ghanaopendata/ghanaian-research-english huggingface dataset:
Decentralized premix fuel management and the development of coastal communities in the Komenda-Edina-Eguafo-Abirem Municipality

Community development can be promoted through the decentralized Premix fuel Management policy. However, irregular supply of premix fuel was identified by government as a major hindrance to fishing activities in the Komenda-Edina-Eguafo-Abirem Municipality. The short supply of premix fuel led to low catch of fish as well as low income levels for fishermen and fishing was steadily becoming a less attractive means of livelihood. In the quest to curb the challenge of irregular supply of premix fuel and its associated problems, the government in 2001 formulated and implemented the decentralised Premix Fuel Management policy. The study sought to examin e the contribution of the decentralised Premix Fuel Management policy to infrastructural development and access to premix in the Komenda-Edina-Eguafo-Abirem Municipality. It also assessed the levels of stakeholder participation and accountability in decentralised premix fuel management. The study adopted the descriptive and explanatory study designs to examine the contribution of the decentralised Premix Fuel Management policy to community development. Both simple random sampling and purposive sampling techniques were used to select the sample for the study. An interview schedule and interview guides were employed for the primary data collection. The main findings of the study were that the decentralised Premix Fuel Management policy has led to an increase in the supply of premix fuel since its passing in 2001 and the sale of premix fuel is managed by the community members themselves instead of private individuals whose major motive is profit maximization. The study also found that the decentralised premix fuel has led to the implementation of developmental projects such as construction of a shed for fishermen and fishmongers at the landing beach, and construction of a clinic and conference hall complex. Other projects financed from the money earned from selling premix fuel are purchase of fishing nets for 150 fishermen and distribution of 450 basins to fishmongers to promote their economic activities. Despite the implementation of these projects, the community members are not pleased with the rewards because they feel the rewards do not reflect their needs, and most iv especially do not match up with the profits accrued from selling premix fuel. The study again found that Stakeholder participation and accountability in PFM are low. The study recommends that government through the National Premix Committee should formulate and enforce laws to ensure that the Landing Beach Committees do not violate the rules and regulations guiding the decentralized Premix Fuel Management policy so as to attain the set goals and objectives.

Source: KNUST
Decision Tree as a Predictive Modeling Tool for General Insurance Claims

The study explored the relationship between the risk factors of policyholders of an insurance company and the occurrence of claim. These relationships were investigated by using decision trees developed by Breiman et al (1984) also referred to as CART analysis. The R software (Rattle) version 0.99.483 was used to analyze the data using the rpart component.The Analysis corroborated the view held by Yeo et al. (2001) and Mayhew et al. (2003) that a major risk factor that a ects the occurrence of claim is the age of policyholders and also proved and ascertained Tranter and Warn (2008) views, that a group of young drivers have higher risk of accident than the same group of older drivers. Most importantly the analysis revealed that the prediction of whether a policyholder would make a claim in the coming year depends in part and in whole on the claim history of the policyholder.

Source: KNUST
Decision-making practices of construction firms in Accra Ghana

Decision-making is critical in any industry and the construction industry is not exempt from this, since the construction process requires many decisions to be made. In terms of strategic decision as well, these companies need to make them so that they can remain competitive in the industry. Therefore, this study was to determine the decision-making practices of construction firms in Ghana, and the objectives included identifying the decisions making practices and the factors, which influenced these decision-making practices. Identifying these would help in proffering recommendations for the best practices in decision-making. Previous works were reviewed to help establish gaps in the area of decision-making. The research relied on a quantitative strategy that allowed for the use of questionnaires to elicit data from respondents made up of building contractors and other construction professionals from firms within the industry using the census sampling. Based on the analysis of the data that is the use of mean score ranking it was revealed that the characteristics of strategic decision-making associated with construction firms focused on their long term organizational objectives and projects, management of firm resources and maintaining their competitive advantage. Their decision-making processes were influenced by certain internal and external factors, and the most significant among them included their implementation plans, past decisions and cognitive biases. It was recommended that decisionmaking processes should be collective, and that there be relevant training for the managers who have to make such strategic decisions for the construction firms. The firms should also have regular internal training for all personnel to aid them in making these strategic decisions and also adhering to them so they can be achieved

Source: KNUST
Decomposition and drivers of energy intensity in Ghana

Ghana's energy intensity trends point to a high energy use necessary to generate a unit of output. The country has also witnessed massive investment in energy infrastructure geared towards meeting its lower middle-income status and achieving universal access to energy. The logical question is: what is the contribution of the current economic and technical infrastructure level to the country's energy intensity? The current study addresses this question by employing the Logarithmic Mean Divisia Index I (LMDI) to decompose energy intensity in Ghana from 2000 to 2020 to examine its trends and sources. The impact of economic-technical factors on aggregate energy intensity in Ghana is then investigated with the aid of the ARDL estimation technique to unearth potential asymmetric and symmetric effects. The decomposition analysis indicates an oscillating pattern in energy intensity in Ghana promoted by structural effect and labour productivity respectively. The results suggest that renewable energy, rural electrification, and digitisation have a direct and secondary long-run asymmetric effect on aggregate energy intensity with labour productivity and household consumption working as the transmission channels. The study recommends the need for government to pursue clean and eco-friendly practices in its economic development agenda for a meaningful reduction in energy intensity.

