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Working with Standard Kiswahili means setting aside the Nairobi Sheng, Kiswahili varieties from the Democratic Republic of Congo (Kisangani Kiswahili, Ituri Kiswahili, Kivu Kiswahili, Katanga Kiswahili), and Northern Kenyan varieties such as Kiamu (Lamu island) and Kimvita (Mombasa City), despite their importance in the history of literature. However, formalization allows us to develop tools for these varieties in a succeeding step. The dictionaries entries and English translations are mainly based on the Internet Living Kiswahili Dictionary data whose proper citation is: M. Benjamin. 2008. editor. Swahili-English Kamusi Project Wordlist for Free Non-Commercial Use, Kamusi Project International. Distributed under Creative Commons Licence: https://creativecommons.org/licenses/by-nc-sa/3.0/ The Kamusi project was a collaborative, open and international project directed by Martin Benjamin of Yale University, USA (now at Polytechnique, Lausanne, Switzerland). Many top Kiswahili scholars contributed as well as non-academic experts. The Kamusi project mixed various dictionaries, published or working papers, and expanded by continuous updates from East Africa. The documents were available at www.kamusi.org as .txt files. A PERL script written by Mirko Westermeier (computer science researcher, Münster University, Germany), available at https://github.com/Feh/kamusi-cli (retrieved the 29th of July 2017) allowed me to convert these text files into a SQLite database and to process it with SQL requests to create NooJ dictionaries entries with a special focus on lemmatization and formalization of generative rules of inflexed or derived words when the entries in the Kamusi Project were already flexed and derived for the most frequent words forms. 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It goes with a 45000 words Kiswahili-English dictionary and morphological grammars that detect all tenses of Kiswahili verbs. It allows multiple automatic text analysis from various purposes from linguistics research to news watch. Unlike Google translate, the text analysis doesn't provide a single result but shows how the Kiswahili module rules and dictionaries understand the text. You can then export the results for further treatments. Don't hesitate to download it, it works with NooJ software, it's free and distributed under Creative Common License CC BY NC SA 3.0. ABOUT THE DICTIONARY Which Kiswahili is it? Kiswahili is generally described as the bantu language G42 in Malcom Guthrie’s classification. But this continentally spread language is diverse from East Africa to Central Africa. Kiswahili ni nyama ya tembo, kila mtu anachumia pake according to Alphonse Lenselaer in the presentation of his Swahili-Français dictionary. It’s a fact that there are many varieties of Kiswahili, to the point that we can seriously talk of Viswahili. Here we have described the Standard Swahili, or Kiswahili sanifu. This is the more widely understood and spoken variety. This is also the written Kiswahili for the medias, law and East African Schools. Finally, it’s the variety used in Dar es Salaam, the economic capital city of Tanzania. Historically the Standard Swahili has originated with the Kiswahili used in Zanzibar also known as Kiunguja. Working with Standard Kiswahili means setting aside the Nairobi Sheng, Kiswahili varieties from the Democratic Republic of Congo (Kisangani Kiswahili, Ituri Kiswahili, Kivu Kiswahili, Katanga Kiswahili), and Northern Kenyan varieties such as Kiamu (Lamu island) and Kimvita (Mombasa City), despite their importance in the history of literature. However, formalization allows us to develop tools for these varieties in a succeeding step. 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Description What is it for? This code automatically calculates the areas of low and tall vegetation to be added to each parcel in a city, based on predefined greening targets. Use cases and applications This calculation applies to private parcels within a user-defined area (metropolitan region, city, or group of cities). It can also be applied to public parcels if desired. However, please note that roadways and parking lots require special attention to maintain vehicle access. The scientific article associated with this code details the case study, methodological development, and results. It also presents an alternative method for greening public spaces (see paper). Dijon Case Study The current version of the code is specifically tailored to the rules established for the Dijon Metropolis (France). This area has defined target values for low vegetation, tall vegetation, and eco-constructed surfaces that should ideally be achieved on private parcels. These targets vary depending on the location within the metropolitan area (see related publication). Want to use this code to create a parcel greening scenario? It can be used in different situations: Working in an area without urban planning regulations defining vegetation requirements per parcel: Code: You can use the code as is. Data: You’ll need to produce a PLU zoning map for your area based on the Dijon model, and find equivalent datasets for buildings, cadastral parcels, and vegetation (see 3. DATA below). Working in an area with urban planning regulations defining vegetation targets per parcel: Code: You can reuse the structure and adapt the CBS calculation to your specific local regulations. Some areas may not use PLT. Data: Your PLU zoning dataset will need to be adapted accordingly (see section 3. DATA below). Working in Dijon Metropolis (France): Code: The code can be used as is (if the 2019 PLUi-HD rules for PLT/CBS are still in effect). Data: The data can be used as is (if the 2019 PLUi-HD rules are still valid). A few definitions The Biotope Area Factor (CBS) is inspired by regulations applied in Berlin since the 1990s. An increasing number of French cities are adopting this approach to encourage private property owners to green their gardens. In the long term, this contributes to