School of Pure and Applied Sciences

A Relativistic Approach to Structure Formation and Evolution in a Friedmann Universe

2026-04-22T13:08:16Z

A Relativistic Approach to Structure Formation and Evolution in a Friedmann Universe Konga, Kennedy Kamuren The advent of modern satellite technology has transformed observational astronomy and astrophysics, offering unprecedented insights into the large-scale behavior of gravitation and challenging established cosmological models. This technological progress has reinvigorated the study of relativistic cosmology, leading to a critical reassessment of foundational assumptions, particularly the cosmological principle, which posits that the universe is homogeneous and isotropic on large scales. While this principle underpins the Standard Cosmological Model (SCM) and the Friedmann-Lemaitre-Robertson-Walker (FLRW) metric, emerging data has increasingly been challenging its validity. Central to this investigation are the redshift-distance and light intensity-distance relations, essential for testing cosmological models. The integration of both parametric and nonparametric redshift models provides a more comprehensive analysis, addressing discrepancies in our understanding of the universe's structure and evolution. However, unresolved mysteries, particularly concerning dark matter and dark energy, complicate these models. This research critically examines the cosmological principle using the latest observational data and scrutinizes the Friedmann model's assumptions. The study reveals that galaxy formation occurred most rapidly in the early universe, particularly within the redshift range of 0 < 𝑧 < 0.4, peaking around 𝑧 ≈ 0.8. It also highlights that dark matter plays a significantly more critical role than dark energy in this process. While dark energy primarily affects the large-scale expansion of the universe, dark matter seems to dominate local galaxy formation and the evolution of cosmic structures. These findings underscore the limitations of current models and contribute to the ongoing refinement of cosmological theories, offering a clearer understanding of the universe’s evolution.

Application of Machine Learning Models for Estimation of Spatial Distribution of Particulate Matter Pollutant in Air

2026-04-18T10:41:30Z

Application of Machine Learning Models for Estimation of Spatial Distribution of Particulate Matter Pollutant in Air Kimuyaa, Alex Mwololo Air pollution, particularly particulate matter, poses significant health and environmental risks. Conventional particulate matter measurement systems provide only single-point instantaneous readings, limiting their ability to generate spatial distribution maps. This study applies machine learning techniques to estimate particulate matter distribution using data from a limited number of sensor nodes. These nsors collected data on PM1.0, PM2.5, and PM10 concentrations, alongside weather parameters (wind speed, temperature, and humidity) and spatial information (longitude, latitude). Various machine learning models, including Artificial Neural Networks, Support Vector Regression, Long Short-Term Memory, and Random Forest, were evaluated for particulate matter distribution prediction. The analysis revealed that Artificial Neural Networks consistently outperformed other models across various feature configurations for predicting particulate matter concentrations. When considering only geometric features (Euclidean_D and Orientation), the Artificial Neural Networks model achieved training scores ranging from 0.9746 to 0.9790(with features: wind speed, temperature, geometric features) for PM1.0 prediction. Similar trends were observed for PM2.5 and PM10, with Artificial Neural Network straining scores between 0.9766 (geometric features only) and 0.9805 (humidity, geometric features) for PM2.5, and 0.9792 (geometric features only) to 0.9798(wind speed, temperature, geometric features) for PM10. Validation R 2 scores remained impressive for Artificial Neural Networks, ranging from 0.9711 to 0.9756 (PM1.0), 0.9692 to 0.9789 (PM2.5), and 0.9775 to 0.9793 (PM10) under the respective particulate matter scales feature combinations. For generalizability on unseen data, Artificial Neural Networks also achieved the highest average prediction accuracy, exceeding 75% consistently across all feature combinations for PM1.0, PM2.5, and PM10 concentrations within a 160-meter radius from a central sensor. Further, the findings revealed that incorporating the weather parameters significantly improved model performance, reducing both RMSE and MAE. Specifically, PM1.0 RMSE decreased from 1.8719 µg/m3 to 1.7201 µg/m3, and MAE from 0.8125 µg/m3 to 0.7952µg/m3. For PM2.5, RMSE reduced from 2.5260 µg/m3 to 2.2139 µg/m3, and MAE from 1.1488 µg/m3 to 1.0012 µg/m3. Lastly, PM10 RMSE decreased from 2.231µg/m3 to 2.143 µg/m3, and MAE from 1.1120 µg/m3 to 1.0037 µg/m3. This research demonstrates a machine learning-based approach to overcome limitations of single- point particulate matter sensors and predict particulate matter distribution across aregion. The research results highlight the effectiveness of Artificial Neural Networks, achieving high accuracy and emphasizing the value of including weather data in model training for improved particulate matter distribution prediction. The proposed approach offers a practical solution for real-world air quality monitoring by enabling data-driven decision-making in pollution management, optimizing sensor placement strategies, and facilitating more accurate identification of pollution hotspots for targeted interventions. The study findings suggest a new approach to designing particulate matter sensors, overcoming limitations of single-point measurement, and emphasize the importance of including weather parameters in machine learning model training for spatial distribution prediction.

