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Full Issue Download Vol. 13 No. 1 2021 The Importance of the Measurement Infrastructure in Economic Recovery from the COVID-19 Pandemic Richard J. C. Brown , Fiona Auty, Eugenio Renedo, Mike King NCSLI Measure | Vol. 13 No. 1 (2021) | doi.org/10.51843/measure.13.1.1 Publisher NCSL International | Published February 2021 | Pages 18-21 Abstract: This paper describes the many, evidenced-based benefits to the economy of a well-developed measurement infrastructure. In particular, it explains how assuring confidence in measurement may be used to accelerate economic recovery from the COVID-19 pandemic including in emerging sectors such as the digital economy. Recommendations are made for providing near term support for national economic recovery whilst also demonstrating the advantages of sustained development of the measurement infrastructure in the medium-term to maximize the potential of future innovative and disruptive technologies. These recommendations, whilst focused on consideration of the UK, should apply globally. References: [1] G. Tassey, 'Underinvestment in public good technologies,' J Technol. Transfer, Vol. 30, pp. 89-113, 2004. https://doi.org/10.1007/s10961-004-4360-0 [2] M. King, and E. Renedo, 'Achieving the 2.4% GDP target: The role of measurement in increasing investment in R&D and innovation,' NPL Report IEA 3, NPL, Teddington, UK, March 2020. [3] M. King and G. Tellett, 'The National Measurement System: A Customer Survey for Three of the Core Labs in the National Measurement System,' NMS Customer Survey Report 2018, NPL Teddington, UK, April 2020 [4] H. Kunzmann, T. Pfeifer, R. Schmitt, H. Schwenke, and A.Weckenmann, 'Productive metrology-adding value to manufacture,' CIRP Annals, vol. 54, pp. 155-168, 2005. https://doi.org/10.1016/S0007-8506(07)60024-9 [5] N. G. Orji, R. G. Dixson, A. Cordes, B. D. Bunday, and J. A. Allgair, 'Measurement traceability and quality assurance in a nanomanufacturing environment,' Instrumentation, Metrology, and Standards for Nanomanufacturing III, Proceedings Vol. 7405, 740505, August 2009. https://doi.org/10.1117/12.826606 [6] Belmana, Analysis for Policy 'Public Support for Innovation and Business Outcomes,' Belmana: London, UK, 2020. [7] R. Hawkins, Standards, systems of innovation and policy in Handbook of Innovation and Standards. Cheltenham, UK: Edward Elgar, 2019. [8] N. Nwaigbo, and M. King, 'Evaluating the Impact of the NMS Consultancy Projects on Supported Firms (Working Paper)' NPL, Teddington, UK, 2020. [9] M. King, R. Lambert, and P. Temple, Measurement, standards and productivity spillovers in Handbook of Innovation and Standards. Cheltenham, UK: Edward Elgar, 2017, p. 162. https://doi.org/10.4337/9781783470082.00016 [10] A. Font, K. de Hoogh, M. Leal-Sanchez, D. C. Ashworth, R. J. C. Brown, A. L. Hansell, and G. W. Fuller, 'Using metal ratios to detect emissions from municipal waste incinerators in ambient air pollution data,' Atmos. Environ., vol. 113, pp. 177-186, July 2015. https://doi.org/10.1016/j.atmosenv.2015.05.002 [11] S. Giannis, M. R. L. Gower, G. D. Sims, G. Pask, and G. Edwards, 'Increasing UK competitiveness by enhancing the composite materials regulatory infrastructure,' NPL Report MAT 90, NPL, Teddington, UK, October 2019. [12] HM Government, UK Research and Development Roadmap, BEIS, London, July 2020. [13] M. R. Mehra, S. S. Desai, F. Ruschitzka, and A. N. Patel, 'Hydroxychloroquine or chloroquine with or without a macrolide for treatment of COVID-19: a multinational registry analysis,' Lancet, 2020, https://doi.org/10.1016/S0140-6736(20)31180-6 (Print: ISSN 1931-5775) (Online: ISSN 2381-0580) ©2021 NCSL International Smart Power Supply Calibration System Iraj Vasaeli , Brandon Umansky NCSLI Measure | Vol. 13 No. 1 (2021) | doi.org/10.51843/measure.13.1.2 Publisher: NCSL International | Published February 2021 | Pages 22-27 Abstract: This paper details the development of an automated procedure to conduct calibrations of power supplies at Jet Propulsion Laboratory, California Institute of Technology (JPL). The fundamentals of power supply calibrations are given, and discussion on the method by which this custom software handles that calibration. Additionally, this technique provides real time uncertainty quantification of the calibrations. This automated system has demonstrated a time savings