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Zeitschriftenartikel zum Thema „Quantification des sources“

Autor: Grafiati

Veröffentlicht am 25. Mai 2024

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1

Ferguson, Christobel M., Katrina Charles, and Daniel A. Deere. "Quantification of Microbial Sources in Drinking-Water Catchments." Critical Reviews in Environmental Science and Technology 39, no. 1 (2008): 1–40. http://dx.doi.org/10.1080/10643380701413294.

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2

Angerer, Jürgen. "Sources and quantification of human backgroudexposure to acrylamide." Toxicology Letters 180 (October 2008): S24—S25. http://dx.doi.org/10.1016/j.toxlet.2008.06.707.

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3

Czerewko, M. A., J. C. Cripps, J. M. Reid, and C. G. Duffell. "Sulfur species in geological materials––sources and quantification." Cement and Concrete Composites 25, no. 7 (2003): 657–71. http://dx.doi.org/10.1016/s0958-9465(02)00066-5.

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4

Clark, Ephraim, and Radu Tunaru. "Quantification of political risk with multiple dependent sources." Journal of Economics and Finance 27, no. 2 (2003): 125–35. http://dx.doi.org/10.1007/bf02827214.

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5

Yang, Jian-Ping, Hong-Zhong Huang, Yu Liu, and Yan-Feng Li. "Quantification Classification Algorithm of Multiple Sources of Evidence." International Journal of Information Technology & Decision Making 14, no. 05 (2015): 1017–34. http://dx.doi.org/10.1142/s0219622014500242.

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Although Dempster–Shafer (D–S) evidence theory and its reasoning mechanism can deal with imprecise and uncertain information by combining cumulative evidences for changing prior opinions of new evidences, there is a deficiency in applying classical D–S evidence theory combination rule when conflict evidence appear — conflict evidence causes counter-intuitive results. To address this issue, alternative combination rules have been proposed for resolving the appeared conflicts of evidence. An underlying assumption is that conflict evidences exist, which, however, is not always true. Moreover, it has been verified that conflict factors may not be accurate to characterize the degree of conflict. Instead, the Jousselme distance has been regarded as a quantification criterion for the degree of conflict because of its promising properties. To avoid the counter-intuitive results, multiple sources of evidence should be classified first. This paper proposes a novel algorithm to quantify the classification of multiple sources of evidence based on a core vector method, and the algorithm is further verified by two examples. This study also explores the relationship between complementary information and conflicting evidence and discusses the stochastic interpretation of basic probability assignment functions.

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Brereton, Carol A., Lucy J. Campbell, and Matthew R. Johnson. "Computationally efficient quantification of unknown fugitive emissions sources." Atmospheric Environment: X 3 (July 2019): 100035. http://dx.doi.org/10.1016/j.aeaoa.2019.100035.

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7

Bielek, Boris, and Milan Bielek. "Common Characteristics of Zero Energy Buildings in Relation to the Energy Distribution Networks." Advanced Materials Research 855 (December 2013): 31–34. http://dx.doi.org/10.4028/www.scientific.net/amr.855.31.

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Physical quantification of the building envelope. Energy quantification of the building. Energy from fossil sources. Energy from ecologically clean renewable sources. Nearly net zero energy buildings. Net zero energy buildings. Net plus energy buildings. The characteristics of zero energy buildings in relation to the energy distribution networks. Requirements for physical quantification of buildings with a zero energy balance in relation to energy distribution networks.

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8

Nafziger, Steven. "Quantification and the Economic History of Imperial Russia." Slavic Review 76, no. 1 (2017): 30–36. http://dx.doi.org/10.1017/slr.2017.5.

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Historians work with sources that are products of specific social, cultural, political, and economic contexts. Thus, understanding how and why sources were produced and why they survived is an essential component of historical scholarship. At the same time, many historians often employ some sort of conceptual framework—implicit or explicit, descriptive or normative—in order to translate the sources into a coherent narrative. Modern economic historians are no different. The sources tend to be quantitative and focused on economic phenomena (with many exceptions), but doing economic history well means interrogating the origins, trustworthiness, and usefulness of the data in question. In doing this, modern economic historians are largely unapologetic about employing the tools—especially statistical—and intellectual apparatus of economics to interrogate their sources, much as social, political, or environmental historians draw on ideas and methods from related disciplines in their own inquiries. This is precisely how we make sense of the historical process of economic development.