Effect of Dolomite on Strength, Rheology and Microstructure of Ternary Blended Cement

2026-04-18T09:10:58Z

Effect of Dolomite on Strength, Rheology and Microstructure of Ternary Blended Cement Masolia, Cleah Minayo Supplementary cementitious materials (SCMs) have long been used in cement production to partially replace clinker, reducing CO₂ emissions and production costs while enhancing sustainability. Materials such as fly ash, silica fume and slag have been widely incorporated into cement formulations, improving performance and durability. However, the availability of these SCMs is diminishing due to declining industrial by-products prompting the search for alternative sources. A promising approach is the use of abundant calcined clay combined with limestone, forming a blended system known as Limestone Calcined Clay Cement (LC3 ). This technology has gained attention as a viable solution to clinker reduction, offering comparable performance while utilizing widely available raw materials. In Africa, particularly in Kenya, the adoption of LC3 technology faces a major challenge: the absence of high-quality limestone. Unlike regions with abundant pure limestone, many African countries primarily have low-grade limestone deposits, including dolomite. Inadequate high-quality limestone sources means that large-scale implementation would necessitate importing it, potentially increasing costs and limiting feasibility. Additionally, the low-quality limestone (dolomite) cannot be used in clinker production since its magnesium carbonate decompose to magnesium oxide that is associated with soundness during hydration. An alternative approach is to explore the use of locally available dolomite when it is not calcined to make a blended cement. In this study, the potential of incorporating dolomite in a ternary blended cement system alongside calcined clay was investigated. A replacement level of 45% was adopted, maintaining a dolomite-to-calcined clay ratio of 1:2. The study assessed the effect of combined clay and dolomite on cement strength, rheology and microstructure. The tested parameters were evaluated using compressive strength and rheometer machine, phase composition through Xray Diffraction (XRD) analysis and Scanning Electron Microscopy (SEM) coupled with Energy Dispersive X-ray Spectroscopy (EDS). The pore was examined using Fiji, an opensource image analysis software program to refine the blends based on the SEM images. Compressive strength of 38 MPa after the 28th day was achieved relatively lower than OPC at 42.5MPa. Shear thinning property and low yield stress was obtained for the dolomitic blends indicating easy workability of the material. The results further showed a synergy between dolomite/limestone and calcined clay whereby additional phases formed on the 28th day including Hemicarboaluminate (Hc) and Monocarboaluminate (Mc). Higher pore refinement was observed with dolomite blends. The results showed that blending dolomite and calcined clay contributes significantly to mechanical properties in the same way as limestone and calcined clay. Microstructural analysis revealed a well-developed cement matrix with promising durability characteristics. These findings suggest that dolomite, when utilized serves as a viable alternative to high-purity limestone in ternary cement formulations, providing a sustainable and locally adaptable solution for clinker reduction in Africa. Dolomite is a suitable clinker substitute to clinker as it contributes to strength development of a ternary blended cement and also it has low yield stress thus enhanced workability.

Evaluation of Physicochemical, Antibacterial, Sensory Properties and Shelf Life of a Bath Soap Containing Camel Milk Cream

2026-04-18T08:45:32Z

Evaluation of Physicochemical, Antibacterial, Sensory Properties and Shelf Life of a Bath Soap Containing Camel Milk Cream Oginga, Elly The growing interest in therapeutic and natural personal care products has fuelled the study for alternative to synthetic ingredients. Camel milk being rich in proteins, vitamins, and antimicrobial properties, is a promising active ingredient for skin care formulations. This study, therefore, explored the possibility of using camel milk creaming bath soap formulation and analysed its physicochemical properties, antibacterial activity and sensory acceptability. Bath soaps were produced using the cold saponification process using camel milk cream, palm oil, and coconut oil as primary ingredients. Fresh camel milk and cream were subjected to compositional and quality analyses, including density (Lactometer), fat content (Gerber method), and protein content (Kjeldahl method). The saponification reaction and resultant soap formulations were further analysed using Fourier Transform Infrared Spectroscopy (FTIR) to monitor functional group changes and confirm reaction completion. The formulated soaps were characterized for physicochemical properties such as pH (multipara meter pH meter), total fatty matter (gravimetric method), moisture content (oven-drying at 110 °C), foam stability (cylinder shake test), hardness (cone penetrometer) and alkali content (acid–base titration). Shelf life was evaluated over 8 weeks by monitoring pH, alkali content, and total fatty matter. Antibacterial activity against S. aureus and E. coli was determined using the agar well diffusion method, with inhibition zones analysed by Duncan’s multiple range Test (p< 0.05) antibacterial activity. The sensory evaluation indicated high consumer acceptability, with positive feedback on the soap's skin-smoothing and moisturizing effects. All the parameters analysed were compared with commercial soaps such as Imperial leather. Remarkably, the soap formulatedwith20% coconut oil and 20% camel milk cream showed a balanced physicochemical properties and antibacterial activities comparable to that of Dettol. These findings suggest that camel milk cream can serve as an effective natural alternative to synthetic antibacterial agents used in commercial soaps.