School of Physical Sciences (Phys-Sciences) Collections
Permanent URI for this collection
Browse
Recent Submissions
1 - 5 of 236
-
ItemA model for the hydroxyl radical in the mesosphere during solar minimum(Makerere Univeristy, 2025)Using particle fluxes from NOAA POES, atmospheric constituents from Aura MLS, and geomagnetic indices and solar wind parameters from CDAWeb, this study develops pre- dictive regression models for mesospheric hydroxyl radical (OH) variability at 62–78 km and 45–80° N during the 2007 - 2009 solar minimum period, with model development focused on 2008. Daily-mean data were processed and correlation analyses performed to identify dominant predictors. Both multiple linear and second-order polynomial regres- sion approaches were applied, and the polynomial models explained 66 – 77 % of OH variability, exceeding the performance of linear models. Validation with independent 2007 data showed high correlation coefficients (r = 0.72–0.79) and low mean-squared prediction errors (MSEP = 0.02–1.90), confirming model robust- ness. These results demonstrate that geomagnetic and solar wind parameters can reliably reproduce mesospheric OH variations linked to particle precipitation. Although the regres- sion coefficients were valid for the solar minimum period, the models provide a foundation for extending OH records beyond the Aura MLS era, supporting ongoing studies of ozone chemistry and climate–space weather interactions.
-
ItemSynthesis and characterization of iron based magnetic nanocomposites for removal of diclofenac and sulfamethoxazole from water(Makerere University, 2025)The continued presence of pharmaceutical contaminants such as diclofenac and sulfamethoxazole in drinking water sources, poses a threat to public health. Although iron oxide (Fe3O₄) nanoparticles have been studied for the treatment of water, their low adsorption efficiency and poor durability preclude their widespread application. This study designed and employed three novel iron oxide-based magnetic nanocomposites; Fe3O₄/SnO₂-MgO, Fe3O₄/SnO₂-AlO₃, and Fe3O₄/SnO₂-CeO₂ to enhance the removal of diclofenac and sulfamethoxazole. The nanocomposites were produced by co-precipitation method and characterized using XRD, SEM/EDX, MPMS, ImageJ, and point of zero charge techniques. Response Surface Methodology (RSM) was used to optimize the adsorption parameters, such as contact duration, adsorbent dose, pH, and initial concentration. Among the materials that were produced, Fe₃O₄/SnO₂-MgO and Fe₃O₄/SnO₂-CeO₂ shown exceptional removal efficiency for diclofenac (97.31 ± 0.04 % and 93.53 ± 0.02 %, respectively), whereas Fe₃O₄/SnO₂-MgO and Fe₃O₄/SnO₂-Al₂O₃ accomplished total removal (100.0 ± 0 %) of sulfamethoxazole. The iron oxide based magnetic nanocomposites displayed remarkable reusability, preserving high removal efficiencies for both diclofenac and sulfamethoxazole across three consecutive cycles, with only a minor drop in performance, demonstrating their promise for practical water treatment applications. For both diclofenac and sulfamethoxazole, kinetic studies used pseudo-second-order models (R2 ≥ 0.998) by the best-performing adsorbents (Fe₃O₄/SnO₂-MgO and Fe₃O₄/SnO₂-CeO₂ for diclofenac and Fe₃O₄/SnO₂-MgO and Fe₃O₄/SnO₂-Al₂O₃ for sulfamethoxazole), indicating chemisorption as the predominant mechanism. Monolayer coverage was suggested as the major mechanism using Langmuir isotherm models (R2 ≥ 0.99), which provided the best description of the diclofenac adsorption processes. For sulfamethoxazole, the Freundlich isotherm model (R2 ≥ 0.9978) shows multilayer adsorption on a heterogeneous surface of the best-performing adsorbents. The practical efficacy of the top-performing nanocomposites was also confirmed by application to actual water samples from Lake Victoria, where both contaminants were completely removed under optimal conditions. These results demonstrate the potential of iron oxide-based magnetic nanocomposites as sustainable and efficient adsorbents for enhancing the safety of drinking water sources tainted with pharmaceutical pollutants.