over existing automated techniques in use today. References: [1] Keysight, 'Low-Profile Modular Power System Series N6700 Service Guide', Part Number: 5969 2938, Edition 7, January 2015. [2] B. N. Taylor and C. E. Kuyatt, 'Guidelines for Evaluating and Expressing the Uncertainty of NIST Measurement Results', NIST Technical Note 1297, 1994. https://doi.org/10.6028/NIST.TN.1297 [3] JCGM, 'Evaluation of measurement data - Guide to the expression of uncertainty in measurement,' first edition (GUM 1995 with minor corrections),' JCGM 100, 2008. (Print: ISSN 1931-5775) (Online: ISSN 2381-0580) © 2021 NCSL International Computer Aided Verification of Voltage Dips and Short Interruption Generators for Electromagnetic Compatibility Immunity Test in Accordance with IEC 61000-4-11: 2004 + AMD: 2017 Hau Wah Lai , Cho Man Tsui , Hing Wah Li NCSLI Measure | Vol. 13 No. 1 (2021) | doi.org/10.51843/measure.13.1.3 Publisher: NCSL International | Published February 2021 | Pages 28-39 Abstract: This paper describes a procedure and a computer-aided system developed by the Standards and Calibration Laboratory (SCL) for verification of voltage dip and short interruption generators in accordance with the international standard IEC 61000-4-11:2004+AMD1:2017. The verification is done by calibrating the specified parameters and comparing with the requirements stated in the standard. The parameters that should be calibrated are the ratios of the residual voltages to the rated voltage, the accuracy of the phase angle at switching, and the rise time, fall time, overshoot and undershoot of the switching waveform. A specially built adapter is used to convert the high voltage output waveforms of the generators to lower level signals to be acquired by a digital oscilloscope. The other circuits required for the testing are also provided. In addition, the paper discusses the uncertainty evaluations for the measured parameters. References: [1] T. Williams, and K. Armstrong, 'EMC for Systems and Installations Part 6 - Low-Frequency Magnetics Fields (Emissions and Immunity) Mains Dips, Dropouts, Interruptions, Sags, Brownouts and Swells,' EMC Compliance Journal, August 2000. [2] M.I. Montrose, and E. M. Nakauchi, Testing for EMC Compliance: Approaches and Techniques, Wiley Interscience, 2004. https://doi.org/10.1002/047164465X [3] International Standard IEC 61000-4-11:2004+AMD1:2017:Electromagnetic Compatibility (EMC) Part 4-11: Testing and measurement techniques - Voltage dips, short interruptions and voltage variations immunity tests. [4] Evaluation of measurement data - Guide to the expression of uncertainty in measurement, First Edition JCGM 100:2008. (Print: ISSN 1931-5775) (Online: ISSN 2381-0580) © 2021 NCSL International Validation of the Photometric Method Used for Micropipette Calibration Elsa Batista , Isabel Godinho, George Rodrigues, Doreen Rumery NCSLI Measure | Vol. 13 No. 1 (2021) | doi.org/10.51843/measure.13.1.4 Publisher: NCSL International | Published February 2021 | Pages 40-45 Abstract: There are two methods generally used for calibration of micropipettes: the gravimetric method described in ISO 8655-6:2002 and the photometric method described in ISO 8655-7:2005. In order to validate the photometric method, several micropipettes of different capacities from 0.1 µL to 1000 µL were calibrated using both methods (gravimetric and photometric) in two different laboratories, IPQ (Portuguese Institute for Quality) and Artel. These tests were performed by six different operators. The uncertainty for both methods was determined and it was verified that the uncertainty component that has a higher contribution to the final uncertainty budget depends on the volume delivered. In the photometric method for small volumes, the repeatability of the pipette is the largest uncertainty component, but for volumes, larger than 100 µL, the photometric instrument is the most significant source of uncertainty. Based on all the results obtained with this study, one may consider the photometric method validated. References: [1] ISO 8655-1/2/6/7, Piston-operated volumetric apparatus, 2002. [2] BIPM, International Vocabulary of Metrology, 3rd edition, JCGM 200:2012. [3] George Rodrigues, Bias and transferability in standards methods of pipette calibration, Artel, June 2003. [4] Taylor, et.al. The definition of primary method of measurement (PMM) of the 'highest