The impact of the COVID-19 pandemic on the communication of Polish speedway clubs in social media : the analysis of the phenomenon

Celem rozważań jest wpływ pandemii COVID-19 i stanu zagrożenia epidemicznego na sposób prowadzenia komunikacji przez najpopularniejsze polskie kluby żużlowe w mediach społecznościowych. Badania rozpoczęto od przeprowadzenia analizy ilościowej profili klubów żużlowych w mediach społecznościowych. Wśród kryteriów ilościowych znalazły się takie aspekty, jak m.in. tematyka postów czy zaangażowanie społeczności. W ramach badań jakościowych dokonano dogłębnej analizy treści publikowanych w mediach społecznościowych. Na podstawie zastosowanej analizy mieszanej stwierdzono, iż pandemia COVID-19 w znaczącym stopniu wpłynęła na sposób komunikacji polskich klubów żużlowych w mediach społecznościowych. Ponadto pandemia zaznaczyła się również w zachowaniu fanów, którzy zmienili sposób konsumowania przedstawianych przez kluby treści w nowych mediach.The purpose of this paper is to analyze the impact of the COVID-19 pandemic and the state of emergency connected with the epidemic threat on how the most popular Polish speedway clubs communicate on social media. The paper begins with a quantitative analysis of the profiles of speedway clubs in social media. The quantitative aspects considered include among others the subject matter of the posts and community involvement. On the other hand, as a part of the qualitative research, an in-depth analysis of the content published on social media was conducted. The mixed analysis proved that the COVID-19 pandemic had a significant impact on the communication of Polish speedway clubs in social media. Furthermore, the SARS-CoV-2 pandemic also affected the behavior of fans who changed their way of consuming the content published by the clubs in new media

Ultrasound-assisted lipase catalyzed hydrolysis of aspirin methyl ester

Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j.ultsonch.2017.08.004.The ultrasound-assisted hydrolysis of aspirin methyl ester (AME) was investigated using immobilized Candida antarctica lipase B (CALB) (1%) in the presence of solvents like triolein, chloroform (CHCl3) and dichloromethane (DCM). The effect of ultrasound and the role of water on the conversion rates have also been investigated. Proton nuclear magnetic resonance spectroscopic (1H NMR) was chosen to calculate hydrolysis convertion rates. We observed that lipase-ultrasound assisted hydrolysis of AME in the presence of triolein and water showed the highest hydrolysis conversion rate (65.3%). Herein low water amount played an important role as a nucleophile being crucial for the hydrolysis yields obtained. Lipase activity was affected by the conjugated action of ultrasound and solvents (35.75% of decrease), however not disturbing its hydrolytic efficiency. It was demonstrated that lipase is able to hydrolyse AME to methyl 2-hydroxy benzoate (methyl salicylate), which applications include fragrance agents in food, beverages and cosmetics, or analgesic agent in liniments.All authors gratefully acknowledge the financial support provided by International Joint Research Laboratory for Textile and Fibre Bioprocesses at Jiangnan University. The authors are also thankful to the Department of Oils, Oleochemicals and Surfactants technology, Institute of Chemical Technology, Mumbai, India and to the Bioprocess and Bio nanotechnology Research Group (BBRG) of University of Minho. Authors would like also to acknowledge the Portuguese Foundation for Science and Technology (FCT) under the scope of the strategic funding of UID/BIO/04469/2013 unit and COMPETE 2020 (POCI-01-0145-FEDER-006684) and BioTecNorte operation (NORTE01-0145-FEDER-000004) funded by European Regional Development Fund under the scope of Norte2020 – Programa Operacional Regional do Norte and to the Fundamental Research Funds for the Central Universities (No. JUSRP51622 A and No. JUSRP115A03), and to the Jiangsu Province Scientific Research Innovation Project for Academic Graduate Students in 2016 (No. KYLX16_0788).info:eu-repo/semantics/publishedVersio

Separation of track- and shower-like energy deposits in ProtoDUNE-SP using a convolutional neural network

International audienceLiquid argon time projection chamber detector technology provides high spatial and calorimetric resolutions on the charged particles traversing liquid argon. As a result, the technology has been used in a number of recent neutrino experiments, and is the technology of choice for the Deep Underground Neutrino Experiment (DUNE). In order to perform high precision measurements of neutrinos in the detector, final state particles need to be effectively identified, and their energy accurately reconstructed. This article proposes an algorithm based on a convolutional neural network to perform the classification of energy deposits and reconstructed particles as track-like or arising from electromagnetic cascades. Results from testing the algorithm on experimental data from ProtoDUNE-SP, a prototype of the DUNE far detector, are presented. The network identifies track- and shower-like particles, as well as Michel electrons, with high efficiency. The performance of the algorithm is consistent between experimental data and simulation

Separation of track- and shower-like energy deposits in ProtoDUNE-SP using a convolutional neural network

International audienceLiquid argon time projection chamber detector technology provides high spatial and calorimetric resolutions on the charged particles traversing liquid argon. As a result, the technology has been used in a number of recent neutrino experiments, and is the technology of choice for the Deep Underground Neutrino Experiment (DUNE). In order to perform high precision measurements of neutrinos in the detector, final state particles need to be effectively identified, and their energy accurately reconstructed. This article proposes an algorithm based on a convolutional neural network to perform the classification of energy deposits and reconstructed particles as track-like or arising from electromagnetic cascades. Results from testing the algorithm on experimental data from ProtoDUNE-SP, a prototype of the DUNE far detector, are presented. The network identifies track- and shower-like particles, as well as Michel electrons, with high efficiency. The performance of the algorithm is consistent between experimental data and simulation