by fully connected layers with dropout. The final segmentation is inferred from the probabilistic output of the network with a 3D fully connected conditional random field, which ensures label agreement between close voxels. The roughly 2.7million parameters in the network are learned with stochastic gradient descent. Our results show that DeepNAT compares favorably to state-of-the-art methods. Finally, the purely learning-based method may have a high potential for the adaptation to young, old, or diseased brains by fine-tuning the pre-trained network with a small training sample on the target application, where the availability of larger datasets with manual annotations may boost the overall segmentation accuracy in the future. Copyright © 2017 Elsevier Inc. All rights reserved.

Deep Learning and Its Applications in Biomedicine.

Science.gov (United States)

Cao, Chensi; Liu, Feng; Tan, Hai; Song, Deshou; Shu, Wenjie; Li, Weizhong; Zhou, Yiming; Bo, Xiaochen; Xie, Zhi

2018-02-01

Advances in biological and medical technologies have been providing us explosive volumes of biological and physiological data, such as medical images, electroencephalography, genomic and protein sequences. Learning from these data facilitates the understanding of human health and disease. Developed from artificial neural networks, deep learning-based algorithms show great promise in extracting features and learning patterns from complex data. The aim of this paper is to provide an overview of deep learning techniques and some of the state-of-the-art applications in the biomedical field. We first introduce the development of artificial neural network and deep learning. We then describe two main components of deep learning, i.e., deep learning architectures and model optimization. Subsequently, some examples are demonstrated for deep learning applications, including medical image classification, genomic sequence analysis, as well as protein structure classification and prediction. Finally, we offer our perspectives for the future directions in the field of deep learning. Copyright © 2018. Production and hosting by Elsevier B.V.

Deep Water Acoustics

Science.gov (United States)

2016-06-28

the Deep Water project and participate in the NPAL Workshops, including Art Baggeroer (MIT), J. Beron- Vera (UMiami), M. Brown (UMiami), T...Kathleen E . Wage. The North Pacific Acoustic Laboratory deep-water acoustic propagation experiments in the Philippine Sea. J. Acoust. Soc. Am., 134(4...estimate of the angle α during PhilSea09, made from ADCP measurements at the site of the DVLA. Sim. A B1 B2 B3 C D E F Prof. # 0 4 4 4 5 10 16 20 α

Mixing driven by transient buoyancy flows. I. Kinematics

Science.gov (United States)

Duval, W. M. B.; Zhong, H.; Batur, C.

2018-05-01

Mixing of two miscible liquids juxtaposed inside a cavity initially separated by a divider, whose buoyancy-driven motion is initiated via impulsive perturbation of divider motion that can generate the Richtmyer-Meshkov instability, is investigated experimentally. The measured Lagrangian history of interface motion that contains the continuum mechanics of mixing shows self-similar nearly Gaussian length stretch distribution for a wide range of control parameters encompassing an approximate Hele-Shaw cell to a three-dimensional cavity. Because of the initial configuration of the interface which is parallel to the gravitational field, we show that at critical initial potential energy mixing occurs through the stretching of the interface, which shows frontogenesis, and folding, owing to an overturning motion that results in unstable density stratification and produces an ideal condition for the growth of the single wavelength Rayleigh-Taylor instability. The initial perturbation of the interface and flow field generates the Kelvin-Helmholtz instability and causes kinks at the interface, which grow into deep fingers during overturning motion and unfold into local whorl structures that merge and self-organize into the Rayleigh-Taylor morphology (RTM) structure. For a range of parametric space that yields two-dimensional flows, the unfolding of the instability through a supercritical bifurcation yields an asymmetric pairwise structure exhibiting smooth RTM that transitions to RTM fronts with fractal structures that contain small length scales for increasing Peclet numbers. The late stage of the RTM structure unfolds into an internal breakwave that breaks down through wall and internal collision and sets up the condition for self-induced sloshing that decays exponentially as the two fluids become stably stratified with a diffusive region indicating local molecular diffusion.

Active Microbial Communities Inhabit Sulphate-Methane Interphase in Deep Bedrock Fracture Fluids in Olkiluoto, Finland

Directory of Open Access Journals (Sweden)

Malin Bomberg

2015-01-01

Full Text Available Active microbial communities of deep crystalline bedrock fracture water were investigated from seven different boreholes in Olkiluoto (Western Finland using bacterial and archaeal 16S rRNA, dsrB, and mcrA gene transcript targeted 454 pyrosequencing. Over a depth range of 296–798 m below ground surface the microbial communities changed according to depth, salinity gradient, and sulphate and methane concentrations. The highest bacterial diversity was observed in the sulphate-methane mixing zone (SMMZ at 250–350 m depth, whereas archaeal diversity was highest in the lowest boundaries of the SMMZ. Sulphide-oxidizing ε-proteobacteria (Sulfurimonas sp. dominated in the SMMZ and γ-proteobacteria (Pseudomonas spp. below the SMMZ. The active archaeal communities consisted mostly of ANME-2D and Thermoplasmatales groups, although Methermicoccaceae, Methanobacteriaceae, and Thermoplasmatales (SAGMEG, TMG were more common at 415–559 m depth. Typical indicator microorganisms for sulphate-methane transition zones in marine sediments, such as ANME-1 archaea, α-, β- and δ-proteobacteria, JS1, Actinomycetes, Planctomycetes, Chloroflexi, and MBGB Crenarchaeota were detected at specific depths. DsrB genes were most numerous and most actively transcribed in the SMMZ while the mcrA gene concentration was highest in the deep methane rich groundwater. Our results demonstrate that active and highly diverse but sparse and stratified microbial communities inhabit the Fennoscandian deep bedrock ecosystems.

Seasonality of Red Sea Mixed-Layer Depth and Density Budget

Science.gov (United States)

Kartadikaria, A. R.; Cerovecki, I.; Krokos, G.; Hoteit, I.

2016-02-01

The Red Sea is an active area of water mass formation. Dense water initially formed in the northern Red Sea, in the Gulf of Aqaba and the Gulf of Suez, spreads southward and finally flows to the open ocean through the Gulf of Aden via the narrow strait of Bab Al Mandeb. The signature of this outflow can be traced until the southern Indian Ocean, and is characterized by potential density of σθ ≈ 27.4. This water mass is important because it represents a significant source of heat and salt for the Indian Ocean. Using a high-resolution 1km regional MITgcm ocean model for the period 1992-2001 configured for the Red Sea, we examine the spatio-temporal characteristics of water mass formation inside the basin by analyzing closed and complete temperature and salinity budgets. The deepest mixed-layers (MLD) always develop in the northern part of the basin where surface ocean buoyancy loss leads to the Red Sea Intermediate and Deep Water formation. As this water is advected south, it is strongly modified by diapycnal mixing of heat and salt.

Overview of deep learning in medical imaging.

Science.gov (United States)

Suzuki, Kenji

2017-09-01

The use of machine learning (ML) has been increasing rapidly in the medical imaging field, including computer-aided diagnosis (CAD), radiomics, and medical image analysis. Recently, an ML area called deep learning emerged in the computer vision field and became very popular in many fields. It started from an event in late 2012, when a deep-learning approach based on a convolutional neural network (CNN) won an overwhelming victory in the best-known worldwide computer vision competition, ImageNet Classification. Since then, researchers in virtually all fields, including medical imaging, have started actively participating in the explosively growing field of deep learning. In this paper, the area of deep learning in medical imaging is overviewed, including (1) what was changed in machine learning before and after the introduction of deep learning, (2) what is the source of the power of deep learning, (3) two major deep-learning models: a massive-training artificial neural network (MTANN) and a convolutional neural network (CNN), (4) similarities and differences between the two models, and (5) their applications to medical imaging. This review shows that ML with feature input (or feature-based ML) was dominant before the introduction of deep learning, and that the major and essential difference between ML before and after deep learning is the learning of image data directly without object segmentation or feature extraction; thus, it is the source of the power of deep learning, although the depth of the model is an important attribute. The class of ML with image input (or image-based ML) including deep learning has a long history, but recently gained popularity due to the use of the new terminology, deep learning. There are two major models in this class of ML in medical imaging, MTANN and CNN, which have similarities as well as several differences. In our experience, MTANNs were substantially more efficient in their development, had a higher performance, and required a

WFIRST: Science from Deep Field Surveys

Science.gov (United States)

Koekemoer, Anton; Foley, Ryan; WFIRST Deep Field Working Group

2018-01-01

WFIRST will enable deep field imaging across much larger areas than those previously obtained with Hubble, opening up completely new areas of parameter space for extragalactic deep fields including cosmology, supernova and galaxy evolution science. The instantaneous field of view of the Wide Field Instrument (WFI) is about 0.3 square degrees, which would for example yield an Ultra Deep Field (UDF) reaching similar depths at visible and near-infrared wavelengths to that obtained with Hubble, over an area about 100-200 times larger, for a comparable investment in time. Moreover, wider fields on scales of 10-20 square degrees could achieve depths comparable to large HST surveys at medium depths such as GOODS and CANDELS, and would enable multi-epoch supernova science that could be matched in area to LSST Deep Drilling fields or other large survey areas. Such fields may benefit from being placed on locations in the sky that have ancillary multi-band imaging or spectroscopy from other facilities, from the ground or in space. The WFIRST Deep Fields Working Group has been examining the science considerations for various types of deep fields that may be obtained with WFIRST, and present here a summary of the various properties of different locations in the sky that may be considered for future deep fields with WFIRST.

The Role of Complex Treatment in Mixed Leg Ulcers - A Case Report of Vascular, Surgical and Physical Therapy.

Science.gov (United States)

Wollina, Uwe; Heinig, Birgit; Stelzner, Christian; Hansel, Gesina; Schönlebe, Jacqueline; Tchernev, Georgi; Lotti, Torello

2018-01-25

Leg ulcers are a burden to patients, their families and society. The second most common cause of chronic leg ulcers is the mixed arterio-venous type. An 80-year-old female patient presented to our department due to painful enlarging chronic leg ulcer of mixed arteriovenous origin on her left lower leg. She suffered from peripheral arterial occlusive disease stage I and chronic venous insufficiency Widmer grade IIIa, and a number of comorbidities. The aim of our ulcer treatment was a complete and stable wound closure that was hampered by arterial occlusion, exposed tendon, and renal insiffuciency. To improve the prognosis for ulcer surgery, we performed percutaneous transluminal angioplasty, transcutaneous CO 2 and deep ulcer shaving. The wound was closed by sandwich transplantation using elastin-collagen dermal template and meshed split skin graft. She had a 100% graft take with rapid reduction of severe wound pain. Complex approaches are necessary, to gain optimum results in leg ulcer therapy in mixed leg ulcers. Therapeutic nihilism should be abandonend.

TOPIC MODELING: CLUSTERING OF DEEP WEBPAGES

OpenAIRE

Muhunthaadithya C; Rohit J.V; Sadhana Kesavan; E. Sivasankar

2015-01-01

The internet is comprised of massive amount of information in the form of zillions of web pages.This information can be categorized into the surface web and the deep web. The existing search engines can effectively make use of surface web information.But the deep web remains unexploited yet. Machine learning techniques have been commonly employed to access deep web content.

DeepSimulator: a deep simulator for Nanopore sequencing

KAUST Repository

Li, Yu; Han, Renmin; Bi, Chongwei; Li, Mo; Wang, Sheng; Gao, Xin

2017-01-01

or assembled contigs, we simulate the electrical current signals by a context-dependent deep learning model, followed by a base-calling procedure to yield simulated reads. This workflow mimics the sequencing procedure more naturally. The thorough experiments

Building Program Vector Representations for Deep Learning

OpenAIRE

Mou, Lili; Li, Ge; Liu, Yuxuan; Peng, Hao; Jin, Zhi; Xu, Yan; Zhang, Lu

2014-01-01

Deep learning has made significant breakthroughs in various fields of artificial intelligence. Advantages of deep learning include the ability to capture highly complicated features, weak involvement of human engineering, etc. However, it is still virtually impossible to use deep learning to analyze programs since deep architectures cannot be trained effectively with pure back propagation. In this pioneering paper, we propose the "coding criterion" to build program vector representations, whi...

[Deep vein thrombosis prophylaxis.

Science.gov (United States)

Sandoval-Chagoya, Gloria Alejandra; Laniado-Laborín, Rafael

2013-01-01

Background: despite the proven effectiveness of preventive therapy for deep vein thrombosis, a significant proportion of patients at risk for thromboembolism do not receive prophylaxis during hospitalization. Our objective was to determine the adherence to thrombosis prophylaxis guidelines in a general hospital as a quality control strategy. Methods: a random audit of clinical charts was conducted at the Tijuana General Hospital, Baja California, Mexico, to determine the degree of adherence to deep vein thrombosis prophylaxis guidelines. The instrument used was the Caprini's checklist for thrombosis risk assessment in adult patients. Results: the sample included 300 patient charts; 182 (60.7 %) were surgical patients and 118 were medical patients. Forty six patients (15.3 %) received deep vein thrombosis pharmacologic prophylaxis; 27.1 % of medical patients received deep vein thrombosis prophylaxis versus 8.3 % of surgical patients (p < 0.0001). Conclusions: our results show that adherence to DVT prophylaxis at our hospital is extremely low. Only 15.3 % of our patients at risk received treatment, and even patients with very high risk received treatment in less than 25 % of the cases. We have implemented strategies to increase compliance with clinical guidelines.

Contemporary deep recurrent learning for recognition

Science.gov (United States)

Iftekharuddin, K. M.; Alam, M.; Vidyaratne, L.

2017-05-01

Large-scale feed-forward neural networks have seen intense application in many computer vision problems. However, these networks can get hefty and computationally intensive with increasing complexity of the task. Our work, for the first time in literature, introduces a Cellular Simultaneous Recurrent Network (CSRN) based hierarchical neural network for object detection. CSRN has shown to be more effective to solving complex tasks such as maze traversal and image processing when compared to generic feed forward networks. While deep neural networks (DNN) have exhibited excellent performance in object detection and recognition, such hierarchical structure has largely been absent in neural networks with recurrency. Further, our work introduces deep hierarchy in SRN for object recognition. The simultaneous recurrency results in an unfolding effect of the SRN through time, potentially enabling the design of an arbitrarily deep network. This paper shows experiments using face, facial expression and character recognition tasks using novel deep recurrent model and compares recognition performance with that of generic deep feed forward model. Finally, we demonstrate the flexibility of incorporating our proposed deep SRN based recognition framework in a humanoid robotic platform called NAO.

Mixing it but not mixed-up: mixed methods research in medical education (a critical narrative review).

Science.gov (United States)

Maudsley, Gillian

2011-01-01

Some important research questions in medical education and health services research need 'mixed methods research' (particularly synthesizing quantitative and qualitative findings). The approach is not new, but should be more explicitly reported. The broad search question here, of a disjointed literature, was thus: What is mixed methods research - how should it relate to medical education research?, focused on explicit acknowledgement of 'mixing'. Literature searching focused on Web of Knowledge supplemented by other databases across disciplines. Five main messages emerged: - Thinking quantitative and qualitative, not quantitative versus qualitative - Appreciating that mixed methods research blends different knowledge claims, enquiry strategies, and methods - Using a 'horses for courses' [whatever works] approach to the question, and clarifying the mix - Appreciating how medical education research competes with the 'evidence-based' movement, health services research, and the 'RCT' - Being more explicit about the role of mixed methods in medical education research, and the required expertise Mixed methods research is valuable, yet the literature relevant to medical education is fragmented and poorly indexed. The required time, effort, expertise, and techniques deserve better recognition. More write-ups should explicitly discuss the 'mixing' (particularly of findings), rather than report separate components.

Towards deep learning with segregated dendrites.

Science.gov (United States)

Guerguiev, Jordan; Lillicrap, Timothy P; Richards, Blake A

2017-12-05

Deep learning has led to significant advances in artificial intelligence, in part, by adopting strategies motivated by neurophysiology. However, it is unclear whether deep learning could occur in the real brain. Here, we show that a deep learning algorithm that utilizes multi-compartment neurons might help us to understand how the neocortex optimizes cost functions. Like neocortical pyramidal neurons, neurons in our model receive sensory information and higher-order feedback in electrotonically segregated compartments. Thanks to this segregation, neurons in different layers of the network can coordinate synaptic weight updates. As a result, the network learns to categorize images better than a single layer network. Furthermore, we show that our algorithm takes advantage of multilayer architectures to identify useful higher-order representations-the hallmark of deep learning. This work demonstrates that deep learning can be achieved using segregated dendritic compartments, which may help to explain the morphology of neocortical pyramidal neurons.

The deep ocean under climate change

Science.gov (United States)

Levin, Lisa A.; Le Bris, Nadine

2015-11-01

The deep ocean absorbs vast amounts of heat and carbon dioxide, providing a critical buffer to climate change but exposing vulnerable ecosystems to combined stresses of warming, ocean acidification, deoxygenation, and altered food inputs. Resulting changes may threaten biodiversity and compromise key ocean services that maintain a healthy planet and human livelihoods. There exist large gaps in understanding of the physical and ecological feedbacks that will occur. Explicit recognition of deep-ocean climate mitigation and inclusion in adaptation planning by the United Nations Framework Convention on Climate Change (UNFCCC) could help to expand deep-ocean research and observation and to protect the integrity and functions of deep-ocean ecosystems.

NATURAL GAS RESOURCES IN DEEP SEDIMENTARY BASINS

Energy Technology Data Exchange (ETDEWEB)

Thaddeus S. Dyman; Troy Cook; Robert A. Crovelli; Allison A. Henry; Timothy C. Hester; Ronald C. Johnson; Michael D. Lewan; Vito F. Nuccio; James W. Schmoker; Dennis B. Riggin; Christopher J. Schenk

2002-02-05

From a geological perspective, deep natural gas resources are generally defined as resources occurring in reservoirs at or below 15,000 feet, whereas ultra-deep gas occurs below 25,000 feet. From an operational point of view, ''deep'' is often thought of in a relative sense based on the geologic and engineering knowledge of gas (and oil) resources in a particular area. Deep gas can be found in either conventionally-trapped or unconventional basin-center accumulations that are essentially large single fields having spatial dimensions often exceeding those of conventional fields. Exploration for deep conventional and unconventional basin-center natural gas resources deserves special attention because these resources are widespread and occur in diverse geologic environments. In 1995, the U.S. Geological Survey estimated that 939 TCF of technically recoverable natural gas remained to be discovered or was part of reserve appreciation from known fields in the onshore areas and State waters of the United. Of this USGS resource, nearly 114 trillion cubic feet (Tcf) of technically-recoverable gas remains to be discovered from deep sedimentary basins. Worldwide estimates of deep gas are also high. The U.S. Geological Survey World Petroleum Assessment 2000 Project recently estimated a world mean undiscovered conventional gas resource outside the U.S. of 844 Tcf below 4.5 km (about 15,000 feet). Less is known about the origins of deep gas than about the origins of gas at shallower depths because fewer wells have been drilled into the deeper portions of many basins. Some of the many factors contributing to the origin of deep gas include the thermal stability of methane, the role of water and non-hydrocarbon gases in natural gas generation, porosity loss with increasing thermal maturity, the kinetics of deep gas generation, thermal cracking of oil to gas, and source rock potential based on thermal maturity and kerogen type. Recent experimental simulations

Deep smarts.

Science.gov (United States)

Leonard, Dorothy; Swap, Walter

2004-09-01

When a person sizes up a complex situation and rapidly comes to a decision that proves to be not just good but brilliant, you think, "That was smart." After you watch him do this a few times, you realize you're in the presence of something special. It's not raw brainpower, though that helps. It's not emotional intelligence, either, though that, too, is often involved. It's deep smarts. Deep smarts are not philosophical--they're not"wisdom" in that sense, but they're as close to wisdom as business gets. You see them in the manager who understands when and how to move into a new international market, in the executive who knows just what kind of talk to give when her organization is in crisis, in the technician who can track a product failure back to an interaction between independently produced elements. These are people whose knowledge would be hard to purchase on the open market. Their insight is based on know-how more than on know-what; it comprises a system view as well as expertise in individual areas. Because deep smarts are experienced based and often context specific, they can't be produced overnight or readily imported into an organization. It takes years for an individual to develop them--and no time at all for an organization to lose them when a valued veteran walks out the door. They can be taught, however, with the right techniques. Drawing on their forthcoming book Deep Smarts, Dorothy Leonard and Walter Swap say the best way to transfer such expertise to novices--and, on a larger scale, to make individual knowledge institutional--isn't through PowerPoint slides, a Web site of best practices, online training, project reports, or lectures. Rather, the sage needs to teach the neophyte individually how to draw wisdom from experience. Companies have to be willing to dedicate time and effort to such extensive training, but the investment more than pays for itself.

Deep Learning and Developmental Learning: Emergence of Fine-to-Coarse Conceptual Categories at Layers of Deep Belief Network.

Science.gov (United States)

Sadeghi, Zahra

2016-09-01

In this paper, I investigate conceptual categories derived from developmental processing in a deep neural network. The similarity matrices of deep representation at each layer of neural network are computed and compared with their raw representation. While the clusters generated by raw representation stand at the basic level of abstraction, conceptual categories obtained from deep representation shows a bottom-up transition procedure. Results demonstrate a developmental course of learning from specific to general level of abstraction through learned layers of representations in a deep belief network. © The Author(s) 2016.

Emplacement of zero-valent metal for remediation of deep contaminant plumes

International Nuclear Information System (INIS)

Hubble, D.W.; Gillham, R.W.; Cherry, J.A.

1997-01-01

Some groundwater plumes containing chlorinated solvent contaminants are found to be so deep that current in situ remediation technologies cannot be economically applied. Also, source zones are often found to be too deep for removal or inaccessible due to surface features. Plumes emanating from these sources require containment or treatment. Containment technologies are available for shallow sites (< 15 m) and are being developed for greater depths. However, it is important to advance the science of reactive treatment - both for cut off of plumes and to contain and treat source zones. Zero-valent metal technology has been used for remediation of solvent plumes at sites in Canada, the UK and at several industrial and military sites in the USA. To date, all of the plumes treated with zero-valent metal (granular iron) have been at depths less than 15 m. This paper gives preliminary results of research into methods to emplace granular iron at depths in the range of 15 to 60 m. The study included review of available and emerging methods of installing barrier or reactive material and the selection, preliminary design and costing of several methods. The design of a treatment system for a 122 m wide PCE plume that, immediately down gradient from its source, extends from a depth of 24 to 37 m below the ground surface is used as a demonstration site. Both Permeable Reactive Wall and Funnel-and-Gate trademark systems were considered. The emplacement methods selected for preliminary design and costing were slurry wall, driven/vibrated beam, deep soil mixing and hydrofracturing injection. For each of these methods, the iron must be slurried for ease of pumping and placement using biodegradable polymer viscosifiers that leave the iron reactive

Groundwater sampling and chemical characterisation of the Laxemar deep borehole KLX02

International Nuclear Information System (INIS)

Laaksoharju, M.; Skaarman, C.; Smellie, J.; Nilsson, A.C.

