ghanaopendata/ghanaian-research-english huggingface dataset:
Econometric analysis of the effect of Ghana’s increasing external debt on foreign direct investment (FDI)

The study explored the relationship between foreign direct investment (FDI) and several covariates including external debt, GDP per capita income, and gross fixed capital formation, for Ghana from 1980 to 2013. It was conducted following Ghana’s bail out by the International Monetary Fund (IMF) to stabilise the Ghanaian currency (the Cedi) as debt levels soared and the Cedi depreciated precipitously against the Dollar. The primary research question that precipitated the study was: by how much will foreign direct investment inflows in Ghana change percentage-point wise per a unit increase in her external debt stock? In the econometric analysis, two OLS regressions were run: the ‘log-level’ model and the ‘log-log’ model. The coefficients on external debt, GDP per capita and gross fixed capital formation (a proxy for infrastructure) were all statistically significant, excluding the GDP deflator, which though had its expected sign, was insignificant at 10% significance level using ‘log-level’ regression. In the ‘log-log’ model, only external debts and gross fixed capital formation were statistically significant at 5% significance level. The R-squared explained 89.98% and 89.64% of the total variation in FDI using the loglevel and log-log models respectively. The semielasticities of log(FDI) with respect to external debts, GDP per capita and gross fixed capital formation were 0.3%, 0.2% and 7.5% respectively, while the elasticities of log(FDI) with respect to external debts and gross fixed capital formation were -1.56 and 1.38 respectively. The magnitude of the proportionate changes in log(FDI) was very large when the percentage increase in the explanatory became very large. To avoid violating the Gauss Markov assumptions of multiple linear regression, the Breusch-Pagan test for heteroscedasticity using ‘log-level’ was employed by regressing residuals-squared on all the independent variable. A correlation test was also run to ensure that there was no perfect correlation between the independent variables. Overall significance of the regression was tested using the F statistics and there was a strong rejection of the null hypothesis that none of the variation in log(FDI) was explained by the independent variables. The study finds that in order for government to attract more FDI, emphasis has to be placed on fiscal discipline, sound economic policies, and infrastructural development. The study will enable government and policy makers to estimate the percentage point fall in FDI per a unit increase in external debts so as to strategise borrowing.

Source: Ashesi
Economic crisis of opportunity? The ageing of Africa 2015–2050

Note: There is no abstract for this article. What follows is the first paragraph of the paper The purpose of this brief discussion is to highlight issues of demographic changes associated with an ageing population in Africa and what, I believe, must happen to avoid the next social crisis among African nations, especially here in Ghana. However, this is not a discussion of doom, gloom and crisis, because the demographic changes that I will discuss are products of development. These are problems to overcome, for certain, but also opportunities that we hope become part of the current agenda throughout Ghanaian society instead of considered only to be a remote or theoretical concern of the future.

Source: Ashesi
Edge computing and machine learning on embedded systems

Running Machine Learning (ML) in embedded systems has fueled the rush for edge computing, where machine learning runs in edge devices. This approach to ML yields many results, such as lower latencies and reduction of network traffic and bandwidth. This project set out to explore machine learning in embedded systems. The Edge Impulse Platform was used to collect data and to a neural network. The neural network created was able to distinguish between five classes of motion. The Neural Network created was tested on two microcontrollers and a desktop. Inferencing on the Arduino Nano BLE took 24ms, and on the desktop, it took 271.8 μs.

Source: Ashesi
EduApp, An educational web and mobile platform

There has been an increase in poor educational performance in the Ghanaian society that has caused many to sort out the root problem. As shown in previous scholarly work, there are many problems that face the Ghanaian education. These can range from stagnating school enrolment, inefficiency of educational administration to teacher training. One research made a note that some of these problems were long standing while others arose because of the unchanging educational system to reflect changing times. For instance, Ghanaian parents continue to grow more concerned about the relevance and quality of education being provided to their wards. Another article by Marzano and Pickering found a correlation between homework given and the performance of children. It is believed that with appropriate homework and parent involvement, children were seen to improve. It is with that in mind that I came up with the solution, EduApp. EduApp provides an assignment approval system that would help parents review their wards submissions. As a mobile and web application, EduApp is geared toward guaranteeing parents the quality of assignments given to their wards is implemented for the Ghanaian context. It is intended to serve as an attendance assessment, assignment distribution, teacher evaluation and student performance overview.