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Méndez, M., M. Perdomo, D. Pose, C. Lindner, J. Torres, and A. Laborde. "Montevideo's health care centers, mercury sources identification and quantification." Toxicology Letters 259 (October 2016): S123. http://dx.doi.org/10.1016/j.toxlet.2016.07.316.

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10

McAuley, Grant, Matthew Schrag, Pál Sipos, et al. "Quantification of punctate iron sources using magnetic resonance phase." Magnetic Resonance in Medicine 63, no. 1 (2009): 106–15. http://dx.doi.org/10.1002/mrm.22185.

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11

Häupl, Peter, and Frank Hansel. "Quantification of the internal space climate." MATEC Web of Conferences 174 (2018): 01003. http://dx.doi.org/10.1051/matecconf/201817401003.

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A practicable model and program CLIMT is put forward for determining the hourly values of indoor air temperature and the interior air humidity in relation to the external climate, the building parameters (geometry, material properties), the ventilation and the use of the internal space (interior heat sources, moisture sources and heating system). In order to generate the outdoor climatic dates (temperature, relative humidity, short and long wave radiation, precipitation, wind velocity and direction, driving rain) for the simulation a climate generator CLIG has been developed additionally. The results have been validated with measurements in actual buildings.

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Bruno, Jack H., Dylan Jervis, Daniel J. Varon, and Daniel J. Jacob. "U-Plume: automated algorithm for plume detection and source quantification by satellite point-source imagers." Atmospheric Measurement Techniques 17, no. 9 (2024): 2625–36. http://dx.doi.org/10.5194/amt-17-2625-2024.

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Abstract. Current methods for detecting atmospheric plumes and inferring point-source rates from high-resolution satellite imagery are labor-intensive and not scalable with regard to the growing satellite dataset available for methane point sources. Here, we present a two-step algorithm called U-Plume for automated detection and quantification of point sources from satellite imagery. The first step delivers plume detection and delineation (masking) with a U-Net machine learning architecture for image segmentation. The second step quantifies the point-source rate from the masked plume using wind speed information and either a convolutional neural network (CNN) or a physics-based integrated mass enhancement (IME) method. The algorithm can process 62 images (each measuring 128 pixels × 128 pixels) per second on a single 2.6 GHz Intel Core i7-9750H CPU. We train the algorithm using large-eddy simulations of methane plumes superimposed on noisy and variable methane background scenes from the GHGSat-C1 satellite instrument. We introduce the concept of point-source observability, Ops=Q/(UWΔB), as a single dimensionless number to predict plume detectability and source rate quantification error from an instrument as a function of source rate Q, wind speed U, instrument pixel size W, and instrument-dependent background noise ΔB. We show that Ops can powerfully diagnose the ability of an imaging instrument to observe point sources of a certain magnitude under given conditions. U-Plume successfully detects and masks plumes from sources as small as 100 kg h−1 in GHGSat-C1 images over surfaces with low background noise and successfully handles larger point sources over surfaces with substantial background noise. We find that the IME method for source quantification is unbiased over the full range of source rates, while the CNN method is biased towards the mean of its training range. The total error in source rate quantification is dominated by wind speed at low wind speeds and by the masking algorithm at high wind speeds. A wind speed of 2–4 m s−1 is optimal for detection and quantification of point sources from satellite data.

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Hayward, Kristen M., Michelle P. Harwood, Stephen C. Lougheed, Zhengxin Sun, Peter Van Coeverden de Groot, and Evelyn L. Jensen. "A real-time PCR assay to accurately quantify polar bear DNA in fecal extracts." PeerJ 8 (April 7, 2020): e8884. http://dx.doi.org/10.7717/peerj.8884.