1995-02-01

The Laxemar deep borehole, KLX02 (1705 m depth), located close to the Aespoe Hard Rock Laboratory (HRL), has been investigated. Groundwater sampling was conducted on two occasions and using different methods. The first sampling was taken in the open borehole using the so-called Tube sampler; the second sampling carried out using the SKB-packer equipment to isolate pre-determined borehole sections. Groundwater compositions consist of two distinct groupings; one shallow to intermediate Sodium-Bicarbonate type (Na(Ca,K):HC 3 Cl(SO 4 )) to a depth of 1000 m, and the other of deep origin, a calcium-chloride type (Ca-Na(K):Cl-SO 4 (Br)), occurring below 1000 m. The deep brines contain up to 46000 mg of Cl per litre. The influence of borehole activities are seen in the tritium data which record significant tritium down to 1000 m, and even to 1420 m. Mixing modelling shows that water from the 1960's is the main source for this tritium. The high tritium values in the 1090-1096.2 m section are due to contamination of 1% shallow water from 1960 and 2% of modern shallow water. The upper 800 m of bedrock at Laxemar lies within a groundwater recharge area; the sub-vertical to moderate angled fracture zones facilitate groundwater circulation to considerable depths, at least to 800 m, thus accounting for some of the low saline brackish groundwaters in these conducting fracture zones. Below 1000 m the system is hydraulically and geochemically 'closed' such that highly saline brines exist in a near-stagnant environment. 30 refs, 22 figs, 8 tabs

The deep ocean under climate change.

Science.gov (United States)

Levin, Lisa A; Le Bris, Nadine

2015-11-13

The deep ocean absorbs vast amounts of heat and carbon dioxide, providing a critical buffer to climate change but exposing vulnerable ecosystems to combined stresses of warming, ocean acidification, deoxygenation, and altered food inputs. Resulting changes may threaten biodiversity and compromise key ocean services that maintain a healthy planet and human livelihoods. There exist large gaps in understanding of the physical and ecological feedbacks that will occur. Explicit recognition of deep-ocean climate mitigation and inclusion in adaptation planning by the United Nations Framework Convention on Climate Change (UNFCCC) could help to expand deep-ocean research and observation and to protect the integrity and functions of deep-ocean ecosystems. Copyright © 2015, American Association for the Advancement of Science.

SEDS: THE SPITZER EXTENDED DEEP SURVEY. SURVEY DESIGN, PHOTOMETRY, AND DEEP IRAC SOURCE COUNTS

International Nuclear Information System (INIS)

Ashby, M. L. N.; Willner, S. P.; Fazio, G. G.; Huang, J.-S.; Hernquist, L.; Hora, J. L.; Arendt, R.; Barmby, P.; Barro, G.; Faber, S.; Guhathakurta, P.; Bell, E. F.; Bouwens, R.; Cattaneo, A.; Croton, D.; Davé, R.; Dunlop, J. S.; Egami, E.; Finlator, K.; Grogin, N. A.

2013-01-01

The Spitzer Extended Deep Survey (SEDS) is a very deep infrared survey within five well-known extragalactic science fields: the UKIDSS Ultra-Deep Survey, the Extended Chandra Deep Field South, COSMOS, the Hubble Deep Field North, and the Extended Groth Strip. SEDS covers a total area of 1.46 deg 2 to a depth of 26 AB mag (3σ) in both of the warm Infrared Array Camera (IRAC) bands at 3.6 and 4.5 μm. Because of its uniform depth of coverage in so many widely-separated fields, SEDS is subject to roughly 25% smaller errors due to cosmic variance than a single-field survey of the same size. SEDS was designed to detect and characterize galaxies from intermediate to high redshifts (z = 2-7) with a built-in means of assessing the impact of cosmic variance on the individual fields. Because the full SEDS depth was accumulated in at least three separate visits to each field, typically with six-month intervals between visits, SEDS also furnishes an opportunity to assess the infrared variability of faint objects. This paper describes the SEDS survey design, processing, and publicly-available data products. Deep IRAC counts for the more than 300,000 galaxies detected by SEDS are consistent with models based on known galaxy populations. Discrete IRAC sources contribute 5.6 ± 1.0 and 4.4 ± 0.8 nW m –2 sr –1 at 3.6 and 4.5 μm to the diffuse cosmic infrared background (CIB). IRAC sources cannot contribute more than half of the total CIB flux estimated from DIRBE data. Barring an unexpected error in the DIRBE flux estimates, half the CIB flux must therefore come from a diffuse component.

The deep lymphatic anatomy of the hand.

Science.gov (United States)

Ma, Chuan-Xiang; Pan, Wei-Ren; Liu, Zhi-An; Zeng, Fan-Qiang; Qiu, Zhi-Qiang

2018-04-03

The deep lymphatic anatomy of the hand still remains the least described in medical literature. Eight hands were harvested from four nonembalmed human cadavers amputated above the wrist. A small amount of 6% hydrogen peroxide was employed to detect the lymphatic vessels around the superficial and deep palmar vascular arches, in webs from the index to little fingers, the thenar and hypothenar areas. A 30-gauge needle was inserted into the vessels and injected with a barium sulphate compound. Each specimen was dissected, photographed and radiographed to demonstrate deep lymphatic distribution of the hand. Five groups of deep collecting lymph vessels were found in the hand: superficial palmar arch lymph vessel (SPALV); deep palmar arch lymph vessel (DPALV); thenar lymph vessel (TLV); hypothenar lymph vessel (HTLV); deep finger web lymph vessel (DFWLV). Each group of vessels drained in different directions first, then all turned and ran towards the wrist in different layers. The deep lymphatic drainage of the hand has been presented. The results will provide an anatomical basis for clinical management, educational reference and scientific research. Copyright © 2018 Elsevier GmbH. All rights reserved.

Physical constraints in the deep hypolimnion of a subalpine lake driving planktonic Bacteria and Archaea distribution

Directory of Open Access Journals (Sweden)

Roberto Bertoni

2010-06-01

Full Text Available The study of the hydrodynamics of the hypolimnion of a deep holo-oligomictic lake (Lake Maggiore, Northern Italy, zmax¼370 m during the last 28 years showed that hypolimnetic waters remained isolated, not exchanging with the mixing zone even in winter when the full overturn conditions are most likely. The thickness of the isolated layer can range from 100 to 300 m. Thus, water masses of variable size reside in the lake for many years, and their physical and chemical conditions remain relatively unaffected by seasonal variability and epilimnetic imputs. In the hypolimnetic waters prokaryote abundance is three times lower than in the mixing layer but cell size is significantly higher. In addition, the relative abundance of Archaea and Crenarchaeota increases with depth in respect to that of Bacteria. The heterogeneous distribution of the two domains within the habitat can be attributed to the existence in the same environment of isolated water masses.

The question of recharge to the deep thermal reservoir underlying the geysers and hot springs of Yellowstone National Park: Chapter H in Integrated geoscience studies in Integrated geoscience studies in the Greater Yellowstone Area—Volcanic, tectonic, and hydrothermal processes in the Yellowstone geoecosystem

Science.gov (United States)

Rye, Robert O.; Truesdell, Alfred Hemingway; Morgan, Lisa A.

2007-01-01

The extraordinary number, size, and unspoiled beauty of the geysers and hot springs of Yellowstone National Park (the Park) make them a national treasure. The hydrology of these special features and their relation to cold waters of the Yellowstone area are poorly known. In the absence of deep drill holes, such information is available only indirectly from isotope studies. The δD-δ18O values of precipitation and cold surface-water and ground-water samples are close to the global meteoric water line (Craig, 1961). δD values of monthly samples of rain and snow collected from 1978 to 1981 at two stations in the Park show strong seasonal variations, with average values for winter months close to those for cold waters near the collection sites. δD values of more than 300 samples from cold springs, cold streams, and rivers collected during the fall from 1967 to 1992 show consistent north-south and east-west patterns throughout and outside of the Park, although values at a given site vary by as much as 8 ‰ from year to year. These data, along with hot-spring data (Truesdell and others, 1977; Pearson and Truesdell, 1978), show that ascending Yellowstone thermal waters are modified isotopically and chemically by a variety of boiling and mixing processes in shallow reservoirs. Near geyser basins, shallow recharge waters from nearby rhyolite plateaus dilute the ascending deep thermal waters, particularly at basin margins, and mix and boil in reservoirs that commonly are interconnected. Deep recharge appears to derive from a major deep thermal-reservoir fluid that supplies steam and hot water to all geyser basins on the west side of the Park and perhaps in the entire Yellowstone caldera. This water (T ≥350°C; δD = –149±1 ‰) is isotopically lighter than all but the farthest north, highest altitude cold springs and streams and a sinter-producing warm spring (δD = –153 ‰) north of the Park. Derivation of this deep fluid solely from present-day recharge is

Mitogenomics does not resolve deep molluscan relationships (yet?).

Science.gov (United States)

Stöger, I; Schrödl, M

2013-11-01

The origin of molluscs among lophotrochozoan metazoans is unresolved and interclass relationships are contradictory between morphology-based, multi-locus, and recent phylogenomic analyses. Within the "Deep Metazoan Phylogeny" framework, all available molluscan mitochondrial genomes were compiled, covering 6 of 8 classes. Genomes were reannotated, and 13 protein coding genes (PCGs) were analyzed in various taxon settings, under multiple masking and coding regimes. Maximum Likelihood based methods were used for phylogenetic reconstructions. In all cases, molluscs result mixed up with lophotrochozoan outgroups, and most molluscan classes with more than single representatives available are non-monophyletic. We discuss systematic errors such as long branch attraction to cause aberrant, basal positions of fast evolving ingroups such as scaphopods, patellogastropods and, in particular, the gastropod subgroup Heterobranchia. Mitochondrial sequences analyzed either as amino acids or nucleotides may perform well in some (Cephalopoda) but not in other palaeozoic molluscan groups; they are not suitable to reconstruct deep (Cambrian) molluscan evolution. Supposedly "rare" mitochondrial genome level features have long been promoted as phylogenetically informative. In our newly annotated data set, features such as genome size, transcription on one or both strands, and certain coupled pairs of PCGs show a homoplastic, but obviously non-random distribution. Apparently congruent (but not unambiguous) signal for non-trivial subclades, e.g. for a clade composed of pteriomorph and heterodont bivalves, needs confirmation from a more comprehensive bivalve sampling. We found that larger clusters not only of PCGs but also of rRNAs and even tRNAs can bear local phylogenetic signal; adding trnG-trnE to the end of the ancestral cluster trnM-trnC-trnY-trnW-trnQ might be synapomorphic for Mollusca. Mitochondrial gene arrangement and other genome level features explored and reviewed herein thus

Effect of Mixing Process on Polypropylene Modified Bituminous Concrete Mix Properties

OpenAIRE

Noor Zainab Habib; Ibrahim Kamaruddin; Madzalan Napiah; Isa Mohd Tan

2011-01-01

This paper presents a research conducted to investigate the effect of mixing process on polypropylene (PP) modified bitumen mixed with well graded aggregate to form modified bituminous concrete mix. Two mode of mixing, namely dry and wet with different concentration of polymer polypropylene was used with 80/100 pen bitumen, to evaluate the bituminous concrete mix properties. Three percentages of polymer varying from 1-3% by the weight of bitumen was used in this study. Three mixes namely cont...

Characterizations of geothermal springs along the Moxi deep fault in the western Sichuan plateau, China

Science.gov (United States)

Qi, Jihong; Xu, Mo; An, Chengjiao; Wu, Mingliang; Zhang, Yunhui; Li, Xiao; Zhang, Qiang; Lu, Guoping

2017-02-01

Abundant geothermal springs occur along the Moxi fault located in western Sichuan Province (the eastern edge of the Qinghai-Tibet plateau), highlighted by geothermal water outflow with an unusually high temperature of 218 °C at 21.5 MPa from a 2010-m borehole in Laoyulin, Kangding. Earthquake activity occurs relatively more frequently in the region and is considered to be related to the strong hydrothermal activity. Geothermal waters hosted by a deep fault may provide evidence regarding the deep underground; their aqueous chemistry and isotopic information can indicate the mechanism of thermal springs. Cyclical variations of geothermal water outflows are thought to work under the effect of solid earth tides and can contribute to understanding conditions and processes in underground geo-environments. This paper studies the origin and variations of the geothermal spring group controlled by the Moxi fault and discusses conditions in the deep ground. Flow variation monitoring of a series of parameters was performed to study the geothermal responses to solid tides. Geothermal reservoir temperatures are evaluated with Na-K-Mg data. The abundant sulfite content, dissolved oxygen (DO) and oxidation-reduction potential (ORP) data are discussed to study the oxidation-reduction states. Strontium isotopes are used to trace the water source. The results demonstrate that geothermal water could flow quickly through the Moxi fault the depth of the geothermal reservoir influences the thermal reservoir temperature, where supercritical hot water is mixed with circulating groundwater and can reach 380 °C. To the southward along the fault, the circulation of geothermal waters becomes shallower, and the waters may have reacted with metamorphic rock to some extent. Our results provide a conceptual deep heat source model for geothermal flow and the reservoir characteristics of the Moxi fault and indicate that the faulting may well connect the deep heat source to shallower depths. The

Deep ECGNet: An Optimal Deep Learning Framework for Monitoring Mental Stress Using Ultra Short-Term ECG Signals.

Science.gov (United States)

Hwang, Bosun; You, Jiwoo; Vaessen, Thomas; Myin-Germeys, Inez; Park, Cheolsoo; Zhang, Byoung-Tak

2018-02-08

Stress recognition using electrocardiogram (ECG) signals requires the intractable long-term heart rate variability (HRV) parameter extraction process. This study proposes a novel deep learning framework to recognize the stressful states, the Deep ECGNet, using ultra short-term raw ECG signals without any feature engineering methods. The Deep ECGNet was developed through various experiments and analysis of ECG waveforms. We proposed the optimal recurrent and convolutional neural networks architecture, and also the optimal convolution filter length (related to the P, Q, R, S, and T wave durations of ECG) and pooling length (related to the heart beat period) based on the optimization experiments and analysis on the waveform characteristics of ECG signals. The experiments were also conducted with conventional methods using HRV parameters and frequency features as a benchmark test. The data used in this study were obtained from Kwangwoon University in Korea (13 subjects, Case 1) and KU Leuven University in Belgium (9 subjects, Case 2). Experiments were designed according to various experimental protocols to elicit stressful conditions. The proposed framework to recognize stress conditions, the Deep ECGNet, outperformed the conventional approaches with the highest accuracy of 87.39% for Case 1 and 73.96% for Case 2, respectively, that is, 16.22% and 10.98% improvements compared with those of the conventional HRV method. We proposed an optimal deep learning architecture and its parameters for stress recognition, and the theoretical consideration on how to design the deep learning structure based on the periodic patterns of the raw ECG data. Experimental results in this study have proved that the proposed deep learning model, the Deep ECGNet, is an optimal structure to recognize the stress conditions using ultra short-term ECG data.

Acute deep venous thrombosis of lower extremity: anatomical distribution, comparison of anticoagulation, thrombolysis and interventional therapy

International Nuclear Information System (INIS)

Zhuang; Naijun; Che Guoping; Gu Jianping; Lou Wensheng; He Xu; Chen Liang; Su Haobo; Song Jinhua; Wang Tao; Xu Ke

2011-01-01

Objective: To investigate the anatomical distribution of acute deep venous thrombosis (DVT) of the lower extremity, and compare different therapeutic methods including anticoagulation alone, thrombolysis through dorsal vein and interventional therapy. Methods: The clinical data, venography and therapies of 204 acute DVT patients were retrospectively studied According to the distribution, DVT were classified into three types including peripheral, central and mixed types. According to the difference of the therapeutic method, each type of DVT was divided into three groups, Group A (37 patients) anticoagulation alone: Group B (55 patients) thrombolysis through dorsal vein: and Group C (112 patients) interventional therapy. The results of different kind of treatment method in each type of DVT were evaluated before the patients were discharged and the Chi-square test was used for statistical analysis. Results: There were 132 patients with DVT in the left lower extremity, 62 in right lower extremity, and 10 in both extremities.. The complication of pulmonary embolism (PE) occurred in 4, 5 and 2 cases respectively, and the morbidity was 3.0%, 8.1% and 20.0% (χ 2 =6.494, P=0.039) respectively. There was significant statistical difference among them. There were 23 cases of peripheral type of DVT, 48 central type and 133 mixed type. The complication of PE were observed in 2, 5 and 4 cases respectively in each type. The morbidity was 8.7%, 10.4% and 3.0% respectively (χ 2 =4.350, P=0.114). There were no statistical significance among them. In the 23 cases of peripheral type DVTs, 2 of 5 in group A and 11 of 18 in group B had excellent therapeutic response. In the 48 cases of central type of DVTs, 1 of 10 in group A, 2 of 5 in in group B and 26 of 33 in group C had excellent therapeutic response. There were statistically significant differences among groups A, B and C (χ 2 =16.157, P=0.000). In the 133 cases of mixed type DVTs, 1 of 22 in group A, 10 of 32 in group B and 65

Deep inelastic electron and muon scattering

International Nuclear Information System (INIS)

Taylor, R.E.

1975-07-01

From the review of deep inelastic electron and muon scattering it is concluded that the puzzle of deep inelastic scattering versus annihilation was replaced with the challenge of the new particles, that the evidence for the simplest quark-algebra models of deep inelastic processes is weaker than a year ago. Definite evidence of scale breaking was found but the specific form of that scale breaking is difficult to extract from the data. 59 references

Fast, Distributed Algorithms in Deep Networks

Science.gov (United States)

2016-05-11

shallow networks, additional work will need to be done in order to allow for the application of ADMM to deep nets. The ADMM method allows for quick...Quock V Le, et al. Large scale distributed deep networks. In Advances in Neural Information Processing Systems, pages 1223–1231, 2012. [11] Ken-Ichi...A TRIDENT SCHOLAR PROJECT REPORT NO. 446 Fast, Distributed Algorithms in Deep Networks by Midshipman 1/C Ryan J. Burmeister, USN

Bacterial Community Response in Deep Faroe-Shetland Channel Sediments Following Hydrocarbon Entrainment With and Without Dispersant Addition

Directory of Open Access Journals (Sweden)

Luis J. Perez Calderon

2018-05-01

Full Text Available Deep sea oil exploration is increasing and presents environmental challenges for deep ocean ecosystems. Marine oil spills often result in contamination of sediments with oil; following the Deepwater Horizon (DwH disaster up to 31% of the released oil entrained in the water column was deposited as oily residues on the seabed. Although the aftermath of DwH was studied intensely, lessons learned may not be directly transferable to other deep-sea hydrocarbon exploration areas, such as the Faroe-Shetland Channel (FSC which comprises cold temperatures and a unique hydrodynamic regime. Here, transport of hydrocarbons into deep FSC sediments, subsequent responses in benthic microbial populations and effects of dispersant application on hydrocarbon fate and microbial communities were investigated. Sediments from 1,000 m in the FSC were incubated at 0°C for 71 days after addition of a 20-hydrocarbon component oil-sediment aggregate. Dispersant was added periodically from day 4. An additional set of cores using sterilized and homogenized sediment was analyzed to evaluate the effects of sediment matrix modification on hydrocarbon entrainment. Sediment layers were independently analyzed for hydrocarbon content by gas chromatography with flame ionization detection and modeled with linear mixed effects models. Oil was entrained over 4 cm deep into FSC sediments after 42 days and dispersant effectiveness on hydrocarbon removal from sediment to the water column decreased with time. Sterilizing and homogenizing sediment resulted in hydrocarbon transport over 4 cm into sediments after 7 days. Significant shifts in bacterial populations were observed (DGGE profiling in response to hydrocarbon exposure after 42 days and below 2 cm deep. Dispersant application resulted in an accelerated and modified shift in bacterial communities. Bacterial 16S rRNA gene sequencing of oiled sediments revealed dominance of Colwellia and of Fusibacter when dispersant was applied over

Deep Learning from Crowds

DEFF Research Database (Denmark)

Rodrigues, Filipe; Pereira, Francisco Camara

Over the last few years, deep learning has revolutionized the field of machine learning by dramatically improving the stateof-the-art in various domains. However, as the size of supervised artificial neural networks grows, typically so does the need for larger labeled datasets. Recently...... networks from crowds. We begin by describing an EM algorithm for jointly learning the parameters of the network and the reliabilities of the annotators. Then, a novel general-purpose crowd layer is proposed, which allows us to train deep neural networks end-to-end, directly from the noisy labels......, crowdsourcing has established itself as an efficient and cost-effective solution for labeling large sets of data in a scalable manner, but it often requires aggregating labels from multiple noisy contributors with different levels of expertise. In this paper, we address the problem of learning deep neural...

Deep learning methods for protein torsion angle prediction.

Science.gov (United States)

Li, Haiou; Hou, Jie; Adhikari, Badri; Lyu, Qiang; Cheng, Jianlin

2017-09-18

Deep learning is one of the most powerful machine learning methods that has achieved the state-of-the-art performance in many domains. Since deep learning was introduced to the field of bioinformatics in 2012, it has achieved success in a number of areas such as protein residue-residue contact prediction, secondary structure prediction, and fold recognition. In this work, we developed deep learning methods to improve the prediction of torsion (dihedral) angles of proteins. We design four different deep learning architectures to predict protein torsion angles. The architectures including deep neural network (DNN) and deep restricted Boltzmann machine (DRBN), deep recurrent neural network (DRNN) and deep recurrent restricted Boltzmann machine (DReRBM) since the protein torsion angle prediction is a sequence related problem. In addition to existing protein features, two new features (predicted residue contact number and the error distribution of torsion angles extracted from sequence fragments) are used as input to each of the four deep learning architectures to predict phi and psi angles of protein backbone. The mean absolute error (MAE) of phi and psi angles predicted by DRNN, DReRBM, DRBM and DNN is about 20-21° and 29-30° on an independent dataset. The MAE of phi angle is comparable to the existing methods, but the MAE of psi angle is 29°, 2° lower than the existing methods. On the latest CASP12 targets, our methods also achieved the performance better than or comparable to a state-of-the art method. Our experiment demonstrates that deep learning is a valuable method for predicting protein torsion angles. The deep recurrent network architecture performs slightly better than deep feed-forward architecture, and the predicted residue contact number and the error distribution of torsion angles extracted from sequence fragments are useful features for improving prediction accuracy.

