The Experts below are selected from a list of 9297 Experts worldwide ranked by ideXlab platform

Ernenwein J.-p. - One of the best experts on this subject based on the ideXlab platform.

  • Signal extraction in atmospheric shower arrays designed for 200 GeV–50 TeV γ-ray astronomy
    'IOP Publishing', 2021
    Co-Authors: Senniappan M., Becherini Y., Punch M., Thoudam S., Bylund T., Mezek G. Kukec, Ernenwein J.-p.
    Abstract:

    International audienceWe present the SEMLA (Signal Extraction using Machine Learning for ALTO) analysis method, developed for the detection of E>200 GeV γ rays in the context of the ALTO wide-field-of-view atmospheric shower array R&D project. The scientific focus of ALTO is extragalactic γ-ray astronomy, so primarily the detection of soft-spectrum γ-ray sources such as Active Galactic Nuclei and Gamma Ray Bursts. The current phase of the ALTO R&D project is the optimization of sensitivity for such sources and includes a number of ideas which are tested and evaluated through the analysis of dedicated Monte Carlo simulations and hardware testing. In this context, it is important to clarify how data are analysed and how results are being obtained. SEMLA takes advantage of machine learning and comprises four Stages: initial event Cleaning (Stage A), filtering out of poorly reconstructed γ-ray events (Stage B), followed by γ-ray signal extraction from proton background events (Stage C) and finally reconstructing the energy of the events (Stage D). The performance achieved through SEMLA is evaluated in terms of the angular, shower core position, and energy resolution, together with the effective detection area, and background suppression. Our methodology can be easily generalized to any experiment, provided that the signal extraction variables for the specific analysis project are considered

  • Signal extraction in atmospheric shower arrays designed for $\rm 200\,GeV-50\,TeV$$\gamma$-ray astronomy
    HAL CCSD, 2021
    Co-Authors: Senniappan M., Becherini Y., Punch M., Thoudam S., Bylund T., Mezek G. Kukec, Ernenwein J.-p.
    Abstract:

    We present the SEMLA (Signal Extraction using Machine Learning for ALTO) analysis method, developed for the detection of $\rm E>200\,GeV$$\gamma$ rays in the context of the ALTO wide-field-of-view atmospheric shower array R&D project. The scientific focus of ALTO is extragalactic $\gamma$-ray astronomy, so primarily the detection of soft-spectrum $\gamma$-ray sources such as Active Galactic Nuclei and Gamma Ray Bursts. The current phase of the ALTO R&D project is the optimization of sensitivity for such sources and includes a number of ideas which are tested and evaluated through the analysis of dedicated Monte Carlo simulations and hardware testing. In this context, it is important to clarify how data are analysed and how results are being obtained. SEMLA takes advantage of machine learning and comprises four Stages: initial event Cleaning (Stage A), filtering out of poorly reconstructed $\gamma$-ray events (Stage B), followed by $\gamma$-ray signal extraction from proton background events (Stage C) and finally reconstructing the energy of the events (Stage D). The performance achieved through SEMLA is evaluated in terms of the angular, shower core position, and energy resolution, together with the effective detection area, and background suppression. Our methodology can be easily generalized to any experiment, provided that the signal extraction variables for the specific analysis project are considered

Thomas Borrmann - One of the best experts on this subject based on the ideXlab platform.

  • n chlorination and orton rearrangement of aromatic polyamides revisited
    Journal of Membrane Science & Technology, 2012
    Co-Authors: Giancarlo M Barassi, Thomas Borrmann
    Abstract:

    Polyamide membranes are widely used in water desalination. It is known that they suffer degradation due to the presence of free chlorine. This communication shows a detailed chemical reaction mechanism for the N-chlorination and Orton rearrangement of poly (m-phenylene isophthalamide), which is the linear aromatic polyamide component of the commonly used B-9 Permasep® membrane. The N-chlorination of this aromatic polyamide causes the loss of hydrogen bonding. This triggers conformational changes in the polymer; the polymer becomes less rigid and, void spaces open up, which decreases solute rejection and increases water flux. The N-Chlorination reaction is reversible in alkaline media. Therefore, if the polymer is suspected to have come into contact with hypochlorite anions or hypochlorous acid immediate Cleaning with sodium hydroxide could reverse the N‑chlorination. Conversely, the N-chlorination is acid catalyzed; hence, special care has to be taken during the Cleaning Stage, when HCl is used. Furthermore, N-chlorinated aromatic polyamides can undergo an Orton rearrangement, which is also promoted in acidic media, resulting in the formation of ortho- or para-chloro substituted analogues of the aromatic amide moiety. The chloro group causes a strong negative inductive effect weakening the amide bond making it more susceptible to hydrolysis, which eventually produces chain scission.

