School of Physical and Mathematical Sciences
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Item Exploring soil pollution patterns in Ghana’s northeastern mining zone using machine learning models(Journal of Hazardous Materials Advances, 2025) Kwayisi, D.; Kazapoe, R.W.; Alidu, S.; et al.This study assessed the pollution status and effectiveness of machine learning models in predicting pollution indices in soils from a mining area in Northeastern Ghana. 552 soil samples were analysed with an Energy Dispersive X-ray Fluorescence (ED-XRF) spectrometer for their elemental concentrations. Four pollution indices; Nemerow Integrated Pollution Index (NIPI), degree of contamination (Cdeg), modified degree of contamination (mCdeg) and Pollution Load Index (PLI). Additionally, the Multivariate Adaptive Regression Splines (MARS) machine learning approach were used. The high CV%, skewness, and kurtosis values show a high degree of variability and uneven distribution patterns which denotes dispersed hotspots that can be interpreted as an influence of gold anomalies and illegal mining activities in the area. V (120.86 mg/L), Cr (242.42 mg/L), Co (30.92 mg/L) Ba (337.62 mg/L), and Zn (35.42 mg/L) recorded values higher than the global and regional contaminant thresholds. The NIPI shows that 46.74% and 26.81% of samples are slightly and moderately polluted respectively. The Cdeg analysis supports these findings, with 36.96% and 41.49% of samples classified as having “moderate” to “considerable” contamination, respectively. The PLI indicates progressive soil quality deterioration (43.84%) of samples reflecting substantial environmental disturbance. The pollution indices show the effect of illegal mining on Shaega, Buin and other areas in the eastern boundary of the study. The MARS models developed for the study demonstrated high predictive capabilities with an R2 value of 0.9665 for model 1 (NIPI), and RMSE and MAE values of 0.8227 and 0.4287 respectively. For model 2 (Cdeg), R2 value of 0.9863, RMSE and MAE of 1.0416 and 0.6181, respectively. Model 3 (mCdeg) produced an R2 value of 0.9844, RMSE and MAE of 0.1225 and 0.0670. These findings suggest MARS models can be an integral tool for soil quality analysis in cooperation with pollution indices. The study suggests that remedial and legislative measures be implemented to address the issue of illegal mining in the area.Item Exploring soil pollution patterns in Ghana’s northeastern mining zone using machine learning models(Journal of Hazardous Materials Advances, 2024) Kwayisi, D.; Kazapoe, R.W.; Alidu, S.; et al.This study assessed the pollution status and effectiveness of machine learning models in predicting pollution indices in soils from a mining area in Northeastern Ghana. 552 soil samples were analysed with an Energy Dispersive X-ray Fluorescence (ED-XRF) spectrometer for their elemental concentrations. Four pollution indices; Nemerow Integrated Pollution Index (NIPI), degree of contamination (Cdeg), modified degree of contamination (mCdeg) and Pollution Load Index (PLI). Additionally, the Multivariate Adaptive Regression Splines (MARS) machine learning approach were used. The high CV%, skewness, and kurtosis values show a high degree of variability and uneven distribution patterns which denotes dispersed hotspots that can be interpreted as an influence of gold anomalies and illegal mining activities in the area. V (120.86 mg/L), Cr (242.42 mg/L), Co (30.92 mg/L) Ba (337.62 mg/L), and Zn (35.42 mg/L) recorded values higher than the global and regional contaminant thresholds. The NIPI shows that 46.74% and 26.81% of samples are slightly and moderately polluted respectively. The Cdeg analysis supports these findings, with 36.96% and 41.49% of samples classified as having “moderate” to “considerable” contamination, respectively. The PLI indicates progressive soil quality deterioration (43.84%) of samples reflecting substantial environmental disturbance. The pollution indices show the effect of illegal mining on Shaega, Buin and other areas in the eastern boundary of the study. The MARS models developed for the study demonstrated high predictive capabilities with an R2 value of 0.9665 for model 1 (NIPI), and RMSE and MAE values of 0.8227 and 0.4287 respectively. For model 2 (Cdeg), R2 value of 0.9863, RMSE and MAE of 1.0416 and 0.6181, respectively. Model 3 (mCdeg) produced an R2 value of 0.9844, RMSE and MAE of 0.1225 and 0.0670. These findings suggest MARS models can be an integral tool for soil quality analysis in cooperation with pollution indices. The study suggests that remedial and legislative measures be implemented to address the issue of illegal mining in the area.Item Crustal evolution of alternating Paleoproterozoic belts and basins in the Birimian terrane in southeastern West African Craton(Journal of African Earth Sciences, 2024) Sakyi, P.A.; Kwayisi, D.; Nunoo, S.; et al.We present a comprehensive review of available geochemical, geochronological and isotopic data on granitoids from the Paleoproterozoic Birimian terrane of Ghana, aimed at providing an in-depth understanding of the geodynamic evolution of southeastern West African Craton. The focus is on plutonic magmatism, crustal recy cling and tectonic setting of the granitoids. The granitoids are mainly TTG suites, calc-alkaline granites, diorites, monzonites, two-mica granites and leucogranites. They are characterized by enrichments in LILE and LREE relative to HREE and HFSE. Their variable positive and negative Eu and Sr anomalies and depletions in Nb-Ta and Ti suggest the presence of residual minerals like hornblende and Fe-Ti oxides (e.g., rutile and ilmenite). The plutons probably formed by partial melting