Deep Learning in Gastrointestinal Endoscopy.

Science.gov (United States)

Patel, Vivek; Armstrong, David; Ganguli, Malika; Roopra, Sandeep; Kantipudi, Neha; Albashir, Siwar; Kamath, Markad V

2016-01-01

Gastrointestinal (GI) endoscopy is used to inspect the lumen or interior of the GI tract for several purposes, including, (1) making a clinical diagnosis, in real time, based on the visual appearances; (2) taking targeted tissue samples for subsequent histopathological examination; and (3) in some cases, performing therapeutic interventions targeted at specific lesions. GI endoscopy is therefore predicated on the assumption that the operator-the endoscopist-is able to identify and characterize abnormalities or lesions accurately and reproducibly. However, as in other areas of clinical medicine, such as histopathology and radiology, many studies have documented marked interobserver and intraobserver variability in lesion recognition. Thus, there is a clear need and opportunity for techniques or methodologies that will enhance the quality of lesion recognition and diagnosis and improve the outcomes of GI endoscopy. Deep learning models provide a basis to make better clinical decisions in medical image analysis. Biomedical image segmentation, classification, and registration can be improved with deep learning. Recent evidence suggests that the application of deep learning methods to medical image analysis can contribute significantly to computer-aided diagnosis. Deep learning models are usually considered to be more flexible and provide reliable solutions for image analysis problems compared to conventional computer vision models. The use of fast computers offers the possibility of real-time support that is important for endoscopic diagnosis, which has to be made in real time. Advanced graphics processing units and cloud computing have also favored the use of machine learning, and more particularly, deep learning for patient care. This paper reviews the rapidly evolving literature on the feasibility of applying deep learning algorithms to endoscopic imaging.

Mixing and remineralization in waters detrained from the surface into Subantarctic Mode Water and Antarctic Intermediate Water in the southeastern Pacific

Science.gov (United States)

Carter, B. R.; Talley, L. D.; Dickson, A. G.

2014-06-01

A hydrographic data set collected in the region and season of Subantarctic Mode Water and Antarctic Intermediate Water (SAMW and AAIW) formation in the southeastern Pacific allows us to estimate the preformed properties of surface water detrained into these water masses from deep mixed layers north of the Subantarctic Front and Antarctic Surface Water south of the front. Using 10 measured seawater properties, we estimate: the fractions of SAMW/AAIW that originate as surface source waters, as well as fractions that mix into these water masses from subtropical thermocline water above and Upper Circumpolar Deep Water below the subducted SAMW/AAIW; ages associated with the detrained surface water; and remineralization and dissolution rates and ratios. The mixing patterns imply that cabbeling can account for ˜0.005-0.03 kg m-3 of additional density in AAIW, and ˜0-0.02 kg m-3 in SAMW. We estimate a shallow depth (˜300-700 m, above the aragonite saturation horizon) calcium carbonate dissolution rate of 0.4 ± 0.2 µmol CaCO3 kg-1 yr-1, a phosphate remineralization rate of 0.031 ± 0.009 µmol P kg-1 yr-1, and remineralization ratios of P:N:-O2:Corg of 1:(15.5 ± 0.6):(143 ± 10):(104 ± 22) for SAMW/AAIW. Our shallow depth calcium carbonate dissolution rate is comparable to previous estimates for our region. Our -O2:P ratio is smaller than many global averages. Our model suggests neglecting diapycnal mixing of preformed phosphate has likely biased previous estimates of -O2:P and Corg:P high, but that the Corg:P ratio bias may have been counteracted by a second bias in previous studies from neglecting anthropogenic carbon gradients.

Neuromorphic Deep Learning Machines

OpenAIRE

Neftci, E; Augustine, C; Paul, S; Detorakis, G

2017-01-01

An ongoing challenge in neuromorphic computing is to devise general and computationally efficient models of inference and learning which are compatible with the spatial and temporal constraints of the brain. One increasingly popular and successful approach is to take inspiration from inference and learning algorithms used in deep neural networks. However, the workhorse of deep learning, the gradient descent Back Propagation (BP) rule, often relies on the immediate availability of network-wide...

Design optimization of single mixed refrigerant LNG process using a hybrid modified coordinate descent algorithm

Science.gov (United States)

Qyyum, Muhammad Abdul; Long, Nguyen Van Duc; Minh, Le Quang; Lee, Moonyong

2018-01-01

Design optimization of the single mixed refrigerant (SMR) natural gas liquefaction (LNG) process involves highly non-linear interactions between decision variables, constraints, and the objective function. These non-linear interactions lead to an irreversibility, which deteriorates the energy efficiency of the LNG process. In this study, a simple and highly efficient hybrid modified coordinate descent (HMCD) algorithm was proposed to cope with the optimization of the natural gas liquefaction process. The single mixed refrigerant process was modeled in Aspen Hysys® and then connected to a Microsoft Visual Studio environment. The proposed optimization algorithm provided an improved result compared to the other existing methodologies to find the optimal condition of the complex mixed refrigerant natural gas liquefaction process. By applying the proposed optimization algorithm, the SMR process can be designed with the 0.2555 kW specific compression power which is equivalent to 44.3% energy saving as compared to the base case. Furthermore, in terms of coefficient of performance (COP), it can be enhanced up to 34.7% as compared to the base case. The proposed optimization algorithm provides a deep understanding of the optimization of the liquefaction process in both technical and numerical perspectives. In addition, the HMCD algorithm can be employed to any mixed refrigerant based liquefaction process in the natural gas industry.

Deep Restricted Kernel Machines Using Conjugate Feature Duality.

Science.gov (United States)

Suykens, Johan A K

2017-08-01

The aim of this letter is to propose a theory of deep restricted kernel machines offering new foundations for deep learning with kernel machines. From the viewpoint of deep learning, it is partially related to restricted Boltzmann machines, which are characterized by visible and hidden units in a bipartite graph without hidden-to-hidden connections and deep learning extensions as deep belief networks and deep Boltzmann machines. From the viewpoint of kernel machines, it includes least squares support vector machines for classification and regression, kernel principal component analysis (PCA), matrix singular value decomposition, and Parzen-type models. A key element is to first characterize these kernel machines in terms of so-called conjugate feature duality, yielding a representation with visible and hidden units. It is shown how this is related to the energy form in restricted Boltzmann machines, with continuous variables in a nonprobabilistic setting. In this new framework of so-called restricted kernel machine (RKM) representations, the dual variables correspond to hidden features. Deep RKM are obtained by coupling the RKMs. The method is illustrated for deep RKM, consisting of three levels with a least squares support vector machine regression level and two kernel PCA levels. In its primal form also deep feedforward neural networks can be trained within this framework.

Preliminary analyses of the deep geoenvironmental characteristics for the deep borehole disposal of high-level radioactive waste in Korea

Energy Technology Data Exchange (ETDEWEB)

Lee, Jong Youl; Lee, Min Soo; Choi, Heui Joo; Kim, Geon Young; Kim, Kyung Su [Korea Atomic Energy Research Institute, Daejeon (Korea, Republic of)

2016-06-15

Spent fuels from nuclear power plants, as well as high-level radioactive waste from the recycling of spent fuels, should be safely isolated from human environment for an extremely long time. Recently, meaningful studies on the development of deep borehole radioactive waste disposal system in 3-5 km depth have been carried out in USA and some countries in Europe, due to great advance in deep borehole drilling technology. In this paper, domestic deep geoenvironmental characteristics are preliminarily investigated to analyze the applicability of deep borehole disposal technology in Korea. To do this, state-of-the art technologies in USA and some countries in Europe are reviewed, and geological and geothermal data from the deep boreholes for geothermal usage are analyzed. Based on the results on the crystalline rock depth, the geothermal gradient and the spent fuel types generated in Korea, a preliminary deep borehole concept including disposal canister and sealing system, is suggested.

Preliminary analyses of the deep geoenvironmental characteristics for the deep borehole disposal of high-level radioactive waste in Korea

International Nuclear Information System (INIS)

Lee, Jong Youl; Lee, Min Soo; Choi, Heui Joo; Kim, Geon Young; Kim, Kyung Su

2016-01-01

Spent fuels from nuclear power plants, as well as high-level radioactive waste from the recycling of spent fuels, should be safely isolated from human environment for an extremely long time. Recently, meaningful studies on the development of deep borehole radioactive waste disposal system in 3-5 km depth have been carried out in USA and some countries in Europe, due to great advance in deep borehole drilling technology. In this paper, domestic deep geoenvironmental characteristics are preliminarily investigated to analyze the applicability of deep borehole disposal technology in Korea. To do this, state-of-the art technologies in USA and some countries in Europe are reviewed, and geological and geothermal data from the deep boreholes for geothermal usage are analyzed. Based on the results on the crystalline rock depth, the geothermal gradient and the spent fuel types generated in Korea, a preliminary deep borehole concept including disposal canister and sealing system, is suggested

Toolkits and Libraries for Deep Learning.

Science.gov (United States)

Erickson, Bradley J; Korfiatis, Panagiotis; Akkus, Zeynettin; Kline, Timothy; Philbrick, Kenneth

2017-08-01

Deep learning is an important new area of machine learning which encompasses a wide range of neural network architectures designed to complete various tasks. In the medical imaging domain, example tasks include organ segmentation, lesion detection, and tumor classification. The most popular network architecture for deep learning for images is the convolutional neural network (CNN). Whereas traditional machine learning requires determination and calculation of features from which the algorithm learns, deep learning approaches learn the important features as well as the proper weighting of those features to make predictions for new data. In this paper, we will describe some of the libraries and tools that are available to aid in the construction and efficient execution of deep learning as applied to medical images.

Mixed-mode modelling mixing methodologies for organisational intervention

CERN Document Server

Clarke, Steve; Lehaney, Brian

2001-01-01

The 1980s and 1990s have seen a growing interest in research and practice in the use of methodologies within problem contexts characterised by a primary focus on technology, human issues, or power. During the last five to ten years, this has given rise to challenges regarding the ability of a single methodology to address all such contexts, and the consequent development of approaches which aim to mix methodologies within a single problem situation. This has been particularly so where the situation has called for a mix of technological (the so-called 'hard') and human­ centred (so-called 'soft') methods. The approach developed has been termed mixed-mode modelling. The area of mixed-mode modelling is relatively new, with the phrase being coined approximately four years ago by Brian Lehaney in a keynote paper published at the 1996 Annual Conference of the UK Operational Research Society. Mixed-mode modelling, as suggested above, is a new way of considering problem situations faced by organisations. Traditional...

Deep-sea coral research and technology program: Alaska deep-sea coral and sponge initiative final report

Science.gov (United States)

Rooper, Chris; Stone, Robert P.; Etnoyer, Peter; Conrath, Christina; Reynolds, Jennifer; Greene, H. Gary; Williams, Branwen; Salgado, Enrique; Morrison, Cheryl L.; Waller, Rhian G.; Demopoulos, Amanda W.J.

2017-01-01

Deep-sea coral and sponge ecosystems are widespread throughout most of Alaska’s marine waters. In some places, such as the central and western Aleutian Islands, deep-sea coral and sponge resources can be extremely diverse and may rank among the most abundant deep-sea coral and sponge communities in the world. Many different species of fishes and invertebrates are associated with deep-sea coral and sponge communities in Alaska. Because of their biology, these benthic invertebrates are potentially impacted by climate change and ocean acidification. Deepsea coral and sponge ecosystems are also vulnerable to the effects of commercial fishing activities. Because of the size and scope of Alaska’s continental shelf and slope, the vast majority of the area has not been visually surveyed for deep-sea corals and sponges. NOAA’s Deep Sea Coral Research and Technology Program (DSCRTP) sponsored a field research program in the Alaska region between 2012–2015, referred to hereafter as the Alaska Initiative. The priorities for Alaska were derived from ongoing data needs and objectives identified by the DSCRTP, the North Pacific Fishery Management Council (NPFMC), and Essential Fish Habitat-Environmental Impact Statement (EFH-EIS) process.This report presents the results of 15 projects conducted using DSCRTP funds from 2012-2015. Three of the projects conducted as part of the Alaska deep-sea coral and sponge initiative included dedicated at-sea cruises and fieldwork spread across multiple years. These projects were the eastern Gulf of Alaska Primnoa pacifica study, the Aleutian Islands mapping study, and the Gulf of Alaska fish productivity study. In all, there were nine separate research cruises carried out with a total of 109 at-sea days conducting research. The remaining projects either used data and samples collected by the three major fieldwork projects or were piggy-backed onto existing research programs at the Alaska Fisheries Science Center (AFSC).

Image Captioning with Deep Bidirectional LSTMs

OpenAIRE

Wang, Cheng; Yang, Haojin; Bartz, Christian; Meinel, Christoph

2016-01-01

This work presents an end-to-end trainable deep bidirectional LSTM (Long-Short Term Memory) model for image captioning. Our model builds on a deep convolutional neural network (CNN) and two separate LSTM networks. It is capable of learning long term visual-language interactions by making use of history and future context information at high level semantic space. Two novel deep bidirectional variant models, in which we increase the depth of nonlinearity transition in different way, are propose...

An overview of latest deep water technologies

International Nuclear Information System (INIS)

Anon.

1995-01-01

The 8th Deep Offshore Technology Conference (DOT VIII, Rio de Janeiro, October 30 - November 3, 1995) has brought together renowned specialists in deep water development projects, as well as managers from oil companies and engineering/service companies to discuss state-of-the-art technologies and ongoing projects in the deep offshore. This paper is a compilation of the session summaries about sub sea technologies, mooring and dynamic positioning, floaters (Tension Leg Platforms (TLP) and Floating Production Storage and Off loading (FPSO)), pipelines and risers, exploration and drilling, and other deep water techniques. (J.S.)

Deep learning in neural networks: an overview.

Science.gov (United States)

Schmidhuber, Jürgen

2015-01-01

In recent years, deep artificial neural networks (including recurrent ones) have won numerous contests in pattern recognition and machine learning. This historical survey compactly summarizes relevant work, much of it from the previous millennium. Shallow and Deep Learners are distinguished by the depth of their credit assignment paths, which are chains of possibly learnable, causal links between actions and effects. I review deep supervised learning (also recapitulating the history of backpropagation), unsupervised learning, reinforcement learning & evolutionary computation, and indirect search for short programs encoding deep and large networks.

Combining shallow and deep processing for a robust, fast, deep-linguistic dependency parser

OpenAIRE

Schneider, G

2004-01-01

This paper describes Pro3Gres, a fast, robust, broad-coverage parser that delivers deep-linguistic grammatical relation structures as output, which are closer to predicate-argument structures and more informative than pure constituency structures. The parser stays as shallow as is possible for each task, combining shallow and deep-linguistic methods by integrating chunking and by expressing the majority of long-distance dependencies in a context-free way. It combines statistical and rule-base...

DeepVel: Deep learning for the estimation of horizontal velocities at the solar surface

Science.gov (United States)

Asensio Ramos, A.; Requerey, I. S.; Vitas, N.

2017-07-01

Many phenomena taking place in the solar photosphere are controlled by plasma motions. Although the line-of-sight component of the velocity can be estimated using the Doppler effect, we do not have direct spectroscopic access to the components that are perpendicular to the line of sight. These components are typically estimated using methods based on local correlation tracking. We have designed DeepVel, an end-to-end deep neural network that produces an estimation of the velocity at every single pixel, every time step, and at three different heights in the atmosphere from just two consecutive continuum images. We confront DeepVel with local correlation tracking, pointing out that they give very similar results in the time and spatially averaged cases. We use the network to study the evolution in height of the horizontal velocity field in fragmenting granules, supporting the buoyancy-braking mechanism for the formation of integranular lanes in these granules. We also show that DeepVel can capture very small vortices, so that we can potentially expand the scaling cascade of vortices to very small sizes and durations. The movie attached to Fig. 3 is available at http://www.aanda.org

Particle–Mixing Simulations Using DEM and Comparison of the Performance of Mixing Indices

International Nuclear Information System (INIS)

Cho, Migyung

2017-01-01

Mixing of molecular grains having different characteristics is very important in many industries such as the food and pharmaceutical industries. With the development of computer simulations, it is common practice to find the optimal mixing conditions through a simulation before the actual mixing task to estimate the proper level of mixing. Accordingly, there has been an increasing need for a mixing index to measure the mix of particles in the simulation process. Mixing indices, which have been widely used so far, can largely be classified into two types: first is the statistical-based mixing index, which is prepared using the sampling method, and the second is the mixing index that is prepared using all the particles. In this paper, we calculated mixing indices in different ways for the data in the course of mixing the particles using the DEM simulation. Additionally, we compared the performance, advantages, and disadvantages of each mixing index. Therefore, I propose a standard that can be used to select an appropriate mixing index.

Particle–Mixing Simulations Using DEM and Comparison of the Performance of Mixing Indices

Energy Technology Data Exchange (ETDEWEB)

Cho, Migyung [Tongmyong Univ., Busan (Korea, Republic of)

2017-02-15

Mixing of molecular grains having different characteristics is very important in many industries such as the food and pharmaceutical industries. With the development of computer simulations, it is common practice to find the optimal mixing conditions through a simulation before the actual mixing task to estimate the proper level of mixing. Accordingly, there has been an increasing need for a mixing index to measure the mix of particles in the simulation process. Mixing indices, which have been widely used so far, can largely be classified into two types: first is the statistical-based mixing index, which is prepared using the sampling method, and the second is the mixing index that is prepared using all the particles. In this paper, we calculated mixing indices in different ways for the data in the course of mixing the particles using the DEM simulation. Additionally, we compared the performance, advantages, and disadvantages of each mixing index. Therefore, I propose a standard that can be used to select an appropriate mixing index.

Geochemical constraints on sources of metabolic energy for chemolithoautotrophy in ultramafic-hosted deep-sea hydrothermal systems.

Science.gov (United States)

McCollom, Thomas M

2007-12-01

Numerical models are employed to investigate sources of chemical energy for autotrophic microbial metabolism that develop during mixing of oxidized seawater with strongly reduced fluids discharged from ultramafic-hosted hydrothermal systems on the seafloor. Hydrothermal fluids in these systems are highly enriched in H(2) and CH(4) as a result of alteration of ultramafic rocks (serpentinization) in the subsurface. Based on the availability of chemical energy sources, inferences are made about the likely metabolic diversity, relative abundance, and spatial distribution of microorganisms within ultramafic-hosted systems. Metabolic reactions involving H(2) and CH(4), particularly hydrogen oxidation, methanotrophy, sulfate reduction, and methanogenesis, represent the predominant sources of chemical energy during fluid mixing. Owing to chemical gradients that develop from fluid mixing, aerobic metabolisms are likely to predominate in low-temperature environments (energy per kilogram of hydrothermal fluid, while anaerobic metabolic reactions can supply about 1 kJ, which is sufficient to support a maximum of approximately 120 mg (dry weight) of primary biomass production by aerobic organisms and approximately 20-30 mg biomass by anaerobes. The results indicate that ultramafic-hosted systems are capable of supplying about twice as much chemical energy as analogous deep-sea hydrothermal systems hosted in basaltic rocks.

Deep Learning in Drug Discovery.

Science.gov (United States)

Gawehn, Erik; Hiss, Jan A; Schneider, Gisbert

2016-01-01

Artificial neural networks had their first heyday in molecular informatics and drug discovery approximately two decades ago. Currently, we are witnessing renewed interest in adapting advanced neural network architectures for pharmaceutical research by borrowing from the field of "deep learning". Compared with some of the other life sciences, their application in drug discovery is still limited. Here, we provide an overview of this emerging field of molecular informatics, present the basic concepts of prominent deep learning methods and offer motivation to explore these techniques for their usefulness in computer-assisted drug discovery and design. We specifically emphasize deep neural networks, restricted Boltzmann machine networks and convolutional networks. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

How diffusivity, thermocline and incident light intensity modulate the dynamics of deep chlorophyll maximum in Tyrrhenian Sea.

Directory of Open Access Journals (Sweden)

Davide Valenti

Full Text Available During the last few years theoretical works have shed new light and proposed new hypotheses on the mechanisms which regulate the spatio-temporal behaviour of phytoplankton communities in marine pelagic ecosystems. Despite this, relevant physical and biological issues, such as effects of the time-dependent mixing in the upper layer, competition between groups, and dynamics of non-stationary deep chlorophyll maxima, are still open questions. In this work, we analyze the spatio-temporal behaviour of five phytoplankton populations in a real marine ecosystem by using a one-dimensional reaction-diffusion-taxis model. The study is performed, taking into account the seasonal variations of environmental variables, such as light intensity, thickness of upper mixed layer and profiles of vertical turbulent diffusivity, obtained starting from experimental findings. Theoretical distributions of phytoplankton cell concentration was converted in chlorophyll concentration, and compared with the experimental profiles measured in a site of the Tyrrhenian Sea at four different times (seasons of the year, during four different oceanographic cruises. As a result we find a good agreement between theoretical and experimental distributions of chlorophyll concentration. In particular, theoretical results reveal that the seasonal changes of environmental variables play a key role in the phytoplankton distribution and determine the properties of the deep chlorophyll maximum. This study could be extended to other marine ecosystems to predict future changes in the phytoplankton biomass due to global warming, in view of devising strategies to prevent the decline of the primary production and the consequent decrease of fish species.

Iris Transponder-Communications and Navigation for Deep Space

Science.gov (United States)

Duncan, Courtney B.; Smith, Amy E.; Aguirre, Fernando H.