  • n chlorination and orton rearrangement of aromatic polyamides
    2012
    Co-Authors: Thomas Borrmann
    Abstract:

    Polyamide membranes are widely used in water desalination. It is known that they suffer degradation due to the presence of free chlorine. This communication shows a detailed chemical reaction mechanism for the N-chlorination and Orton rearrangement of poly (m-phenylene isophthalamide), which is the linear aromatic polyamide component of the commonly used B-9 Permasep® membrane. The N-chlorination of this aromatic polyamide causes the loss of hydrogen bonding. This triggers conformational changes in the polymer; the polymer becomes less rigid and, void spaces open up, which decreases solute rejection and increases water flux. The N-Chlorination reaction is reversible in alkaline media. Therefore, if the polymer is suspected to have come into contact with hypochlorite anions or hypochlorous acid immediate Cleaning with sodium hydroxide could reverse the N-chlorination. Conversely, the N-chlorination is acid catalyzed; hence, special care has to be taken during the Cleaning Stage, when HCl is used. Furthermore, N-chlorinated aromatic polyamides can undergo an Orton rearrangement, which is also promoted in acidic media, resulting in the formation of ortho- or para-chloro substituted analogues of the aromatic amide moiety. The chloro group causes a strong negative inductive effect weakening the amide bond making it more susceptible to hydrolysis, which eventually produces chain scission.

Necati Ozkan - One of the best experts on this subject based on the ideXlab platform.

  • Cleaning rate in the uniform Cleaning Stage for whey protein gel deposits
    Food and Bioproducts Processing, 2002
    Co-Authors: H Xin, Xiao Dong Chen, Necati Ozkan
    Abstract:

    The removal of a thermally induced whey protein concentrate (WPC) gel layer from the inner surface of a stainless steel tube was determined continuously and non-invasively by a rapid UV spectrophotometric method. Based on mass transfer theory and polymer dissolution concepts, the constant Cleaning rates in the uniform Stage were predicted. The viscosity and solubility of WPC gels at different temperatures were measured and used in the modelling prediction. The deviation of the prediction from the experimental results indicates a possible change of protein fragment size with temperature. The investigation of the dissolution of WPC gel in 0.5 wt% NaOH solutions showed a break-up of disulphide bonds between protein molecules. The signi. cant increase of the SH group content with increasing dissolution temperature was in agreement with the assumption that protein fragment size decreases with increasing temperature.

Kenneth H Mellits - One of the best experts on this subject based on the ideXlab platform.

  • Paper Paper Inclusion of detergent in a Cleaning regime and effect on microbial load in livestock housing
    2020
    Co-Authors: Laura R Hancox, Le M Bon, Christine E R Dodd, Kenneth H Mellits
    Abstract:

    Determining effective Cleaning and disinfection regimes of livestock housing is vital to improving the health of resident animals and reducing zoonotic disease. A Cleaning regime consisting of scraping, soaking with or without detergent (treatment and control), pressure washing, disinfection and natural drying was applied to multiple pig pens. After each Cleaning Stage, samples were taken from different materials and enumerated for total aerobic count (TAC) and Enterobacteriaceae (ENT). Soaking with detergent (Blast-Off, Biolink) caused significantly greater reductions of TAC and ENT on metal, and TAC on concrete, compared with control. Disinfection effect (Virkon S, DuPont) was not significantly associated with prior detergent treatment. Disinfection significantly reduced TAC and ENT on concrete and stock board but not on metal. Twenty-four hours after disinfection TAC and ENT on metal and stock board were significantly reduced, but no significant reductions occurred in the subsequent 96 hours. Counts on concrete did not significantly reduce during the entire drying period (120 hours). Detergent and disinfectant have varying bactericidal effects according to the surface and bacterial target; however, both can significantly reduce microbial numbers so should be used during Cleaning, with a minimum drying period of 24 hours, to lower bacterial counts effectively

  • inclusion of detergent in a Cleaning regime and effect on microbial load in livestock housing
    Veterinary Record, 2013
    Co-Authors: Laura R Hancox, Le M Bon, Christine E R Dodd, Kenneth H Mellits
    Abstract:

