Showing 21 - 40 results of 53 for search '"Naivasha"', query time: 0.08s Refine Results
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    An extended singular spectrum transformation (SST) for the investigation of Kenyan precipitation data by N. Itoh, N. Marwan

    Published 2013-07-01
    “…With regards to the application, we find change points in the precipitation data of Kenyan towns (Nakuru, Naivasha, Narok, and Kisumu) which coincide with the variability of the Indian Ocean Dipole (IOD) suggesting its impact of extreme climate in East Africa.…”
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    A creative approach to participatory mapping on climate change impacts among very young adolescents in Kenya by Carmen H Logie, Sarah Van Borek, Anoushka Lad, Lesley Gittings, Julia Kagunda, Humphres Evelia, Clara Gachoki, Kevin Oyugi, Mercy Wanjiru Chege, Beldine Omondi, Maryline Okuto, Lina Taing

    Published 2023-06-01
    “…The aim of this paper is to describe the design of a multi-media participatory mapping tool to identify and address the interconnections between social, health, and environmental well-being with very young adolescents (aged 10-14 years) in six geographically-diverse, climate-affected regions of Kenya (Nairobi, Kisumu, Kilifi, Naivasha, Isiolo, and Kalobeyei Refugee Settlement). …”
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    The quantification of the extent of flooding on selected major Afrotropical lakes to guide management implications by Christopher Mulanda Aura, Chrisphine Nyamweya, Safina Musa, Zachary Ogari, Winnie Owoko, Melckzedeck Osore, Linda May, James M. Njiru

    Published 2023-03-01
    “…Lakes with the smallest surface areas such as Baringo and Naivasha showed the greatest increase in flooding of 52.63% and 42.62%, respectively. …”
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    Article
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    Declines in an augur buzzard Buteo augur population in a region of increasing human development by Eichenwald, AJ, Amar, A, Tyrrell, PD, Buechley, ER, Virani, MZ

    Published 2021
    “…In this study, we explore trends in Augur Buzzard (Buteo augur) territory occupancy over nearly two decades around Lake Naivasha, Kenya, in relation to land-use changes, particularly expansion in human housing and flower farms. …”
    Journal article
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    Assessment of hepatitis B vaccination status and hepatitis B surface antibody titres among health care workers in selected public health hospitals in Kenya. by Irene Ann Mwangi, Jesca O Wesongah, Victor Moses Musyoki, Gloria S Omosa-Manyonyi, Bashir Farah, Laura Gwahalla Edalia, Margaret Mbuchi

    Published 2023-01-01
    “…This was a cross-sectional study carried out among HCWs at Kenyatta National Hospital (KNH), Naivasha and Mbagathi County hospitals. Data on participants' demographics and hepatitis B vaccination status was collected using an interviewer-guided questionnaire. …”
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    Simulation of Maize Lethal Necrosis (MLN) Damage Using the CERES-Maize Model by William D. Batchelor, L. M. Suresh, Xiaoxing Zhen, Yoseph Beyene, Mwaura Wilson, Gideon Kruseman, Boddupalli Prasanna

    Published 2020-05-01
    “…A field experiment consisting of 17 maize hybrids with different levels of MLN tolerance was planted under MLN virus-inoculated and non-inoculated conditions in 2016 and 2018 at the MLN Screening Facility in Naivasha, Kenya. Time series disease progress scores were recorded and translated into daily damage, including leaf necrosis and death, as inputs in the crop model. …”
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    A cluster-randomised controlled equivalence trial of the Surprise Soap handwashing intervention among older children living in a refugee settlement in Sudan by Julie Watson, Oliver Cumming, Amy MacDougall, Ibtihal Mohammed-Elhassan Osman, Maud Amon-Tanoh, Claudio Deola

    Published 2023-10-01
    “…Here, we evaluate this intervention against a standard intervention in a complex emergency setting.Methods A cluster-randomised controlled equivalence trial was conducted in Naivasha refugee settlement, Sudan. Blinding was not possible. 203 randomly selected households, with at least one child aged 5–12, were randomised to receive the Surprise Soap intervention (n=101) or a standard intervention comprising a short household session with health messaging and plain soap distribution (n=102). …”
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