2014-01-01

The Jet Propulsion Laboratory has developed the Iris CubeSat compatible deep space transponder for INSPIRE, the first CubeSat to deep space. Iris is 0.4 U, 0.4 kg, consumes 12.8 W, and interoperates with NASA's Deep Space Network (DSN) on X-Band frequencies (7.2 GHz uplink, 8.4 GHz downlink) for command, telemetry, and navigation. This talk discusses the Iris for INSPIRE, it's features and requirements; future developments and improvements underway; deep space and proximity operations applications for Iris; high rate earth orbit variants; and ground requirements, such as are implemented in the DSN, for deep space operations.

Injection grout for deep repositories - Low-pH cementitious grout for larger fractures. Field testing in Finland, Pilot tests

International Nuclear Information System (INIS)

Sievaeen, U.; Syrjaenen, P.; Ranta-aho, S.

2005-10-01

Posiva, SKB and NUMO have cooperated for developing a low pH injection grout for sealing of the deep repositories for spent nuclear fuel. A project 'Injection grout for deep repositories' was divided into four subprojects. The development of low pH cementitious grout for > 100 μm fractures was carried out in Finland. The development of non-cementitious low pH grout for < 100 μm fractures was carried out in Sweden. This report concerns the cementitious grout. Requirements for pH and penetration ability were set for the grouts to be developed. Besides these, the grouts were desired to fulfil certain targets set for viscosity, bleeding, shear strength, yield value, compressive strength and open time. Also durability, availability of the components and known history in practical engineering were given as requirements. The object of the work presented here was to test if the grout properties developed in laboratory can be met in field conditions. Only the most promising binder material combinations, which have fulfilled the main requirements in laboratory, were tested in field. Evaluations of environmental aspects are included in this report. In the pilot test 1, carried out in a multi-purpose tunnel in Helsinki, Portland cement-cilicasystem and blast furnace slag-based system were chosen to be tested. In field conditions, mixed with ordinary mixer, all grout properties achieved in laboratory, were not verified. Penetration ability was typically good, but fluidity and strength development were not satisfying. The main conclusion was that water to dry material ratio should be diminished. In order to get better rheological properties at the same time, superplastizicer was needed in further development of the mixes. Also accurate dosing and mixing seemed to be very important. Blast furnace slag - system was after this pilot test ruled out due to high leaching of sulphide from the product, not due to the bad technical properties. The development work continued with

Context and Deep Learning Design

Science.gov (United States)

Boyle, Tom; Ravenscroft, Andrew

2012-01-01

Conceptual clarification is essential if we are to establish a stable and deep discipline of technology enhanced learning. The technology is alluring; this can distract from deep design in a surface rush to exploit the affordances of the new technology. We need a basis for design, and a conceptual unit of organization, that are applicable across…

Deep Generative Models for Molecular Science

DEFF Research Database (Denmark)

Jørgensen, Peter Bjørn; Schmidt, Mikkel Nørgaard; Winther, Ole

2018-01-01

Generative deep machine learning models now rival traditional quantum-mechanical computations in predicting properties of new structures, and they come with a significantly lower computational cost, opening new avenues in computational molecular science. In the last few years, a variety of deep...... generative models have been proposed for modeling molecules, which differ in both their model structure and choice of input features. We review these recent advances within deep generative models for predicting molecular properties, with particular focus on models based on the probabilistic autoencoder (or...

Too Deep or Not Too Deep?: A Propensity-Matched Comparison of the Analgesic Effects of a Superficial Versus Deep Serratus Fascial Plane Block for Ambulatory Breast Cancer Surgery.

Science.gov (United States)

Abdallah, Faraj W; Cil, Tulin; MacLean, David; Madjdpour, Caveh; Escallon, Jaime; Semple, John; Brull, Richard

2018-07-01

Serratus fascial plane block can reduce pain following breast surgery, but the question of whether to inject the local anesthetic superficial or deep to the serratus muscle has not been answered. This cohort study compares the analgesic benefits of superficial versus deep serratus plane blocks in ambulatory breast cancer surgery patients at Women's College Hospital between February 2014 and December 2016. We tested the joint hypothesis that deep serratus block is noninferior to superficial serratus block for postoperative in-hospital (pre-discharge) opioid consumption and pain severity. One hundred sixty-six patients were propensity matched among 2 groups (83/group): superficial and deep serratus blocks. The cohort was used to evaluate the effect of blocks on postoperative oral morphine equivalent consumption and area under the curve for rest pain scores. We considered deep serratus block to be noninferior to superficial serratus block if it were noninferior for both outcomes, within 15 mg morphine and 4 cm·h units margins. Other outcomes included intraoperative fentanyl requirements, time to first analgesic request, recovery room stay, and incidence of postoperative nausea and vomiting. Deep serratus block was associated with postoperative morphine consumption and pain scores area under the curve that were noninferior to those of the superficial serratus block. Intraoperative fentanyl requirements, time to first analgesic request, recovery room stay, and postoperative nausea and vomiting were not different between blocks. The postoperative in-hospital analgesia associated with deep serratus block is as effective (within an acceptable margin) as superficial serratus block following ambulatory breast cancer surgery. These new findings are important to inform both current clinical practices and future prospective studies.

ADVANCED MIXING MODELS

International Nuclear Information System (INIS)

Lee, S; Richard Dimenna, R; David Tamburello, D

2008-01-01

The process of recovering the waste in storage tanks at the Savannah River Site (SRS) typically requires mixing the contents of the tank with one to four dual-nozzle jet mixers located within the tank. The typical criteria to establish a mixed condition in a tank are based on the number of pumps in operation and the time duration of operation. To ensure that a mixed condition is achieved, operating times are set conservatively long. This approach results in high operational costs because of the long mixing times and high maintenance and repair costs for the same reason. A significant reduction in both of these costs might be realized by reducing the required mixing time based on calculating a reliable indicator of mixing with a suitably validated computer code. The work described in this report establishes the basis for further development of the theory leading to the identified mixing indicators, the benchmark analyses demonstrating their consistency with widely accepted correlations, and the application of those indicators to SRS waste tanks to provide a better, physically based estimate of the required mixing time. Waste storage tanks at SRS contain settled sludge which varies in height from zero to 10 ft. The sludge has been characterized and modeled as micron-sized solids, typically 1 to 5 microns, at weight fractions as high as 20 to 30 wt%, specific gravities to 1.4, and viscosities up to 64 cp during motion. The sludge is suspended and mixed through the use of submersible slurry jet pumps. To suspend settled sludge, water is added to the tank as a slurry medium and stirred with the jet pump. Although there is considerable technical literature on mixing and solid suspension in agitated tanks, very little literature has been published on jet mixing in a large-scale tank. If shorter mixing times can be shown to support Defense Waste Processing Facility (DWPF) or other feed requirements, longer pump lifetimes can be achieved with associated operational cost and

ADVANCED MIXING MODELS

Energy Technology Data Exchange (ETDEWEB)

Lee, S; Richard Dimenna, R; David Tamburello, D

2008-11-13

The process of recovering the waste in storage tanks at the Savannah River Site (SRS) typically requires mixing the contents of the tank with one to four dual-nozzle jet mixers located within the tank. The typical criteria to establish a mixed condition in a tank are based on the number of pumps in operation and the time duration of operation. To ensure that a mixed condition is achieved, operating times are set conservatively long. This approach results in high operational costs because of the long mixing times and high maintenance and repair costs for the same reason. A significant reduction in both of these costs might be realized by reducing the required mixing time based on calculating a reliable indicator of mixing with a suitably validated computer code. The work described in this report establishes the basis for further development of the theory leading to the identified mixing indicators, the benchmark analyses demonstrating their consistency with widely accepted correlations, and the application of those indicators to SRS waste tanks to provide a better, physically based estimate of the required mixing time. Waste storage tanks at SRS contain settled sludge which varies in height from zero to 10 ft. The sludge has been characterized and modeled as micron-sized solids, typically 1 to 5 microns, at weight fractions as high as 20 to 30 wt%, specific gravities to 1.4, and viscosities up to 64 cp during motion. The sludge is suspended and mixed through the use of submersible slurry jet pumps. To suspend settled sludge, water is added to the tank as a slurry medium and stirred with the jet pump. Although there is considerable technical literature on mixing and solid suspension in agitated tanks, very little literature has been published on jet mixing in a large-scale tank. If shorter mixing times can be shown to support Defense Waste Processing Facility (DWPF) or other feed requirements, longer pump lifetimes can be achieved with associated operational cost and

A Deep Chandra Observation of the Centaurus Cluster:Bubbles, Filaments and Edges

Energy Technology Data Exchange (ETDEWEB)

Fabian, A.C.

2005-03-14

X-ray images and gas temperatures taken from a deep {approx}200 ks Chandra observation of the Centaurus cluster are presented. Multiple inner bubbles and outer semicircular edges are revealed, together with wispy filaments of soft X-ray emitting gas. The frothy central structure and eastern edge are likely due to the central radio source blowing bubbles in the intracluster gas. The semicircular edges to the surface brightness maps 32 kpc to the east and 17.5 kpc to the west are marked by sharp temperature increases and abundance drops. The edges could be due to sloshing motions of the central potential, or are possibly enhanced by earlier radio activity. The high abundance of the innermost gas (about 2.5 times Solar) limits the amount of diffusion and mixing taking place.

Beyond emission targets: how to decarbonize the passenger transport sector? Results from the Deep Decarbonization Pathways Project for Transport (DDPP-T)

International Nuclear Information System (INIS)

2017-11-01

Reaching the ambitious climate objective of the Paris Agreement requires decreasing significantly sectoral emissions from the transport sector. However, the ambition pledged for the transport sector under the Nationally Determined Contributions (NDCs) remains very limited. The DDPP-T analyzes Paris-compatible sectoral strategies for the passenger transport that can serve to inform the 2018 Facilitative Dialogue and the preparation of future, more ambitious, NDCs by 2020. In a context of an expected steep increase in global mobility demand, deep decarbonization will require a mix of different 'well-known' options: the rapid diffusion of low-carbon vehicles and low-carbon fuels and the modal shift towards low-carbon modes like public transport and non-motorized transport (cycling and walking). However, while crucial, these options are not 'silver bullets' that on their own meet the decarbonization challenge, given their intrinsic individual limitations. The project adopts an integrated approach of sectoral deep decarbonization strategies articulating the diffusion of low-carbon technologies with the future of mobility and all its drivers, such as the demographic and economic situation, the localization of population centers, the transport and urban planning, the lifestyles and the features of mobility services. The strategies are context-specific in order to capture different country circumstances, and consider a long-term horizon to inform the short-term conditions enabling structural changes of the transport system. Building on four country analyses (France, Japan, Mexico and the United Kingdom), this Issue Brief derives five cross-cutting messages for a deep decarbonization of the passenger transport sector. Key messages: - Deep decarbonization of the passenger transport sector requires strong actions on four pillars of transformation. Only a consistent articulation of these synergistic pillars allows an effective deep decarbonization. - Deep

Deep learning architecture for iris recognition based on optimal Gabor filters and deep belief network

Science.gov (United States)

He, Fei; Han, Ye; Wang, Han; Ji, Jinchao; Liu, Yuanning; Ma, Zhiqiang

2017-03-01

Gabor filters are widely utilized to detect iris texture information in several state-of-the-art iris recognition systems. However, the proper Gabor kernels and the generative pattern of iris Gabor features need to be predetermined in application. The traditional empirical Gabor filters and shallow iris encoding ways are incapable of dealing with such complex variations in iris imaging including illumination, aging, deformation, and device variations. Thereby, an adaptive Gabor filter selection strategy and deep learning architecture are presented. We first employ particle swarm optimization approach and its binary version to define a set of data-driven Gabor kernels for fitting the most informative filtering bands, and then capture complex pattern from the optimal Gabor filtered coefficients by a trained deep belief network. A succession of comparative experiments validate that our optimal Gabor filters may produce more distinctive Gabor coefficients and our iris deep representations be more robust and stable than traditional iris Gabor codes. Furthermore, the depth and scales of the deep learning architecture are also discussed.

A mechanistic model of an upper bound on oceanic carbon export as a function of mixed layer depth and temperature

Directory of Open Access Journals (Sweden)

Z. Li

2017-11-01

Full Text Available Export production reflects the amount of organic matter transferred from the ocean surface to depth through biological processes. This export is in large part controlled by nutrient and light availability, which are conditioned by mixed layer depth (MLD. In this study, building on Sverdrup's critical depth hypothesis, we derive a mechanistic model of an upper bound on carbon export based on the metabolic balance between photosynthesis and respiration as a function of MLD and temperature. We find that the upper bound is a positively skewed bell-shaped function of MLD. Specifically, the upper bound increases with deepening mixed layers down to a critical depth, beyond which a long tail of decreasing carbon export is associated with increasing heterotrophic activity and decreasing light availability. We also show that in cold regions the upper bound on carbon export decreases with increasing temperature when mixed layers are deep, but increases with temperature when mixed layers are shallow. A meta-analysis shows that our model envelopes field estimates of carbon export from the mixed layer. When compared to satellite export production estimates, our model indicates that export production in some regions of the Southern Ocean, particularly the subantarctic zone, is likely limited by light for a significant portion of the growing season.

Molecular analysis of deep subsurface bacteria

International Nuclear Information System (INIS)

Jimenez Baez, L.E.

1989-09-01

Deep sediments samples from site C10a, in Appleton, and sites, P24, P28, and P29, at the Savannah River Site (SRS), near Aiken, South Carolina were studied to determine their microbial community composition, DNA homology and mol %G+C. Different geological formations with great variability in hydrogeological parameters were found across the depth profile. Phenotypic identification of deep subsurface bacteria underestimated the bacterial diversity at the three SRS sites, since bacteria with the same phenotype have different DNA composition and less than 70% DNA homology. Total DNA hybridization and mol %G+C analysis of deep sediment bacterial isolates suggested that each formation is comprised of different microbial communities. Depositional environment was more important than site and geological formation on the DNA relatedness between deep subsurface bacteria, since more 70% of bacteria with 20% or more of DNA homology came from the same depositional environments. Based on phenotypic and genotypic tests Pseudomonas spp. and Acinetobacter spp.-like bacteria were identified in 85 million years old sediments. This suggests that these microbial communities might have been adapted during a long period of time to the environmental conditions of the deep subsurface

Impact of an intense water column mixing (0-1500 m) on prokaryotic diversity and activities during an open-ocean convection event in the NW Mediterranean Sea.

Science.gov (United States)

Severin, Tatiana; Sauret, Caroline; Boutrif, Mehdi; Duhaut, Thomas; Kessouri, Fayçal; Oriol, Louise; Caparros, Jocelyne; Pujo-Pay, Mireille; Durrieu de Madron, Xavier; Garel, Marc; Tamburini, Christian; Conan, Pascal; Ghiglione, Jean-François

2016-12-01

Open-ocean convection is a fundamental process for thermohaline circulation and biogeochemical cycles that causes spectacular mixing of the water column. Here, we tested how much the depth-stratified prokaryotic communities were influenced by such an event, and also by the following re-stratification. The deep convection event (0-1500 m) that occurred in winter 2010-2011 in the NW Mediterranean Sea resulted in a homogenization of the prokaryotic communities over the entire convective cell, resulting in the predominance of typical surface Bacteria, such as Oceanospirillale and Flavobacteriales. Statistical analysis together with numerical simulation of vertical homogenization evidenced that physical turbulence only was not enough to explain the new distribution of the communities, but acted in synergy with other parameters such as exported particulate and dissolved organic matters. The convection also stimulated prokaryotic abundance (+21%) and heterotrophic production (+43%) over the 0-1500 m convective cell, and resulted in a decline of cell-specific extracellular enzymatic activities (-67%), thus suggesting an intensification of the labile organic matter turnover during the event. The rapid re-stratification of the prokaryotic diversity and activities in the intermediate layer 5 days after the intense mixing indicated a marked resilience of the communities, apart from the residual deep mixed water patch. © 2016 The Authors. Environmental Microbiology published by Society for Applied Microbiology and John Wiley & Sons Ltd.

Joint Training of Deep Boltzmann Machines

OpenAIRE

Goodfellow, Ian; Courville, Aaron; Bengio, Yoshua

2012-01-01

We introduce a new method for training deep Boltzmann machines jointly. Prior methods require an initial learning pass that trains the deep Boltzmann machine greedily, one layer at a time, or do not perform well on classifi- cation tasks.

Generalizing Pooling Functions in CNNs: Mixed, Gated, and Tree.

Science.gov (United States)

Lee, Chen-Yu; Gallagher, Patrick; Tu, Zhuowen

2018-04-01

In this paper, we seek to improve deep neural networks by generalizing the pooling operations that play a central role in the current architectures. We pursue a careful exploration of approaches to allow pooling to learn and to adapt to complex and variable patterns. The two primary directions lie in: (1) learning a pooling function via (two strategies of) combining of max and average pooling, and (2) learning a pooling function in the form of a tree-structured fusion of pooling filters that are themselves learned. In our experiments every generalized pooling operation we explore improves performance when used in place of average or max pooling. We experimentally demonstrate that the proposed pooling operations provide a boost in invariance properties relative to conventional pooling and set the state of the art on several widely adopted benchmark datasets. These benefits come with only a light increase in computational overhead during training (ranging from additional 5 to 15 percent in time complexity) and a very modest increase in the number of model parameters (e.g., additional 1, 9, and 27 parameters for mixed, gated, and 2-level tree pooling operators, respectively). To gain more insights about our proposed pooling methods, we also visualize the learned pooling masks and the embeddings of the internal feature responses for different pooling operations. Our proposed pooling operations are easy to implement and can be applied within various deep neural network architectures.

Fluid mixing III

International Nuclear Information System (INIS)

Harnby, N.

1988-01-01

Covering all aspects of mixing, this work presents research and developments in industrial applications, flow patterns and mixture analysis, mixing of solids into liquids, and mixing of gases into liquids

Sylgard® Mixing Study

Energy Technology Data Exchange (ETDEWEB)

Bello, Mollie [Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Welch, Cynthia F. [Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Goodwin, Lynne Alese [Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Keller, Jennie [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

2014-08-22

Sylgard® 184 and Sylgard® 186 silicone elastomers form Dow Corning® are used as potting agents across the Nuclear Weapons Complex. A standardized mixing procedure is required for filled versions of these products. The present study is a follow-up to a mixing study performed by MST-7 which established the best mixing procedure to use when adding filler to either 184 or 186 base resins. The most effective and consistent method of mixing resin and curing agent for three modified silicone elastomer recipes is outlined in this report. For each recipe, sample size, mixing type, and mixing time was varied over 10 separate runs. The results show that the THINKY™ Mixer gives reliable mixing over varying batch sizes and mixing times. Hand Mixing can give improved mixing, as indicated by reduced initial viscosity; however, this method is not consistent.

Deep boreholes; Tiefe Bohrloecher

Energy Technology Data Exchange (ETDEWEB)

Bracke, Guido [Gesellschaft fuer Anlagen- und Reaktorsicherheit gGmbH Koeln (Germany); Charlier, Frank [NSE international nuclear safety engineering gmbh, Aachen (Germany); Geckeis, Horst [Karlsruher Institut fuer Technologie (Germany). Inst. fuer Nukleare Entsorgung; and others

2016-02-15

The report on deep boreholes covers the following subject areas: methods for safe enclosure of radioactive wastes, requirements concerning the geological conditions of possible boreholes, reversibility of decisions and retrievability, status of drilling technology. The introduction covers national and international activities. Further chapters deal with the following issues: basic concept of the storage in deep bore holes, status of the drilling technology, safe enclosure, geomechanics and stability, reversibility of decisions, risk scenarios, compliancy with safe4ty requirements and site selection criteria, research and development demand.

DeepLoc: prediction of protein subcellular localization using deep learning

DEFF Research Database (Denmark)

Almagro Armenteros, Jose Juan; Sønderby, Casper Kaae; Sønderby, Søren Kaae

2017-01-01

The prediction of eukaryotic protein subcellular localization is a well-studied topic in bioinformatics due to its relevance in proteomics research. Many machine learning methods have been successfully applied in this task, but in most of them, predictions rely on annotation of homologues from...... knowledge databases. For novel proteins where no annotated homologues exist, and for predicting the effects of sequence variants, it is desirable to have methods for predicting protein properties from sequence information only. Here, we present a prediction algorithm using deep neural networks to predict...... current state-of-the-art algorithms, including those relying on homology information. The method is available as a web server at http://www.cbs.dtu.dk/services/DeepLoc . Example code is available at https://github.com/JJAlmagro/subcellular_localization . The dataset is available at http...

Pre-cementation of deep shaft

Science.gov (United States)

Heinz, W. F.

1988-12-01

Pre-cementation or pre-grouting of deep shafts in South Africa is an established technique to improve safety and reduce water ingress during shaft sinking. The recent completion of several pre-cementation projects for shafts deeper than 1000m has once again highlighted the effectiveness of pre-grouting of shafts utilizing deep slimline boreholes and incorporating wireline technique for drilling and conventional deep borehole grouting techniques for pre-cementation. Pre-cementation of deep shaft will: (i) Increase the safety of shaft sinking operation (ii) Minimize water and gas inflow during shaft sinking (iii) Minimize the time lost due to additional grouting operations during sinking of the shaft and hence minimize costly delays and standing time of shaft sinking crews and equipment. (iv) Provide detailed information of the geology of the proposed shaft site. Informations on anomalies, dykes, faults as well as reef (gold bearing conglomerates) intersections can be obtained from the evaluation of cores of the pre-cementation boreholes. (v) Provide improved rock strength for excavations in the immediate vicinity of the shaft area. The paper describes pre-cementation techniques recently applied successfully from surface and some conclusions drawn for further considerations.

The use of "mixing" procedure of mixed methods in health services research.

Science.gov (United States)

Zhang, Wanqing; Creswell, John

2013-08-01

Mixed methods research has emerged alongside qualitative and quantitative approaches as an important tool for health services researchers. Despite growing interest, among health services researchers, in using mixed methods designs, little has been done to identify the procedural aspects of doing so. To describe how mixed methods researchers mix the qualitative and quantitative aspects of their studies in health services research. We searched the PubMed for articles, using mixed methods in health services research, published between January 1, 2006 and December 30, 2010. We identified and reviewed 30 published health services research articles on studies in which mixed methods had been used. We selected 3 articles as illustrations to help health services researcher conceptualize the type of mixing procedures that they were using. Three main "mixing" procedures have been applied within these studies: (1) the researchers analyzed the 2 types of data at the same time but separately and integrated the results during interpretation; (2) the researchers connected the qualitative and quantitative portions in phases in such a way that 1 approach was built upon the findings of the other approach; and (3) the researchers mixed the 2 data types by embedding the analysis of 1 data type within the other. "Mixing" in mixed methods is more than just the combination of 2 independent components of the quantitative and qualitative data. The use of "mixing" procedure in health services research involves the integration, connection, and embedding of these 2 data components.

