University of Mines and Technology

Groundwater quality in the Voltaian aquifer of Krachi East Municipality, Oti Region, remains poorly understood, creating a gap in sustainable water resource management. Therefore, this study aimed to characterize groundwater chemistry, identify controlling processes, and assess its suitability for domestic and agricultural use. Sixteen groundwater samples were analyzed using hydrochemical parameters, spatial mapping, Gibbs diagrams, bivariate plots, and multivariate statistical techniques (factor analysis, correlation matrix, and cluster analysis). Results show slightly alkaline pH (6.85–7.87; mean = 7.32), low electrical conductivity (403–708 μS/cm, mean = 489 μS/cm), and low total dissolved solids (276–388 mg/L, mean = 363 mg/L). The dominant hydrochemical facies (81.25% of samples) was Na–HCO3 type. The WQI values ranged from 22.4 to 57.1, with an average of 33.4, confirming excellent quality for drinking. However, Na% ranged from 49.9 to 98.4% (average 81.6%), signalling high sodicity risk for irrigation. Spatial maps revealed heterogeneity in major ions, while Gibbs plots and saturation indices indicated that rock weathering, carbonate and silicate dissolution, and ion exchange are the primary geogenic processes. Anthropogenic influences, notably agriculture, also contribute to groundwater chemistry. These findings highlight that while the Voltaian aquifer provides safe drinking water, elevated sodium and magnesium levels pose challenges for long-term agricultural sustainability. The findings constitute the first integrated chemometric and geochemical modeling assessment in the Krachi East segment of the Voltaian Basin, clarifying dominant processes and revealing irrigation sodicity risks that are critical for sustainable groundwater management. The study offers critical insights into hydrogeochemical evolution and informs strategies for sustainable groundwater management in Krachi East Municipality.

Abstract Satellite imagery plays a crucial role in exploring land use inventories of urban areas. However, accurate land cover classification from satellite imagery remains a longstanding challenge. With recent advancements in artificial intelligence technology, Deep Learning algorithms have achieved success in understanding satellite images by means of Convolutional Neural Networks (CNNs). While there has been a notable emphasis on satellite image analysis to improve the accuracy of land cover classifications, it is imperative to emphasise the significance of data-driven optimisation techniques. This paper introduces a hybrid UNet-ResNet-50 architecture, which integrates the metaheuristic Particle Swarm Algorithm (PSA) in dynamic hyperparameter optimisation for multi-class semantic segmentation. The approach of this research leverages a UNet extractor with ResNet-50 backbone (UResNet-50) and augments it with a Particle Swarm Optimiser (PSO) to automate the hyperparameter tuning process for segmenting the DeepGlobe satellite dataset into seven meaningful classes, namely: urban, forest, rangeland, barren land, agriculture, water bodies and unknown. The PSO-UResNet-50 model demonstrated robust performance across four distinct locations, in terms of accuracy, precision, recall, F1-score and mIoU as follows: Location-1 (95.74%, 98.12%, 86.95%, 92.04%,88.17%); Location-2 (91.88%, 79.23%, 80.75%, 81.42%, 83.03%); Location-3 (99.44%, 93.97%, 87.42%, 88.68%, 90.77%); and Location-4 (96.20%, 94.03%, 89.75%, 92.16%, 88.97%). The proposed PSO-UResNet-50 model outperformed the conventional U-Net and hybrid UResNet-50, demonstrating the advantage of applying PSO in multi-class segmentation of satellite imagery. The principal contribution of this work lies in the development and validation of a novel, metaheuristic-optimised deep learning framework that addresses the land cover classification challenge inherent in satellite images.

Concerns about climate change and its possible impacts on human activities have increased the ‎awareness that climatic conditions are dynamic. West Africa, of which Ghana is part is one of the ‎areas in the world that had experienced major climatic anomalies in the past century. The purpose ‎of this research is to model rainfall data using block maxima approach of extreme value theorem. ‎Monthly rainfall data covering the period 1960 – 2022 were obtained from the Ghana ‎Meteorological Agency, Accra Airport substation. Statistical properties of the data indicated data ‎was stationary, however it was not normally distributed. Time series analysis of the data indicated ‎consistent increase in rainfall values with both downward and upward spikes indicating ‎fluctuations in the rainfall values. The Generalized Extreme value distribution was used to fit the ‎model of rainfall values. The rainfall values were fitted using the Generalized Extreme value ‎distribution. The Fréchet distribution was found to be the most appropriate model for the monthly ‎rainfall data. Additionally, it was discovered that the amplitude of the extreme values grows with ‎return periods, with higher return levels predicted to become more uncommon but severe over ‎time‎.