-
ItemEvaluation of radionuclide contaminations in water sources in Kaabong District(Makerere University, 2025)This study investigates the activity concentrations of naturally occurring radionuclides and associated radiological health risks in borehole groundwater from Kaabong District, Uganda. The work is driven by concerns over ionizing radiation exposure in areas underlain by granitic and metamorphic rocks enriched in uranium and thorium. Groundwater samples from five sub-counties were analyzed using gamma-ray spectrometry with a NaI(Tl) detector to quantify 226Ra, 232Th, and 40K. Radiological parameters including absorbed dose rate (D), radium equivalent activity (Raeq), internal and external hazard indices (Hin, Hex), annual effective dose equivalent (AEDE), and excess lifetime cancer risk (ELCR) were determined following ICRP and UNSCEAR standards. Results reveal spatial variations in radionuclide concentrations, with some boreholes exceeding international reference levels. The absorbed dose rate ranged from 32.96 to 106.18 nGy h−1 (mean: 67.93 nGy h−1 ), while AEDE values (0.20–0.65 mSv y−1 ; mean: 0.39 mSv y−1 ) surpassed the recommended global limit of 0.07 mSv y−1 . Radium equivalent activity (75.42–249.70 Bq kg−1 ; mean: 158.60 Bq kg−1 ) and hazard indices (Hin = 0.49, Hex = 0.43) remained below unity, indicating that groundwater use does not pose significant radiological danger. ELCR values ranged from 0.00085 × 10−3 to 0.00295 × 10−3 (mean: 0.002 × 10−3), all well below the world average of 0.29 × 10−3. The negligible lifetime cancer risk is attributed to the relatively low concentrations of the most radiotoxic nuclides (226Ra and 232Th), coupled with small ingestion dose conversion factors. The district’s geology, dominated by weathered gneisses and low-radioelement sedimentary formations, further limits radionuclide mobility into groundwater. Comparisons with studies from Nigeria, Ethiopia, and Saudi Arabia show that Kaabong District exhibits generally lower radiological hazard levels. The findings highlight the importance of regular groundwater monitoring, public awareness, and mitigation strategies to safeguard community health.
-
ItemLong-range contact process: theory and applications(Makerere University, 2025)We consider a general class of contact processes on a d-dimensional integer lattice (Zd), allowing for long-range interactions. By adapting classical renormalization arguments, we extend well-known results for the case where the infection parameter has a finite range to this more general setting under certain assumptions on the decay rate. Particularly, we show that a supercritical process remains supercritical after truncation of the interaction parameter at a sufficiently large distance. Further, for families of parameters satisfying this latter truncation property, we conclude that the probability of the process never to recover is continuous. To further assess the impact of long-range dynamics on complex networks, we extend this concept into environments that incorporate aging, cooperation, and competing strain models. Using discrete-time nonlinear dynamical systems, we show that contagion dynamics are highly sensitive to both environmental randomness and long-range couplings in both cooperative and competitive models. Furthermore, statistical analyses reveal that the epidemic survival significantly depends on the spatial decay exponent (α) and the scale-free graph exponent (γ). Particularly, these exert pronounced, nonlinear, and time-dependent effects on the survival of competing strains. Finally, by means of a mean-field analysis, we demonstrate that the survival function in a contact process with aging model depends on three exponents: the spatial decay exponent (α), the recovery exponent (δ), and the infectivity exponent (γ). We show that (α) predominantly controls the threshold behavior. However, as spatial interactions become increasingly localized, the temporal exponents (δ , γ) play a dominant role. In particular, slower recovery (δ < 1) enhances memory effects and spatial correlations, promoting infection persistence and lowering the critical contagion rate (λc), whereas faster recovery suppresses local clustering and raises the threshold. These results reveal how non-Markovian temporal dynamics and long-range spatial coupling interact to shape critical behavior in epidemic processes on complex networks.
-
ItemRegime-switching approaches for dynamic risk and dependence modeling of insurance claim frequency and severity(Makerere University, 2025)This study advances dynamic risk and dependence modeling in general insurance by applying regime-switching approaches that aim to accurately capture nonlinear, asymmetric, time-varying structures and regime shifts in claim frequency and severity, limitations often overlooked by traditional methods such as Pearson correlation, static copulas, and single-regime models. The Local Gaussian Correlation (LGC) framework is used to analyze monthly and weekly insurance severity data from Kenya and Norway. By combining LGC with Hidden Markov Models (LGC-HMM), the study reveals time-varying dependencies across different lines of business. Diagnostic checks using Auto Correlation Functions (ACFs) confirm the validity of the framework. Furthermore, comparisons of Value-at-Risk (VaR) and Tail Value-at-Risk (TVaR) show that LGC-HMM models achieve higher accuracy and exhibit asymmetric diversification benefits. For Claim Frequency modeling, weekly motor insurance data from Uganda, covering periods before, during, and after COVID-19, are analyzed using the Regime-Switching Integer-Valued Generalized Autoregressive Conditional Heteroskedasticity (RS-INGARCH) framework, estimated via the Extended Hamilton-Gray algorithm. Among the lag options, RS-INGARCH(1,1) is chosen for its simplicity and effectiveness. A similar analysis with Kenyan motor insurance data enhances regional generalizability. Comparisons with INAR(1) and INGARCH models indicate that RS-INGARCH provides improved in-sample fitting and out-of-sample forecasting, supported by appropriate residual diagnostics using ACFs and Ljung-Box tests. The findings highlight the need for regime-switching models to manage volatility and structural changes in insurance claims. The LGC-HMM framework aids dependence analysis, while RS-INGARCH enhances claim frequency modeling. Together, these approaches offer insurers and regulators valuable tools for solvency monitoring and riskbased decision-making, especially in developing markets facing uncertainty from regulatory reforms and systemic shocks like the COVID-19 pandemic.