Source: KNUST
Decomposition and drivers of energy intensity in Ghana

Ghana’s energy intensity trends point to a high energy use necessary to generate a unit of output. The country has also witnessed massive investment in energy infrastructure geared towards meeting its lower middle-income status and achieving universal access to energy. The logical question is: what is the contribution of the cur rent economic and technical infrastructure level to the country’s energy intensity? The current study addresses this question by employing the Logarithmic Mean Divisia Index I (LMDI) to decompose energy intensity in Ghana from 2000 to 2020 to examine its trends and sources. The impact of economic-technical factors on aggregate energy intensity in Ghana is then investigated with the aid of the ARDL estimation technique to unearth potential asymmetric and symmetric effects. The decomposition analysis indicates an oscillating pattern in energy in tensity in Ghana promoted by structural effect and labour productivity respectively. The results suggest that renewable energy, rural electrification, and digitisation have a direct and secondary long-run asymmetric effect on aggregate energy intensity with labour productivity and household consumption working as the transmission channels. The study recommends the need for government to pursue clean and eco-friendly practices in its economic development agenda for a meaningful reduction in energy intensity.

Source: KNUST
Decomposition and nutrient release patterns of leaf mulches of Leucaena leucocephala, Gliricidia sepium and Cassia spectabilis

The rate of decomposition and nutrient release patterns of leaf mulches of Leucaena leucocephala, Gliricidia sepium and Cassia spectab1is were investigated under field conditions using wooden square frames over a period of 70 days. The experimental design used was a complete randomized design with three replications using the tree species as treatments. Fresh leaf mulch weighing the equivalent of 100g dry weight of the species were spread in the wooden frames, sampled every two weeks and the dry weights and the concentration of C, N, P, K, Ca and Mg determined. Soil under the wooden frames were also sampled at 10-15cm and 15-30cm profile depths every two weeks and analyzed for C, N, P. K, Ca and Mg. The initial chemical characteristics of the leaf mulches were also determined. Decomposition rates of the leaf mulches differed significantly (P < 0.05) and followed the order: Leucaena leucocephala > Gliricidia sepium > Cassia spectabilis. The half-life values were; 18 days for L. leucocephala, 35 days for G. sepium and 69 days for C. spectabilis Initial concentrations of nutrients and percent lignin in leaf mulch of the species varied significantly (P < 0.05). Gliricidia sepium had the highest concentration of Phosphorus (0.45%), Potassium (2.87%), Calcium (1.40%) and Magnesium (0.44%). Leucaena leucocephala had the highest Nitrogen (3.85%) and Carbon (46.20%). Percent lignin was 5.6 in L. leucocephala, 7.6 in G. sepiu.m and 8.7 in C. spectabilis. Generally, Potassium showed the highest release rate with nutrient release constant (K) ranging between 0.018 in C. spectabilis and 0.045 in G. sepium. The release of the nutrients from the leaf mulch was ranked as follows: K > Mg > N > P > C > Ca for L. leucocephala, K > Mg > P > N = Ca > C for G. sepium and K > Mg > P > N > Ca = C for C. spectabilis. The chemical characteristics of the mulch material especially percent nitrogen and lignin played key roles in determining the rate of decomposition and nutrient release. Soil nutrient levels improved significantly under the decomposing leaves at both 0-15cm and 15-30cm profile depths: The general order of improvement in soil nutrient content at 0-15cm depth of profile was N > K > Mg > Ca > C > P for L. leucocephala and G. sepium and N > K > Mg > C > Ca > P for C. spectabilis. At the 15-30cm depth the trend was N > K > Mg > Ca > P > C for the three species.