There are extant studies on the knowledge, attitudes and practices of air pollution in sub-Saharan Africa and Ghana, however, studies among commercial drivers and traders in transport stations in Ghana are sparse. This study examined the mediation effect of attitude on the association between knowledge and practice toward air pollution among two high-risk workers in the Greater Accra Metropolitan Area, Ghana. A population-based cross-sectional study was conducted among commercial drivers and traders between November 2023 and June 2024. A consecutive sampling technique was used to select 1011 participants (drivers [n = 619, 61.2%] and traders [n = 392, 38.8%]). A structured questionnaire was used, including questions on knowledge (9 items), attitude (7 items) and practices (5 items) toward air pollution. Statistical analysis (descriptive, hierarchical linear regression and partial least squares structural equation modelling) was performed using Stata 17/MP. The primary sources of air pollution information for drivers and traders were social media and radio. The PLS-SEM revealed a significant direct effect of knowledge for both drivers/traders (β = 0.23/0.26, p < 0.001). Moreover, attitude partially mediated the relationship between knowledge and practices, with a direct significant effect observed for drivers/traders (β = 0.10/0.02, p < 0.001). HLR further confirmed that knowledge strongly predicted protective practices for both drivers (β = 0.23, p < 0.001) and traders (β = 0.26, p < .001. Higher educational level consistently predicted better knowledge and protective practices but old age had an inverse relationship with protective behaviour toward air pollution. Although a cross-sectional design precludes causality, attitude partially mediated the association between knowledge and practices, but the effect was more substantial among drivers than traders. The findings have implications for social and traditional media education and attitudinal change campaigns to effectively reduce air pollution exposure risk among these high-risk occupational groups.

Drying agricultural products and other porous media is typically done in the open sun, which is unhygienic, labour intensive, and dependent on varying weather conditions. Designing an efficient drying system requires a comprehensive computational analysis of relevant drying phenomena. In this study, numerical simulations and experimental investigations of three-dimensional simultaneous heat and mass transfer in a photovoltaic (PV) - powered drying system operating under forced convection were conducted under no load conditions. The equations governing continuity, momentum, energy, and species were solved using ANSYS Fluent’s Finite Volume Method (FVM), a computational fluid dynamics tool. The highest simulated temperature and velocity in the drying chamber, 393 K and 1.55 m/s, respectively, closely matched the experimental values of 386 K and 1.40 m/s. It is recommended that the developed drying system serve as a practical alternative to traditional open sun drying, as the numerical results were within an acceptable range.

To improve the bioelectrical conductivity, bioactivity, and biocompatibility of titanium implants, an electroconductive tantalum (Ta) coating was fabricated on Ti6Al4V (TC4) substrates via the cold spray technique. Microstructural and phase characterizations were performed using Scanning Electron Microscopy (SEM), X-ray Diffraction (XRD), and Fourier-Transform Infrared Spectroscopy (FTIR). Four-point probe resistivity, electrochemical impedance spectroscopy (EIS), and cyclic voltammetry (CV) were used to characterize electrical and electrochemical properties relevant to neural interfaces. Mechanical and bioactivity evaluations included tensile adhesion, three-point bending, and Vickers microhardness tests, alongside simulated body fluid (SBF) immersion to assess osteointegration potential. Results show that the cold-sprayed Ta coating exhibits a bulk electrical conductivity of 6.8 × 106 S/m, approximately 40 % that of pure bulk Ta, due to the presence of inter-particle interfaces and porosity. The coating’s impedance at 1 kHz-a frequency range relevant to neural signal transmission was measured at ~8.2 kΩ•cm², indicating favorable charge transfer capability for neural electrode applications. The double-layer capacitance extracted from the EIS Nyquist plots was approximately 2.6 mF/cm², signifying strong electrochemical surface activity. Mechanically, the coatings exhibited an adhesive strength of 10.6 MPa, microhardness of 300 HV0.1, and Young’s modulus of 11.08 GPa, closely matching the modulus of natural cortical bone (10-30 GPa). Bioactivity assessment showed dense apatite layer formation within 14 days of immersion, while electrical stimulation (±100 μA, 1 Hz) significantly enhanced apatite nucleation rates, confirming electro-assisted biomineralization. These findings show improvement in bone-implant integration with emphasis on electrical performance suitable for bioelectronic and neural stimulation implants.