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DNA extracted from fecal samples contains DNA from the focal species, food, bacteria and pathogens. Most DNA quantification methods measure total DNA and cannot differentiate among sources. Despite the desirability of noninvasive fecal sampling for studying wildlife populations, low amounts of focal species DNA make it difficult to use for next-generation sequencing (NGS), where accurate DNA quantification is critical for normalization. Two factors are required prior to using fecal samples in NGS libraries: (1) an accurate quantification method for the amount of target DNA and (2) a determination of the relative amount of target DNA needed for successful single nucleotide polymorphism genotyping assays. Here, we address these needs by developing primers to amplify a 101 bp region of the nuclear F2 gene and a quantitative PCR (qPCR) assay that allows the accurate quantification of the amount of polar bear (Ursus maritimus) DNA in fecal extracts. We test the assay on pure polar bear DNA extracted from muscle tissue and find a high correlation between fluorometric and qPCR quantifications. The qPCR assay was also successfully used to quantify the amount of DNA derived from polar bears in fecal extractions. Orthologs of the F2 gene have been identified across vertebrates; thus, similar qPCR assays could be developed for other species to enable noninvasive studies.

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Ramsey, Michael H. "Sampling as a source of measurement uncertainty: techniques for quantification and comparison with analytical sources." Journal of Analytical Atomic Spectrometry 13, no. 2 (1998): 97–104. http://dx.doi.org/10.1039/a706815h.

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15

Arahchige, Buddhi, and Suresh Perinpanayagam. "Uncertainty quantification in aircraft gas turbine engines." Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering 232, no. 9 (2017): 1628–38. http://dx.doi.org/10.1177/0954410017699001.

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In a complex system like the jet engine, engine variables are affected by many systems making fault identification difficult. Therefore, Prognostics and Health Management Systems should be able to factor in the different sources of uncertainty. The different sources of uncertainty integral to diagnostics and prognostics must be accounted for in a probabilistic method for the approach to make any sense. This article aims to address the effect of uncertainty mainly in the form of random sensor noise on the modelled CFM56-7B27 engine output variables. Statistical Interpretations are utilised to investigate this random noise effect.

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16

Chen, Yuan, Yuwei Zhou, Weimin Feng, Yuan Fang, and Anqi Feng. "Factors That Influence the Quantification of the Embodied Carbon Emission of Prefabricated Buildings: A Systematic Review, Meta-Analysis and the Way Forward." Buildings 12, no. 8 (2022): 1265. http://dx.doi.org/10.3390/buildings12081265.

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Prefabricated buildings and off-site construction are increasingly adopted in modern construction. As one of the most concerning environmental impacts, the embodied carbon emission of prefabricated buildings has been extensively investigated in recent years. Due to the various influencing factors of carbon quantification, such as building characteristics, quantification boundary, emission sources, and quantification methods, no consensus has been reached so far. The impacts of the influencing factors on carbon quantification remain unclear. To fill this gap, this paper provides a systematic review and meta-analysis to comprehensively evaluate the recent research concerning the quantification of the embodied carbon emission of prefabricated buildings. In total, 43 peer-reviewed articles (96 building cases) were screened and analyzed. Twelve influencing factors of embodied carbon quantification have been identified and analyzed to give rise to a synthesized conclusion. The results of the meta-analysis indicated that the embodied carbon emission of prefabricated buildings varied significantly from 26.6 to 1644.4 kgCO2e/m2 in the reviewed literature. The results showed that some of the quantification factors could significantly influence the quantification results, such as the building structure forms, level of prefabrication, type of greenhouse gas considered, and data sources, while some factors have a lesser impact on carbon quantification results, such as the function of the building, quantification methods adopted, quantification tools/software used, and carbon inventory databases applied. The findings of this research provide readers with an in-depth and critical understanding of the quantification of the embodied carbon emission of prefabricated buildings. Research gaps and suggestions for future research are also provided based on the results of this work.

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Chiementin, X., F. Bolaers, J. P. Dron, and L. Rasolofondraibe. "Inverse Approach to the Reconstruction and Quantification of Vibratory Sources." Journal of Vibration and Control 13, no. 8 (2007): 1169–90. http://dx.doi.org/10.1177/1077546307076889.

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18

Spassov, S., R. Egli, F. Heller, D. K. Nourgaliev, and J. Hannam. "Magnetic quantification of urban pollution sources in atmospheric particulate matter." Geophysical Journal International 159, no. 2 (2004): 555–64. http://dx.doi.org/10.1111/j.1365-246x.2004.02438.x.