of hydrous basaltic/mafic crust metasomatized by slab-derived melts at different depths. The εHf (− 14.5 to +7.6) and εNd (− 5.3 to +3.5) values and Nd model ages (2.21–2.53 Ga) indicate their crystallization from juvenile magmas derived from a depleted mantle with significant recycling of older crustal material. The older (≥2200 Ma) and younger (<2100 Ma) ages recorded in both belt- and basin type granitoids indicate that magmatism in both types was contemporaneous. Nonetheless, the basins recorded younger peak emplacement ages compared to adjacent belts. The presence of inherited older zircon grains (Archean zircon cores?), is widespread in southeastern WAC. The granitoids formed in a continental arc setting via subduction–accretion processes. Furthermore, the magmatic time-span is more prolonged in southern Ghana, with the sedimentary basins recording the longest intervals of magma emplacement. The sub-chondritic εHf data and Hf model ages strongly suggest the existence of Neoarchean to Mesoarchean crustal material in eastern Ghana during the Birimian crust formation. We propose that the subduction-accretion processes during the Paleoproterozoic Eburnean orogeny in the WAC contributed to the formation of the Columbia supercontinent in the Late Paleoproterozoic-Mesoproterozoic.Item Determinants of under-fve mortality in informal settlements in Nairobi, Kenya from 2002 to 2018(BMC Public Health, 2024) Iddi, S.; Akeyo, D.; Sanya, R.E.; Wamukoya, M.; Asiki, G.Background Childhood mortality persists as a significant public health challenge in low and middle-income countries and is uneven within countries, with poor communities such as urban informal settlements bearing the highest burden. There is limited literature from urban informal settlements on the risk factors of mortality. We assessed under-five mortality and associated risk factors from the period 2002 to 2018 in Nairobi urban informal settlements. Methods We used secondary data from the Nairobi Urban Health and Demographic Surveillance System (NUH DSS), a longitudinal surveillance platform that routinely collects individual and household-level data in two informal settlements (Viwandani and Korogocho) in Nairobi, Kenya. We used Kaplan-Meier curves to estimate overall survival and the Cox proportional hazard model with a frailty term to evaluate the impact of risk factors on survival time. Results Overall under-five survival rate was 96.8% and this improved from 82.6% (2002-2006) to 95% (2007-2012) and 98.4% (2012-2018). There was a reduced risk of mortality among children who had BCG vaccination, those born to a married mother or a mother not engaging in any income-generating activity (both from 2007 to 2011), children from singleton pregnancy, children born in Viwandani slum and ethnicity of the child. Conclusion Under-five mortality is still high in urban informal settlements. Targeted public health interventions such as vaccinations and interventions empowering women such as single mothers, those with multiple pregnancies, and more impoverished slums are needed to further reduce under-five mortality in urban informal settlements.Item Does the environmental Phillips curve hypothesis hold within the Ghanaian context?(Scientific African, 2024) Addison, R.; Akutcha, E.; Debrah, G.This study examines the relationship between environmental quality and unemployment in Ghana using annual data spanning the period from 1990 to 2019. It also assesses the impact of gender-segregated unemployment rate on environmental quality. The study employed the Autoregressive Distributive Lag (ARDL) error correction model to estimate the relationship among the variables. In addition, the Fully Modified Ordinary Least Squares (FMOLS) and the Dynamic Ordinary Least Squares (DOLS) estimation procedures were employed to check for robustness of the ARDL results. Findings indicate a positive effect of total unemployment rate on environmental quality in Ghana in the long-run and also in the short-run. In the case of the gender-segregated unemployment, the findings reveal that in both short-run and long-run, a rise in female unemployment causes a deterioration in environmental quality in Ghana. The results also validated the Environmental Phillips Curve (EPC) hypothesis in the case of male unemployment. Thus, given that there is no general pattern in the findings, the study concludes that the Environmental Phillips Curve (EPC) hypothesis does not hold within the Ghanaian context.Item Geological evaluation of black shale as a suitable Supplementary Cementitious Material (SCM) to optimize the use of clinker in cement production(Heliyon, 2024) Nunoo, S.; Owusu-Sasu, T.A.; Amponsah, P. O.; et al.Faced with challenges like resource depletion and climate change, the cement industry needs sustainable solutions. This study explores the potential of geologically-delinaeated black shale from Apersua, Ghana, as a supplementary cementitious material (SCM) to reduce reliance on traditional methods. The researchers analysed the shale’s chemical composition and mineralogy, then created laboratory cement formulations with varying black shale content. These were compared to standard formulations without shale. The results show cement with black shale has comparable compressive strength, meeting standard requirements. Even a formulation with only black shale (excluding limestone, a common ingredient) passed strength tests. Overall, the black shale demonstrated good potential as a SCM based on strength, chemical makeup, setting time, and its possible contribution to durability. This research suggests that black shales from Apersua are worth exploring further as a sustainable and potentially cost-effective