Source: Ashesi
Effect of IMF Conditionality Outcome on Debt Accumulation And Financial Instability in Sub-Saharan Africa

The International Monetary Fund aims to support countries in growing, reducing their debt, and improving their living standards. Given this, many sub-Saharan African countries relied on their financial support to embark on effective developmental projects. Examples include South Africa and Ethiopia. As it stands now, the current GDP of South Africa is US$ 373 billion, and that of Ethiopia is US$ 205 billion, with each of them maintaining lower debt. However, the IMF report shows that over 40 sub-Saharan African countries that have gone to the IMF several times have experienced otherwise. Their debt, GDP, employment rate, and government tax revenue keep on falling, which contradicts the mission of the IMF. The study then assessed whether the IMF conditionality outcomes are the major contributors to the fall in government tax revenue and employment levels issues in these sub-Saharan countries and whether their debt patterns due to frequent visits to the IMF adversely affect national savings via the Minsky model. The study then collected data from the IMF, the World Bank, and the International Labor Organization to perform a panel data analysis using the fixed and random effect models. The findings revealed that there is a positive relationship between the IMF conditionality outcome and government tax revenue and employment levels as well. The study also unveiled that national savings have been affected by the debt patterns of these sub-Saharan countries through the Minsky model due to their frequent visit to the IMF. Given this, the study recommends that stakeholders such as policymakers, development practitioners, and the governments of these sub-Saharan African countries should not depend solely on the IMF for financial assistance because, despite its growth, it skyrockets debts that affect other variables like national savings.

Source: Ashesi
Effect of hands-on science activities on Ghanaian student learning, attitudes, and career interest: A preliminary control study

A quasi-experimental study was carried out with 309 Form 3 students across 9 public Junior High Schools in the Greater Accra Region of Ghana. The effect of Practical Education Network (PEN)’s approach of training STEM teachers to employ hands-on activities using low-cost, locally-available resources was studied in terms of student learning outcomes, attitudes towards learning science, and interest in STEM majors/ careers. Over a 2.5-month period, the science teacher at each experimental school received a weekly training on a hands-on activity and lesson observation by the respective PEN Trainer. A survey on attitudes towards science and a previous edition of the national exam (BECE) were administered to all students before and after the intervention. The mean pre-post differences were compared between the experimental and control schools. The intervention caused an average of 10.9% increase in exam scores (difference-in-differences), but the results were mixed at the school-level. Unpaired t-tests and Hedge’s g tests were used to determine statistical significance between the two groups. Student engagement increased significantly (p = 3 x 10 -7 , g = 0.85), and student enjoyment of science increased 22% more, on average. The intervention disproportionately affected the females positively, enabling greater learning gains (14.5% vs. 5.3% for the males), greater increase in engagement, and a significant shift in interest towards STEM majors and careers, which their male counterparts did not experience. Results from this study should inform the design of future studies with longer duration and which account for factors such as school infrastructure quality.

Source: Ashesi
Effect of particle size and sintering time on the mechanical properties of porous Ti–6Al–4V implant

Titanium alloys have been extensively used in biomedical applications owing to its low density, excellent biocompatibility (i.e., biological and chemical inertness), and unique mechanical properties. However, there is a high disparity between Young’s moduli of the implant and the natural bone. This disparity causes stress shielding in the body. This paper presents the effect of particle size and sintering time of Ti–6Al–4V powder used in the formation of a porous implant, sintered at 980 °C. Morphological characteristics of the sintered samples were obtained with a scanning electron microscope. The effect of surface hydrophilicity of the samples was elucidated via surface wettability testing using contact angle measurement with bio-fluid. Mechanical characterization was also evaluated with nanoindentation and a universal testing machine. The relation between Young’s modulus and sintering time was presented. It was observed that the wettability decreases with sintering time and the Ti alloy powder with particle size < 150 µm had the Young’s modulus that is closer to the modulus of the bone; the optimum sintering time was 5 h.

Source: Ashesi
Electric autonomous lawn mower

This project aims to develop an electric autonomous lawn mower that can efficiently maintain lawns with less human intervention. Using electric power, the mower eliminates the need for gasoline, known to emit harmful pollutants into the environment, oil changes, and air filter replacements. It uses a rechargeable battery that powers electric motors to execute its task. Additionally, it has autonomous capabilities in the form of a web interface and remote control to increase its efficiency and minimize the risk of human injury. The mower has a web interface to set the defined area to mow when in automatic mode. This feature saves homeowners time and money by eliminating the need to hire a professional or manually mow their lawns. Overall, the project offers a sustainable and efficient solution for lawn maintenance. It represents a significant step towards a greener future and a more sustainable way of life.