Source: KNUST
Deep learning model for predicting daily IGS zenith tropospheric delays in West Africa using TensorFlow and Keras

Accessibility and precise modelling of tropospheric delay play a significant role in the precise Global Navigation satellite system (GNSS) positioning applications as well as meteorological studies and weather forecasting. However, if in the event that a GNSS Continuously Operating Reference Station (CORS) is inaccessible due to power outages, poor internet connectivity, equipment failure, and firmware issues, gaps are created in the data archive, and the quality of the tropospheric delay estimation is degraded. Over the years, several modelling approaches and methodologies have been proposed towards the precise estimation of tropospheric delay, owing to the spatiotemporal variability of water vapour content in the atmosphere. This study employs Deep learning (DL) approach with TensorFlow and Keras to develop a predictive model (DLztd) for predicting daily IGS final ZTDs over four selected IGS stations in West Africa. Daily surface meteorological parameters (Pressure (P), Temperature (T), and Water vapour partial pressure (e)), as well as daily ZTD and stations’ coordinates (latitude, and ellipsoidal height) obtained from the site-wise VMF3-ZTD products for the period 2015–2018, were used as input variables to train and test the model, while data from 2019 were used to evaluate the predictive performance of the developed model. Statistical performance indicators such as Mean Bias (MB), Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), coefficient of determination (R2), Nash-Sutcliffe coefficient of Efficiency (NSE), and the fraction of prediction within a Factor of Two (FAC2) were employed to determine the degree of agreement between the DLztd model predictions and IGS final ZTD data. The results from the various analyses indicate exceptionally good prediction capability of the DLztd model with average MB, RMSE, MAPE, R2, NSE and FAC2 of 3.25 mm, 9.62 mm, 0.30%, 0.959, 0.947, and 1.00 respectively. This demonstrates that the DLztd model provides a remarkable alternative for improving the availability of the ZTD data over the IGS stations under study should the stations' data be inaccessible or unavailable.

Source: KNUST
Decomposition Studies on Fine Roots of Gliricidia Sepium, Leucaena Leucocephala and Senna Siamea

Nutrient composition, rate of decomposition and nutrient release pattern of fine roots (≤ 2mm) of Leucaena leucocephala, Gliricidia sepium and Senna siamea buried within 15cm and 30cm soil depths were studied biweekly under field conditions using litter pot method over an 84 day period. Generally, Gliricidia sepium had significantly higher (P < 0.05) initial levels of nutrients among species and could be described as high quality litter, Leucaena Leucocephala of moderate quality litter and Senna siamea of low quality litter. Gliricidia sepium fine roots showed the most rapid root weight loss within both 15cm and 30cm soil depths, followed by Leucaena leucocephala and then Senna siamea. Mean decomposition rate constants (Kd) was significantly higher for Gliricidia sepium and similar for Leucaena leucocephala and Senna siamea within both 15 and 30cm soil depths. The different decomposition rate could be related to litter quality (initial nitrogen, lignin and lignin/nitrogen ratio). Depths of placement did not significantly influence decomposition of fine roots. Nutrient release followed the order K > P > Mg > N > Ca > C for Gliricidia sepium, K > Ca > P > N > Mg > C for Leucaena leucocephala and K > P > Ca > Mg > C = N for Senna siamea within 15cm soil depth and K > P > Ca > Mg = N > C for Gliricidia sepium, K > Ca > P > N > Mg > C for Leucaena leucocephala, K > P >Ca > Mg > N = C for Senna siamea within 30cm soil depth. Nutrient release could be related to initial nutrient and lignin levels in fine roots. Depths of placement significantly influenced the release of potassium and magnesium and this could be attributed to leaching as the predominant mode for the release of potassium and magnesium.

Source: KNUST
Deep learning based capsule networks for breast cancer classification using ultrasound images

Abstract: Purposes: Breast cancer (BC) is a disease in which the breast cells multiply uncon trolled. Breast cancer is one of the most often diagnosed malignancies in women worldwide. Early identification of breast cancer is critical for limiting the impact on affected people’s health conditions. The influence of technology and artificial intelligence approaches (AI) in the health industry is tremendous as technology advances. Deep learning (DL) techniques are used in this study to classify breast lumps. Materials and Methods: The study makes use of two distinct breast ultrasound images (BUSI) with binary and multiclass classification. To assist the models in understanding the data, the datasets are exposed to numerous preprocessing and hyperparameter approaches. With data imbalance being a key difficulty in health analysis, due to the likelihood of not having a condition exceeding that of having the disease, this study applies a cutoff stage to impact the decision threshold in the datasets data augmentation procedures. The capsule neural network (CapsNet), Gabor capsule network (GCN), and convolutional neural network (CNN) are the DL models used to train the various datasets. Results: The findings showed that the CapsNet earned the maximum accuracy value of 93.62% while training the multiclass data, while the GCN achieved the highest model accuracy of 97.08% when training the binary data. The models were also evaluated using a variety of performance assessment parameters, which yielded consistent results across all datasets. Conclusion: The study provides a non-invasive approach to detect breast cancer; and enables stakeholders, medical practitioners, and health research enthusiasts a fresh view into the analysis of breast cancer detection with DL techniques to make educated judgements.