enhancing vegetation in urban areas, which brings numerous ecosystem benefits (climate regulation, biodiversity, environmental quality, social well-being, etc.). Method In Dijon, the calculation is split into two coefficients: PLT (permeable land ratio): applies to low vegetation areas. A minimum of one tree is required per 100 m² of low vegetation. CBS (Biotope Area Factor): applies to tall vegetation and eco-constructed surfaces (e.g., raised decks, permeable gravel pathways, green roofs and walls, etc.). These coeifficients are detailed in the associated publication. Calculation steps This tool performs (1) the calculation of the current situation and (2) the estimation of additional vegetation areas needed per parcel, adjusted to parcel size. To better visualize the results, we recommend highlighting parcels that are too small to meet the greening targets. Limitations In this version, CBS is calculated only from tall vegetation. Parcels that are too small to reach the target should rely on adding eco-constructed surfaces. Outlook A future version of the code could incorporate the addition of eco-constructed surfaces into CBS calculations. Feel free to contact the team for any questions or issues. Please let us know if you use this method in your work—we’d love to hear about it! 2. Requirements These scripts were tested with QGIS versions 3.34 and 3.38 3. DATA All layers must use the same coordinate system and be correctly configured You will need the following layers: Parcels: vector layer for private parcels Buildings: vector layer of buildings Raster vegetation: vegetation raster layer Raster tall vegetation: vegetation raster layer taller than 2 meters PLU zoning: city PLU zoning, providing PLT and CBS values 4. Scripts 4.1 vegetalisation_preparation.py The aim of this script is to provide a layer with all the variables necessary for our revegetation scenario. Parameters : Parcels: vector layer for private parcels Buildings: buildings vector layer Raster vegetation: vegetation raster layer Raster tall vegetation: vegetation raster layer greater than 2 meters Raster resolution : resolution of raster layers PLU zoning: city PLU zoning, providing PLT and CBS values CBS: the field containing the CBS value PLT: the field containing the PLT value Storage directory: directory to store the geopackage database that contains output layers Output As output, the script provides the \"parcelles_PourVegetalisation\" layer containing the fields: id_parcelle: parcel identifier SurfParcelle: parcel area SurfParcelleNoBati: parcel's area without buildings SurVeg: vegetation surface in the parcel SurfHVeg: tall (&gt; 2m) vegetation surface in the parcel PLU_CBS: CBS value provided by city's PLU PLU_PLT: PLT value provided by city's PLU For complementary output, the script provides two more layers: entite_nonJoignable: parcels with no remaining unbuilt area entite_PLU_nonJoignable: parcels located outside the PLU zoning 4.2 vegetalisation.py The aim of this script is to provide the areas of vegetation to be added per parcel in order to approach the theoretical value of the CBS and PLT while considering the space available. Parameters : db_vegetalization: geopackage database created in step 1 treeSurface: average tree surface area (m2) modulation: bonus per additional tree Output As output, the script provides the vegetalisation layer. Main results are stored in the fields: PLT : the PLT is calculated as close as possible to the PLT recommended in the PLU, based on the space available for vegetation. CBS : the CBS is calculated as close as possible to the CBS recommended in the PLU, based on the space available for vegetation. sVegAjout : low vegetation to be planted corresponding to the calculated PLT sHVegAjout : tall vegetation to be planted corresponding to the calculated CBS 5. 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Calculation steps This tool performs (1) the calculation of the current situation and (2) the estimation of additional vegetation areas needed per parcel, adjusted to parcel size. To better visualize the results, we recommend highlighting parcels that are too small to meet the greening targets. Limitations In this version, CBS is calculated only from tall vegetation. Parcels that are too small to reach the target should rely on adding eco-constructed surfaces. Outlook A future version of the code could incorporate the addition of eco-constructed surfaces into CBS calculations. Feel free to contact the team for any questions or issues. Please let us know if you use this method in your work—we’d love to hear about it! 2. Requirements These scripts were tested with QGIS versions 3.34 and 3.38 3. 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Parameters : Parcels: vector layer for private parcels Buildings: buildings vector layer Raster vegetation: vegetation raster layer Raster tall vegetation: vegetation raster layer greater than 2 meters Raster resolution : resolution of raster layers PLU zoning: city PLU zoning, providing PLT and CBS values CBS: the field containing the CBS value PLT: the field containing the PLT value Storage directory: directory to store the geopackage database that contains output layers Output As output, the script provides the \"parcelles_PourVegetalisation\" layer containing the fields: id_parcelle: parcel identifier SurfParcelle: parcel area SurfParcelleNoBati: parcel's area without buildings SurVeg: vegetation surface in the parcel SurfHVeg: tall (&gt; 2m) vegetation surface in the parcel PLU_CBS: CBS value provided by city's PLU PLU_PLT: PLT value provided by city's PLU For complementary output, the script provides two more layers: entite_nonJoignable: parcels with no remaining unbuilt area entite_PLU_nonJoignable: parcels located outside the PLU zoning 4.2 vegetalisation.py The aim of this script is to provide the areas of vegetation to be added per parcel in order to approach the theoretical value of the CBS and PLT while considering the space available. Parameters : db_vegetalization: geopackage database created in step 1 treeSurface: average tree surface area (m2) modulation: bonus per additional tree Output As output, the script provides the vegetalisation layer. Main results are stored in the fields: PLT : the PLT is calculated as close as possible to the PLT recommended in the PLU, based on the space available for vegetation. CBS : the CBS is calculated as close as possible to the CBS recommended in the PLU, based on the space available for vegetation. sVegAjout : low vegetation to be planted corresponding to the calculated PLT sHVegAjout : tall vegetation to be planted corresponding to the calculated CBS 5. 