Source: Ashesi
Electric vehicle charging infrastructure with three-way automatic switch.

Electric Vehicle (EV) technology is one of the steps in battling climate change through electrification of transport systems. Great advances in energy to size and energy to cost ratios have presented a great opportunity allowing all auto manufacturers to jumped for the idea of electrification as the future of transportation. The journey for better charging technologies is still a very important area of research. Among the main challenges in electrification of transport is inadequate charging stations on road networks to provide quick and reliable charging whenever EVs run out of power. This problem hinders people from adopting EVs especially in sub Saharan Africa where unreliable power supply and unstable grid energy infrastructure have become setbacks to deployment of current fast charging technologies. This capstone project showed that complimenting grid power with alternative decentralized energy sources will allow provision of reliable power to charging stations for charging of EVs. In this project eagle CAD is used in designing power switching and system controller to interface with pilot signaling, GFCI and relay circuits in controlling the charging of EV. Also, monitoring of the designed charging station as an IoT, a web application was built to monitor power sources and their frequency of switching on the main power circuit. While testing of most of the design modules on breadboard produced expected results, eagle designs of main power and system controller circuits generated 100% and 98% routed complete working circuits.

Source: Ashesi
Electricity bill calculation for compound houses in Ghana using Artificial Neural Network (ANN)

In Ghana, people live in a housing arrangement where members (tenants) of the household share basic facilities such as washrooms, kitchens and electricity meter - this is called a compound house. Over the years, there have been several complaints regarding these shared facilities but one remains paramount and is the lectric meters. Tenants mostly have concerns about how electricity bills are shared amongst themselves. It is assumed that tenants may not consume electric power as much as others do and as a result conclude bills are not shared fairly. Previous research has been done to verify the ability to spolit bills by implementing the Non-Intrusive Load monitoring system with focus on identifying different appliances used by different tenants. However, this did not take into consideration similar appliances used by different tenants. This paper investigates the possibility of distinguisihing between similar appliances used by different teanants. It was discovered that the Time Series Classification Algorithm had a higher overall performance when compared to the Multilayer Perceptron (MLP) henece is efficient for distinguising between similar applicances used by tenants.

Source: Ashesi
Elev8.com: An information technology based freelancing and skill learning platform for the Ghanaian society

Society today preaches that the world has become so interconnected that life has become seamless without the need for in person interaction and transactions, however, how are we using this seemingly connected society to our advantage, especially when some of our biggest challenges today are the high unemployment rate and access to a higher form of education than the book oriented we are used to, especially in the Ghanaian context? In this paper, a proposed solution is discussed and the Architecture, Implementation and testing are expounded on to see how best the problem of unemployment and book oriented education in Ghana, taking the Information Technology sector; Elev8, a web application designed for students to learn, workers to find freelance work and employers to hire and outsource, is suggested as a potential fit to the problem society faces today. A key feature it has is its ability to link freelancers or employees to potential employers and vice versa; a feature which will ultimately serve as the panacea for the problem identified.

Source: Ashesi
Embedded machine learning: Without Edge impulse

This paper presents an implementation of embedded machine learning using TensorFlow Lite Micro on two microcontroller boards, namely, KL25Z and Nana BLE. The implementation aims to recognize a wake word using a convolutional neural network (CNN). The paper details the design and implementation of the CNN architecture and the training and deployment process using TensorFlow Lite Micro. The results of the implementation show that the training of the CNN was successful and achieved high accuracy in wake word detection. However, deploying the trained model on the microcontroller boards was unsuccessful.

Source: Ashesi
Embedding information about road surface quality into Google Maps to improve navigation

