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    Interpretation of EkeGusii Pop Songs within the Great Chain of Being Metaphor
    (Chuka University, 2017-10) Ntabo, Victor Ondara; Gathigia, Moses Gatambuki; Moraa, Noam N
    A review of literature on pop songs reveals that composers use metaphors to communicate their feelings. In particular, the meaning of the metaphors in EkeGusii pop songs (EPS) needs to be interpreted to reveal the intention of the composers. The EkeGusii pop singer Christopher Mosioma’s songs have gained fame in Kenya because of their use of metaphors. The song amasomo (education) has gained acclaim from Kenyans since its launch in 2014. The song amasomo (education) is basically presented as a piece of advice to students to embrace education in order to optimally reap from its benefits. It is against this backdrop that this study identifies the metaphors in the song through the Metaphor Identification Procedure Vrije Universiteit (MIPVU) and interprets them. The study employs four coders (including the researchers) in the identification of the metaphors. The study also takes into account the folk conception of the generic Great Chain of Being Metaphor (GCBM) whose main aim is to assign a place for any phenomenon in the universe in a strict hierarchical system. The study found that, inter alia, human, animal, plant, object and vehicle metaphors are used in the song amasomo. The study concludes that the metaphors in the EkeGusii pop songs belong inherently to different levels of the generic Great Chain of Being Metaphor (GCBM).
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    Conceptual Metaphors in Ken Walibora's Novel "Kidagaa Kimemwozea"
    (Chuka University, 2016-10) Ntabo, Victor Ondara; Kangangi, Beatrice
    The deficiency of grammar in unearthing literary gist necessitates the borrowing of a Cognitive Linguist’s lenses for a fuller explication of a text. This motivates the blast-off point in pursuit of meaning where “backstage cognition” fills a lacuna whose origin is the apparent mismatch between the writer’s background and the reader’s linguistic resources. Whereas intellectual endeavors unclothing the correlation between language and cognition cannot be controverted, the diligence paid to the study of metaphor in literary texts within a cognitive-semantics perspective has hitherto been hemmed in. We, therefore, analyze the conceptual metaphors in Kidagaa Kimemwozea by the Kenyan novelist Ken Walibora. The novel reflects a bedeviled state whose unfeeling king abuses power to amass wealth as sounds of anguish rent the air. Luckily, the protagonist (Amani) conspires with the king’s son to exploit the father’s weakness for the benefit of common citizens. This chapter establishes, classifies and annotates the conceptual metaphors using survey descriptive research design within the backup of Conceptual Metaphor Theory. It utilizes the Great Chain of Being metaphor whose chief objective slots a place for any phenomenon in a set hierarchical system. Animals, plants, objects and natural things are stratified source domains richly used to depict the characters in the novel. For a better appreciation of conceptual metaphors, it is salient to use the spectacles of a cognitive linguist to understand contextual language against the cultural, historical and geographical backdrop. Conceptual metaphors are conduits of communication and should be explained using a cognitive linguistics approach. Language is embodied and situated in a specific environment, making it possible for the meaning of some of the metaphors to elude the reader.
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    Integration of RNA-Seq and Metabolite Analysis Reveals the Key Floral Scent Biosynthetic Genes in Herbaceous Peony
    (Horticulturae, 2024-06-10) Kimani, Shadrack Kanyonji; Wang, Shuxian; Xie, Jinyi; Bao, Tingting; Shan, Xiaotong; Li, Hongjie; Adnan; Wang, Li; Gao, Xiang; Li, Yueqing
    Floral scent is an essential and genetically complex trait in herbaceous peonies (Paeonia lactiflora Pall.); however, specific genes related to metabolic and regulatory networks remain scantily studied. Our study integrated metabolite profiling and RNA-sequencing to screen floral scent biosynthetic genes. Hence, the major molecules identified by headspace collection combined with cultivar-specific GC-MS analysis were geraniol, β-caryophyllene, 2-phenylethanol (2-PE), citronellol, and 1,8-cineole. Genes related to terpenoids and 2-PE biosynthesis were identified after the assembly and annotation of the P. lactiflora transcriptomes. Eight angiosperm-specific terpene synthases (TPSs) from the TPS-a and TPS-b clades, as well as enzymes linked to 2-PE synthesis such as aromatic amino acid decarboxylase (AADC), phenylacetaldehyde reductase (PAR), and geranial reductase (GER) were identified. The biochemical analysis of the enzymes encoded by PlPAR1 and PlGER1 generated 2-PE from phenylacetaldehyde (PAld). The pairwise alignment of AADC1 reveals a splice variant lacking a 124 bp fragment, thus highlighting the possible role of alternative splicing in modulating floral scent composition. This study offers insights into the molecular-level biosynthesis of terpenoids and 2-PE in Peonia taxa, and provides the basis for the functional haracterization, breeding, and bioengineering of prospective candidate genes for the production of floral volatiles in the Paeonia genus.