metrological quality': a challenge in understanding and communication, Accred. Qual.Assur (2001) 6:103-106. https://doi.org/10.1007/PL00010444 [5] EURAMET project 1353, Volume comparison on Calibration of micropipettes - Gravimetric and photometric methods. [6] ASTM E542: Standard Practice for Calibration of laboratory Volumetric Apparatus, 2000. [7] ISO 4787; Laboratory glassware - Volumetric glassware - Methods for use and testing of capacity, 2010 . [8] ISO 13528:2005 - Statistical methods used in proficiency testing by interlaboratory comparisons. [9] BIPM et al, Guide to the Expression of Uncertainty in Measurement (GUM), 2nd ed., International Organization for Standardization, Genève, 1995. [10] EURAMET guide, cg 19, - Guidelines on the determination of uncertainty in gravimetric volume calibration, version 3.0, 2012. [11] E. Batista et all, A Study of Factors that Influence Micropipette Calibrations, Measure Vol. 10 No. 1, 2015 https://doi.org/10.1080/19315775.2015.11721717 [12] www.BIPM.org. (Print: ISSN 1931-5775) (Online: ISSN 2381-0580) © 2021 NCSL International Material Flow Rate Estimation in Material Extrusion Additive Manufacturing G. P. Greeff NCSLI Measure | Vol. 13 No. 1 (2021) | doi.org/10.51843/measure.13.1.5 Publisher: NCSL International | Published February 2021 | Pages 46-56 Abstract: The additive manufacturing of products promises exciting possibilities. Measurement methodologies, which measure an in-process dataset of these products and interpret the results, are essential. However, before developing such a level of quality assurance several in-process measurands must be realized. One of these is the material flow rate, or rate of adding material during the additive manufacturing process. Yet, measuring this rate directly in material extrusion additive manufacturing presents challenges. This work presents two indirect methods to estimate the volumetric flow rate at the liquefier exit in material extrusion, specifically in Fused Deposition Modeling or Fused Filament Fabrication. The methods are cost effective and may be applied in future sensor integration. The first method is an optical filament feed rate and width measurement and the second is based on the liquefier pressure. Both are used to indirectly estimate the volumetric flow rate. The work also includes a description of linking the G-code command to the final print result, which may be used to create a per extrusion command model of the part. References: [1] T. Wohlers, I. Campbell, O. Diegel, J. Kowen, I. Fidan, and D.L. Bourell, 'Wohlers Report 2017: 3D Printing and Additive Manufacturing State of the Industry Annual Worldwide Progress Report,' 2017. [2] Additive manufacturing -- General principles -- Terminology. Geneva, CH: International Organization for Standardization, 2015. [3] R. Jones et al., 'Reprap - The replicating rapid prototyper,' Robotica, vol. 29, no. 1 SPEC. ISSUE, pp. 177-191, 2011, https://doi.org/10.1017/S026357471000069X [4] T. Wohlers and T. Gornet, 'History of Additive Manufacturing 2017,' 2017. [5] S. A. M. Tofail, E. P. Koumoulos, A. Bandyopadhyay, S. Bose, L. O'Donoghue, and C. Charitidis, 'Additive manufacturing: scientific and technological challenges, market uptake and opportunities, 'Materials Today, vol. 21, no. 1, pp. 22-37, Jan. 2018, https://doi.org/10.1016/j.mattod.2017.07.001 [6] G. Moroni and S. Petrò, 'Managing uncertainty in the new manufacturing era,' Procedia CIRP, vol. 75, pp. 1-2, 2018, https://doi.org/10.1016/j.procir.2018.07.001 [7] R. Leach et al., 'Information-rich manufacturing metrology,'in Eighth International Precision Assembly Seminar (IPAS), 2018, no. January. https://doi.org/10.1007/978-3-030-05931-6_14 [8] S. Moylan, J. Slotwinski, A. Cooke, K. Jurrens, M. A. Donmez, and A. Donmez, 'Proposal for a Standardized Test Artifact for Additive Manufacturing Machines and Processes,' Solid Freeform Fabrication Symposium Proceedings, pp. 902-920, 2012. https://doi.org/10.6028/NIST.IR.7858 [9] ASME Y14.46-2017 Product Definition for Additive Manufacturing. New York:The American Society of Mechanical Engineers, 2017. [10] H. Li, T. Wang, J. Sun, and Z. Yu, 'The effect of process parameters in fused deposition modelling on bonding degree and mechanical properties,' Rapid Prototyping Journal, vol. 24, no. 1, pp. 80-92, Jan. 2018, https://doi.org/10.1108/RPJ-06-2016-0090 [11] A. W. Gebisa and