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19

Long, James, Will Long, Nick Bottenus, and Gregg Trahey. "Coherence-based quantification of acoustic clutter sources in medical ultrasound." Journal of the Acoustical Society of America 148, no. 4 (2020): 2486–87. http://dx.doi.org/10.1121/1.5146891.

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20

Long, James, Will Long, Nick Bottenus, and Gregg Trahey. "Coherence-based quantification of acoustic clutter sources in medical ultrasound." Journal of the Acoustical Society of America 148, no. 2 (2020): 1051–62. http://dx.doi.org/10.1121/10.0001790.

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21

Tian, Hanqin, Rongting Xu, Josep G. Canadell, et al. "A comprehensive quantification of global nitrous oxide sources and sinks." Nature 586, no. 7828 (2020): 248–56. http://dx.doi.org/10.1038/s41586-020-2780-0.

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22

Conway, Tim M., and Seth G. John. "Quantification of dissolved iron sources to the North Atlantic Ocean." Nature 511, no. 7508 (2014): 212–15. http://dx.doi.org/10.1038/nature13482.

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23

Smidts, C., and D. Sova. "An architectural model for software reliability quantification: sources of data." Reliability Engineering & System Safety 64, no. 2 (1999): 279–90. http://dx.doi.org/10.1016/s0951-8320(98)00068-4.

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24

Liu, Xi, Rongqiao Wang, Dianyin Hu, and Gaoxiang Chen. "Rigorous uncertainty quantification with correlated random variables from multiple sources." Engineering Failure Analysis 121 (March 2021): 105114. http://dx.doi.org/10.1016/j.engfailanal.2020.105114.

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25

Blaauw, M., M. J. J. Ammerlaan, and P. Bode. "Quantification of some sources of variation in neutron activation analysis." Applied Radiation and Isotopes 44, no. 3 (1993): 547–51. http://dx.doi.org/10.1016/0969-8043(93)90168-a.

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26

Oerlemans, S., P. Sijtsma, and B. Méndez López. "Location and quantification of noise sources on a wind turbine." Journal of Sound and Vibration 299, no. 4-5 (2007): 869–83. http://dx.doi.org/10.1016/j.jsv.2006.07.032.

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27

Crowther, Ashley R., Carrie Janello, and Rajendra Singh. "Quantification of clearance-induced impulsive sources in a torsional system." Journal of Sound and Vibration 307, no. 3-5 (2007): 428–51. http://dx.doi.org/10.1016/j.jsv.2007.05.055.

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28

Dean, Kirk E., James M. Patek, and Mel A. Vargas. "TOOLS TO ASSIST IDENTIFICATION AND QUANTIFICATION OF INDICATOR BACTERIAL SOURCES." Proceedings of the Water Environment Federation 2005, no. 8 (2005): 7179–89. http://dx.doi.org/10.2175/193864705783858648.

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29

Robinson, R. Bruce, and Brian T. Hurst. "Statistical Quantification of the Sources of Variance in Uncertainty Analyses." Risk Analysis 17, no. 4 (1997): 447–53. http://dx.doi.org/10.1111/j.1539-6924.1997.tb00885.x.

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30

Hellar-Kihampa, Harieth. "Quantification of micropollutants in some water sources in northern Tanzania." Journal of Applied Sciences and Environmental Management 20, no. 3 (2016): 549. http://dx.doi.org/10.4314/jasem.v20i3.8.

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31

Brumovsky, L. A., J. O. Brumovsky, M. R. Fretes, and J. M. Peralta. "Quantification of Resistant Starch in Several Starch Sources Treated Thermally." International Journal of Food Properties 12, no. 3 (2009): 451–60. http://dx.doi.org/10.1080/10942910701867673.

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32

Kamstra, Haye, Erik Wilmes, and Frans C. T. van der Helm. "Quantification of Error Sources with Inertial Measurement Units in Sports." Sensors 22, no. 24 (2022): 9765. http://dx.doi.org/10.3390/s22249765.

