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Duchesne S, Mailhot A, Dequidt E, Villeneuve JP (2001) Mathematical modeling of sewers under surcharge for real time control of combined sewer overflows. Elliott AH, Trowsdale SA (2007) A review of models for low impact urban stormwater drainage. Finaud-Guyot P, Delenne C, Guinot V, Llovel C (2011) 1D-2D coupling for river flow modeling. Fiori A, Volpi E (2020) On the effectiveness of LID infrastructures for the attenuation of urban flooding at the catchment scale. Freni G, Mannina G, Viviani G (2010) Urban storm-water quality management: centralized versus source control. Gaborit E, Muschalla D, Vallet B, Vanrolleghem PA et al (2013) Improving the performance of stormwater detention basins by real-time control using rainfall forecasts. Gagliano A, Nocera F, Detommaso M, Evola G (2016) Thermal behavior of an extensive green roof: numerical simulations and experimental investigations. Garcia L, Barreiro-Gomez J, Escobar E, Tellez D et al (2015) Modeling and real-time control of urban drainage systems: a review. Gejadze IY, Monnier J (2007) On a 2D zoom for 1D shallow-water model: coupling and data assimilation.
Always add a polite sign-off (kind regards, best regards, etc.), proofread, and then hit send! To explain mouse click the up coming webpage principles above, here are six examples of effective quotation emails for various products, services, and situations. As well as the samples themselves, we provide a short analysis of the audience and why they're effective. Samples and templates are, by nature, generic - so before using them in business, be sure to edit and adapt them! This quotation request email is one you can send to multiple suppliers. It outlines what you want when you want it, and why. This quotation request email reflects the appropriate tone and language when requesting a quotation. The content and structure also adhere to the tips outlined above, and you can see how we've asked for critical information. If the proposal you've received doesn't cover everything, then ask for a revised quotation. When sending a revised quotation request email, specify precisely what you need to avoid confusion. Be sure to clearly explain the reason for requesting a revision of the quotation.
These two steps provided a means to identify LDA models that were consistent with my intuitive understanding of the topics, and to build confidence in these models. The model with 16 topics was selected. Having selected this model, each document was assigned a dominant topic, which is the topic that most strongly contributes to that documents meaning. Each topic was then labelled based on qualitative assessment of its most common keywords as well as re-reviewing both its most representative documents and a random selection. LDAvis (Mabey 2018), a python port of the R package LDAVis (Sievert and Shirley 2014), was used to visualize the distribution of keywords in each topic. LDAvis includes an option to adjust how the relevance of terms to their parent topic is calculated by modified the λ value used in this determination. Sievert and Shirley (2014) note that human accuracy in categorizing topics was best when λ was 0.6, and I followed this recommendation. Upon labelling the topics, it became evident that some overlapped and could be grouped together.
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