Source: KNUST
Default risk management in the internal marketing of cocoa in Ghana: a case study of Kuapa Kokoo Limited

Default risk is the uncertainty surrounding an individual or a firm's ability to service its debts and obligations. Prior to default, there is no way to discriminate unambiguously between individuals or firms that will default and those that will not. Default among Purchasing Clerks has led to losses from purchasing activities and this threatens the modest gains made from the tight margins of operations of Licensed Buying Companies. The general purpose of the study was to assess the risk of default among Purchasing Clerks, the contributing factors, and how this is being managed by Licensed Buying Companies in the internal marketing of cocoa in Ghana. Semi structured questionnaires were administered to two groups of employees of Kuapa Kokoo Limited. The sample population of the study was made up of two hundred and fifty eight (258) employees, comprising forty four (44) District Managers, and two hundred and fourteen (214) Purchasing Clerks. The study found that the estimated rate of default among the Purchasing Clerks was 29.91%. The major significant contributing factors to default were, pre-financing of cocoa purchasing, misapplication of funds, Purchasing Clerks absconding with funds, quality related problems, low numbers of dependants, sub-contracting of cocoa purchasing, volume handling and theft of cocoa and funds. It is recommended that a proper and effective risk management system should be put in place by Kuapa Kokoo Limited to reduce the incidence of default among Purchasing Clerks.

Source: KNUST
Defects and failures on newly constructed public buildings by indigenous contractors in Ghana: A study within Ketu-South Municipal Assembly

The fitness of building in meeting client requirements goes further to create value in construction products. Poor constructional works affects the building quality over time and also have negative impact on overall investments for metropolitan, municipal and districts assemblies as well as the budget of the local contractors. Preventing nonconformance to construction building requirements and appraising conformance to requirements results into failures found in and after constructional works. Majority of local assembly’s buildings and construction products are vastly exposed to numerous factors including building defects. These are critical as the building defects can physically affect the building appearances as well as damage the structure. This can further affect the safety of the end users. It therefore called for appropriate solutions to mitigate building and or constructional defects for Ketu-South Municipal Assembly. A failed newly constructed building requires all stakeholders including local authorities, consultants, contractors and public to be involved in remedial works. This ultimately increases the risk, delay schedule and cost for the client whiles further defeating the objective of public supplies. The study was undertaken to assess defects and failures on newly constructed buildings projects among indigenous contractors in Ghana. The analysis was done based on the sample size of sixty (60) contractors with ninety one percent (91%) response rate. This was based on four-point Likert-scale. The respondents for this study include construction industry players including K1K2, D1D2 contractors; Project Engineers and Directors from the Ketu-South Municipal Assembly (KSMA) which formed the sample frame of the study. The data was analysed to obtain mean values which were presented in tabular format and ranked accordingly (1st, 2nd, 3rd etc.) for easy reading and interpretation. It was found from the study that peeling paint, foundation failure, frequent design technology changes and dampness among others were common defects and failures on newly constructed buildings in the study area. The findings impact on the cost of construction, increase risk and delays construction scheduled for the client. Mitigating measures including adhering to technical specifications, strict adherence to health and safety regulations, engaging construction professionals among others were suggested

Source: KNUST
Deforestation and its effect on livelihood patterns of forest fringe communities in the Asunafo North Municipality

In spite of their immeasurable benefits to life sustenance, the sustainable management of forest and forest resources in Ghana is fraught with innumerable challenges such as the conflicting roles of the various stakeholders involved in forest management. Forests have since ancient times played an important role in the lives of people and the environment in general. That is, forests provided and continue to provide numerous benefits to humanity. This has repercussions for the environmental and livelihood patterns on the people especially the poor and the people who depend on the forest. It is in this vein that this study was undertaken to assess the effects of deforestation on the livelihood patterns of the forest fringe communities (farmers) in the Asunafo North District. The study adopted the case study research design in undertaking this systematic enquiry. This was adopted to help acquire knowledge on the current situation with regards to the phenomenon under consideration. Both primary and secondary data were collected and used for the study. The study employed both qualitative and quantitative data gathering techniques to collect the necessary data and was analyzed in great depth to determine their implications for changing forest cover and livelihood patterns in the study area. The respondents for the study comprised Farmers, Municipal Meteorological Department, Forestry Commission and Municipal Agricultural Development Units. The study showed that, the farmers in the study area are largely engaged in the cultivation of food crops which are mainly subsistence in nature. It was realized that the farmers practice mixed cropping with slash and burn as the predominant land preparation method. The study showed that deforestation has affected crop production in the areas of delayed commencement of planting seasons, pest and diseases infestation, level and quality of crop yields and reduction in the income levels of farmers. The study recommended among other things, the continuous education and sensitization of farmers, strengthening of the public institution stakeholders and promotion of active research that will ensure a decline in deforestation.

Source: KNUST