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Background: Inertial measurement units (IMUs) offer the possibility to capture the lower body motions of players of outdoor team sports. However, various sources of error are present when using IMUs: the definition of the body frames, the soft tissue artefact (STA) and the orientation filter. Methods to minimize these errors are currently being used without knowing their exact influence on the various sources of errors. The goal of this study was to present a method to quantify each of the sources of error of an IMU separately. Methods: An optoelectronic system was used as a gold standard. Rigid marker clusters (RMCs) were designed to construct a rigid connection between the IMU and four markers. This allowed for the separate quantification of each of the sources of error. Ten subjects performed nine different football-specific movements, varying both in the type of movement, and in movement intensity. Results: The error of the definition of the body frames (11.3–18.7 deg RMSD), the STA (3.8–9.1 deg RMSD) and the error of the orientation filter (3.0–12.7 deg RMSD) were all quantified separately for each body segment. Conclusions: The error sources of IMU-based motion analysis were quantified separately. This allows future studies to quantify and optimize the effects of error reduction techniques.

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Koopmann, Inga K., Annemarie Kramer, and Antje Labes. "Development and validation of reliable astaxanthin quantification from natural sources." PLOS ONE 17, no. 12 (2022): e0278504. http://dx.doi.org/10.1371/journal.pone.0278504.

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Astaxanthin derived from natural sources occurs in the form of various esters and stereomers, which complicates its quantitative and qualitative analysis. To simplify and standardize astaxanthin measurement with high precision, an enzymolysis-based astaxanthin quantification method was developed to hydrolyze astaxanthin esters and determine free astaxanthin in all its diastereomeric forms. Astaxanthin standards and differently processed Haematococcus pluvialis biomass were investigated. Linear correlation of standards of all-E-astaxanthin was observed in a measurement range between extract concentrations of 1.0 μg/mL and 11.2 μg/mL with a coefficient of variation below 5%. The diastereomers 9Z-, and 13Z-astaxanthin, and two di-Z-forms were detected. In contrast to the measurement of standards, the observed measurement range was extended to 30 μg/mL in extracts from H. pluvialis. The nature of the sample had to be taken into account for measurement, as cell, respectively, sample composition altered the optimal concentration for astaxanthin determination. The measurement precision of all-E-astaxanthin quantification in dried H. pluvialis biomass (1.2–1.8 mg dried biomass per sample) was calculated with a coefficient of variation of maximum 1.1%, whereas it was below 10% regarding the diastereomers. Complete enzymolysis was performed with 1.0 to 2.0 units of cholesterol esterase in the presence of various solvents with up to 2.0 mg biomass (dry weight). The method was compared with other astaxanthin determination approaches in which astaxanthin is converted to acetone in a further step before measurement. The developed method resulted in a higher total astaxanthin recovery but lower selectivity of the diastereomers. The reliability of photometric astaxanthin estimations was assessed by comparing them with the developed chromatographic method. At later stages in the cell cycle of H. pluvialis, all methods yielded similar results (down to 0.1% deviation), but photometry lost precision at earlier stages (up to 31.5% deviation). To optimize sample storage, the shelf life of astaxanthin-containing samples was investigated. Temperatures below -20°C, excluding oxygen, and storing intact H. pluvialis cells instead of dried or disrupted biomass reduced astaxanthin degradation.

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Helmers, Eckard, and Klaus Kümmerer. "Anthropogenic platinum fluxes: Quantification of sources and sinks, and outlook." Environmental Science and Pollution Research 6, no. 1 (1999): 29–36. http://dx.doi.org/10.1007/bf02987118.

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35

Skybova, Marie. "Quantification, Sources, and Control of Ammonia Emissions in the Czech Republic." Scientific World JOURNAL 1 (2001): 363–70. http://dx.doi.org/10.1100/tsw.2001.382.

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The exact quantification of ammonia (NH3) emissions is the basic presumption for the fulfilment of obligations set by the CLRTAP (Convention on Long Range Transboundary Air Pollution) Protocol which was signed by the Czech Republic in 1999. Most NH3emissions in the Czech Republic are produced during the breeding of cattle, pigs, and poultry; therefore, determinating emission factors for these kinds of animals by studying their total number, type of breeding, and subsequent disposal of manure is the solution to the problem of NH3emissions quantification. This paper summarizes the results of 4 years of research in this area, determining the emission factors and ways of decreasing emissions from the breeding of cattle, pigs, and poultry.

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Allen, Steven P. "Detection and quantification of bubble activity in therapeutic ultrasound: Magnetic resonance imaging for cavitation detection and quantification." Journal of the Acoustical Society of America 152, no. 4 (2022): A215. http://dx.doi.org/10.1121/10.0016056.