    Determining effective Cleaning and disinfection regimes of livestock housing is vital to improving the health of resident animals and reducing zoonotic disease. A Cleaning regime consisting of scraping, soaking with or without detergent (treatment and control), pressure washing, disinfection and natural drying was applied to multiple pig pens. After each Cleaning Stage, samples were taken from different materials and enumerated for total aerobic count (TAC) and Enterobacteriaceae (ENT). Soaking with detergent (Blast-Off, Biolink) caused significantly greater reductions of TAC and ENT on metal, and TAC on concrete, compared with control. Disinfection effect (Virkon S, DuPont) was not significantly associated with prior detergent treatment. Disinfection significantly reduced TAC and ENT on concrete and stock board but not on metal. Twenty-four hours after disinfection TAC and ENT on metal and stock board were significantly reduced, but no significant reductions occurred in the subsequent 96 hours. Counts on concrete did not significantly reduce during the entire drying period (120 hours). Detergent and disinfectant have varying bactericidal effects according to the surface and bacterial target; however, both can significantly reduce microbial numbers so should be used during Cleaning, with a minimum drying period of 24 hours, to lower bacterial counts effectively.

Senniappan M. - One of the best experts on this subject based on the ideXlab platform.

  • Signal extraction in atmospheric shower arrays designed for 200 GeV–50 TeV γ-ray astronomy
    'IOP Publishing', 2021
    Co-Authors: Senniappan M., Becherini Y., Punch M., Thoudam S., Bylund T., Mezek G. Kukec, Ernenwein J.-p.
    Abstract:

    International audienceWe present the SEMLA (Signal Extraction using Machine Learning for ALTO) analysis method, developed for the detection of E>200 GeV γ rays in the context of the ALTO wide-field-of-view atmospheric shower array R&D project. The scientific focus of ALTO is extragalactic γ-ray astronomy, so primarily the detection of soft-spectrum γ-ray sources such as Active Galactic Nuclei and Gamma Ray Bursts. The current phase of the ALTO R&D project is the optimization of sensitivity for such sources and includes a number of ideas which are tested and evaluated through the analysis of dedicated Monte Carlo simulations and hardware testing. In this context, it is important to clarify how data are analysed and how results are being obtained. SEMLA takes advantage of machine learning and comprises four Stages: initial event Cleaning (Stage A), filtering out of poorly reconstructed γ-ray events (Stage B), followed by γ-ray signal extraction from proton background events (Stage C) and finally reconstructing the energy of the events (Stage D). The performance achieved through SEMLA is evaluated in terms of the angular, shower core position, and energy resolution, together with the effective detection area, and background suppression. Our methodology can be easily generalized to any experiment, provided that the signal extraction variables for the specific analysis project are considered

  • Signal extraction in atmospheric shower arrays designed for $\rm 200\,GeV-50\,TeV$$\gamma$-ray astronomy
    HAL CCSD, 2021
    Co-Authors: Senniappan M., Becherini Y., Punch M., Thoudam S., Bylund T., Mezek G. Kukec, Ernenwein J.-p.
    Abstract:

    We present the SEMLA (Signal Extraction using Machine Learning for ALTO) analysis method, developed for the detection of $\rm E>200\,GeV$$\gamma$ rays in the context of the ALTO wide-field-of-view atmospheric shower array R&D project. The scientific focus of ALTO is extragalactic $\gamma$-ray astronomy, so primarily the detection of soft-spectrum $\gamma$-ray sources such as Active Galactic Nuclei and Gamma Ray Bursts. The current phase of the ALTO R&D project is the optimization of sensitivity for such sources and includes a number of ideas which are tested and evaluated through the analysis of dedicated Monte Carlo simulations and hardware testing. In this context, it is important to clarify how data are analysed and how results are being obtained. SEMLA takes advantage of machine learning and comprises four Stages: initial event Cleaning (Stage A), filtering out of poorly reconstructed $\gamma$-ray events (Stage B), followed by $\gamma$-ray signal extraction from proton background events (Stage C) and finally reconstructing the energy of the events (Stage D). The performance achieved through SEMLA is evaluated in terms of the angular, shower core position, and energy resolution, together with the effective detection area, and background suppression. Our methodology can be easily generalized to any experiment, provided that the signal extraction variables for the specific analysis project are considered