Ho Technical University, formerly Ho Polytechnic, is a public tertiary institution in the Volta Region of Ghana. The Polytechnic started in 1968 as a technical institute with the primary goal of providing pre-technical education. By 1972, the Institute made tremendous progress and upgraded its courses. In 1986, the institution was upgraded into a Polytechnic. However, it was not until 1993 that it got full backing of the law (Polytechnic Law 321) to become a fully-fledged tertiary institution, charged with the responsibility of training students to the Higher National Diploma (HND) and Degree Levels.
Abstract Copula models offer a flexible statistical framework for describing complex dependence structures between variables by linking marginal distributions to a joint distribution. In recent years, they have attracted growing interest in air pollution research due to their ability to capture nonlinear relationships and extreme dependencies among pollutants. This study presents a bibliometric and content analysis of research applying copula models in air pollution studies using data retrieved from the Scopus database for the period 2005–2026. The review examines publication trends, leading contributors, and emerging research directions. A synthesis matrix for the relevant publications, allowed the identification of methodological developments and key research themes. The results showed increasing scholarly attention to modelling pollutant interdependence and environmental risk. Three main thematic areas were identified: dependence structure modelling of air pollutants, copula-based forecasting and hybrid machine learning approaches, and risk assessment of extreme pollution events. Recent studies show a methodological shift toward more advanced techniques like vine copulas, hybrid AI-copula frameworks, and spatiotemporal dependence models, which enhance predictive performance and interpretability. Overall, the review highlights the expanding role of copula models in air pollution analysis and emphasizes the need for methodological consistency, and stronger integration with public health and climate resilience research.
This study evaluates the structural feasibility of using shredded plastic waste remnant (SPWR) as a partial replacement for fine aggregate in conventional reinforced concrete (RC) beams. Unlike previous studies limited to mortar or small-scale specimens, this research investigates full-scale reinforced concrete beams using locally sourced SPWR from Ghanaian waste streams under realistic flexural loading conditions. Concrete mixes were prepared with SPWR replacement levels of 0 to 25
The rise of malware in highly interconnected and resource-limited distributed edge networks poses a considerable challenge for traditional security measures. Effective malware detection in these environments requires real-time analysis capabilities, minimal computational overhead on edge devices, strong resilience against adversarial evasion techniques, and the preservation of data privacy across distributed nodes. This paper presents EdgeFence, an innovative framework aimed at lightweight adversarial malware detection within distributed edge networks, utilising Federated Temporal Graph Neural Networks (FTGNNs). EdgeFence represents the dynamic behaviour of processes and system interactions at individual edge nodes through the use of temporal graphs. In contrast to centralised methods, it utilises a federated learning framework, enabling edge devices to work together in training a global detection model by exchanging model updates instead of raw data, which helps maintain data privacy and minimises communication overhead. A significant contribution is the incorporation of Temporal Graph Neural Networks refined for efficiency, adept at capturing sequential dependencies and structural anomalies in dynamic graph data streams produced at the edge. Additionally, EdgeFence integrates adversarial training methods into the federated learning framework to improve the model’s resilience against advanced malware intended to bypass GNN-based detection. Our evaluation shows that EdgeFence attains high accuracy and low false positive rates in detecting various malware families in real-time on resource-limited edge devices, while also demonstrating considerable resilience to adversarial attacks. EdgeFence offers a practical and scalable solution for securing large-scale distributed edge computing infrastructures against evolving cyber threats, thanks to its lightweight architecture and federated learning approach.
Tuo zaafi, a traditional dish prepared from cereals and often accompanied by dark green vegetable soup, is a revered dish well known for its medicinal and nutritional attributes. The cereals are usually prone to mycotoxin contamination. This study aimed to determine fungal diversity, mycotoxin contamination (Ochratoxin A, Aflatoxins, Fumonisins), and consumer risk associated with tuo-zaafi in the northern regions of Ghana. Fungi were identified using standard mycology protocols, and a high-performance liquid chromatography-fluorescence detector (HPLC-FLD) was used to analyze mycotoxin levels in the samples. Cancer risk assessments were done using deterministic models proposed by a Joint FAO/WHO Expert Committee on Additives. The fungal counts were between the ranges of 3.19 and 4.27 log10 CFU/g. Some species of the genera Aspergillus, Fusarium, Trichoderma, Penicillium, Mucor, Rhizopus, Cladosporium, Alternaria, and Saccharomyces contaminated the food samples. Additionally, Aflatoxins (13.05-24.51 µg/kg) and Fumonisins (101.59-126.18 µg/kg) exceeded regulatory limits set by the Ghana Standards Authority (GSA) and the European Food Safety Authority (EFSA) in a majority of samples. Risk assessment based on Margin of Exposure (MOE) calculations revealed values below 10,000, indicating a significant carcinogenic public health concern. These findings highlight the urgent need for regulatory interventions and public awareness campaigns to mitigate mycotoxin exposure.
Sentiment analysis (also known as opinion mining) is a natural language processing (NLP) technique for determining data’s positive, negative, or neutral nature. The rise of social media platforms such as X (formally Twitter) and Facebook have become great arenas for discourse on racism and mediums of racism ideologies. This study utilized a hybrid sentiment analysis to detect racist tweets using lexicon-based sentiment analysis and a Support Vector Machine. The models’ success in accurately classifying sentiments related to racism highlights its potential for broader applications in the analysis of other social issues. Furthermore, this study contributes to the ongoing discourse on combating racism in the digital age. By shedding light on the sentiments expressed online, it provides valuable insights that can inform policy decisions, advocacy efforts, and public awareness campaigns. The findings underscore the importance of addressing racism not just in the physical world but also in the digital sphere, where harmful ideologies can spread rapidly and widely.