H. G. Lemu, 'Investigating effects of Fused-deposition modeling (FDM) processing parameters on flexural properties of ULTEM 9085 using designed experiment, 'Materials, vol.11, no. 4, pp. 1-23, 2018, https://doi.org/10.3390/ma11040500 PMid:29584674 PMCid:PMC5951346 [12] B. Wittbrodt and J. M. Pearce, 'The effects of PLA color on material properties of 3-D printed components,' Additive Manufacturing, vol. 8, pp. 110-116, 2015, https://doi.org/10.1016/j.addma.2015.09.006 [13] O. A. Mohamed, S. H. Masood, and J. L. Bhowmik, 'Optimization of fused deposition modeling process parameters: a review of current research and future prospects,' Advances in Manufacturing, vol. 3, no. 1, pp. 42-53, Mar. 2015, https://doi.org/10.1007/s40436-014-0097-7 [14] S. K. Everton, M. Hirsch, P. Stravroulakis, R. K. Leach and A. T. Clare, 'Review of in-situ process monitoring and in-situ metrology for metal additive manufacturing,' Materials and Design, vol. 95, pp. 431-445, 2016, https://doi.org/10.1016/j.matdes.2016.01.099 [15] P. K. Rao, J. P. Liu, D. Roberson, Z. J. Kong, and C. Williams,'Online Real-Time Quality Monitoring in Additive Manufacturing Processes Using Heterogeneous Sensors,' Journal of Manufacturing Science and Engineering, vol. 137, no. 6, p.061007, Sep. 2015, https://doi.org/10.1115/1.4029823 [16] J. Pellegrino, T. Makila, S. McQueen, and E. Taylor, 'Measurement science roadmap for polymer-based additive manufacturing,' Gaithersburg, MD, Dec. 2016. https://doi.org/10.6028/NIST.AMS.100-5 [17] T. R. Kramer, F. M. Proctor, and E. Messina, 'The NIST RS274NGC Interpreter -Version 3,' Gaithersburg, Maryland, 2000. https://doi.org/10.6028/NIST.IR.6556 [18] B. N. Turner, R. Strong, and S. A. Gold, 'A review of melt extrusion additive manufacturing processes: I. Process design and modeling,' Rapid Prototyping Journal, vol. 20, no. 3, pp.192-204, Apr. 2014, https://doi.org/10.1108/RPJ-01-2013-0012 [19] Conrad Electronic, 'Renkforce RF1000 3D Drucker,' 2016. https://www.conrad.de/de/renkforce-rf1000-3d-drucker-single-extruder-inkl-software-franzis-designcad-v24-3d-printrenkforce-edition-1007508.html (accessed Sep. 20, 2016). [20] G. Hodgson, A. Ranellucci, and J. Moe, 'Slic3r Manual - Flow Math,' 2016. http://manual.slic3r.org/advanced/flow-math (accessed Jun. 21, 2016). [21] Repetier, 'Repetier-Firmware Documentation.' https://www.repetier.com/documentation/repetier firmware/repetier-firmware-introduction/ (accessed Apr. 17, 2018). [22] B. Weiss, D. W. Storti, and M. A. Ganter, 'Low-cost closedloop control of a 3D printer gantry,' Rapid Prototyping Journal, vol. 21, no. 5, pp. 482-490, Aug. 2015, https://doi.org/10.1108/RPJ-09-2014-0108 [23] R. L. Zinniel and J. S. Batchelder, 'Volumetric Feed Control for Flexible Filament,' US 6085957, 2000. [24] W. J. Heij, Applied Metrology in Additive Manufacturing. Delft: Delft University of Technology, 2016. [25] G. P. Greeff and M. Schilling, 'Closed loop control of slippage during filament transport in molten material extrusion,' Additive Manufacturing, vol. 14, pp. 31-38, 2017, https://doi.org/10.1016/j.addma.2016.12.005 [26] G. P. Greeff, Applied Metrology in Additive Manufacturing, vol. 60. Berlin: Mensch und Buch, 2019. [27] G. P. Greeff and M. Schilling, 'Comparing Retraction Methods with Volumetric Exit Flow Measurement in Molten Material Extrusion,' in Special Interest Group meeting on Dimensional Accuracy and Surface Finish in Additive Manufacturing, 2017, no. October, pp. 70-74. [28] G. P. Greeff and M. Schilling, 'Single print optimisation of fused filament fabrication parameters,' The International Journal of Advanced Manufacturing Technology, Aug. 2018, https://doi.org/10.1007/s00170-018-2518-4 [29] A. Bellini, S. Güçeri, and M. Bertoldi, 'Liquefier Dynamics in Fused Deposition,' Journal of Manufacturing Science and Engineering, vol. 126, no. 2, p. 237, 2004, https://doi.org/10.1115/1.1688377 [30] P. Virtanen et al., 'SciPy 1.0: fundamental algorithms for scientific computing in Python,' Nature Methods, vol. 17, no. 3, pp. 261-272, Mar. 2020, https://doi.org/10.1038/s41592-019-0686-2 PMid:32015543 PMCid:PMC7056644 (Print: ISSN 1931-5775) (Online: ISSN 2381-0580) © 2021 NCSL International Software to Maximize End-User Uptake of Conformity Assessment with Measurement Uncertainty, Including Bivariate Cases. The European EMPIR CASoft Project
L.R. Pendrill ; A. Allard ; N. Fischer ; et al.