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It also presents an alternative method for greening public spaces (see paper). Dijon Case Study The current version of the code is specifically tailored to the rules established for the Dijon Metropolis (France). This area has defined target values for low vegetation, tall vegetation, and eco-constructed surfaces that should ideally be achieved on private parcels. These targets vary depending on the location within the metropolitan area (see related publication). Want to use this code to create a parcel greening scenario? It can be used in different situations: Working in an area without urban planning regulations defining vegetation requirements per parcel: Code: You can use the code as is. Data: You’ll need to produce a PLU zoning map for your area based on the Dijon model, and find equivalent datasets for buildings, cadastral parcels, and vegetation (see 3. DATA below). Working in an area with urban planning regulations defining vegetation targets per parcel: Code: You can reuse the structure and adapt the CBS calculation to your specific local regulations. Some areas may not use PLT. Data: Your PLU zoning dataset will need to be adapted accordingly (see section 3. DATA below). Working in Dijon Metropolis (France): Code: The code can be used as is (if the 2019 PLUi-HD rules for PLT/CBS are still in effect). Data: The data can be used as is (if the 2019 PLUi-HD rules are still valid). A few definitions The Biotope Area Factor (CBS) is inspired by regulations applied in Berlin since the 1990s. An increasing number of French cities are adopting this approach to encourage private property owners to green their gardens. In the long term, this contributes to enhancing vegetation in urban areas, which brings numerous ecosystem benefits (climate regulation, biodiversity, environmental quality, social well-being, etc.). 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Parameters : Parcels: vector layer for private parcels Buildings: buildings vector layer Raster vegetation: vegetation raster layer Raster tall vegetation: vegetation raster layer greater than 2 meters Raster resolution : resolution of raster layers PLU zoning: city PLU zoning, providing PLT and CBS values CBS: the field containing the CBS value PLT: the field containing the PLT value Storage directory: directory to store the geopackage database that contains output layers Output As output, the script provides the \"parcelles_PourVegetalisation\" layer containing the fields: id_parcelle: parcel identifier SurfParcelle: parcel area SurfParcelleNoBati: parcel's area without buildings SurVeg: vegetation surface in the parcel SurfHVeg: tall (&gt; 2m) vegetation surface in the parcel PLU_CBS: CBS value provided by city's PLU PLU_PLT: PLT value provided by city's PLU For complementary output, the script provides two more layers: entite_nonJoignable: parcels with no remaining unbuilt area entite_PLU_nonJoignable: parcels located outside the PLU zoning 4.2 vegetalisation.py The aim of this script is to provide the areas of vegetation to be added per parcel in order to approach the theoretical value of the CBS and PLT while considering the space available. 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They can be found in the folder notebooks at : https://gricad-gitlab.univ-grenoble-alpes.fr/omegalpes/omegalpes_examples OMEGAlpes-web interface : An web interface is available at https://omegalpes-ote.univ-grenoble-alpes.fr/ This interface enable you to generate scripts more easily using the graphical representation detailed in https://omegalpes.readthedocs.io/en/latest/OMEGAlpes_grah_representation.html ## OMEGAlpes' Community Please subscribe to our mailing lists : for our newsletters: https://groupes.renater.fr/sympa/subscribe/omegalpes-news as an OMEGAlpes' developer: https://groupes.renater.fr/sympa/subscribe/omegalpes-users Please use the git issues system to report an error: https://gricad-gitlab.univ-grenoble-alpes.fr/omegalpes/omegalpes.git Otherwise you can also contact de developer team using the following email adress: omegalpes-users@groupes.renater.fr To see the details of the contributions, see the Contributors in OMEGAlpes repository: https://gricad-gitlab.univ-grenoble-alpes.fr/omegalpes/omegalpes/-/graphs/master","fr":"# OMEGAlpes Lib for linear energy systems modelling OMEGAlpes stands for Generation of Optimization Models As Linear Programming for Energy Systems. 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One of the current algorithmic challenges for exploiting these data on a global scale consists in providing efficient query engines on these petabyte-scale datasets. Most methods indexing those datasets rely on indexing words of fixed length k, called k-mers. Many applications, such as metagenomics, require the abundance of indexed k-mers as well as their simple presence or absence, but no method scales up to petabyte-scaled datasets. This deficiency is primarily because storing abundance requires explicit storage of the k-mers in order to associate them with their counts. Using counting Approximate Membership Queries (cAMQ) data structures, such as counting Bloom filters, provides a way to index large amounts of k-mers with their abundance, but at the expense of a sensible false positive rate. Results We propose a novel algorithm, called fimpera, that enables the improvement of any cAMQ performance. 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