alternative in cement production.Item A cascading approach using se-resnext, resnet and feature pyramid network for kidney tumor segmentation(Heliyon, 2024) Appati, J. K.; Yirenkyi, I. A.Accurate segmentation of kidney tumors in CT images is very important in the diagnosis of kidney cancer. Automatic semantic segmentation of the kidney tumor has shown promising results to wards developing advance surgical planning techniques in the treatment of kidney tumor. However, the relatively small size of kidney tumor volume in comparison to the overall kidney volume, and its irregular distribution and shape makes it difficult to accurately segment the tu mors. In addressing this issue, we proposed a coarse to fine segmentation which leverages on transfer learning using SE-ResNeXt model for the initial segmentation and ResNet and Feature Pyramid Network for the final segmentation. The processes are related and the output of the initial results was used for the final training. We trained and evaluated our method on the KITS19 dataset and achieved a dice score of 0.7388 and Jaccard score 0.7321 for the final segmentation demonstrating promising results when compared to other approaches.Item Multi-method machine learning techniques in gold pathfinder elements prediction in central parts of Tanzania using stream sediment geochemical data(Physics and Chemistry of the Earth, 2024) Nunoo, S.; Abu, M.; Ayitey, E.; Mvile, B. N.; Kalimenze, J. D.Prediction models using machine learning techniques have proven to be a reliable technique in mineral explo ration. A combination of these techniques is very robust and reliable in exploration targeting and much dependable as an approach in greenfield. In this study, multi-machine learning methods: random forest (RF), support vector machine (SVM), and artificial neural network (ANN) were employed to conduct a data-driven gold (Au) prospectivity modelling in the Central parts of the Tanzania Craton (TC). A total of 166 samples with Au concentrations from stream sediment samples were considered. Based on the modeling results, the RF model demonstrates superior prediction accuracy compared to the SVM (MSE of 0.89) and ANN models (MSE of 1.21), achieving an MSE of less than 0.82. In terms of overall predictive performance and efficiency, the RF model outperforms other ML models deployed in this research. Therefore, it is deemed the suitable model for gold (Au) prediction in the TC catchments. According to the geological interpretation derived from the model, anomalies in arsenic (As), nickel (Ni), and tungsten (W) now emerge as significant predictors in the quest for gold. This implies that the association of As–Ni–W are potential pathfinder elements in the exploration of gold in the central part of the TC.Item Enhancing corporate bankruptcy prediction via a hybrid genetic algorithm and domain adaptation learning architecture.(Expert Systems With Applications., 2024-08-15) Narh,A.T.; Nortey,N.N.E.; Adzri,P.E.; Sarkodie,O.R.In the contemporary business landscape, accurately evaluating a company’s financial health is essential for stakeholders to mitigate risks and avert bankruptcy. This study presents an innovative approach to improving business bankruptcy prediction through the hybrid integration of Domain Adaptation Learning (DAL) and Genetic Algorithm (GA) techniques. The hybrid model harnesses DAL to address distributional changes in the real world scenarios and utilize GA’s proficiency in feature selection. Six machine learning models are rigorously evaluated against the proposed hybrid model: Random Forest (RF), Support Vector Machine (SVM), Logistic Regression (LR), Gradient Boosting (GB), k-Nearest Neighbours (k-NN), and Stacking Ensemble (SE). Our hybrid model performs well on imbalanced target datasets using the Area Under the Precision–Recall Curve metric: 0.93 (RF), 0.93 (SVM), 0.89 (LR), 0.91 (GB), 0.88 (k-NN), and 0.92 (SE). These findings highlight the model’s ability to overcome the limitations of traditional approaches, offering a more reliable predictive framework for stakeholders to make informed decisions and proactively manage financial stability. Future research directions may explore the applicability of this hybrid model across different industries and the integration of additional techniques to further enhance its performance.Item Synthesis and Biological Properties of Ferrocenyl and Organic Methotrexate Derivatives(ACS Omega, 2024) Rózga, K.; Błauz, A.; Ayine-Tora, D.M.; et al.Synthesis and biological activity of two series of modified side chain methotrexate (MTX) derivatives are presented, one with a ferrocenyl moiety inserted between the pteroyl and glutamate portions of the molecule and the other with glutamate substituted for short chain amino acids. Ferrocenyl derivatives of MTX turned out to be rather moderate inhibitors of dihydrofolate reductase (DHFR) although molecular modeling suggested more effective interactions between these compounds and the target enzyme. More interestingly, ferrocene-decorated MTX derivatives were able to impede the proliferation of four murine and human cell lines as well as their methotrexate-resistant counterparts, overcoming the multidrug resistance (MDR) barrier. They were also able to directly interact with Abcc1, an MDR protein. Of the amino acid pteroyl conjugates, the γ-aminobutyric acid derivative was an efficient inhibitor of DHFR but had no effect on cell proliferation in the concentration range studied while a taurine conjugate was a poor DHFR inhibitor but able to affect cell viability. We postulate that modification of the methotrexate side chain may be an efficient strategy to overcome efflux-dependent methotrexate resistance.