Refereed Journals

Accurate estimation of runoff depth and volume is essential for effective watershed management. Runoff, resulting from rainfall, is influenced by numerous factors, including soil type, vegetation, land use patterns, and rainfall characteristics. The Densu River Basin, located in the Greater Accra region of Ghana, has experienced flooding incidents, partly attributed to changes in land use and land cover. To address these challenges and facilitate proper flood management, drainage network design, hydropower generation, and other applications, this study aims to estimate surface runoff depth in the Densu River Basin, Ghana. The Natural Resources Conservation Services Curve Number (NRCS-CN) method, combined with Geographic Information System (GIS) and Remote Sensing (RS), is employed for runoff depth estimation. The research involves supervised classification of Landsat images from 2001, 2011, and 2022 to determine land use patterns, calculate grass cover percentages, identify hydrologic soil categories, extract rainfall intensity data, compute maximum soil storage, and estimate runoff depths for 10 year, 25 year, and 50 year return periods. The study reveals a significant increase in direct surface runoff depth, from 138.29 mm to 144.70 mm, for soil type D (Clay loam), the dominant soil type in the basin, during the 10 year return period, attributed to changes in land use and climate within the basin. The findings from this study hold valuable insights for mitigating environmental hazards in the area and improving water resource management.

In recent years, the use of GFRP reinforcing bars in place of steel reinforcing bars in concrete structures such as, buildings, roads, and bridges cannot be overlooked as they offer advantages such as higher tensile strength, corrosion resistance, reduced weight and cost effectiveness compared to steel reinforcing bars. The use of PKS as partial coarse aggregate in steel reinforced concrete has been studied by several researchers and found to produce lightweight concrete and reduce construction cost but, the application of GFRP reinforcing bars in LWC such as PKSC, presents a unique structural material with possibly different mechanical and structural properties which requires further studies and this study specifically focused on determining the anchorage bond strength of Glass Fibre Polymer reinforced concrete with PKS as partial coarse aggregatesince the bond strength between concrete and reinforcing bars is a crucial prerequisite for the design of reinforced concrete as a composite material. Normal weight concrete of mix ratio 1:1.5:3 with water-cement ratio (w/c) of 0.5 and lightweight concrete with 10% of the volume of coarse aggregate replaced by PKS were used in a total of forty-eight (48) double pull-out prismatic specimens of dimension 100mm x 100mm x 300mm for control and test specimens respectively, embedded with 12mm and 16mm diameter GFRP reinforcing bars at varying end-to-end embedment lengths (100mm, 125mm and 150mm) and 300mm continuous embedment. Average anchorage bond strength of 4.684N/mm2 and 3.558N/mm2 were respectively recorded for the PKSC with 12mm and 16mm diameter bars and 100mm embedment length and 3.051N/mm2 and 2.899N/mm2respectively for PKSC specimens with 12mm and 16mm diameter bars and 150mm embedment length, indicating a decrease in anchorage bond strength with increasing (end-to-end) embedment length. However, the highest average anchorage bond strength of 6.174N/mm2 and 4.581N/mm2 were respectively recorded for PKSC specimens with 12mm and 16mm GFRP reinforcing bars and continuous (300mm) embedment length. Comparatively, the average percentage anchorage bond strength values ranging between 75.5-97.9% of that of NWC were recorded for PKSC and an increase in GFRP reinforcing bar diameter resulted in a decrease in anchorage bond strength. Splitting failure was observed for most of the specimens with longitudinal and transverse crack patterns developed after load application regardless of the size of GFRP reinforcing bar or concrete mix but the extent and visibility of the cracks formed reduced in specimens with continuous bar embedment.