Applications of Deep Learning in Biomedicine.

Science.gov (United States)

Mamoshina, Polina; Vieira, Armando; Putin, Evgeny; Zhavoronkov, Alex

2016-05-02

Increases in throughput and installed base of biomedical research equipment led to a massive accumulation of -omics data known to be highly variable, high-dimensional, and sourced from multiple often incompatible data platforms. While this data may be useful for biomarker identification and drug discovery, the bulk of it remains underutilized. Deep neural networks (DNNs) are efficient algorithms based on the use of compositional layers of neurons, with advantages well matched to the challenges -omics data presents. While achieving state-of-the-art results and even surpassing human accuracy in many challenging tasks, the adoption of deep learning in biomedicine has been comparatively slow. Here, we discuss key features of deep learning that may give this approach an edge over other machine learning methods. We then consider limitations and review a number of applications of deep learning in biomedical studies demonstrating proof of concept and practical utility.

Deep Complementary Bottleneck Features for Visual Speech Recognition

NARCIS (Netherlands)

Petridis, Stavros; Pantic, Maja

Deep bottleneck features (DBNFs) have been used successfully in the past for acoustic speech recognition from audio. However, research on extracting DBNFs for visual speech recognition is very limited. In this work, we present an approach to extract deep bottleneck visual features based on deep

Producing deep-water hydrocarbons

International Nuclear Information System (INIS)

Pilenko, Thierry

2011-01-01

Several studies relate the history and progress made in offshore production from oil and gas fields in relation to reserves and the techniques for producing oil offshore. The intention herein is not to review these studies but rather to argue that the activities of prospecting and producing deep-water oil and gas call for a combination of technology and project management and, above all, of devotion and innovation. Without this sense of commitment motivating men and women in this industry, the human adventure of deep-water production would never have taken place

A simple model for the dispersion of radioactive wastes dumped on the deep-sea bed

International Nuclear Information System (INIS)

Shepherd, J.G.

1976-01-01

A simple model has been developed for the dispersion of radioactive materials in a closed and finite ocean. It allows for the simultaneous action of both diffusion and horizontal (but not vertical) advection, and thus avoids the major limitations of previous models such as that of Webb and Morley. It is sufficiently versatile to handle non-Fickian diffusion and radioactive decay, but requires numerical integration using some semi-empirical form for the Green's function of diffusion from a point source. The model has been used to estimate equilibrium concentrations of radioactive materials in sea water arising from the continuous release of material from a dump on the bottom of the deep ocean, using parameters appropriate for the North Atlantic. It is found that except under rather extreme conditions the surface concentrations do not exceed the long-term average value which would be established in a perfectly mixed ocean. The concentrations are also rather insensitive to the values of the diffusion and advection parameters used, except for that for vertical diffusion, but depend strongly on the overall removal rate of material from the ocean, including processes other than radioactive decay. It is suggested that safety assessments of deep-sea dumping should utilize estimates of the environmental capacities of the oceans based on the long-term 'well-mixed' average concentrations (which are very easily calculated) using a safety factor of no more than ten to allow for the possible effects of pluming and upwelling. In so far as their results are comparable, the present model yields estimates which are close to those of the Webb-Morley model for overall half-lives between 30 and 3000 years, but which become increasingly more restrictive for longer-lived materials. (author)

Using Argo-O2 data to examine the impact of deep-water formation events on oxygen uptake in the Labrador Sea

Science.gov (United States)

Wolf, M. K.; Hamme, R. C.; Gilbert, D.; Yashayaev, I.

2016-02-01

Deep-water formation allows the deep ocean to communicate with the atmosphere, facilitating exchanges of heat as well as important gases such as CO2 and oxygen. The Labrador Sea is the most studied location of deep convection in the North Atlantic Ocean and a strong contributor to the global thermohaline circulation. Since there are no internal sources of oxygen below the euphotic zone, deep-water formation is vital for oxygen transport to the deep ocean. Recent studies document large interannual variability in the strength and depth of convection in the Labrador Sea, from mixed layers of 100m to greater than 1000m. A weakening of this deep convection starves the deep ocean of oxygen, disrupting crucial deep sea biological processes, as well as reducing oceanic CO2 uptake and ocean circulation. We used data from the extensive Argo float network to examine these deep-water formation events in the Labrador Sea. The oxygen optodes onboard many Argo floats suffer from biases whose amplitude must be determined; therefore we investigated and applied various optode calibration methods. Using calibrated vertical profiles of oxygen, temperature, and salinity, we observed the timing, magnitude, and location of deep convection, restratification, and spring phytoplankton blooms. In addition, we used surface oxygen values along with NCEP wind speeds to calculate the air-sea oxygen flux using a range of air-sea gas exchange parameterizations. We then compared this oxygen flux to the rate of change of the measured oxygen inventory. Where the inventory and flux did not agree, we identified other oceanic processes such as biological activity or lateral advection of water masses occurring, or advection of the float itself into a new area. The large role that horizontal advection of water or the float has on oxygen uptake and cycling leads us to conclude that this data cannot be easily interpreted as a 1-D system. Oxygen exchanges with the atmosphere at a faster rate than CO2, is

Deep inelastic processes and the parton model

International Nuclear Information System (INIS)

Altarelli, G.

The lecture was intended as an elementary introduction to the physics of deep inelastic phenomena from the point of view of theory. General formulae and facts concerning inclusive deep inelastic processes in the form: l+N→l'+hadrons (electroproduction, neutrino scattering) are first recalled. The deep inelastic annihilation e + e - →hadrons is then envisaged. The light cone approach, the parton model and their relation are mainly emphasized

Life Support for Deep Space and Mars

Science.gov (United States)

Jones, Harry W.; Hodgson, Edward W.; Kliss, Mark H.

2014-01-01

How should life support for deep space be developed? The International Space Station (ISS) life support system is the operational result of many decades of research and development. Long duration deep space missions such as Mars have been expected to use matured and upgraded versions of ISS life support. Deep space life support must use the knowledge base incorporated in ISS but it must also meet much more difficult requirements. The primary new requirement is that life support in deep space must be considerably more reliable than on ISS or anywhere in the Earth-Moon system, where emergency resupply and a quick return are possible. Due to the great distance from Earth and the long duration of deep space missions, if life support systems fail, the traditional approaches for emergency supply of oxygen and water, emergency supply of parts, and crew return to Earth or escape to a safe haven are likely infeasible. The Orbital Replacement Unit (ORU) maintenance approach used by ISS is unsuitable for deep space with ORU's as large and complex as those originally provided in ISS designs because it minimizes opportunities for commonality of spares, requires replacement of many functional parts with each failure, and results in substantial launch mass and volume penalties. It has become impractical even for ISS after the shuttle era, resulting in the need for ad hoc repair activity at lower assembly levels with consequent crew time penalties and extended repair timelines. Less complex, more robust technical approaches may be needed to meet the difficult deep space requirements for reliability, maintainability, and reparability. Developing an entirely new life support system would neglect what has been achieved. The suggested approach is use the ISS life support technologies as a platform to build on and to continue to improve ISS subsystems while also developing new subsystems where needed to meet deep space requirements.

Turbulence and finestructure in a deep ocean channel with sill overflow on the mid-Atlantic ridge

Science.gov (United States)

Tippenhauer, Sandra; Dengler, Marcus; Fischer, Tim; Kanzow, Torsten

2015-05-01

Diapycnal mixing in the deep ocean is known to be much stronger in the vicinity of rough topography of mid-ocean ridges than above abyssal plains. In this study a horizontally profiling microstructure probe attached to an autonomous underwater vehicle (AUV) is used to infer the spatial distribution of the dissipation rate of turbulent kinetic energy (ε) in the central valley of the Mid-Atlantic Ridge. To the authors' knowledge, this is the first successful realization of a horizontal, deep-ocean microstructure survey. More than 22 h of horizontal, near-bottom microstructure data from the Lucky Strike segment (37°N) are presented. The study focuses on a channel with unidirectional sill overflow. Density was found to decrease along the channel following the mean northward flow of 3 to 8 cm/s. The magnitude of the rate of turbulent kinetic energy dissipation was distributed asymmetrically relative to the position of the sill. Elevated dissipation rates were present in a segment 1-4 km downstream (north) of the sill with peak values of 1 ×10-7 W/kg. Large flow speeds and elevated density finestructure were observed within this segment. Lowered hydrographic measurements indicated unstable stratification in the same region. The data indicate that hydraulic control was established at least temporarily. Inside the channel at wavelengths between 1 m and 250 m the slopes of AUV-inferred horizontal temperature gradient spectra were found to be consistent with turbulence in the inertial-convective subrange. Integrated temperature gradient variance in this wavelength interval was consistent with an ε2/3 dependence. The results illustrate that deep-reaching AUVs are a useful tool to study deep ocean turbulence over complex terrain where free-falling and lowered turbulence measurements are inefficient and time-consuming.

Automatic Segmentation and Deep Learning of Bird Sounds

NARCIS (Netherlands)

Koops, Hendrik Vincent; Van Balen, J.M.H.; Wiering, F.

2015-01-01

We present a study on automatic birdsong recognition with deep neural networks using the BIRDCLEF2014 dataset. Through deep learning, feature hierarchies are learned that represent the data on several levels of abstraction. Deep learning has been applied with success to problems in fields such as

Deep Learning: A Primer for Radiologists.

Science.gov (United States)

Chartrand, Gabriel; Cheng, Phillip M; Vorontsov, Eugene; Drozdzal, Michal; Turcotte, Simon; Pal, Christopher J; Kadoury, Samuel; Tang, An

2017-01-01

Deep learning is a class of machine learning methods that are gaining success and attracting interest in many domains, including computer vision, speech recognition, natural language processing, and playing games. Deep learning methods produce a mapping from raw inputs to desired outputs (eg, image classes). Unlike traditional machine learning methods, which require hand-engineered feature extraction from inputs, deep learning methods learn these features directly from data. With the advent of large datasets and increased computing power, these methods can produce models with exceptional performance. These models are multilayer artificial neural networks, loosely inspired by biologic neural systems. Weighted connections between nodes (neurons) in the network are iteratively adjusted based on example pairs of inputs and target outputs by back-propagating a corrective error signal through the network. For computer vision tasks, convolutional neural networks (CNNs) have proven to be effective. Recently, several clinical applications of CNNs have been proposed and studied in radiology for classification, detection, and segmentation tasks. This article reviews the key concepts of deep learning for clinical radiologists, discusses technical requirements, describes emerging applications in clinical radiology, and outlines limitations and future directions in this field. Radiologists should become familiar with the principles and potential applications of deep learning in medical imaging. © RSNA, 2017.

A Fuel Microanalysis for a Deep Burn-High Temperature Reactor

International Nuclear Information System (INIS)

Kim, Young Min; Jo, Chang Keun; Jun, Ji Su; Cho, Moon Sung

2010-08-01

The microanalysis for a deep burn-high temperature reactor (DB-HTR) covers the gas pressure buildup in a coated fuel particle (CFP), the thermo-mechanical behavior of a CFP, the failure probabilities of CFPs, the thermal analysis for a fuel element and a CFP, and the fission product transport into a coolant. The fuel performance analysis code of KAERI, COPA, is used in the microanalysis. The considered fuel materials are 0.2% UO 2 + 99.8% (5% NpO 2 + 95% PuO 1.8 ) mixed with 0.6 moles of silicon carbide (SiC) per mole of heavy metal and 30% UO 2 + 70% (5% NpO 2 + 95% PuO 1.8 ) mixed with 0.6 moles SiC per mole of heavy metal. Two thermal powers, 600 and 450 MW th , are taken into account. It was assumed that the DB-HTR was operated at constant temperature and power for normal operation and then was subjected to a low pressure conduction cooling (LPCC) accident for 250 hours. All the fuels of the DB-HTRs had good mechanical and thermal integrity during normal operation. But in the LPCC accident, whole particle failure occurred in the 600 MW DB-HTRs and the failure fractions in the 450 MW DB-HTRs are below 0.03. In order to secure the integrity of CFPs during the LPCC accident, it is necessary to reduce the excessive temperatures and the gas pressure in a CFP

Protein Secondary Structure Prediction Using Deep Convolutional Neural Fields.

Science.gov (United States)

Wang, Sheng; Peng, Jian; Ma, Jianzhu; Xu, Jinbo

2016-01-11

Protein secondary structure (SS) prediction is important for studying protein structure and function. When only the sequence (profile) information is used as input feature, currently the best predictors can obtain ~80% Q3 accuracy, which has not been improved in the past decade. Here we present DeepCNF (Deep Convolutional Neural Fields) for protein SS prediction. DeepCNF is a Deep Learning extension of Conditional Neural Fields (CNF), which is an integration of Conditional Random Fields (CRF) and shallow neural networks. DeepCNF can model not only complex sequence-structure relationship by a deep hierarchical architecture, but also interdependency between adjacent SS labels, so it is much more powerful than CNF. Experimental results show that DeepCNF can obtain ~84% Q3 accuracy, ~85% SOV score, and ~72% Q8 accuracy, respectively, on the CASP and CAMEO test proteins, greatly outperforming currently popular predictors. As a general framework, DeepCNF can be used to predict other protein structure properties such as contact number, disorder regions, and solvent accessibility.

Stratification-Based Outlier Detection over the Deep Web.

Science.gov (United States)

Xian, Xuefeng; Zhao, Pengpeng; Sheng, Victor S; Fang, Ligang; Gu, Caidong; Yang, Yuanfeng; Cui, Zhiming

2016-01-01

For many applications, finding rare instances or outliers can be more interesting than finding common patterns. Existing work in outlier detection never considers the context of deep web. In this paper, we argue that, for many scenarios, it is more meaningful to detect outliers over deep web. In the context of deep web, users must submit queries through a query interface to retrieve corresponding data. Therefore, traditional data mining methods cannot be directly applied. The primary contribution of this paper is to develop a new data mining method for outlier detection over deep web. In our approach, the query space of a deep web data source is stratified based on a pilot sample. Neighborhood sampling and uncertainty sampling are developed in this paper with the goal of improving recall and precision based on stratification. Finally, a careful performance evaluation of our algorithm confirms that our approach can effectively detect outliers in deep web.

Deep Learning and Bayesian Methods

Directory of Open Access Journals (Sweden)

Prosper Harrison B.

2017-01-01

Full Text Available A revolution is underway in which deep neural networks are routinely used to solve diffcult problems such as face recognition and natural language understanding. Particle physicists have taken notice and have started to deploy these methods, achieving results that suggest a potentially significant shift in how data might be analyzed in the not too distant future. We discuss a few recent developments in the application of deep neural networks and then indulge in speculation about how such methods might be used to automate certain aspects of data analysis in particle physics. Next, the connection to Bayesian methods is discussed and the paper ends with thoughts on a significant practical issue, namely, how, from a Bayesian perspective, one might optimize the construction of deep neural networks.

Eric Davidson and deep time.

Science.gov (United States)

Erwin, Douglas H

2017-10-13

Eric Davidson had a deep and abiding interest in the role developmental mechanisms played in generating evolutionary patterns documented in deep time, from the origin of the euechinoids to the processes responsible for the morphological architectures of major animal clades. Although not an evolutionary biologist, Davidson's interests long preceded the current excitement over comparative evolutionary developmental biology. Here I discuss three aspects at the intersection between his research and evolutionary patterns in deep time: First, understanding the mechanisms of body plan formation, particularly those associated with the early diversification of major metazoan clades. Second, a critique of early claims about ancestral metazoans based on the discoveries of highly conserved genes across bilaterian animals. Third, Davidson's own involvement in paleontology through a collaborative study of the fossil embryos from the Ediacaran Doushantuo Formation in south China.

Effect of edible coating ingredients incorporated into predusting mix on moisture content, fat content and consumer acceptability of fried breaded product

Directory of Open Access Journals (Sweden)

Nongnuch Raksakulthai

2008-04-01

Full Text Available The effect of edible coatings and their concentrations on moisture and fat contents of fried breaded potato were investigated. Hydroxypropyl methylcellulose (HPMC, methylcellulose (MC or wheat gluten (WG were incorporate into predusting mix to achieve coating material concentration of 3-12% (w/w. Blanched potatoes were first coated with predusting mix and followed sequentially by battering, breading and deep frying at 170°C for 3 min. Moisture and fat contents in the core and crust of sample and intact samples were determined. It was found that HPMC and MC could reduce moisture loss and fat absorption than WG. Predusting mix with 6% MC was the most effective to retain moisture and reduce fat absorption. This predusting mix was then applied to commercial breaded shrimps. In both prefried and fried products, treated breaded shrimps had more moisture and less fat than untreated breaded shrimps. They also were lower in product hardness and crust hardness than untreated samples. Sensory evaluation showed that treated and untreated shrimp samples had similar rating for appearance, color, flavor, taste, texture and overall. Treated breaded shrimp was acceptable to the consumers. The application of edible coatings into predusting mix can be easily introduced into the production process and is beneficial to both food industry and consumers.

More Far-Side Deep Moonquake Nests Discovered

Science.gov (United States)

Nakamura, Y.; Jackson, John A.; Jackson, Katherine G.

2004-01-01

As reported last year, we started to reanalyze the seismic data acquired from 1969 to 1977 with a network of stations established on the Moon during the Apollo mission. The reason for the reanalysis was because recent advances in computer technology make it possible to employ much more sophisticated analysis techniques than was possible previously. The primary objective of the reanalysis was to search for deep moonquakes on the far side of the Moon and, if found, to use them to infer the structure of the Moon's deep interior, including a possible central core. The first step was to identify any new deep moonquakes that escaped our earlier search by applying a combination of waveform cross-correlation and single-link cluster analysis, and then to see if any of them are from previously unknown nests of deep moonquakes. We positively identified 7245 deep moonquakes, more than a five-fold increase from the previous 1360. We also found at least 88 previously unknown deep-moonquake nests. The question was whether any of these newly discovered nets were on the far side of the Moon, and we now report that our analysis of the data indicates that some of them are indeed on the far side.

The Value of Mixed Methods Research: A Mixed Methods Study

Science.gov (United States)

McKim, Courtney A.

2017-01-01

The purpose of this explanatory mixed methods study was to examine the perceived value of mixed methods research for graduate students. The quantitative phase was an experiment examining the effect of a passage's methodology on students' perceived value. Results indicated students scored the mixed methods passage as more valuable than those who…

DeepAnomaly: Combining Background Subtraction and Deep Learning for Detecting Obstacles and Anomalies in an Agricultural Field

Directory of Open Access Journals (Sweden)

Peter Christiansen

2016-11-01

Full Text Available Convolutional neural network (CNN-based systems are increasingly used in autonomous vehicles for detecting obstacles. CNN-based object detection and per-pixel classification (semantic segmentation algorithms are trained for detecting and classifying a predefined set of object types. These algorithms have difficulties in detecting distant and heavily occluded objects and are, by definition, not capable of detecting unknown object types or unusual scenarios. The visual characteristics of an agriculture field is homogeneous, and obstacles, like people, animals and other obstacles, occur rarely and are of distinct appearance compared to the field. This paper introduces DeepAnomaly, an algorithm combining deep learning and anomaly detection to exploit the homogenous characteristics of a field to perform anomaly detection. We demonstrate DeepAnomaly as a fast state-of-the-art detector for obstacles that are distant, heavily occluded and unknown. DeepAnomaly is compared to state-of-the-art obstacle detectors including “Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks” (RCNN. In a human detector test case, we demonstrate that DeepAnomaly detects humans at longer ranges (45–90 m than RCNN. RCNN has a similar performance at a short range (0–30 m. However, DeepAnomaly has much fewer model parameters and (182 ms/25 ms = a 7.28-times faster processing time per image. Unlike most CNN-based methods, the high accuracy, the low computation time and the low memory footprint make it suitable for a real-time system running on a embedded GPU (Graphics Processing Unit.

ADVANCED MIXING MODELS

Energy Technology Data Exchange (ETDEWEB)

Lee, S; Dimenna, R; Tamburello, D

2011-02-14

The process of recovering and processing High Level Waste (HLW) the waste in storage tanks at the Savannah River Site (SRS) typically requires mixing the contents of the tank with one to four mixers (pumps) located within the tank. The typical criteria to establish a mixed condition in a tank are based on the number of pumps in operation and the time duration of operation. To ensure that a mixed condition is achieved, operating times are typically set conservatively long. This approach results in high operational costs because of the long mixing times and high maintenance and repair costs for the same reason. A significant reduction in both of these costs might be realized by reducing the required mixing time based on calculating a reliable indicator of mixing with a suitably validated computer code. The focus of the present work is to establish mixing criteria applicable to miscible fluids, with an ultimate goal of addressing waste processing in HLW tanks at SRS and quantifying the mixing time required to suspend sludge particles with the submersible jet pump. A single-phase computational fluid dynamics (CFD) approach was taken for the analysis of jet flow patterns with an emphasis on the velocity decay and the turbulent flow evolution for the farfield region from the pump. Literature results for a turbulent jet flow are reviewed, since the decay of the axial jet velocity and the evolution of the jet flow patterns are important phenomena affecting sludge suspension and mixing operations. The work described in this report suggests a basis for further development of the theory leading to the identified mixing indicators, with benchmark analyses demonstrating their consistency with widely accepted correlations. Although the indicators are somewhat generic in nature, they are applied to Savannah River Site (SRS) waste tanks to provide a better, physically based estimate of the required mixing time. Waste storage tanks at SRS contain settled sludge which varies in

Learning Transferable Features with Deep Adaptation Networks

OpenAIRE

Long, Mingsheng; Cao, Yue; Wang, Jianmin; Jordan, Michael I.

2015-01-01

Recent studies reveal that a deep neural network can learn transferable features which generalize well to novel tasks for domain adaptation. However, as deep features eventually transition from general to specific along the network, the feature transferability drops significantly in higher layers with increasing domain discrepancy. Hence, it is important to formally reduce the dataset bias and enhance the transferability in task-specific layers. In this paper, we propose a new Deep Adaptation...