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    Wind power density characterization in arid and semi-arid Taita-Taveta and Garissa counties of Kenya.
    (Elsevier, 2023-12) Rotich, Ibrahim Kipngeno; Musyimi, Peter K.
    Wind Power Density (WPD) is a crucial parameter that can be used in assessing the potential of a given site for energy development and determining the suitability of wind turbine installation. A 7-year long-term data (2014–2020) of temperature, relative humidity, and wind speeds were obtained from Voi and Garissa synoptic station with a 3-h resolution. The objective of the study was to characterize wind power density in selected arid regions in Kenya. Analysis was performed using Weibull distribution parameters statistical tools i.e. Moment of Methods, Empirical Method (Justus), and Empirical Method (Lyssen), and error analysis using Mean Absolute Percentage Error, Mean Absolute Deviation (MAD), Coefficient of determination (R2) and Root Mean squared Error to determine the WPD accurate characteristics. Results show that Moment of Methods (MoM) performed better compared to other statistical tools, while the Taita Taveta had a better coefficient of Variance (CoV) ranging between 0.20 and 0.28% compared to 0.28–0.43% in Garissa. Based on the wind power density, the sites were found to be within Class II on the wind power classification from IEC and thus not viable for commercial power purposes. Results imply that power produced can be used in supplementing Kenya Offgrid Solar Access Project (KoSAP) which supplements power production used in gazetted marginalized counties by Kenya Power.
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    Crop Yield Estimation Using Sentinel-3 SLSTR, Soil Data, and Topographic Features Combined with Machine Learning Modeling: A Case Study of Nepal
    (AgriEngineering, 2023-10-09) Sahbeni, Ghada; Székely, Balázs; Musyimi , Peter K.; Timár, Gábor; Sahajpal, Ritvik
    Effective crop monitoring and accurate yield estimation are fundamental for informed decision-making in agricultural management. In this context, the present research focuses on estimating wheat yield in Nepal at the district level by combining Sentinel-3 SLSTR imagery with soil data and topographic features. Due to Nepal’s high-relief terrain, its districts exhibit diverse geographic and soil properties, leading to a wide range of yields, which poses challenges for modeling efforts. In light of this, we evaluated the performance of two machine learning algorithms, namely, the gradient boosting machine (GBM) and the extreme gradient boosting (XGBoost). The results demonstrated the superiority of the XGBoost-based model, achieving a determination coefficient (R2) of 0.89 and an RMSE of 0.3 t/ha for training, with an R2 of 0.61 and an RMSE of 0.42 t/ha for testing. The calibrated model improved the overall accuracy of yield estimates by up to 10% compared to GBM. Notably, total nitrogen content, slope, total column water vapor (TCWV), organic matter, and fractional vegetation cover (FVC) significantly influenced the predicted values. This study highlights the effectiveness of combining multi-source data and Sentinel-3 SLSTR, particularly proposing XGBoost as an alternative tool for accurately estimating yield at lower costs. Consequently, the findings suggest comprehensive and robust estimation models for spatially explicit yield forecasting and near-future yield projection using satellite data acquired two months before harvest. Future work can focus on assessing the suitability of agronomic practices in the region, thereby contributing to the early detection of yield anomalies and ensuring food security at the national level.

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