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        Chapter Introductory Chapter: Agricultural Waste as a Source of Raw Materials 

        Aladjadjiyan, Anna (2018)
        Agricultural science
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        Chapter Estimating the Effect of Voters’ Media Awareness on the 2016 US Presidential Election 

        Gallego, Maria; Dique, Lauren (2019)
        We examine whether voter media awareness of the 2016 US Presidential election campaign influenced the election using a logit model to estimate the probability that a voter with certain characteristics votes for one of the ...
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        Chapter Machine Learning in Volcanology: A Review 

        Carniel, Roberto; Guzmán, Silvina (2020)
        A volcano is a complex system, and the characterization of its state at any given time is not an easy task. Monitoring data can be used to estimate the probability of an unrest and/or an eruption episode. These can include ...
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        Chapter Machine Learning Techniques to Mitigate Nonlinear Phase Noise in Moderate Baud Rate Optical Communication Systems 

        Bogoni, A.; Fern&#225, o; ndez, E.; C&#225, a; rdenas Soto, A.; Guerrero Gonzalez, N.; Serafino, G.; Ghelfi, P. (2020)
        Nonlinear phase noise (NLPN) is the most common impairment that degrades the performance of radio-over-fiber networks. The effect of NLPN in the constellation diagram consists of a shape distortion of symbols that increases ...
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        Chapter Business Process Linguistic Modeling: Theory and Practice Part I: BPLM Strategy Creator 

        Jozef, Stašák; Jaroslav, Kultan; Peter, Schmidt; Abu, Urinbasarovich (2020)
        The business activities provided within any firm or company should be checked and controlled continuously, while two principal approaches should be applied: (a) qualitative monitoring, (b) quantitative evaluations, while ...
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        License

        • If not noted otherwise all contents are available under Attribution 4.0 International (CC BY 4.0)

        Credits

        • logo EU
        • This project received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 683680, 810640, 871069 and 964352.

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