(Table 2, Page 252) Average chemical composition of 11 manganese nodules and crust from the Pacific Ocean

The ability of the hydrated oxides of manganese and iron to adsorb ions from solution (scavenging) is considered in relation to some problems in marine geology, chemistry, and biology. In the ferruginous sediments of the Pacific Ocean, iron oxides are accompanied by titanium, cobalt, and zirconium in amounts proportional to the iron content. Similarly, copper and nickel are linearly related to the manganese content. These observations are explained on the basis of scavenging. An electrochemical theory for the formation of manganese nodules is presented. Marine sediments are classified on the basis of the geosphere in which the solid phases originate. The distribution of certain ionic species in sea water between the solid and aqueous phases is considered on the basis of scavenging and co-ordination compound theory. The concentration of minor elements by members of the marine biosphere is explained either by the direct uptake of the element or by the uptake of iron or manganese oxides with the accompanying scavenged element.

Comparison of available measurements of the absolute air-fluorescence yield and determination of its global average value

Experimental results of the absolute air-fluorescence yield are given very often in different units (photons/MeV or photons/m) and for different wavelength intervals. In this work we present a comparison of available results normalized to its value in photons/MeV for the 337 nm band at 1013 hPa and 293 K. The conversion of photons/m to photons/MeV requires an accurate determination of the energy deposited by the electrons in the field of view of the experimental set-up. We have calculated the energy deposition for each experiment by means of a detailed Monte Carlo simulation and the results have been compared with those assumed or calculated by the authors. As a result, corrections to the reported fluorescence yields are proposed. These corrections improve the compatibility between measurements in such a way that a reliable average value with uncertainty at the level of 5% is obtained.

Evaluating New Policy Instruments of the Corporate Average Fuel Economy Standards : Footprints, Credit Transferring and Credit Trading

Publisher PDF

Evaluating New Policy Instruments of the Corporate Average Fuel Economy Standards : Footprints, Credit Transferring and Credit Trading

Publisher PDF

Design concept evaluation based on rough number and information entropy theory

Concept evaluation at the early phase of product development plays a crucial role in new product development. It determines the direction of the subsequent design activities. However, the evaluation information at this stage mainly comes from experts' judgments, which is subjective and imprecise. How to manage the subjectivity to reduce the evaluation bias is a big challenge in design concept evaluation. This paper proposes a comprehensive evaluation method which combines information entropy theory and rough number. Rough number is first presented to aggregate individual judgments and priorities and to manipulate the vagueness under a group decision-making environment. A rough number based information entropy method is proposed to determine the relative weights of evaluation criteria. The composite performance values based on rough number are then calculated to rank the candidate design concepts. The results from a practical case study on the concept evaluation of an industrial robot design show that the integrated evaluation model can effectively strengthen the objectivity across the decision-making processes.

Single particle diversity and mixing state measurements

A newly developed framework for quantifying aerosol particle diversity and mixing state based on information-theoretic entropy is applied for the first time to single particle mass spectrometry field data. Single particle mass fraction estimates for black carbon, organic aerosol, ammonium, nitrate and sulfate, derived using single particle mass spectrometer, aerosol mass spectrometer and multi-angle absorption photometer measurements are used to calculate single particle species diversity (Di). The average single particle species diversity (Dα) is then related to the species diversity of the bulk population (Dγ) to derive a mixing state index value (χ) at hourly resolution. The mixing state index is a single parameter representation of how internally/externally mixed a particle population is at a given time. The index describes a continuum, with values of 0 and 100% representing fully external and internal mixing, respectively. This framework was applied to data collected as part of the MEGAPOLI winter campaign in Paris, France, 2010. Di values are low (∼ 2) for fresh traffic and wood-burning particles that contain high mass fractions of black carbon and organic aerosol but low mass fractions of inorganic ions. Conversely, Di values are higher (∼ 4) for aged carbonaceous particles containing similar mass fractions of black carbon, organic aerosol, ammonium, nitrate and sulfate. Aerosol in Paris is estimated to be 59% internally mixed in the size range 150-1067 nm, and mixing state is dependent both upon time of day and air mass origin. Daytime primary emissions associated with vehicular traffic and wood-burning result in low χ values, while enhanced condensation of ammonium nitrate on existing particles at night leads to higher χ values. Advection of particles from continental Europe containing ammonium, nitrate and sulfate leads to increases in Dα, Dγ and χ. The mixing state index represents a useful metric by which to compare and contrast ambient particle mixing state at other locations globally.

Average chemical composition of lake ores of Finland and North-Eastern Russia extracted in the late nineteen century

According to Solitander C. P., the extraction of lake ore from Eastern Finland lakes considerably rose in the 1870 - 1880 period in relation with the increasing demand from the ironworks being operated in the region. In St. Petersburg, Nicholas Putiloff, a business tycoon and State Minister owned the Haapakosken, Huutokosken and Oravin ironworks which were using 99% of lake ore for their supply. During this period the biggest production came from lake Sysmäjärvi in the Joroinen county with 3676 tonnes at an average concentration of 35.94% Fe, 4.55% Mn, 0.26% P and 0.04% S. The Värtsilä ironworks used the lake ore coming from 49 lakes, the biggest production coming from lake Loitimojärvi with 14535 tonnes of ore with a medium at concentration of 30.8% Fe. Möhkö ironworks took advantage of the 59 lakes, the largest of which was from lake Koitere with 4301 tonnes at 41.3% Fe. The Karttula ironworks were also significant in the consumption of ferromanganese lake ore.