The significance of adjustment and computation studies has grown in recent years, influencing allied fields like arithmetic and satellite geodesy. This empirical study explores the effectiveness of various soft and traditional regression methods in correcting survey field data. Specifically, it investigates soft computing techniques such as back-propagation artificial neural network (BPANN), radial basis function artificial neural network (RBFANN), generalized regression artificial neural network (GRANN), and traditional regression methods like polynomial regression model (PRM) and least square regression (LSR) techniques. The study aims to fill the knowledge gap regarding soft computing strategies for modifying real-time kinematics (RTK) GPS field data and the ongoing debate between artificial intelligence techniques (ANN) and traditional methods on which technique offers the best results in modifying survey field data. Performance criteria, including horizontal displacement (HE), arithmetic mean error (AME), arithmetic mean square error (AMSE), minimum and maximum error values, and arithmetic standard deviation (ASD), were used to assess each model technique. Statistical analysis revealed that RBFANN, BPANN, and GRANN achieved superior accuracy compared to conventional techniques (PRM and LSR) in adjusting real-time kinematics GPS data. RBFANN outperformed BPANN and GRANN in terms of AME, AMSE, and ASD of their horizontal displacement. These findings suggest that soft computing techniques enhance real-time kinematics GPS field data adjustment, addressing critical issues in accurate positioning, particularly in Ghana. This study contributes to the knowledge base for developing an accurate geodetic datum in Ghana for national and local objectives. This will lay a foundation for the global determination of exact positions in Ghana. RBFANN emerges as a promising option for real-time kinematics GPS field data adjustment in topographic surveys. However, care should be taken to check issues of data overfitting.

Aim: The study examined the potential roles of Human Resources (HR) in ensuring efficient change management in a Ghanaian research organization, the Council for Scientific and Industrial Research (CSIR). The study was premised on the assumption that employees, when well engaged through HR activities, can be better informed and understand the need for change during any change management process. Methodology: The study tested the assumption using a quantitative research design of survey questionnaire among 64 employees and a review of institutional documents at the CSIR - Forestry Research Institute of Ghana (CSIR-FORIG). The responses were analysed using statistical means. Findings: The findings show that HR functions, such as; communication, training, and monitoring of performance that make the Human Resource Manager a change agent are challenged at the CSIR-FORIG, especially in the communication role. While the level of understanding of change appears not to depend on the category of employee, the expectations and commitment of employees seem to be associated with the category of employee. Four predictor variables of change management were found not to have been adequately met due to factors such as lack of feedback mechanism in the communication strategy limiting full understanding of the change process. Conclusions: The study concludes that change management implementation at the CSIR-FORIG did not yield the needed outcome and this could partly be attributed to HR role which was not adequately incorporated in the implementation strategy. Therefore, policy and practice implications on any change management process need to recognize the role of HR and incorporate HR professionals in the change management strategy.

Road construction involves activities that emit pollutants, including particulate matter, which harms humans. This study determined and compared particulate matter (PM_1.0, PM_2.5 and PM₁₀) levels and air quality index (AQI) at unpaved roads, asphalt overlay, chip-sealed and asphalt-producing sites in Ghana and the health risks posed by their exposure. It was conducted in the Ashanti and Ahafo Regions, Ghana and was cross-sectional using the low-cost sensor, PCE-RCM16. Data collection took place, January-May, 2020. The asphalt-producing, asphalt overlay, chip-sealed and unpaved road sites had mean PM₁₀ concentrations of 12.7-fold, 7.4-fold, 6.1-fold and 2.6-fold respectively of the 2021 World Health Organisation (WHO) air quality guideline (AQG) daily limit, 45 μg m⁻³. The mean PM_2.5 concentrations were 30.4-fold, 17.2-fold, 14.1-fold and 6.1-fold greater than the daily AQG limit, 15 μg m⁻³ respectively. The mean PM_1.0 values were of grave concern. Using the AQI, the asphalt-producing and asphalt overlay sites were considered “hazardous”, chip-sealed site was “very unhealthy” and the unpaved sites were “unhealthy for sensitive groups”. Type of activity influenced pollution levels (p < 0.01). All the sites were polluted above the WHO limits. Authorities should ensure the wearing of personal protective equipment, strict adherence to the WHO AQG and apply appropriate sanctions to offending firms/workers.