Theory of deep inelastic lepton-hadron scattering

International Nuclear Information System (INIS)

Geyer, B.; Robaschik, D.; Wieczorek, E.

1979-01-01

The description of deep inelastic lepton-nucleon scattering in the lowest order of the electromagnetic and weak coupling constants leads to a study of virtual Compton amplitudes and their absorptive parts. Some aspects of quantum chromodynamics are discussed. Deep inelastic scattering enables a central quantity of quantum field theory, namely the light cone behaviour of the current commutator. The moments of structure functions are used for the description of deep inelastic scattering. (author)

DeepQA: Improving the estimation of single protein model quality with deep belief networks

OpenAIRE

Cao, Renzhi; Bhattacharya, Debswapna; Hou, Jie; Cheng, Jianlin

2016-01-01

Background Protein quality assessment (QA) useful for ranking and selecting protein models has long been viewed as one of the major challenges for protein tertiary structure prediction. Especially, estimating the quality of a single protein model, which is important for selecting a few good models out of a large model pool consisting of mostly low-quality models, is still a largely unsolved problem. Results We introduce a novel single-model quality assessment method DeepQA based on deep belie...

DeepDive: Declarative Knowledge Base Construction.

Science.gov (United States)

De Sa, Christopher; Ratner, Alex; Ré, Christopher; Shin, Jaeho; Wang, Feiran; Wu, Sen; Zhang, Ce

2016-03-01

The dark data extraction or knowledge base construction (KBC) problem is to populate a SQL database with information from unstructured data sources including emails, webpages, and pdf reports. KBC is a long-standing problem in industry and research that encompasses problems of data extraction, cleaning, and integration. We describe DeepDive, a system that combines database and machine learning ideas to help develop KBC systems. The key idea in DeepDive is that statistical inference and machine learning are key tools to attack classical data problems in extraction, cleaning, and integration in a unified and more effective manner. DeepDive programs are declarative in that one cannot write probabilistic inference algorithms; instead, one interacts by defining features or rules about the domain. A key reason for this design choice is to enable domain experts to build their own KBC systems. We present the applications, abstractions, and techniques of DeepDive employed to accelerate construction of KBC systems.

Pathways to deep decarbonization - 2015 report

International Nuclear Information System (INIS)

Ribera, Teresa; Colombier, Michel; Waisman, Henri; Bataille, Chris; Pierfederici, Roberta; Sachs, Jeffrey; Schmidt-Traub, Guido; Williams, Jim; Segafredo, Laura; Hamburg Coplan, Jill; Pharabod, Ivan; Oury, Christian

2015-12-01

In September 2015, the Deep Decarbonization Pathways Project published the Executive Summary of the Pathways to Deep Decarbonization: 2015 Synthesis Report. The full 2015 Synthesis Report was launched in Paris on December 3, 2015, at a technical workshop with the Mitigation Action Plans and Scenarios (MAPS) program. The Deep Decarbonization Pathways Project (DDPP) is a collaborative initiative to understand and show how individual countries can transition to a low-carbon economy and how the world can meet the internationally agreed target of limiting the increase in global mean surface temperature to less than 2 degrees Celsius (deg. C). Achieving the 2 deg. C limit will require that global net emissions of greenhouse gases (GHG) approach zero by the second half of the century. In turn, this will require a profound transformation of energy systems by mid-century through steep declines in carbon intensity in all sectors of the economy, a transition we call 'deep decarbonization'

Deep electrical resistivity tomography and geothermal analysis of Bradano foredeep deposits in Venosa area (Southern Italy: preliminary results

Directory of Open Access Journals (Sweden)

V. Lapenna

2008-06-01

Full Text Available Geophysical surveys have been carried out to characterize the stratigraphical and structural setting and to better understand the deep water circulation system in the Venosa area (Southern Italy located in the frontal portion of the southern Appenninic Subduction. In this area there are some deep water wells from which a water conductivity of about 3 mS/cm and a temperature of about 35°C was measured. A deep geoelectrical tomography with dipole-dipole array has been carried out along a profile of 10000 m and an investigation depth of about 900 m. Furthermore a broad band magnetotelluric profile consisting of six stations was performed to infer the resistivity distribution up to some kilometres of depth. The MT profile was almost coincident with the geoelectrical outline. The applied methods allow us to obtain a mutual control and integrated interpretation of the data. The high resolution of the data was the key to reconstruct the structural asset of buried carbonatic horst whose top is located at about 600 m depth. The final results coming from data wells, geothermal analysis and geophysical data, highlighted a horst saturated with salted water and an anomalous local gradient of 60°C/km. The proposed mechanism is that of a mixing of fossil and fresh water circulation system.

Negative stereotypes of the Scottish diet: A qualitative analysis of deep-fried Mars bar references in bestselling newspapers in Scotland, 2011-14.

Science.gov (United States)

Knight, Christine

2016-08-01

The Scottish diet is associated in the UK media and popular discourse with unhealthy deep-fried foods. In addition to the stereotype's negative effects on perceptions of Scottish food, culture and people, there is evidence that the stereotype of the Scottish diet has negative effects on food behaviour and public health in Scotland, having been shown to encourage consumption of deep-fried foods and discourage positive dietary change. The most notorious deep-fried food associated with Scotland is the deep-fried Mars bar (DFMB), arguably invented in Stonehaven (near Aberdeen), and first reported in the Scottish and UK press in 1995. This article reports findings from an analysis of newspaper references to the DFMB in the two highest selling newspapers in Scotland, the Scottish Sun and the Daily Record, between 2011 and 2014. A keyword search ("deep fried Mars bar") using the online media database Lexis Library generated 97 unique records, and the resulting dataset was analysed thematically and discursively. Analysis showed that both newspapers clearly associated the DFMB with Scotland. Further, both newspapers portrayed the DFMB and the broader "deep-fried" Scottish diet stereotype ambivalently (mixed positive and negative associations). However, the Daily Record actively criticised the DFMB stereotype much more often than did the Scottish Sun. These findings suggest that the Scottish population encounters different messages in the press about food and nutrition from people elsewhere in the UK, and that these messages vary depending on choice of media in Scotland. Given the known negative effects of the stereotype, differences in Scottish media discourse should be considered a potential factor in persistent health inequalities affecting Scotland. Educational efforts, and opening discussion with journalists and amongst the Scottish public, may be helpful. Copyright © 2016 The Author. Published by Elsevier Ltd.. All rights reserved.

Origins of the deep-sea sediments and their variations with time. Annual progress report No. 6, May 1975

International Nuclear Information System (INIS)

Biscaye, P.E.

1975-05-01

Techniques for studying water mass mixing and sediment transport in the benthic layer of the deep sea and the effects of these processes on the continental shelf specifically in the New York Bight were studied. Results are presented for the first major combined geochemistry-physical oceanography cruise. Data on the nature of the bottom sediments are presented principally in context of their potential as sources of both radon and methane in the lower water column. Preliminary data on the nature and composition of suspended particulates also indicate potential for tracing the mechanisms of solids dispersal in the Bight. (PCS)

Lead isotopes in deep-sea coral skeletons: Ground-truthing and a first deglacial Southern Ocean record

Science.gov (United States)

Wilson, David J.; van de Flierdt, Tina; Adkins, Jess F.

2017-05-01

Past changes in seawater lead (Pb) isotopes record the temporal evolution of anthropogenic pollution, continental weathering inputs, and ocean current transport. To advance our ability to reconstruct this signature, we present methodological developments that allow us to make precise and accurate Pb isotope measurements on deep-sea coral aragonite, and apply our approach to generate the first Pb isotope record for the glacial to deglacial mid-depth Southern Ocean. Our refined methodology includes a two-step anion exchange chemistry procedure and measurement using a 207Pb-204Pb double spike on a Thermo Finnigan Triton TIMS instrument. By employing a 1012 Ω resistor (in place of a 1011 Ω resistor) to measure the low-abundance 204Pb ion beam, we improve the internal precision on 206,207,208Pb/204Pb for a 2 ng load of NIST-SRM-981 Pb from typically ∼420 ppm to ∼230 ppm (2 s.e.), and the long term external reproducibility from ∼950 ppm to ∼550 ppm (2 s.d.). Furthermore, for a typical 500 mg coral sample with low Pb concentrations (∼6-10 ppb yielding ∼3-5 ng Pb for analysis), we obtain a comparable internal precision of ∼150-250 ppm for 206,207,208Pb/204Pb, indicating a good sensitivity for tracing natural Pb sources to the oceans. Successful extraction of a seawater signal from deep-sea coral aragonite further relies on careful physical and chemical cleaning steps, which are necessary to remove anthropogenic Pb contaminants and obtain results that are consistent with ferromanganese crusts. Applying our approach to a collection of late glacial and deglacial corals (∼12-40 ka BP) from south of Tasmania at ∼1.4-1.7 km water depth, we generated the first intermediate water Pb isotope record from the Southern Ocean. That record reveals millennial timescale variability, controlled by binary mixing between two Pb sources, but no distinct glacial-interglacial Pb isotope shift. Mixing between natural endmembers is fully consistent with our data and points to

The Impact of the Aerosol Direct Radiative Forcing on Deep Convection and Air Quality in the Pearl River Delta Region

Science.gov (United States)

Liu, Z.; Yim, Steve H. L.; Wang, C.; Lau, N. C.

2018-05-01

Literature has reported the remarkable aerosol impact on low-level cloud by direct radiative forcing (DRF). Impacts on middle-upper troposphere cloud are not yet fully understood, even though this knowledge is important for regions with a large spatial heterogeneity of emissions and aerosol concentration. We assess the aerosol DRF and its cloud response in June (with strong convection) in Pearl River Delta region for 2008-2012 at cloud-resolving scale using an air quality-climate coupled model. Aerosols suppress deep convection by increasing atmospheric stability leading to less evaporation from the ground. The relative humidity is reduced in middle-upper troposphere due to induced reduction in both evaporation from the ground and upward motion. The cloud reduction offsets 20% of the aerosol DRF. The weaker vertical mixing further increases surface aerosol concentration by up to 2.90 μg/m3. These findings indicate the aerosol DRF impact on deep convection and in turn regional air quality.

Mixing Ventilation. Guide on mixing air distribution design

DEFF Research Database (Denmark)

Kandzia, Claudia; Kosonen, Risto; Melikov, Arsen Krikor

In this guidebook most of the known and used in practice methods for achieving mixing air distribution are discussed. Mixing ventilation has been applied to many different spaces providing fresh air and thermal comfort to the occupants. Today, a design engineer can choose from large selection...

Text feature extraction based on deep learning: a review.

Science.gov (United States)

Liang, Hong; Sun, Xiao; Sun, Yunlei; Gao, Yuan

2017-01-01

Selection of text feature item is a basic and important matter for text mining and information retrieval. Traditional methods of feature extraction require handcrafted features. To hand-design, an effective feature is a lengthy process, but aiming at new applications, deep learning enables to acquire new effective feature representation from training data. As a new feature extraction method, deep learning has made achievements in text mining. The major difference between deep learning and conventional methods is that deep learning automatically learns features from big data, instead of adopting handcrafted features, which mainly depends on priori knowledge of designers and is highly impossible to take the advantage of big data. Deep learning can automatically learn feature representation from big data, including millions of parameters. This thesis outlines the common methods used in text feature extraction first, and then expands frequently used deep learning methods in text feature extraction and its applications, and forecasts the application of deep learning in feature extraction.

Review on Carbon Dioxide Absorption by Choline Chloride/Urea Deep Eutectic Solvents

Directory of Open Access Journals (Sweden)

Rima J. Isaifan

2018-01-01

Full Text Available In the recent past few years, deep eutectic solvents (DESs were developed sharing similar characteristics to ionic liquids but with more advantageous features related to preparation cost, environmental impact, and efficiency for gas separation processes. Amongst many combinations of DES solvents that have been prepared, reline (choline chloride as the hydrogen bond acceptor mixed with urea as the hydrogen bond donor was the first DES synthesized and is still the one with the lowest melting point. Choline chloride/urea DES has proven to be a promising solvent as an efficient medium for carbon dioxide capture when compared with amine alone or ionic liquids under the same conditions. This review sheds light on the preparation method, physical and chemical characteristics, and the CO2 absorption capacity of choline chloride/urea DES under different temperatures and pressures reported up to date.

The isotope composition of inorganic germanium in seawater and deep sea sponges

Science.gov (United States)

Guillermic, Maxence; Lalonde, Stefan V.; Hendry, Katharine R.; Rouxel, Olivier J.

2017-09-01

Although dissolved concentrations of germanium (Ge) and silicon (Si) in modern seawater are tightly correlated, uncertainties still exist in the modern marine Ge cycle. Germanium stable isotope systematics in marine systems should provide additional constraints on marine Ge sources and sinks, however the low concentration of Ge in seawater presents an analytical challenge for isotopic measurement. Here, we present a new method of pre-concentration of inorganic Ge from seawater which was applied to measure three Ge isotope profiles in the Southern Ocean and deep seawater from the Atlantic and Pacific Oceans. Germanium isotopic measurements were performed on Ge amounts as low as 2.6 ng using a double-spike approach and a hydride generation system coupled to a MC-ICP-MS. Germanium was co-precipitated with iron hydroxide and then purified through anion-exchange chromatography. Results for the deep (i.e. >1000 m depth) Pacific Ocean off Hawaii (nearby Loihi Seamount) and the deep Atlantic off Bermuda (BATS station) showed nearly identical δ74/70Ge values at 3.19 ± 0.31‰ (2SD, n = 9) and 2.93 ± 0.10‰ (2SD, n = 2), respectively. Vertical distributions of Ge concentration and isotope composition in the deep Southern Ocean for water depth > 1300 m yielded an average δ74/70Ge = 3.13 ± 0.25‰ (2SD, n = 14) and Ge/Si = 0.80 ± 0.09 μmol/mol (2SD, n = 12). Significant variations in δ74/70Ge, from 2.62 to 3.71‰, were measured in the first 1000 m in one station of the Southern Ocean near Sars Seamount in the Drake Passage, with the heaviest values measured in surface waters. Isotope fractionation by diatoms during opal biomineralization may explain the enrichment in heavy isotopes for both Ge and Si in surface seawater. However, examination of both oceanographic parameters and δ74/70Ge values suggest also that water mass mixing and potential contribution of shelf-derived Ge also could contribute to the variations. Combining these results with new Ge isotope data

Deep-seated sarcomas of the penis

Directory of Open Access Journals (Sweden)

Alberto A. Antunes

2005-06-01

Full Text Available Mesenchymal neoplasias represent 5% of tumors affecting the penis. Due to the rarity of such tumors, there is no agreement concerning the best method for staging and managing these patients. Sarcomas of the penis can be classified as deep-seated if they derive from the structures forming the spongy body and the cavernous bodies. Superficial lesions are usually low-grade and show a small tendency towards distant metastasis. In contrast, deep-seated lesions usually show behavior that is more aggressive and have poorer prognosis. The authors report 3 cases of deep-seated primary sarcomas of the penis and review the literature on this rare and aggressive neoplasia.

How do changes in warm-phase microphysics affect deep convective clouds?

Science.gov (United States)

Chen, Qian; Koren, Ilan; Altaratz, Orit; Heiblum, Reuven H.; Dagan, Guy; Pinto, Lital

2017-08-01

Understanding aerosol effects on deep convective clouds and the derived effects on the radiation budget and rain patterns can largely contribute to estimations of climate uncertainties. The challenge is difficult in part because key microphysical processes in the mixed and cold phases are still not well understood. For deep convective clouds with a warm base, understanding aerosol effects on the warm processes is extremely important as they set the initial and boundary conditions for the cold processes. Therefore, the focus of this study is the warm phase, which can be better resolved. The main question is: How do aerosol-derived changes in the warm phase affect the properties of deep convective cloud systems? To explore this question, we used a weather research and forecasting (WRF) model with spectral bin microphysics to simulate a deep convective cloud system over the Marshall Islands during the Kwajalein Experiment (KWAJEX). The model results were validated against observations, showing similarities in the vertical profile of radar reflectivity and the surface rain rate. Simulations with larger aerosol loading resulted in a larger total cloud mass, a larger cloud fraction in the upper levels, and a larger frequency of strong updrafts and rain rates. Enlarged mass both below and above the zero temperature level (ZTL) contributed to the increase in cloud total mass (water and ice) in the polluted runs. Increased condensation efficiency of cloud droplets governed the gain in mass below the ZTL, while both enhanced condensational and depositional growth led to increased mass above it. The enhanced mass loading above the ZTL acted to reduce the cloud buoyancy, while the thermal buoyancy (driven by the enhanced latent heat release) increased in the polluted runs. The overall effect showed an increased upward transport (across the ZTL) of liquid water driven by both larger updrafts and larger droplet mobility. These aerosol effects were reflected in the larger ratio

How do changes in warm-phase microphysics affect deep convective clouds?

Directory of Open Access Journals (Sweden)

Q. Chen

2017-08-01

Full Text Available Understanding aerosol effects on deep convective clouds and the derived effects on the radiation budget and rain patterns can largely contribute to estimations of climate uncertainties. The challenge is difficult in part because key microphysical processes in the mixed and cold phases are still not well understood. For deep convective clouds with a warm base, understanding aerosol effects on the warm processes is extremely important as they set the initial and boundary conditions for the cold processes. Therefore, the focus of this study is the warm phase, which can be better resolved. The main question is: How do aerosol-derived changes in the warm phase affect the properties of deep convective cloud systems? To explore this question, we used a weather research and forecasting (WRF model with spectral bin microphysics to simulate a deep convective cloud system over the Marshall Islands during the Kwajalein Experiment (KWAJEX. The model results were validated against observations, showing similarities in the vertical profile of radar reflectivity and the surface rain rate. Simulations with larger aerosol loading resulted in a larger total cloud mass, a larger cloud fraction in the upper levels, and a larger frequency of strong updrafts and rain rates. Enlarged mass both below and above the zero temperature level (ZTL contributed to the increase in cloud total mass (water and ice in the polluted runs. Increased condensation efficiency of cloud droplets governed the gain in mass below the ZTL, while both enhanced condensational and depositional growth led to increased mass above it. The enhanced mass loading above the ZTL acted to reduce the cloud buoyancy, while the thermal buoyancy (driven by the enhanced latent heat release increased in the polluted runs. The overall effect showed an increased upward transport (across the ZTL of liquid water driven by both larger updrafts and larger droplet mobility. These aerosol effects were reflected in the

In Brief: Deep-sea observatory

Science.gov (United States)

Showstack, Randy

2008-11-01

The first deep-sea ocean observatory offshore of the continental United States has begun operating in the waters off central California. The remotely operated Monterey Accelerated Research System (MARS) will allow scientists to monitor the deep sea continuously. Among the first devices to be hooked up to the observatory are instruments to monitor earthquakes, videotape deep-sea animals, and study the effects of acidification on seafloor animals. ``Some day we may look back at the first packets of data streaming in from the MARS observatory as the equivalent of those first words spoken by Alexander Graham Bell: `Watson, come here, I need you!','' commented Marcia McNutt, president and CEO of the Monterey Bay Aquarium Research Institute, which coordinated construction of the observatory. For more information, see http://www.mbari.org/news/news_releases/2008/mars-live/mars-live.html.

Deep Learning for Computer Vision: A Brief Review

Science.gov (United States)

Doulamis, Nikolaos; Doulamis, Anastasios; Protopapadakis, Eftychios

2018-01-01

Over the last years deep learning methods have been shown to outperform previous state-of-the-art machine learning techniques in several fields, with computer vision being one of the most prominent cases. This review paper provides a brief overview of some of the most significant deep learning schemes used in computer vision problems, that is, Convolutional Neural Networks, Deep Boltzmann Machines and Deep Belief Networks, and Stacked Denoising Autoencoders. A brief account of their history, structure, advantages, and limitations is given, followed by a description of their applications in various computer vision tasks, such as object detection, face recognition, action and activity recognition, and human pose estimation. Finally, a brief overview is given of future directions in designing deep learning schemes for computer vision problems and the challenges involved therein. PMID:29487619

Deep Learning for Computer Vision: A Brief Review

Directory of Open Access Journals (Sweden)

Athanasios Voulodimos

2018-01-01

Full Text Available Over the last years deep learning methods have been shown to outperform previous state-of-the-art machine learning techniques in several fields, with computer vision being one of the most prominent cases. This review paper provides a brief overview of some of the most significant deep learning schemes used in computer vision problems, that is, Convolutional Neural Networks, Deep Boltzmann Machines and Deep Belief Networks, and Stacked Denoising Autoencoders. A brief account of their history, structure, advantages, and limitations is given, followed by a description of their applications in various computer vision tasks, such as object detection, face recognition, action and activity recognition, and human pose estimation. Finally, a brief overview is given of future directions in designing deep learning schemes for computer vision problems and the challenges involved therein.

Deep Learning for Computer Vision: A Brief Review.

Science.gov (United States)

Voulodimos, Athanasios; Doulamis, Nikolaos; Doulamis, Anastasios; Protopapadakis, Eftychios

2018-01-01

Over the last years deep learning methods have been shown to outperform previous state-of-the-art machine learning techniques in several fields, with computer vision being one of the most prominent cases. This review paper provides a brief overview of some of the most significant deep learning schemes used in computer vision problems, that is, Convolutional Neural Networks, Deep Boltzmann Machines and Deep Belief Networks, and Stacked Denoising Autoencoders. A brief account of their history, structure, advantages, and limitations is given, followed by a description of their applications in various computer vision tasks, such as object detection, face recognition, action and activity recognition, and human pose estimation. Finally, a brief overview is given of future directions in designing deep learning schemes for computer vision problems and the challenges involved therein.

Is deep dreaming the new collage?

Science.gov (United States)

Boden, Margaret A.

2017-10-01

Deep dreaming (DD) can combine and transform images in surprising ways. But, being based in deep learning (DL), it is not analytically understood. Collage is an art form that is constrained along various dimensions. DD will not be able to generate collages until DL can be guided in a disciplined fashion.

Density functionals from deep learning

OpenAIRE

McMahon, Jeffrey M.

2016-01-01

Density-functional theory is a formally exact description of a many-body quantum system in terms of its density; in practice, however, approximations to the universal density functional are required. In this work, a model based on deep learning is developed to approximate this functional. Deep learning allows computational models that are capable of naturally discovering intricate structure in large and/or high-dimensional data sets, with multiple levels of abstraction. As no assumptions are ...

