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Journal articles on the topic 'Quantification des sources'

Author: Grafiati

Published: 25 May 2024

Last updated: 31 July 2025

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1

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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2

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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3

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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4

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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5

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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6

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

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7

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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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

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9

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 win

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10

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

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11

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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12

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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13

Gerstoft, Peter, and Ishan D. Khurjekar. "Uncertainty quantification for acoustical problems." Journal of the Acoustical Society of America 155, no. 3_Supplement (2024): A213. http://dx.doi.org/10.1121/10.0027342.

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Acoustical parameter estimation is a routine task in many domains and is typically done using signal processing methods. The performance of existing estimation methods is affected due to external uncertainty and yet the methods provide no measure of confidence in the outputs. Hence it is crucial to quantify uncertainty in the estimates before real-world deployment. Conformal prediction is a simple method to obtain statistically valid prediction intervals from an estimation model. In this work, conformal prediction is used for obtaining statistically valid uncertainty intervals for various acou

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14

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 determinat

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15

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 re

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16

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 i

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17

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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18

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. Rig

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19

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 y

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20

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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21

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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22

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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23

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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24

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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25

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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26

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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27

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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28

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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29

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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30

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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31

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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32

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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33

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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34

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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35

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 con

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36

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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37

Manguy, Jean, Georgios I. Papoutsidakis, Ben Doyle, and Sanja Trajkovic. "Quantification of Peptides in Food Hydrolysate from Vicia faba." Foods 14, no. 7 (2025): 1180. https://doi.org/10.3390/foods14071180.

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The hydrolysis of raw food sources by commercially available food-grade enzymes releases thousands of peptides. The full characterization of bioactive hydrolysates requires robust methods to identify and quantify key peptides in these food sources. For this purpose, the absolute quantification of specific peptides, part of a complex peptide network, is necessary. Protein quantification with synthetic tryptic peptides as internal standards is a well-known approach, yet the quantification of non-tryptic peptides contained in food hydrolysates is still largely unaddressed. Similarly, data analyse

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38

Zolani, Ndlovu. "Assessing the role of area sources in air quality: A comprehensive review." World Journal of Advanced Research and Reviews 23, no. 2 (2024): 1462–72. https://doi.org/10.5281/zenodo.14859427.

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Air pollution poses significant challenges to environmental quality and public health. The array of factors contributing to air pollution necessitates a focus on three primary source categories namely point sources, mobile sources, and area sources. Point sources, typified by industrial facilities and power plants, emit pollutants from stationary positions, facilitating monitoring and regulatory interventions. Conversely, mobile sources including vehicles and aircraft, which emit pollutants while in motion, present a level of logistical challenges for control measures. Area sources, encompassi

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39

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 fro

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40

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 explanation

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41

Mishra, Sudeep, Neelam Richhariya, Rachana Rani, and Lalitesh K Thakur. "Uncertanity of Measurement During Estimation of 23 Organophosphorus Pesticides Residue Present in Bottle Gourd." Journal of Forensic Chemistry and Toxicology 5, no. 2 (2019): 131–36. http://dx.doi.org/10.21088/jfct.2454.9363.5219.6.

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46The study presents the assessment of uncertainty calculation generated, within the analysis of selected 23 organophosphrus pesticides residues of bottle guard. The samples were prepared by using a modified quick, easy, cheap, effective, rugged and safe (QuEChERS) analytical protocol. Multiresidue method used for analysis of samples consisted of (i) acetonitrile extraction, (ii) PSA/C18 clean-up and (iii) identification/quantification of residues by GC utilizing either (nitrogen–phosphorus) or mass-selective detectors (quadrupole analyzer) were evaluated. Major sources like weighing of standa

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42

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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43

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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44

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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45

Liu, Liangye, and Weilin Yi. "Research on identification and quantification methods for loss sources of compressors." Journal of Physics: Conference Series 2882, no. 1 (2024): 012044. http://dx.doi.org/10.1088/1742-6596/2882/1/012044.

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Abstract A deep understanding of the internal loss mechanisms and specific values of turbomachinery is crucial for its design and analysis. In this paper, based on the theory of entropy production, the loss sources in the NASA Rotor67 transonic fan and Radiver centrifugal compressor were quantified by using numerical simulation methods and physical flow characteristic description methods. The proportion of losses in various regions was presented. The results show that in the transonic fan, boundary layer losses and wake losses account for more than 70%, while direct shock losses account for le

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46

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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47

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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48

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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49

Risal, Avay, and Prem B. Parajuli. "Quantification and simulation of nutrient sources at watershed scale in Mississippi." Science of The Total Environment 670 (June 2019): 633–43. http://dx.doi.org/10.1016/j.scitotenv.2019.03.233.

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50

Mummullage, Sandya, Prasanna Egodawatta, Godwin A. Ayoko, and Ashantha Goonetilleke. "Sources of hydrocarbons in urban road dust: Identification, quantification and prediction." Environmental Pollution 216 (September 2016): 80–85. http://dx.doi.org/10.1016/j.envpol.2016.05.042.

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