The Esaase gold deposit is located in the Asankrangwa gold belt and within the Paleoproterozoic Birimian Supergroup of the Kumasi basin. The deposit represents an important example of a structurally controlled turbidite-hosted gold system. Despite its economic significance, the geochemical processes controlling the element mobility during mineralisation remain poorly understood. This study presents an integrated petrographic and mass balance investigation of the Esaase gold deposit to characterise alteration processes and quantify the elemental mass transfer associated with the hydrothermal mineralisation. Petrographic studies revealed two major stages: (I) metamorphic and (II) hydrothermal. Stage II is associated with gold mineralisation. Metamorphic alteration is characterised by fine-grained sericite, chlorite, paragonite, and albite, while hydrothermal alteration is characterised by coarse-grained sericite, chlorite, carbonates, quartz, albite, paragonite, pyrite, arsenopyrite, and traces of chalcopyrite and gold. (Na 2 O/Al 3 O 2 vs. K 2 O/Al 3 O 2 ) Isocon-based mass balance calculations reveal a net mass gain of 16.67%, with significant enrichment in Au, As, Sb, Bi, Pb, Te, Se, Sb, Cr, Ni, and Cu, and marked depletion in Cao without significant changes in K 2 O and Na 2 O. These geochemical trends reflect localised but efficient fluid-rock interaction along structurally controlled hydrothermal pathways. The alteration assemblages are dominated by sericite-chlorite over sericite-albite, as supported by alkali-alumina molar ratios (Na 2 O/Al 3 O 2 vs. K 2 O/Al 3 O 2 ), consistent with alteration haloes surrounding sheeted quartz-carbonate veins. The deposit geology, alteration mineralogy, and lithogeochemistry of the Esaase turbidite-hosted gold deposit are similar to other world-class turbidite-hosted gold deposits, such as Bendigo in Australia, Meguma in Canada, Reefton in New Zealand, and Shangxu in China. This study establishes the quantitative mass balance assessment of the hydrothermal alteration at Esaase, demonstrating how moderate net mass gains reflect structurally controlled fluid flow and mineralisation. The findings provide new constraints on the genetic interpretations and support targeted exploration strategies in the Kumasi basin and similar geological settings.

This study examined twenty-three (23) groundwater samples to determine the hydrogeochemistry of water in Ghana’s Asutifi North District. The primary objective was to characterize and assess the groundwater quality in the district, which is underlain by Paleoproterozoic supracrustal and intrusive rocks of the Birimian Supergroup. This was accomplished by employing a multi-method approach that included geochemical analysis, multivariate statistical analysis, water quality assessment, and geochemical modeling. Unlike previous studies in the district that examined geochemical mechanisms without assessing water potability, this study integrates hydrogeochemical analysis with water quality assessment for both drinking and agricultural use of groundwater in the district. The pH (6.2–7.6) of groundwater is moderately alkaline owing to aquifer chemistry. The major ion concentrations fall within the World Health Organization guidelines for potable water. The cationic composition followed the order Na+ >Mg2+ >K+ >Ca2+, while the anionic composition was HCO3− >NO3− >Cl− >F−. The NO₃⁻ concentrations ranged from 0.20 to 95.6 mg/L with ~ 9% of the samples exceeding the guideline value of 50 mg/L. Piper diagram identifies Na-HCO3 as the dominant water type, with other mixed water types also present. Factor Analysis (FA) identified five factors with eigenvalues greater than 1, explaining 85.56% of the total variance in the hydrochemical parameters. The FA results highlight natural processes, such as the dissolution of silicate and carbonate minerals, and anthropogenic activities, including the use of phosphate and nitrate fertilizers, as key contributors to the aquifer’s chemical composition. The computed water quality index ranged from 11.8 to 47.5 for the sampled groundwater, indicating that the groundwater is of excellent quality and fit for human consumption. However, the high magnesium ratio (> 50) and sodium percent values (> 60%) show that the water is not suitable for irrigation usage.

In this study, activated carbons were prepared from palm kernel shells by physical activation. The methodology of experimental design was used to optimise the preparation conditions. While varying the particle size of the precursor palm kernel shells, carbonisation was done for about one hour to yield char. Activated carbon was prepared from the product char at a steam flow rate of 0.06 mol/h/g char and at a temperature of about 900°C for five hours. The empirical results were investigated to estimate the yield and particle size distribution of char and activated carbon produced from the palm kernel shells. Based on the yield and particle size analysis, a model was generated to predict product particle size and quantity in the production of activated carbon, thereby effectively utilising available raw materials and reducing preparation costs. In gold adsorption process, activated carbon of + 2.00 mm is mainly used to recover dissolved gold complexes from solutions. From data analysis and the model generated in this study, a relatively high composition of activated carbon with particle size + 2.00 mm was produced by utilising palm kernel shells precursor with particle size - 6.70 mm + 5.60 mm for carbonisation and char particle size –3.35 mm +2.80 mm for activation.