NCSL International measure. 13:58-69

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The component structure of memory during development.
Cohen, Samantha S. ; Olson, Ingrid R. ; Newcombe, Nora S.
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Community Alcohol Sales and Related Problems: Filling the Critical Research Gap
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Do spatial navigation and episodic memory rely on the same systems? Evidence from a naturalistic experience with children and adults.
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Uso de inteligencia artificial para identificar posibles errores en el pintado de piezas
Archila Atehortúa, Cristian ; Posada Acosta, Simón ; Hernández Marulanda, Andrés Felipe
Y. Ben Salem y M. N. Abdelkrim, «Texture classification of fabric defects using machine learning», International Journal of Electrical and Computer Engineering, vol. 10, n.° 4, págs. 4390-4399, 2020, ISSN: 20888708. DOI:10.11591/ijece.v10i4.pp4390-4399. ; B. Ngendangenzwa, «Defect detection and classification on painted specular surfaces», 2017. dirección: https://umu.diva-portal.org/smash/get/diva2:1193907/FULLTEXT01.pdf. ; L. Armesto, J. Tornero, A. Herraez y J. Asensio, «ICRA Communications Inspection System Basedon Artificial Vision for Paint Defects Detection on Cars Bodies», págs. 3-6, 2011. ; R. McBride, «Human-Like Implemented A.I. and Human Problem-Solving A.I.», International Journal of Business, Humanities and Technology, vol. 9, n.° 3, págs. 1-10, 2019, 21621357. DOI: ....

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An automated, unobtrusive, formative assessment of creativity in a computer science and music remixing learning environment.
Rahimi, Seyedahmad ; Smith, Jason Brent ; Truesdell, Erin J. K. ; et al.
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Diseño de la arquitectura de un sistema de apoyo a decisiones clínicas para facilitar el proceso de clasificación de urgencias hospitalarias, en el contexto de la salud en Colombia
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BERTAgent: The development of a novel tool to quantify agency in textual data.
Nikadon, Jan ; Suitner, Caterina ; Erseghe, Tomaso ; et al.
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Causal relationships in longitudinal observational data: An integrative modeling approach.
Biazoli, Claudinei E., Jr. ; Sato, João R. ; Pluess, Michael
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Gordon, Ilanit ; Tomashin, Alon ; Mayo, Oded
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Using theory-informed learning analytics to understand how homework behavior predicts achievement.
Deininger, Hannah ; Pieronczyk, Ines ; Parrisius, Cora ; et al.
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Disability discrimination, disability permanence, and mental health among U.S. adults.
Rubin-McGregor, Jordan ; Hunger, Jeffrey M.
Psychological Aspects of Disability

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Brief glance, lasting effect: How pointing gestures influence the perception of paintings.
Dimova, Temenuzhka ; Lapique, Nicolas ; Rosenberg, Raphael
Psychol Aesthet Creat Arts

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The role of asking more complex questions in creative thinking.
Raz, Tuval ; Reiter-Palmon, Roni ; Kenett, Yoed N.
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Language-based AI modeling of personality traits and pathology from life narrative interviews.
Oltmanns, Joshua R. ; Khandelwal, Ritik ; Ma, Jerry ; et al.
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Leveraging natural language processing to enhance feedback-informed group therapy: A proof of concept.
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Signal processing and machine learning with transdermal alcohol concentration to predict natural environment alcohol consumption.
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