A Survey: Time Travel in Deep Learning Space: An Introduction to Deep Learning Models and How Deep Learning Models Evolved from the Initial Ideas

OpenAIRE

Wang, Haohan; Raj, Bhiksha

2015-01-01

This report will show the history of deep learning evolves. It will trace back as far as the initial belief of connectionism modelling of brain, and come back to look at its early stage realization: neural networks. With the background of neural network, we will gradually introduce how convolutional neural network, as a representative of deep discriminative models, is developed from neural networks, together with many practical techniques that can help in optimization of neural networks. On t...

Assessment of deep geological environment condition

International Nuclear Information System (INIS)

Bae, Dae Seok; Han, Kyung Won; Joen, Kwan Sik

2003-05-01

The main tasks of geoscientific study in the 2nd stage was characterized focusing mainly on a near-field condition of deep geologic environment, and aimed to generate the geologic input data for a Korean reference disposal system for high level radioactive wastes and to establish site characterization methodology, including neotectonic features, fracture systems and mechanical properties of plutonic rocks, and hydrogeochemical characteristics. The preliminary assessment of neotectonics in the Korean peninsula was performed on the basis of seismicity recorded, Quarternary faults investigated, uplift characteristics studied on limited areas, distribution of the major regional faults and their characteristics. The local fracture system was studied in detail from the data obtained from deep boreholes in granitic terrain. Through this deep drilling project, the geometrical and hydraulic properties of different fracture sets are statistically analysed on a block scale. The mechanical properties of intact rocks were evaluated from the core samples by laboratory testing and the in-situ stress conditions were estimated by a hydro fracturing test in the boreholes. The hydrogeochemical conditions in the deep boreholes were characterized based on hydrochemical composition and isotopic signatures and were attempted to assess the interrelation with a major fracture system. The residence time of deep groundwater was estimated by C-14 dating. For the travel time of groundwater between the boreholes, the methodology and equipment for tracer test were established

Deep Carbon Observatory investigates Carbon from Crust to Core: An Academic Record of the History of Deep Carbon Science

Science.gov (United States)

Mitton, S. A.

2017-12-01

Carbon plays an unparalleled role in our lives: as the element of life, as the basis of most of society's energy, as the backbone of most new materials, and as the central focus in efforts to understand Earth's variable and uncertain climate. Yet in spite of carbon's importance, scientists remain largely ignorant of the physical, chemical, and biological behavior of many of Earth's carbon-bearing systems. The Deep Carbon Observatory (DCO) is a global research program to transform our understanding of carbon in Earth. At its heart, DCO is a community of scientists, from biologists to physicists, geoscientists to chemists, and many others whose work crosses these disciplinary lines, forging a new, integrative field of deep carbon science. As a historian of science, I specialise in the history of planetary science and astronomy since 1900. This is directed toward understanding of the history of the steps on the road to discovering the internal dynamics of our planet. Within a framework that describes the historical background to the new field of Earth System Science, I present the first history of deep carbon science. This project will identifies the key discoveries of deep carbon science. It will assess the impact of new knowledge on geochemistry, geodynamics, and geobiology. The project will lead to publication, in book form in 2019, of an illuminating narrative that will highlight the engaging human stories of many remarkable scientists and natural philosophers from whom we have learned about the complexity of Earth's internal world. On this journey of discovery we will encounter not just the pioneering researchers of deep carbon science, but also their institutions, their instrumental inventiveness, and their passion for exploration. The book is organised thematically around the four communities of the Deep Carbon Observatory: Deep Life, Extreme Physics and Chemistry, Reservoirs and Fluxes, and Deep Energy. The presentation has a gallery and list of Deep Carbon

Deep Energy Retrofit

DEFF Research Database (Denmark)

Zhivov, Alexander; Lohse, Rüdiger; Rose, Jørgen

Deep Energy Retrofit – A Guide to Achieving Significant Energy User Reduction with Major Renovation Projects contains recommendations for characteristics of some of core technologies and measures that are based on studies conducted by national teams associated with the International Energy Agency...... Energy Conservation in Buildings and Communities Program (IEA-EBC) Annex 61 (Lohse et al. 2016, Case, et al. 2016, Rose et al. 2016, Yao, et al. 2016, Dake 2014, Stankevica et al. 2016, Kiatreungwattana 2014). Results of these studies provided a base for setting minimum requirements to the building...... envelope-related technologies to make Deep Energy Retrofit feasible and, in many situations, cost effective. Use of energy efficiency measures (EEMs) in addition to core technologies bundle and high-efficiency appliances will foster further energy use reduction. This Guide also provides best practice...

A Non-catalytic Deep Desulphurization Process using Hydrodynamic Cavitation

Science.gov (United States)

Suryawanshi, Nalinee B.; Bhandari, Vinay M.; Sorokhaibam, Laxmi Gayatri; Ranade, Vivek V.

2016-09-01

A novel approach is developed for desulphurization of fuels or organics without use of catalyst. In this process, organic and aqueous phases are mixed in a predefined manner under ambient conditions and passed through a cavitating device. Vapor cavities formed in the cavitating device are then collapsed which generate (in-situ) oxidizing species which react with the sulphur moiety resulting in the removal of sulphur from the organic phase. In this work, vortex diode was used as a cavitating device. Three organic solvents (n-octane, toluene and n-octanol) containing known amount of a model sulphur compound (thiophene) up to initial concentrations of 500 ppm were used to verify the proposed method. A very high removal of sulphur content to the extent of 100% was demonstrated. The nature of organic phase and the ratio of aqueous to organic phase were found to be the most important process parameters. The results were also verified and substantiated using commercial diesel as a solvent. The developed process has great potential for deep of various organics, in general, and for transportation fuels, in particular.

Sneutrino mixing

International Nuclear Information System (INIS)

Grossman, Y.

1997-10-01

In supersymmetric models with nonvanishing Majorana neutrino masses, the sneutrino and antisneutrino mix. The conditions under which this mixing is experimentally observable are studied, and mass-splitting of the sneutrino mass eigenstates and sneutrino oscillation phenomena are analyzed

Deep-well injection of radioactive waste in Russia

International Nuclear Information System (INIS)

Hoek, J.

1998-01-01

In the Russian federation, deep borehole injection of liquid radioactive waste has been established practice since at least 1963. The liquid is injected into sandy or other formations with high porosity, which are isolated by water-tight layers. This technique has also been used elsewhere for toxic liquid waste and residues from mining operations. Deep-well injection of radioactive waste is not currently used in any of the European Commission (EC) countries. In this paper the results of a EC-funded study were presented. The study is entitled 'Measurements, modelling of migration and possible radiological consequences at deep well injection sites for liquid radioactive waste in Russia', COSU-CT94-0099-UK. The study was carried out jointly by AEA Technology, CAG and the Research Institute for Nuclear Reactors (NIIAR) at Dimitrovgrad. Many scientists have contributed to the results reported here. The aims of the study are: Provision of extensive information on the deep-well injection repositories and their use in the former Soviet Union; Provision of a methodology to assess safety aspects of deep-well injection of liquid radioactive waste in deep geological formations; This will allow evaluation of proposals to use deep-well injection techniques in other regions; Support for Russian regulatory bodies through evaluation of the suitability of the sites, including estimates of the maximum amount of waste that can be safely stored in them; and Provision of a methodology to assess the use of deep-well injection repositories as an alternative disposal technique for EC countries. 7 refs

DEEP: a general computational framework for predicting enhancers

KAUST Repository

Kleftogiannis, Dimitrios A.

2014-11-05

Transcription regulation in multicellular eukaryotes is orchestrated by a number of DNA functional elements located at gene regulatory regions. Some regulatory regions (e.g. enhancers) are located far away from the gene they affect. Identification of distal regulatory elements is a challenge for the bioinformatics research. Although existing methodologies increased the number of computationally predicted enhancers, performance inconsistency of computational models across different cell-lines, class imbalance within the learning sets and ad hoc rules for selecting enhancer candidates for supervised learning, are some key questions that require further examination. In this study we developed DEEP, a novel ensemble prediction framework. DEEP integrates three components with diverse characteristics that streamline the analysis of enhancer\\'s properties in a great variety of cellular conditions. In our method we train many individual classification models that we combine to classify DNA regions as enhancers or non-enhancers. DEEP uses features derived from histone modification marks or attributes coming from sequence characteristics. Experimental results indicate that DEEP performs better than four state-of-the-art methods on the ENCODE data. We report the first computational enhancer prediction results on FANTOM5 data where DEEP achieves 90.2% accuracy and 90% geometric mean (GM) of specificity and sensitivity across 36 different tissues. We further present results derived using in vivo-derived enhancer data from VISTA database. DEEP-VISTA, when tested on an independent test set, achieved GM of 80.1% and accuracy of 89.64%. DEEP framework is publicly available at http://cbrc.kaust.edu.sa/deep/.

DEEP: a general computational framework for predicting enhancers

KAUST Repository

Kleftogiannis, Dimitrios A.; Kalnis, Panos; Bajic, Vladimir B.

2014-01-01

Transcription regulation in multicellular eukaryotes is orchestrated by a number of DNA functional elements located at gene regulatory regions. Some regulatory regions (e.g. enhancers) are located far away from the gene they affect. Identification of distal regulatory elements is a challenge for the bioinformatics research. Although existing methodologies increased the number of computationally predicted enhancers, performance inconsistency of computational models across different cell-lines, class imbalance within the learning sets and ad hoc rules for selecting enhancer candidates for supervised learning, are some key questions that require further examination. In this study we developed DEEP, a novel ensemble prediction framework. DEEP integrates three components with diverse characteristics that streamline the analysis of enhancer's properties in a great variety of cellular conditions. In our method we train many individual classification models that we combine to classify DNA regions as enhancers or non-enhancers. DEEP uses features derived from histone modification marks or attributes coming from sequence characteristics. Experimental results indicate that DEEP performs better than four state-of-the-art methods on the ENCODE data. We report the first computational enhancer prediction results on FANTOM5 data where DEEP achieves 90.2% accuracy and 90% geometric mean (GM) of specificity and sensitivity across 36 different tissues. We further present results derived using in vivo-derived enhancer data from VISTA database. DEEP-VISTA, when tested on an independent test set, achieved GM of 80.1% and accuracy of 89.64%. DEEP framework is publicly available at http://cbrc.kaust.edu.sa/deep/.

DEWS (DEep White matter hyperintensity Segmentation framework): A fully automated pipeline for detecting small deep white matter hyperintensities in migraineurs.

Science.gov (United States)

Park, Bo-Yong; Lee, Mi Ji; Lee, Seung-Hak; Cha, Jihoon; Chung, Chin-Sang; Kim, Sung Tae; Park, Hyunjin

2018-01-01

Migraineurs show an increased load of white matter hyperintensities (WMHs) and more rapid deep WMH progression. Previous methods for WMH segmentation have limited efficacy to detect small deep WMHs. We developed a new fully automated detection pipeline, DEWS (DEep White matter hyperintensity Segmentation framework), for small and superficially-located deep WMHs. A total of 148 non-elderly subjects with migraine were included in this study. The pipeline consists of three components: 1) white matter (WM) extraction, 2) WMH detection, and 3) false positive reduction. In WM extraction, we adjusted the WM mask to re-assign misclassified WMHs back to WM using many sequential low-level image processing steps. In WMH detection, the potential WMH clusters were detected using an intensity based threshold and region growing approach. For false positive reduction, the detected WMH clusters were classified into final WMHs and non-WMHs using the random forest (RF) classifier. Size, texture, and multi-scale deep features were used to train the RF classifier. DEWS successfully detected small deep WMHs with a high positive predictive value (PPV) of 0.98 and true positive rate (TPR) of 0.70 in the training and test sets. Similar performance of PPV (0.96) and TPR (0.68) was attained in the validation set. DEWS showed a superior performance in comparison with other methods. Our proposed pipeline is freely available online to help the research community in quantifying deep WMHs in non-elderly adults.

Development and application of new composite grouting material for sealing groundwater inflow and reinforcing wall rock in deep mine.

Science.gov (United States)

Jinpeng, Zhang; Limin, Liu; Futao, Zhang; Junzhi, Cao

2018-04-04

With cement, bentonite, water glass, J85 accelerator, retarder and water as raw materials, a new composite grouting material used to seal groundwater inflow and reinforce wall rock in deep fractured rock mass was developed in this paper. Based on the reaction mechanism of raw material, the pumpable time, stone rate, initial setting time, plastic strength and unconfined compressive strength of multi-group proportion grouts were tested by orthogonal experiment. Then, the optimum proportion of composite grouting material was selected and applied to the grouting engineering for sealing groundwater inflow and reinforcing wall rock in mine shaft lining. The results show the mixing proportion of the maximum pumpable time, maximum stone rate and minimum initial setting time of grout are A K4 B K1 C K4 D K2 , A K3 B K1 C K1 D K4 and A K3 B K3 C K4 D K1 , respectively. The mixing proportion of the maximum plastic strength and unconfined compressive strength of grouts concretion bodies are A K1 B K1 C K1 D K3 and A K1 B K1 C K1 D K1 , respectively. Balanced the above 5 indicators overall and determined the optimum proportion of grouts: bentonite-cement ratio of 1.0, water-solid ratio of 3.5, accelerator content of 2.9% and retarder content of 1.45%. This new composite grouting material had good effect on the grouting engineering for sealing groundwater inflow and reinforcing wall rock in deep fractured rock mass.

Deep-Sea Corals: A New Oceanic Archive

National Research Council Canada - National Science Library

Adkins, Jess

1998-01-01

Deep-sea corals are an extraordinary new archive of deep ocean behavior. The species Desmophyllum cristagalli is a solitary coral composed of uranium rich, density banded aragonite that I have calibrated for several paleoclimate tracers...

Mixing ventilation guide on mixing air distribution design

CERN Document Server

Kandzia, Claudia; Kosonen, Risto; Krikor Melikov, Arsen; Nielsen, Peter Vilhelm

2013-01-01

In this guidebook most of the known and used in practice methods for achieving mixing air distribution are discussed. Mixing ventilation has been applied to many different spaces providing fresh air and thermal comfort to the occupants. Today, a design engineer can choose from large selection of air diffusers and exhaust openings.

Challenging oil bioremediation at deep-sea hydrostatic pressure

Directory of Open Access Journals (Sweden)

Alberto Scoma

2016-08-01

Full Text Available The Deepwater Horizon (DWH accident has brought oil contamination of deep-sea environments to worldwide attention. The risk for new deep-sea spills is not expected to decrease in the future, as political pressure mounts to access deep-water fossil reserves, and poorly tested technologies are used to access oil. This also applies to the response to oil-contamination events, with bioremediation the only (biotechnology presently available to combat deep-sea spills. Many questions about the fate of petroleum-hydrocarbons at deep-sea remain unanswered, as much as the main constraints limiting bioremediation under increased hydrostatic pressures and low temperatures. The microbial pathways fueling oil take up are unclear, and the mild upregulation observed for beta-oxidation-related genes in both water and sediments contrasts with the high amount of alkanes present in the spilled-oil. The fate of solid alkanes (tar and that of hydrocarbons degradation rates was largely overlooked, as the reason why the most predominant hydrocarbonoclastic genera were not enriched at deep-sea, despite being present at hydrocarbon seeps at the Gulf of Mexico. This mini-review aims at highlighting the missing information in the field, proposing a holistic approach where in situ and ex situ studies are integrated to reveal the principal mechanisms accounting for deep-sea oil bioremediation.

Deep Crustal Melting and the Survival of Continental Crust

Science.gov (United States)

Whitney, D.; Teyssier, C. P.; Rey, P. F.; Korchinski, M.

2017-12-01

Plate convergence involving continental lithosphere leads to crustal melting, which ultimately stabilizes the crust because it drives rapid upward flow of hot deep crust, followed by rapid cooling at shallow levels. Collision drives partial melting during crustal thickening (at 40-75 km) and/or continental subduction (at 75-100 km). These depths are not typically exceeded by crustal rocks that are exhumed in each setting because partial melting significantly decreases viscosity, facilitating upward flow of deep crust. Results from numerical models and nature indicate that deep crust moves laterally and then vertically, crystallizing at depths as shallow as 2 km. Deep crust flows en masse, without significant segregation of melt into magmatic bodies, over 10s of kms of vertical transport. This is a major mechanism by which deep crust is exhumed and is therefore a significant process of heat and mass transfer in continental evolution. The result of vertical flow of deep, partially molten crust is a migmatite dome. When lithosphere is under extension or transtension, the deep crust is solicited by faulting of the brittle upper crust, and the flow of deep crust in migmatite domes traverses nearly the entire thickness of orogenic crust in Recognition of the importance of migmatite (gneiss) domes as archives of orogenic deep crust is applicable to determining the chemical and physical properties of continental crust, as well as mechanisms and timescales of crustal differentiation.

Deep Learning and Bayesian Methods

OpenAIRE

Prosper Harrison B.

2017-01-01

A revolution is underway in which deep neural networks are routinely used to solve diffcult problems such as face recognition and natural language understanding. Particle physicists have taken notice and have started to deploy these methods, achieving results that suggest a potentially significant shift in how data might be analyzed in the not too distant future. We discuss a few recent developments in the application of deep neural networks and then indulge in speculation about how such meth...

The dynamics of biogeographic ranges in the deep sea.

Science.gov (United States)

McClain, Craig R; Hardy, Sarah Mincks

2010-12-07

Anthropogenic disturbances such as fishing, mining, oil drilling, bioprospecting, warming, and acidification in the deep sea are increasing, yet generalities about deep-sea biogeography remain elusive. Owing to the lack of perceived environmental variability and geographical barriers, ranges of deep-sea species were traditionally assumed to be exceedingly large. In contrast, seamount and chemosynthetic habitats with reported high endemicity challenge the broad applicability of a single biogeographic paradigm for the deep sea. New research benefiting from higher resolution sampling, molecular methods and public databases can now more rigorously examine dispersal distances and species ranges on the vast ocean floor. Here, we explore the major outstanding questions in deep-sea biogeography. Based on current evidence, many taxa appear broadly distributed across the deep sea, a pattern replicated in both the abyssal plains and specialized environments such as hydrothermal vents. Cold waters may slow larval metabolism and development augmenting the great intrinsic ability for dispersal among many deep-sea species. Currents, environmental shifts, and topography can prove to be dispersal barriers but are often semipermeable. Evidence of historical events such as points of faunal origin and climatic fluctuations are also evident in contemporary biogeographic ranges. Continued synthetic analysis, database construction, theoretical advancement and field sampling will be required to further refine hypotheses regarding deep-sea biogeography.

Survey on deep learning for radiotherapy.

Science.gov (United States)

Meyer, Philippe; Noblet, Vincent; Mazzara, Christophe; Lallement, Alex

2018-05-17

More than 50% of cancer patients are treated with radiotherapy, either exclusively or in combination with other methods. The planning and delivery of radiotherapy treatment is a complex process, but can now be greatly facilitated by artificial intelligence technology. Deep learning is the fastest-growing field in artificial intelligence and has been successfully used in recent years in many domains, including medicine. In this article, we first explain the concept of deep learning, addressing it in the broader context of machine learning. The most common network architectures are presented, with a more specific focus on convolutional neural networks. We then present a review of the published works on deep learning methods that can be applied to radiotherapy, which are classified into seven categories related to the patient workflow, and can provide some insights of potential future applications. We have attempted to make this paper accessible to both radiotherapy and deep learning communities, and hope that it will inspire new collaborations between these two communities to develop dedicated radiotherapy applications. Copyright © 2018 Elsevier Ltd. All rights reserved.

Continuous mixing of solids

NARCIS (Netherlands)

Raouf, M.S.

1963-01-01

The most important literature on theoretical aspects of mixing solids was reviewed.

Only when the mixed materials showed no segregation it was possible to analyse the mixing process quantitatively. In this case the mixture could be described by the 'χ' Square test. Longitudinal mixing could be

Fundamentals of Highly Non-Degenerate Cascaded Four-Wave Mixing

Directory of Open Access Journals (Sweden)

Rosa Weigand

2015-09-01

Full Text Available By crossing two intense ultrashort laser pulses with different colors in a transparent medium, like a simple piece of glass, a fan of multicolored broadband light pulses can be simultaneously generated. These newly generated pulses are emitted in several well-defined directions and can cover a broad spectral range, from the infrared to the ultraviolet and beyond. This beautiful phenomenon, first observed and described 15 years ago, is due to highly-nondegenerate cascaded four-wave mixing (cascaded FWM, or CFWM. Here, we present a review of our work on the generation and measurement of multicolored light pulses based on third-order nonlinearities in transparent solids, from the discovery and first demonstration of highly-nondegenerate CFWM, to the coherent synthesis of single-cycle pulses by superposition of the multicolored light pulses produced by CFWM. We will also present the development and main results of a dedicated 2.5-D nonlinear propagation model, i.e., with propagation occurring along a two-dimensional plane while assuming cylindrically symmetric pump beam profiles, capable of adequately describing noncollinear FWM and CFWM processes. A new method for the generation of femtosecond pulses in the deep-ultraviolet (DUV based on FWM and CFWM will also be described. These experimental and theoretical results show that highly-nondegenerate third-order nonlinear optical processes are formally well understood and provide broader bandwidths than other nonlinear optical processes for the generation of ultrashort light pulses with wavelengths extending from the near-infrared to the deep-ultraviolet, which have many applications in science and technology.

Contemplating case mix: A primer on case mix classification and management.

Science.gov (United States)

Costa, Andrew P; Poss, Jeffery W; McKillop, Ian

2015-01-01

Case mix classifications are the frameworks that underlie many healthcare funding schemes, including the so-called activity-based funding. Now more than ever, Canadian healthcare administrators are evaluating case mix-based funding and deciphering how they will influence their organization. Case mix is a topic fraught with technical jargon and largely relegated to government agencies or private industries. This article provides an abridged review of case mix classification as well as its implications for management in healthcare. © 2015 The Canadian College of Health Leaders.

Hello World Deep Learning in Medical Imaging.

Science.gov (United States)

Lakhani, Paras; Gray, Daniel L; Pett, Carl R; Nagy, Paul; Shih, George

2018-05-03

There is recent popularity in applying machine learning to medical imaging, notably deep learning, which has achieved state-of-the-art performance in image analysis and processing. The rapid adoption of deep learning may be attributed to the availability of machine learning frameworks and libraries to simplify their use. In this tutorial, we provide a high-level overview of how to build a deep neural network for medical image classification, and provide code that can help those new to the field begin their informatics projects.