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For some applications of therapeutic ultrasound, magnetic resonance imaging (MRI) may serve as a useful tool for detecting, measuring, and quantifying cavitation activity—especially in cases where direct sampling of acoustic emissions are difficult. Doing so can be challenging because the physical phenomena that drive ultrasonic cavitation (e.g., pressure, surface tension, micro-second time scales) have little overlap with the phenomena that drive MRI (e.g., quantum spin, Faraday induction, milli-second time scales). However, this same principle also protects MRI-based cavitation detection from common confounds that plague traditional direct acoustic detection. The proposed paper will first review known methods for encoding cavitation behavior into MR images. These methods generally employ one of two strategies: (1) Encode transient cavitation activities into otherwise approximately static MR signal sources.; and (2) Leverage the time-average effects of many, many cycles of cavitation activity to alter MR signal sources. This paper will then discuss how these methods may be useful to therapeutic ultrasound applications. Finally, this paper will pose opportunities for future development.

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Adatrao, S., and A. Sciacchitano. "Survey On PIV Errors And Uncertainty Quantification." Proceedings of the International Symposium on the Application of Laser and Imaging Techniques to Fluid Mechanics 20 (July 11, 2022): 1–12. http://dx.doi.org/10.55037/lxlaser.20th.56.

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A survey on PIV error sources and uncertainty quantification (UQ) is performed. The aim of the survey is to understand how users and researchers in academia and industry perceive the PIV technique, especially for what concerns the measurement errors and uncertainties. A questionnaire is designed to determine the respondents’ areas of work/research, type of PIV setup they typically employ, flow properties they measure, challenges they encounter, most significant error sources and their UQ strategies. Over 100 respondents have provided valuable answers to the questions and supporting explanations. The responses are analyzed both quantitatively and qualitatively. The quantitative results are presented in form of figures, such as pie charts, bar graphs, bubble plots, and are supported by the analysis of the descriptive answers from the respondents. Overall, this work not only provides a picture of the current status of PIV perceived by the users and researchers but also highlights areas where further development is needed. To participate in the survey, follow the link- https://forms.gle/gxhD9CcKGFSBpNqW8

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Tokunaga, Kyoko, Takayoshi Fuchida, Shigeru Okada, Toshio Soda, Nobumitsu Hata, and Michio Tsuchiya. "Quantification of the Color Reproduction Balance of Light Sources for HDTV." JOURNAL OF THE ILLUMINATING ENGINEERING INSTITUTE OF JAPAN 79, no. 2 (1995): 108–15. http://dx.doi.org/10.2150/jieij1980.79.2_108.

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39

De Ketelaere, B., J. Stulens, J. Lammertyn, and J. De Baerdemaeker. "IDENTIFICATION AND QUANTIFICATION OF SOURCES OF BIOLOGICAL VARIANCE: A METHODOLOGICAL APPROACH." Acta Horticulturae, no. 674 (May 2005): 523–29. http://dx.doi.org/10.17660/actahortic.2005.674.68.

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40

KUMAGAI, Hiroyuki, and Masaru NAKANO. "Recent Advances in Quantification of the Sources of Volcano-seismic Signals." Zisin (Journal of the Seismological Society of Japan. 2nd ser.) 61, Supplement (2009): 379–90. http://dx.doi.org/10.4294/zisin.61.379.

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41

Thompson, L. R., and J. E. Rowntree. "Invited Review: Methane sources, quantification, and mitigation in grazing beef systems." Applied Animal Science 36, no. 4 (2020): 556–73. http://dx.doi.org/10.15232/aas.2019-01951.

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42

Tratt, David M., Kerry N. Buckland, Jeffrey L. Hall, et al. "Airborne visualization and quantification of discrete methane sources in the environment." Remote Sensing of Environment 154 (November 2014): 74–88. http://dx.doi.org/10.1016/j.rse.2014.08.011.

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43

Scheiber, Laura, Carlos Ayora, and Enric Vázquez-Suñé. "Quantification of proportions of different water sources in a mining operation." Science of The Total Environment 619-620 (April 2018): 587–99. http://dx.doi.org/10.1016/j.scitotenv.2017.11.172.

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