(Table 1) Average sea surface temperature, average sea surface salinity, C37 concentration, palmitic acid concentration, Uk'37, C37/C38 ratio, dD of water, dD of C37 and dD of palmitic acid of water samples from a transect across the Amazon Plume

The stable hydrogen isotope composition of lipid biomarkers, such as alkenones, is a promising new tool for the improvement of palaeosalinity reconstructions. Laboratory studies confirmed the correlation between lipid biomarker dD composition (dDLipid), water dD composition (dDH2O) and salinity; yet there is limited insight into the applicability of this proxy in oceanic environments. To fill this gap, we test the use of the dD composition of alkenones (dDC37) and palmitic acid (dDPA) as salinity proxies using samples of surface suspended material along the distinct salinity gradient induced by the Amazon Plume. Our results indicate a positive correlation between salinity and dDH2O, while the relationship between dDH2O and dDLipid is more complex: dDPAM correlates strongly with dDH2O (r2 = 0.81) and shows a salinity-dependent isotopic fractionation factor. dDC37 only correlates with dDH2O in a small number (n = 8) of samples with alkenone concentrations > 10 ng L**-1, while there is no correlation if all samples are taken into account. These findings are mirrored by alkenone-based temperature reconstructions, which are inaccurate for samples with low alkenone concentrations. Deviations in dDC37 and temperature are likely to be caused by limited haptophyte algae growth due to low salinity and light limitation imposed by the Amazon Plume. Our study confirms the applicability of dDLipid as a salinity proxy in oceanic environments. But it raises a note of caution concerning regions where low alkenone production can be expected due to low salinity and light limitation, for instance, under strong riverine discharge.

(Table 2, Page 270) Average chemical composition of manganese nodules and crust collected at different localities the Pacific Ocean

Considerable regional variations in the chemical composition of manganese nodules from a wide range of the Pacific Ocean have been observed. These variations can be more exactly expressed in terms of inter-element relationships. In particular, Cu-Mn and Cu-Ni associations reveal that Cu content in pelagic nodules increases rapidly in proportion to those of Mn or Ni. In nodules from continental borderland and hemipelagic areas, even if Mn or Ni contents increase, that of Cu increases only slightly. It is suggested that the considerable chemical differences within individual nodules and between nodules from the same site, at a limited pelagic area where there is no marked change in depositional conditions of nodules, are due to the role of hydrolyzable trace elements in the formation of nodules.

A suite of global, cross-scale topographic variables for environmental and biodiversity modeling, links to files in GeoTIFF format

Topographic variation, the spatial variation in elevation and terrain features, underpins a myriad of patterns and processes in geography and ecology and is key to understanding the variation of life on the planet. The characterization of this variation is scale-dependent, i.e. it varies with the distance over which features are assessed and with the spatial grain (grid cell resolution) of analysis. A fully standardized and global multivariate product of different terrain features has the potential to support many large-scale basic research and analytical applications, however to date, such technique is unavailable. Here we used the digital elevation model products of global 250 m GMTED and near-global 90 m SRTM to derive a suite of topographic variables: elevation, slope, aspect, eastness, northness, roughness, terrain roughness index, topographic position index, vector ruggedness measure, profile and tangential curvature, and 10 geomorphological landform classes. We aggregated each variable to 1, 5, 10, 50 and 100 km spatial grains using several aggregation approaches (median, average, minimum, maximum, standard deviation, percent cover, count, majority, Shannon Index, entropy, uniformity). While a global cross-correlation underlines the high similarity of many variables, a more detailed view in four mountain regions reveals local differences, as well as scale variations in the aggregated variables at different spatial grains. All newly-developed variables are available for download at http://www.earthenv.org and can serve as a basis for standardized hydrological, environmental and biodiversity modeling at a global extent.

Entropy feature extraction of EEG signals for automatic person identification

The use of human brain electroencephalography (EEG) signals for automatic person identi cation has been investigated for a decade. It has been found that the performance of an EEG-based person identication system highly depends on what feature to be extracted from multi-channel EEG signals. Linear methods such as Power Spectral Density and Autoregressive Model have been used to extract EEG features. However these methods assumed that EEG signals are stationary. In fact, EEG signals are complex, non-linear, non-stationary, and random in nature. In addition, other factors such as brain condition or human characteristics may have impacts on the performance, however these factors have not been investigated and evaluated in previous studies. It has been found in the literature that entropy is used to measure the randomness of non-linear time series data. Entropy is also used to measure the level of chaos of braincomputer interface systems. Therefore, this thesis proposes to study the role of entropy in non-linear analysis of EEG signals to discover new features for EEG-based person identi- cation. Five dierent entropy methods including Shannon Entropy, Approximate Entropy, Sample Entropy, Spectral Entropy, and Conditional Entropy have been proposed to extract entropy features that are used to evaluate the performance of EEG-based person identication systems and the impacts of epilepsy, alcohol, age and gender characteristics on these systems. Experiments were performed on the Australian EEG and Alcoholism datasets. Experimental results have shown that, in most cases, the proposed entropy features yield very fast person identication, yet with compatible accuracy because the feature dimension is low. In real life security operation, timely response is critical. The experimental results have also shown that epilepsy, alcohol, age and gender characteristics have impacts on the EEG-based person identication systems.