One key tool for navigation is a map; it helps users to identify locations and features of a particular area. Anecdotally, Google Maps is one of the most commonly used navigation tools. Google Maps indicates the specific locations of a user and gives possible routes to a desired destination. Among the features indicated on the map are roads. They show where to move from one point to the other; but the map provides no indication of road surface quality. In some countries such as Ghana, not all roads indicated on the map are good. There are some poor roads, which when encountered by motorists while driving, results in bad driving experience. However, motorists can only tell on Google Maps the various routes to their desired destination without knowing the nature of the roads. Therefore, having a feature that would show the road surface quality on Google Maps would help motorists especially in Ghana plan their routes or even plan to reduce their speed on a given road due to the nature of the road. The focus of this project is to design a "road surface quality" feature that would be embedded into Google Maps to assist motorists determine the quality of roads during navigation. Ultimately, the road surface quality information would be obtained by crowdsourcing this information from many users running a mobile application that classifies roads based on accelerometer readings. Crowdsourcing is a method of obtaining information from a large group of people. The application for road classification is being developed by a colleague, Delali Vorgbe, but the specifics of how the classification is done are outside the scope of this work. This thesis focuses on how the information gathered about the road surface quality would be displayed on the map to the users, and whether having this information improves the navigation of users. The premise of this thesis is that when motorists continually provide their road data, the road surface quality information would accurately reflect the state of the road on the map. This feature on the map would then have an impact not only on motorists but on the nation as a whole. This is because when motorists know the road surface quality ahead of time, it would help them decide in advance the best, safest and most comfortable route to use. They can also plan to drive carefully on the poor roads if no other routes are available.

Source: Ashesi
EMG based control of assistive devices

This project aims to develop a smart, EMG-based system for use in assistive devices. This entire system is incorporated primarily through an EMG sensor which records muscle activity using electrodes, a raspberry pi which houses the machine learning algorithm and a robotic arm, which serves as the end effector. The system leverages electromyography (EMG) signals to interpret user-intended motions and control a robotic arm, providing enhanced mobility and functionality for individuals with physical disabilities. The EMG signals are acquired through an EMG spiker shield attached to an Arduino, which transmits the data serially to a Raspberry Pi. The Raspberry Pi processes the real-time EMG signals using a machine learning model trained to recognize four specific hand gestures, which are then mapped to control commands for the robotic arm equipped with SG90 servos. The hand gestures are clench fist, open hand, wrist flexion and wrist extension. Each of these hand gestures correspond to a specific hand movement. Underneath the high level overview of the project is an integration of several key components which work together to achieve the desired end. These include signal acquisition, preprocessing of acquired data, machine model training and testing real-time processing, and machine learning-based gesture recognition. The primary focus is on creating a reliable, responsive and user-friendly interface for controlling assistive devices, specifically a bionic arm emphasizing the practical application of EMG signal processing and embedded systems. Through this approach, the project aims to provide a cost-effective, scalable solution that can significantly improve the quality of life for users, enabling greater independence and ease in performing daily tasks.

Source: Ashesi
Emotion recognition from speech: An implementation in MATLAB

Human Computer Interaction now focuses more on being able to relate to human emotions. Recognizing human emotions from speech is an area that a lot of research is being done into with the rise of robots and Virtual reality. In this paper, emotion recognition from speech is done in MATLAB. Feature extraction is done based on the pitch and 13 MFCCs of the audio files. Two classification methods are used and compared to determine the one with the highest accuracy for the data set.

Source: Ashesi
Emotion recognition using image processing

Emotion recognition is an active field of research that has seen a lot of interest over the past decade. Historically, people’s emotions were analysed and determined through human observation and psychological counselling and later evolved to electrophysiological and largely intrusive methods such as Electroencephalography (EEG) because of how complex of a task it is and how extensive its application could be. Currently, with the entrance of machine learning and computer vision-related technologies, computers and robots can now be trained to learn and predict the emotions of human beings either in real-time or with their static facial images. In this research, emotion recognition is explored with respect to its three widely recognised stages: face detection, feature extraction and emotion recognition. At each of these stages, different image processing methods and learning techniques are explored and tested. A Convolutional Neural Network was trained and tested and recorded an accuracy of 50.7%. A Support Vector Machine was also trained and tested and recorded an accuracy of 81.5%. Both classifiers were trained on 7 emotion categories. The results show that it is possible for computers to predict the emotions of humans using image processing techniques and deep learning models. ; deep learning; ; image processing;

Source: Ashesi
Emotional support system for supporting children on the autism spectrum using an embedded system

Autism Spectrum Disorder is a group of pervasive neurodevelopmental disorders mainly characterised by aberrant restrictive behaviour patterns such as self-injurious behaviours (SIB). These behaviours are attributed to the social impairments of the autistic child. This project proposes the design of a miniature robot toy that responds to tactile triggers through vocal communication, facial expressions, and body gestures. The robot is designed to be low powered and low cost to ensure that parents with children on the spectrum can have an affordable intervention medium for their children. The different triggers for the different emotional states regarding the requirements suited to an autistic child are met by the robot design.

Source: Ashesi