Technical note: Using k-means to identify soot aggregates in transmission electron microscopy images

Sipkens, Timothy A.; Rogak, Steven N.

doi:10.1016/j.jaerosci.2020.105699

Cited by 19 publications

(25 citation statements)

References 25 publications

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“…The difference we observed lies more within the structure of the instrument and its mouthpiece, as mentioned above. He et al [61] also point out the difference between brass and woodwinds, which we can relate to but not support to its full content, since air velocity measurements did not diversify significantly.…”

Section: Discussioncontrasting

confidence: 57%

“…This finding also corresponds with Becher et al’s [55] finding on the importance of individual blowing techniques for air movements. We thus affiliate with Becher et al’s [56] as well as He et al’s [57] assumption that the structure on an instrument as well as the way a mouthpiece is blown has a significant influence on the generated air velocity while playing, the distance it reaches, and in the following, its generation of aerosols [58].…”

Section: Discussionmentioning

confidence: 99%

“…At the University of Minnesota a study on aerosol generation from different wind instruments analyzed 15 musicians from the Minnesota Orchestra [13] (playing trumpet, bass trombone, French horn, tuba, piccolo, bassoon, oboe, clarinet and bass clarinet) while playing their instruments, breathing and speaking. By use of an aerodynamic particle sizer, the aerosol concentration and size was measured.…”

Section: Introductionmentioning

confidence: 99%

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Airflow and air velocity measurements while playing wind instruments, with respect to risk assessment of a SARS-CoV-2 infection

Spahn

Hipp

Schubert³

et al. 2020

Preprint

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Due to airborne transmission of infection with the coronavirus, the question arose as to how high the risk of spreading infectious particles can be while playing a wind instrument.To contribute to this question and to help clarify the possible risks, we analyzed 14 wind instruments, first qualitative by making airflows visible while playing and second quantitative by measuring air velocities at three distances (1m, 1.5m and 2m) in direction of the instrument’s bell.Measurements took place with wind instrumentalists of the Bamberg Symphony in their concert hall.Our findings highlight that while playing all wind instruments no airflow escaping from the instruments – from the bell with brass instruments, from the mouthpiece, keyholes and bell with woodwinds – was measured beyond a distance of 1.5m from the instruments’ bell, regardless of volume, pitch or what was played. With that, air velocity while playing corresponded to the usual value of hall-like rooms, of 0.1 m/s. For air-jet woodwinds, alto flute and piccolo, significant air movements were seen close to their mouthpieces, which escaped directly into the room without passing through the instrument and therefore generating directed air movements.

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Section: Discussioncontrasting

confidence: 57%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Airflow and air velocity measurements while playing wind instruments, with respect to risk assessment of a SARS-CoV-2 infection

Spahn

Hipp

Schubert³

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…At the University of Minnesota, a study on aerosol generation by different wind instruments analyzed 15 musicians from the Minnesota Orchestra [ 8 ] (playing the trumpet, bass trombone, French horn, tuba, piccolo, bassoon, oboe, clarinet and bass clarinet) while playing their instruments, breathing and speaking. The aerosol concentration and size was measured using an aerodynamic particle sizer.…”

Section: Introductionmentioning

confidence: 99%

Airflow and Air Velocity Measurements While Playing Wind Instruments, with Respect to Risk Assessment of a SARS-CoV-2 Infection

Spahn

Hipp

Schubert³

et al. 2021

IJERPH

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Due to airborne transmission of the coronavirus, the question arose as to how high the risk of spreading infectious particles can be while playing a wind instrument. To examine this question and to help clarify the possible risk, we analyzed 14 wind instruments, first qualitatively by making airflows visible while playing, and second quantitatively by measuring air velocity at three distances (1, 1.5, 2 m) in the direction of the instruments’ bells. Measurements took place with wind instrumentalists of the Bamberg Symphony in their concert hall. Our findings highlight that while playing, no airflows escaping from any of the wind instruments—from the bell with brass instruments or from the mouthpiece, keyholes or bell with woodwinds—were measurable beyond a distance of 1.5 m, regardless of volume, pitch or what was played. With that, air velocity while playing corresponded to the usual value of 1 m/s in hall-like rooms. For air-jet woodwinds, alto flute and piccolo, significant air movements were seen close to the mouthpiece, which escaped directly into the room.

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“…The k-means clustering algorithm [27] is a fundamental clustering technique in the field of machine learning. Clustering begins by randomly initializing a centroid for each of a total of k groups.…”

Section: K-means Algorithmsmentioning

confidence: 99%

Level of Budget Execution According to the Professional Profile of Regional Governors Applying Machine Learning Models

Rocha¹,

Aurelio²,

Irazema³

et al. 2020

IJACSA

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Machine Learning is a discipline of artificial intelligence that implements computer systems capable of learning complex patterns automatically and predicting future behaviors. The objective was to implement a Machine Learning model that allows to identify, classify and predict the influence of the professional training of the governors in the execution of the public spending of the regional governments of Peru. Of the 14 indicators of academic training, professional experience and university studies were selected as significant indicators that contribute to the execution of public spending by the 25 governors of Peru. For the prediction of the execution of the public spending of the regional governors, a supervised learning algorithm was implemented. The mean square error for the Machine Learning regression model was 4.20 and the coefficient of determination was 0.726, which indicates that the execution of public spending by regional governments is explained with 72.6% by the professional experience and university studies of the governors. The regional governors of Peru with university studies and professional experience achieve better results in the execution of public spending in the regional governments of Peru.

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Technical note: Using k-means to identify soot aggregates in transmission electron microscopy images

Cited by 19 publications

References 25 publications

Airflow and air velocity measurements while playing wind instruments, with respect to risk assessment of a SARS-CoV-2 infection

Airflow and air velocity measurements while playing wind instruments, with respect to risk assessment of a SARS-CoV-2 infection

Airflow and Air Velocity Measurements While Playing Wind Instruments, with Respect to Risk Assessment of a SARS-CoV-2 Infection

Level of Budget Execution According to the Professional Profile of Regional Governors Applying Machine Learning Models

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