http://hdl.handle.net/123456789/6743 2026-04-04T19:33:13Z 2026-04-04T19:33:13Z Enoch Santeto Kuntai, Jared Ombiro Gwaro, Ogaro Elijah Nyakang’o http://hdl.handle.net/123456789/19043 2026-02-13T07:48:44Z 2026-02-01T00:00:00Z

The role of atomic coherence in enhanced absorption line-profile Enoch Santeto Kuntai, Jared Ombiro Gwaro, Ogaro Elijah Nyakang’o In this paper, we present a theoretical analysis of the burning of a narrow transparency window on the enhanced absorption spectrum of a probe laser in a triple resonance configuration visualized as a V-type plus a two-level system. The transparency window is due to coherence effects causing electromagnetically induced transparency (EIT) in a V-type system (i.e. Autler–Townes splitting plus quantum interference), for atomic vapor at a finite temperature. Additionally, the EIT window is enhanced by velocity induced population oscillation effect, velocity selective saturation effect, optical pumping effect (a source of ground state level population dynamics) and wavelength mismatch of the resonant laser fields forming a V-type configuration.

2026-02-01T00:00:00Z Josephate O. Bosire · Aloys M. Osano · Justin K. Maghanga · Patricia B.C. Forbes http://hdl.handle.net/123456789/18603 2026-01-14T07:22:57Z 2023-02-01T00:00:00Z

Assessment of Local Domestic Solid Fuel Sources: A Kenyan Case Study in Kisii, Bomet and Narok Counties Josephate O. Bosire · Aloys M. Osano · Justin K. Maghanga · Patricia B.C. Forbes Proximate analyses and decomposition profiles of solid fuels commonly used in Kenya were studied to determine their relative suitability for use as a clean and efficient source of energy in households. The moisture, volatile matter, ash, and fixed carbon content of firewood, charcoal, and briquette samples were investigated, as well as their decomposition profiles under various temperature regimes. Except for the ash content of the briquette sample, which deviated slightly likely due to the presence of binders, all the values were within acceptable limits according to International Energy Agency and World Health Organization. Decomposition profiles revealed that mass change during combustion tends to occur primarily between 350 and 500 °C once the majority of the volatiles had been released. Briquette samples proved to be the most dependable and suitable household fuel due to their longer combustion time and lower volatile matter content, implying lower emissions

2023-02-01T00:00:00Z Saaya Abel Kanai Wilson Ombati Robinson Ndegwa Jared Ombiro Gwaro http://hdl.handle.net/123456789/18602 2026-01-14T07:17:49Z 2025-01-01T00:00:00Z

Application of Raman Spectroscopy Coupled With Chemometrics for the Detection and Quantification of Mancozeb Residues in Collard Green Saaya Abel Kanai Wilson Ombati Robinson Ndegwa Jared Ombiro Gwaro The presence of pesticide residues in food crops poses serious health concerns, necessitating precise, rapid and accessible detection techniques. This study investigates the use of Raman spectroscopy combined with advanced data analysis techniques to detect and quantify Mancozeb residues in collard greens. The primary objective was to evaluate the viability of this approach for accurate pesticide residue monitoring in leafy vegetables. Raman spectral data were collected and preprocessed using a standard normalization technique to reduce spectral noise and enhance signal quality. Dimensionality reduction was achieved through a statistical method that extracted key spectral features and successfully differentiated control from treated samples, explaining a combined variance of 86% across the first two principal components. Graphical score plots revealed clear clustering patterns across various residue concentrations, ranging from 0.01 to 0.5 parts per million, with samples categorized according to regulatory residue limits. To further assess predictive capability, several machine learning models were developed for classification and quantification, including deep learning–based and ensemble-based approaches. Among these, the support vector model achieved the highest classification precision of 95% and demonstrated strong calibration and prediction accuracy. A convolutional neural network achieved 99% training accuracy and 98% testing accuracy, effectively recognizing complex spectral patterns. Statistical validation using analysis of variance confirmed that the observed model differences were significant, supporting the robustness of the selected algorithms. The proposed method accurately quantified Mancozeb residues within the tested range and demonstrated high sensitivity even at low concentration levels. This study highlights the potential of Raman spectroscopy, integrated with computational modelling, as a non-destructive, fast and cost-effective tool for pesticide residue detection in food safety applications.

2025-01-01T00:00:00Z Marwa Hassan Chacha, Joseph Ouno, Boniface Kwach,Cornelius Nyakundi http://hdl.handle.net/123456789/18479 2025-12-10T07:42:48Z 2025-01-01T00:00:00Z

Modelling Spatial and Non-Linear Trends in Climate Data Using Gaussian Process Regression and Generalized Additive Model Marwa Hassan Chacha, Joseph Ouno, Boniface Kwach,Cornelius Nyakundi Accurate modeling of climate variability is critical for understanding the impacts of climate change and supporting data-driven adaptation strategies. Traditional parametric models, while widely used, often struggle to capture the complex non-linear relationships and spatial dependencies that characterize climate systems, especially in regions with diverse geography such as Kenya. This study aimed to apply two non-parametric statistical approaches—Generalized Additive Models (GAM) and Gaussian Process Regression (GPR)—to model spatial and non-linear trends in climate data over Kenya. Daily climate variables, including temperature and precipitation, were obtained from the ERA5-Land dataset using Google Earth Engine, spanning the period from 2015 to 2024. GAM was used to model the smooth effects of covariates such as time, elevation, and precipitation, while GPR was implemented using a Mat´ern covariance kernel to capture residual spatial autocorrelation. The models were evaluated using RMSE, MAE, and 2 , and parameter estimation was conducted via penalized likelihood and L-BFGS optimization techniques. The results demonstrated that GAM effectively captured structured non-linear effects and provided interpretable smooth functions, while GPR performed better in modeling spatial variability and uncertainty. Both models outperformed traditional linear approaches, with GPR offering superior accuracy in areas with high spatial heterogeneity. The findings affirm that GAM and GPR are powerful and complementary tools for climate modeling in complex environmental contexts. In conclusion, this study confirms the suitability of non-parametric approaches for climate modeling in data-rich, spatially heterogeneous settings. Further research is recommended to explore integrated hybrid GAM–GPR models, extend the methodology to multivariate climate indicators, and evaluate its performance in other regions or under future climate scenarios.

2025-01-01T00:00:00Z