Deep Neuromuscular Blockade Improves Laparoscopic Surgical Conditions

DEFF Research Database (Denmark)

Rosenberg, Jacob; Herring, W Joseph; Blobner, Manfred

2017-01-01

INTRODUCTION: Sustained deep neuromuscular blockade (NMB) during laparoscopic surgery may facilitate optimal surgical conditions. This exploratory study assessed whether deep NMB improves surgical conditions and, in doing so, allows use of lower insufflation pressures during laparoscopic cholecys...

Development of Hydro-Mechanical Deep Drawing

DEFF Research Database (Denmark)

Zhang, Shi-Hong; Danckert, Joachim

1998-01-01

The hydro-mechanical deep-drawing process is reviewed in this article. The process principles and features are introduced and the developments of the hydro-mechanical deep-drawing process in process performances, in theory and in numerical simulation are described. The applications are summarized....... Some other related hydraulic forming processes are also dealt with as a comparison....

Mixed Waste Management Facility

International Nuclear Information System (INIS)

Brummond, W.; Celeste, J.; Steenhoven, J.

1993-08-01

The DOE has developed a National Mixed Waste Strategic Plan which calls for the construction of 2 to 9 mixed waste treatment centers in the Complex in the near future. LLNL is working to establish an integrated mixed waste technology development and demonstration system facility, the Mixed Waste Management Facility (MWMF), to support the DOE National Mixed Waste Strategic Plan. The MWMF will develop, demonstrate, test, and evaluate incinerator-alternatives which will comply with regulations governing the treatment and disposal of organic mixed wastes. LLNL will provide the DOE with engineering data for design and operation of new technologies which can be implemented in their mixed waste treatment centers. MWMF will operate under real production plant conditions and process samples of real LLNL mixed waste. In addition to the destruction of organic mixed wastes, the development and demonstration will include waste feed preparation, material transport systems, aqueous treatment, off-gas treatment, and final forms, thus making it an integrated ''cradle to grave'' demonstration. Technologies from offsite as well as LLNL's will be tested and evaluated when they are ready for a pilot scale demonstration, according to the needs of the DOE

76 FR 66078 - Notice of Industry Workshop on Technical and Regulatory Challenges in Deep and Ultra-Deep Outer...

Science.gov (United States)

2011-10-25

...-0087] Notice of Industry Workshop on Technical and Regulatory Challenges in Deep and Ultra-Deep Outer... discussions expected to help identify Outer Continental Shelf (OCS) challenges and technologies associated... structured venue for consultation among offshore deepwater oil and gas industry and regulatory experts in...

Deep Corals, Deep Learning: Moving the Deep Net Towards Real-Time Image Annotation

OpenAIRE

Lea-Anne Henry; Sankha S. Mukherjee; Neil M. Roberston; Laurence De Clippele; J. Murray Roberts

2016-01-01

The mismatch between human capacity and the acquisition of Big Data such as Earth imagery undermines commitments to Convention on Biological Diversity (CBD) and Aichi targets. Artificial intelligence (AI) solutions to Big Data issues are urgently needed as these could prove to be faster, more accurate, and cheaper. Reducing costs of managing protected areas in remote deep waters and in the High Seas is of great importance, and this is a realm where autonomous technology will be transformative.

Evolutionary process of deep-sea bathymodiolus mussels.

Science.gov (United States)

Miyazaki, Jun-Ichi; de Oliveira Martins, Leonardo; Fujita, Yuko; Matsumoto, Hiroto; Fujiwara, Yoshihiro

2010-04-27

Since the discovery of deep-sea chemosynthesis-based communities, much work has been done to clarify their organismal and environmental aspects. However, major topics remain to be resolved, including when and how organisms invade and adapt to deep-sea environments; whether strategies for invasion and adaptation are shared by different taxa or unique to each taxon; how organisms extend their distribution and diversity; and how they become isolated to speciate in continuous waters. Deep-sea mussels are one of the dominant organisms in chemosynthesis-based communities, thus investigations of their origin and evolution contribute to resolving questions about life in those communities. We investigated worldwide phylogenetic relationships of deep-sea Bathymodiolus mussels and their mytilid relatives by analyzing nucleotide sequences of the mitochondrial cytochrome c oxidase subunit I (COI) and NADH dehydrogenase subunit 4 (ND4) genes. Phylogenetic analysis of the concatenated sequence data showed that mussels of the subfamily Bathymodiolinae from vents and seeps were divided into four groups, and that mussels of the subfamily Modiolinae from sunken wood and whale carcasses assumed the outgroup position and shallow-water modioline mussels were positioned more distantly to the bathymodioline mussels. We provisionally hypothesized the evolutionary history of Bathymodilolus mussels by estimating evolutionary time under a relaxed molecular clock model. Diversification of bathymodioline mussels was initiated in the early Miocene, and subsequently diversification of the groups occurred in the early to middle Miocene. The phylogenetic relationships support the "Evolutionary stepping stone hypothesis," in which mytilid ancestors exploited sunken wood and whale carcasses in their progressive adaptation to deep-sea environments. This hypothesis is also supported by the evolutionary transition of symbiosis in that nutritional adaptation to the deep sea proceeded from extracellular

Ultra Deep Wave Equation Imaging and Illumination

Energy Technology Data Exchange (ETDEWEB)

Alexander M. Popovici; Sergey Fomel; Paul Sava; Sean Crawley; Yining Li; Cristian Lupascu

2006-09-30

In this project we developed and tested a novel technology, designed to enhance seismic resolution and imaging of ultra-deep complex geologic structures by using state-of-the-art wave-equation depth migration and wave-equation velocity model building technology for deeper data penetration and recovery, steeper dip and ultra-deep structure imaging, accurate velocity estimation for imaging and pore pressure prediction and accurate illumination and amplitude processing for extending the AVO prediction window. Ultra-deep wave-equation imaging provides greater resolution and accuracy under complex geologic structures where energy multipathing occurs, than what can be accomplished today with standard imaging technology. The objective of the research effort was to examine the feasibility of imaging ultra-deep structures onshore and offshore, by using (1) wave-equation migration, (2) angle-gathers velocity model building, and (3) wave-equation illumination and amplitude compensation. The effort consisted of answering critical technical questions that determine the feasibility of the proposed methodology, testing the theory on synthetic data, and finally applying the technology for imaging ultra-deep real data. Some of the questions answered by this research addressed: (1) the handling of true amplitudes in the downward continuation and imaging algorithm and the preservation of the amplitude with offset or amplitude with angle information required for AVO studies, (2) the effect of several imaging conditions on amplitudes, (3) non-elastic attenuation and approaches for recovering the amplitude and frequency, (4) the effect of aperture and illumination on imaging steep dips and on discriminating the velocities in the ultra-deep structures. All these effects were incorporated in the final imaging step of a real data set acquired specifically to address ultra-deep imaging issues, with large offsets (12,500 m) and long recording time (20 s).

Displacement potential solution of a guided deep beam of composite materials under symmetric three-point bending

Science.gov (United States)

Rahman, M. Muzibur; Ahmad, S. Reaz

2017-12-01

An analytical investigation of elastic fields for a guided deep beam of orthotropic composite material having three point symmetric bending is carried out using displacement potential boundary modeling approach. Here, the formulation is developed as a single function of space variables defined in terms of displacement components, which has to satisfy the mixed type of boundary conditions. The relevant displacement and stress components are derived into infinite series using Fourier integral along with suitable polynomials coincided with boundary conditions. The results are presented mainly in the form of graphs and verified with finite element solutions using ANSYS. This study shows that the analytical and numerical solutions are in good agreement and thus enhances reliability of the displacement potential approach.

U.V. repair in deep-sea bacteria

International Nuclear Information System (INIS)

Lutz, L.; Yayanos, A.A.

1986-01-01

Exposure of cells to light of less than 320 nanometers wavelengths may lead to lethal lesions and perhaps carcinogenesis. Many organisms have evolved mechanisms to repair U.V. light-induced damage. Organisms such as deep-sea bacteria are presumably never exposed to U.V. light and perhaps occasionally to visible from bioluminescence. Thus, the repair of U.V. damage in deep-sea bacterial DNA might be inefficient and repair by photoreactivation unlikely. The bacteria utilized in this investigation are temperature sensitive and barophilic. Four deep-sea isolates were chosen for this study: PE-36 from 3584 m, CNPT-3 from 5782 m, HS-34 from 5682 m, and MT-41 from 10,476 m, all are from the North Pacific ocean. The deep-sea extends from 1100 m to depths greater than 7000 m. It is a region of relatively uniform conditions. The temperature ranges from 5 to -1 0 C. There is no solar light in the deep-sea. Deep-sea bacteria are sensitive to U.V. light; in fact more sensitive than a variety of terrestrial and sea-surface bacteria. All four isolates demonstrate thymine dimer repair. Photoreactivation was observed in only MT-41. The other strains from shallower depths displayed no photoreactivation. The presence of DNA sequences homologous to the rec A, uvr A, B, and C and phr genes of E. coli have been examined by Southern hybridization techniques

Diabetic retinopathy screening using deep neural network.

Science.gov (United States)

Ramachandran, Nishanthan; Hong, Sheng Chiong; Sime, Mary J; Wilson, Graham A

2017-09-07

There is a burgeoning interest in the use of deep neural network in diabetic retinal screening. To determine whether a deep neural network could satisfactorily detect diabetic retinopathy that requires referral to an ophthalmologist from a local diabetic retinal screening programme and an international database. Retrospective audit. Diabetic retinal photos from Otago database photographed during October 2016 (485 photos), and 1200 photos from Messidor international database. Receiver operating characteristic curve to illustrate the ability of a deep neural network to identify referable diabetic retinopathy (moderate or worse diabetic retinopathy or exudates within one disc diameter of the fovea). Area under the receiver operating characteristic curve, sensitivity and specificity. For detecting referable diabetic retinopathy, the deep neural network had an area under receiver operating characteristic curve of 0.901 (95% confidence interval 0.807-0.995), with 84.6% sensitivity and 79.7% specificity for Otago and 0.980 (95% confidence interval 0.973-0.986), with 96.0% sensitivity and 90.0% specificity for Messidor. This study has shown that a deep neural network can detect referable diabetic retinopathy with sensitivities and specificities close to or better than 80% from both an international and a domestic (New Zealand) database. We believe that deep neural networks can be integrated into community screening once they can successfully detect both diabetic retinopathy and diabetic macular oedema. © 2017 Royal Australian and New Zealand College of Ophthalmologists.

Asymptotics for moist deep convection I: refined scalings and self-sustaining updrafts

Science.gov (United States)

Hittmeir, Sabine; Klein, Rupert

2018-04-01

Moist processes are among the most important drivers of atmospheric dynamics, and scale analysis and asymptotics are cornerstones of theoretical meteorology. Accounting for moist processes in systematic scale analyses therefore seems of considerable importance for the field. Klein and Majda (Theor Comput Fluid Dyn 20:525-551, 2006) proposed a scaling regime for the incorporation of moist bulk microphysics closures in multiscale asymptotic analyses of tropical deep convection. This regime is refined here to allow for mixtures of ideal gases and to establish consistency with a more general multiple scales modeling framework for atmospheric flows. Deep narrow updrafts, the so-called hot towers, constitute principal building blocks of larger scale storm systems. They are analyzed here in a sample application of the new scaling regime. A single quasi-one-dimensional upright columnar cloud is considered on the vertical advective (or tower life cycle) time scale. The refined asymptotic scaling regime is essential for this example as it reveals a new mechanism for the self-sustainance of such updrafts. Even for strongly positive convectively available potential energy, a vertical balance of buoyancy forces is found in the presence of precipitation. This balance induces a diagnostic equation for the vertical velocity, and it is responsible for the generation of self-sustained balanced updrafts. The time-dependent updraft structure is encoded in a Hamilton-Jacobi equation for the precipitation mixing ratio. Numerical solutions of this equation suggest that the self-sustained updrafts may strongly enhance hot tower life cycles.

Extracting Databases from Dark Data with DeepDive.

Science.gov (United States)

Zhang, Ce; Shin, Jaeho; Ré, Christopher; Cafarella, Michael; Niu, Feng

2016-01-01

DeepDive is a system for extracting relational databases from dark data : the mass of text, tables, and images that are widely collected and stored but which cannot be exploited by standard relational tools. If the information in dark data - scientific papers, Web classified ads, customer service notes, and so on - were instead in a relational database, it would give analysts a massive and valuable new set of "big data." DeepDive is distinctive when compared to previous information extraction systems in its ability to obtain very high precision and recall at reasonable engineering cost; in a number of applications, we have used DeepDive to create databases with accuracy that meets that of human annotators. To date we have successfully deployed DeepDive to create data-centric applications for insurance, materials science, genomics, paleontologists, law enforcement, and others. The data unlocked by DeepDive represents a massive opportunity for industry, government, and scientific researchers. DeepDive is enabled by an unusual design that combines large-scale probabilistic inference with a novel developer interaction cycle. This design is enabled by several core innovations around probabilistic training and inference.

Deep groundwater chemistry

International Nuclear Information System (INIS)

Wikberg, P.; Axelsen, K.; Fredlund, F.

1987-06-01

Starting in 1977 and up till now a number of places in Sweden have been investigated in order to collect the necessary geological, hydrogeological and chemical data needed for safety analyses of repositories in deep bedrock systems. Only crystalline rock is considered and in many cases this has been gneisses of sedimentary origin but granites and gabbros are also represented. Core drilled holes have been made at nine sites. Up to 15 holes may be core drilled at one site, the deepest down to 1000 m. In addition to this a number of boreholes are percussion drilled at each site to depths of about 100 m. When possible drilling water is taken from percussion drilled holes. The first objective is to survey the hydraulic conditions. Core drilled boreholes and sections selected for sampling of deep groundwater are summarized. (orig./HP)

Oxygen Saturation Surrounding Deep Water Formation Events in the Labrador Sea From Argo-O2 Data

Science.gov (United States)

Wolf, Mitchell K.; Hamme, Roberta C.; Gilbert, Denis; Yashayaev, Igor; Thierry, Virginie

2018-04-01

Deep water formation supplies oxygen-rich water to the deep sea, spreading throughout the ocean by means of the global thermohaline circulation. Models suggest that dissolved gases in newly formed deep water do not come to equilibrium with the atmosphere. However, direct measurements during wintertime convection are scarce, and the controls over the extent of these disequilibria are poorly quantified. Here we show that, when convection reached deeper than 800 m, oxygen in the Labrador Sea was consistently undersaturated at -6.1% to -7.6% at the end of convection. Deeper convection resulted in greater undersaturation, while convection ending later in the year resulted in values closer to equilibrium, from which we produce a predictive relationship. We use dissolved oxygen data from six profiling Argo floats in the Labrador Sea between 2003 and 2016, allowing direct observations of wintertime convection. Three of the six optode oxygen sensors displayed substantial average in situ drift of -3.03 μmol O2 kg-1 yr-1 (-0.94% O2 yr-1), which we corrected to stable deepwater oxygen values from repeat ship surveys. Observations of low oxygen intrusions during restratification and a simple mixing calculation demonstrate that lateral processes act to lower the oxygen inventory of the central Labrador Sea. This suggests that the Labrador Sea is a net sink for atmospheric oxygen, but uncertainties in parameterizing gas exchange limit our ability to quantify the net uptake. Our results constrain the oxygen concentration of newly formed Labrador Sea Water and allow more precise estimates of oxygen utilization and nutrient regeneration in this water mass.

Endodontic pathogens causing deep neck space infections: clinical impact of different sampling techniques and antibiotic susceptibility.

Science.gov (United States)

Poeschl, Paul W; Crepaz, Valentina; Russmueller, Guenter; Seemann, Rudolf; Hirschl, Alexander M; Ewers, Rolf

2011-09-01

The aims of the present study were to compare microbial populations in patients suffering from deep neck space abscesses caused by primary endodontic infections by sampling the infections with aspiration or swabbing techniques and to determine the susceptibility rates of the isolated bacteria to commonly used antibiotics. A total of 89 patients with deep neck space abscesses caused by primary endodontic infections requiring extraoral incision and drainage under general anesthesia were included. Either aspiration or swabbing was used to sample microbial pus specimens. The culture of the microbial specimens and susceptibility testing were performed following standard procedures. A total of 142 strains were recovered from 76 patients. In 13 patients, no bacteria were found. The predominant bacteria observed were streptococci (36%), staphylococci (13%), Prevotella (8%), and Peptostreptococcus (6%). A statistically significant greater number of obligate anaerobes were found in the aspiration group. The majority of patients presented a mixed aerobic-anaerobic population of bacterial flora (62%). The antibiotic resistance rates for the predominant bacteria were 10% for penicillin G, 9% for amoxicillin, 0% for amoxicillin clavulanate, 24% for clindamycin, and 24% for erythromycin. The results of our study indicated that a greater number of anaerobes were found when sampling using the aspiration technique. Penicillin G and aminopenicillins alone are not always sufficient for the treatment of severe deep neck space abscesses; beta-lactamase inhibitor combinations are more effective. Bacteria showed significant resistant rates to clindamycin. Thus, its single use in penicillin-allergic patients has to be carefully considered. Copyright © 2011 American Association of Endodontists. Published by Elsevier Inc. All rights reserved.

Preface: Deep Slab and Mantle Dynamics

Science.gov (United States)

Suetsugu, Daisuke; Bina, Craig R.; Inoue, Toru; Wiens, Douglas A.

2010-11-01

We are pleased to publish this special issue of the journal Physics of the Earth and Planetary Interiors entitled "Deep Slab and Mantle Dynamics". This issue is an outgrowth of the international symposium "Deep Slab and Mantle Dynamics", which was held on February 25-27, 2009, in Kyoto, Japan. This symposium was organized by the "Stagnant Slab Project" (SSP) research group to present the results of the 5-year project and to facilitate intensive discussion with well-known international researchers in related fields. The SSP and the symposium were supported by a Grant-in-Aid for Scientific Research (16075101) from the Ministry of Education, Culture, Sports, Science and Technology of the Japanese Government. In the symposium, key issues discussed by participants included: transportation of water into the deep mantle and its role in slab-related dynamics; observational and experimental constraints on deep slab properties and the slab environment; modeling of slab stagnation to constrain its mechanisms in comparison with observational and experimental data; observational, experimental and modeling constraints on the fate of stagnant slabs; eventual accumulation of stagnant slabs on the core-mantle boundary and its geodynamic implications. This special issue is a collection of papers presented in the symposium and other papers related to the subject of the symposium. The collected papers provide an overview of the wide range of multidisciplinary studies of mantle dynamics, particularly in the context of subduction, stagnation, and the fate of deep slabs.

Inference of ICF Implosion Core Mix using Experimental Data and Theoretical Mix Modeling

International Nuclear Information System (INIS)

Welser-Sherrill, L.; Haynes, D.A.; Mancini, R.C.; Cooley, J.H.; Tommasini, R.; Golovkin, I.E.; Sherrill, M.E.; Haan, S.W.

2009-01-01

The mixing between fuel and shell materials in Inertial Confinement Fusion (ICF) implosion cores is a current topic of interest. The goal of this work was to design direct-drive ICF experiments which have varying levels of mix, and subsequently to extract information on mixing directly from the experimental data using spectroscopic techniques. The experimental design was accomplished using hydrodynamic simulations in conjunction with Haan's saturation model, which was used to predict the mix levels of candidate experimental configurations. These theoretical predictions were then compared to the mixing information which was extracted from the experimental data, and it was found that Haan's mix model performed well in predicting trends in the width of the mix layer. With these results, we have contributed to an assessment of the range of validity and predictive capability of the Haan saturation model, as well as increased our confidence in the methods used to extract mixing information from experimental data.

Harnessing the Deep Web: Present and Future

OpenAIRE

Madhavan, Jayant; Afanasiev, Loredana; Antova, Lyublena; Halevy, Alon

2009-01-01

Over the past few years, we have built a system that has exposed large volumes of Deep-Web content to Google.com users. The content that our system exposes contributes to more than 1000 search queries per-second and spans over 50 languages and hundreds of domains. The Deep Web has long been acknowledged to be a major source of structured data on the web, and hence accessing Deep-Web content has long been a problem of interest in the data management community. In this paper, we report on where...

Zooplankton at deep Red Sea brine pools

KAUST Repository

Kaartvedt, Stein

2016-03-02

The deep-sea anoxic brines of the Red Sea comprise unique, complex and extreme habitats. These environments are too harsh for metazoans, while the brine–seawater interface harbors dense microbial populations. We investigated the adjacent pelagic fauna at two brine pools using net tows, video records from a remotely operated vehicle and submerged echosounders. Waters just above the brine pool of Atlantis II Deep (2000 m depth) appeared depleted of macrofauna. In contrast, the fauna appeared to be enriched at the Kebrit Deep brine–seawater interface (1466 m).

How to study deep roots - and why it matters

OpenAIRE

Maeght, Jean-Luc; Rewald, B.; Pierret, Alain

2013-01-01

The drivers underlying the development of deep root systems, whether genetic or environmental, are poorly understood but evidence has accumulated that deep rooting could be a more widespread and important trait among plants than commonly anticipated from their share of root biomass. Even though a distinct classification of "deep roots" is missing to date, deep roots provide important functions for individual plants such as nutrient and water uptake but can also shape plant communities by hydr...

Benchmarking State-of-the-Art Deep Learning Software Tools

OpenAIRE

Shi, Shaohuai; Wang, Qiang; Xu, Pengfei; Chu, Xiaowen

2016-01-01

Deep learning has been shown as a successful machine learning method for a variety of tasks, and its popularity results in numerous open-source deep learning software tools. Training a deep network is usually a very time-consuming process. To address the computational challenge in deep learning, many tools exploit hardware features such as multi-core CPUs and many-core GPUs to shorten the training time. However, different tools exhibit different features and running performance when training ...

High-Redshift Radio Galaxies from Deep Fields

Indian Academy of Sciences (India)

2016-01-27

Jan 27, 2016 ... High-Redshift Radio Galaxies from Deep Fields ... Here we present results from the deep 150 MHz observations of LBDS-Lynx field, which has been imaged at 327, ... Articles are also visible in Web of Science immediately.