http://ir.tum.ac.ke/handle/123456789/13 Journal articles Sat, 13 Jun 2026 17:23:26 GMT 2026-06-13T17:23:26Z http://ir.tum.ac.ke/handle/123456789/17674 Flavonoids Diversity and Therapeutic Significance in Terminalia brownii Kahindo J.M., J.M. Terminalia brownii species within the family Combretaceae, is a widely distributed deciduous tree native to tropical and subtropical regions of Africa, particularly in Eastern and Central parts, including Kenya, Ethiopia, Sudan, and Uganda. Traditionally, various parts of T. brownii, including the bark, leaves and roots have been used in folk medicine to manage diarrhea, malaria, wound infections, respiratory disorders, and gastrointestinal conditions. The medicinal value of T. brownii is largely attributed to its rich profile of secondary metabolites, notably tannins, saponins, alkaloids, phenolics, and flavonoids. Flavonoids such as quercetin, kaempferol and luteolin have been identified in different plant parts, contributing to the plant's antioxidant, antimicrobial, anti-inflammatory and hepatoprotective activities. Despite growing interest in the pharmacological properties of T. brownii, comprehensive reviews focusing specifically on the diversity and therapeutic relevance of its flavonoid compounds remain limited. This review aims to bridge that gap by summarizing the subclasses of flavonoids isolated from different parts of T. brownii and highlighting their medicinal applications with a view toward informing future drug discovery and development efforts. : 10.9734/CSJI/2025/v34i4984 Mon, 16 Aug 0202 00:00:00 GMT http://ir.tum.ac.ke/handle/123456789/17674 0202-08-16T00:00:00Z http://ir.tum.ac.ke/handle/123456789/17647 Deep learning models for the early detection of maize streak virus and maize lethal necrosis diseases in Tanzania Mayo, Flavia; Maina, Ciira; Mgala, Mvurya; Mduma, Neema Agriculture is considered the backbone of Tanzania’s economy, with more than 60% of the residents depending on it for survival. Maize is the country’s dominant and primary food crop, accounting for 45% of all farmland production. However, its productivity is challenged by the limitation to detect maize diseases early enough. Maize streak virus (MSV) and maize lethal necrosis virus (MLN) are common diseases often detected too late by farmers. This has led to the need to develop a method for the early detection of these diseases so that they can be treated on time. This study investigated the potential of developing deep-learning models for the early detection of maize diseases in Tanzania. The regions where data was collected are Arusha, Kilimanjaro, and Manyara. Data was collected through observation by a plant. The study proposed convolutional neural network (CNN) and vision transformer (ViT) models. Four classes of imagery data were used to train both models: MLN, Healthy, MSV, and WRONG. The results revealed that the ViT model surpassed the CNN model, with 93.1 and 90.96% accuracies, respectively. Further studies should focus on mobile app development and deployment of the model with greater precision for early detection of the diseases mentioned above in real life. 10.3389/frai.2024.1384709 Fri, 16 Aug 2024 00:00:00 GMT http://ir.tum.ac.ke/handle/123456789/17647 2024-08-16T00:00:00Z http://ir.tum.ac.ke/handle/123456789/17646 Mapping Population Densities and Waste Management Systems in Mombasa County Nyachiro, Asnath; Mgala, Dr. Mvurya Mombasa County is one of the five counties along the coastline of Kenya. Currently, Mombasa County has a human population of 1.2 million people, and the population is rapidly increasing through rural-urban immigration and natural births. However, the inadequacy of well-established waste management systems on the mainland and coastlines of the County is a threat to the well-being of the local residents. Poorly disposed of wastes both on land and at sea can lead to health problems not only for humans and terrestrial animals but also for aquatic animals such as fish, with the latter being one of the main sources of affordable proteins for poor coastal communities. This paper aimed to conduct a review of the spatial human population density of Mombasa County and the geospatial location of waste dumping sites and their proximity to settled areas. This literature review synthesized existing research on the application of Geographic Information Systems (GIS) and Remote Sensing techniques in mapping population densities and assessing waste management systems. The review began by examining studies that investigate population distribution patterns in urban and rural areas, utilizing GIS to analyze demographic data and satellite imagery. It explored methodologies used to estimate population densities, including dasymetric mapping, spatial interpolation, and land use classification techniques. Key findings were that limited studies have utilized GIS technologies to assess the population in Kenya. GRASP and Random Forest (RF) were the main techniques previously used to assess population densities in the rural Taita Hills area and along the coastal region. The population has exponentially increased in Mombasa County since the 1950s and is projected to increase further. Additionally, waste management in Mombasa County is majorly controlled by the county government. Ten geo-tagged waste collection spots were identified during the review, spread across the residential areas. In conclusion, the county should endeavor to employ GIS techniques to assess the rapid population change within the county and have targeted interventions to address the disparities in waste collection systems against population increase 10.24940/ijird/2024/v13/i6/JUN24050 Mon, 01 Jan 2024 00:00:00 GMT http://ir.tum.ac.ke/handle/123456789/17646 2024-01-01T00:00:00Z http://ir.tum.ac.ke/handle/123456789/17645 A Systematic Review of Computer Science Solutions for Addressing Violence Against Women in Educational Institutions Omar, Amina S.; Mgala, Mvurya : Approximately one in three women worldwide experience physical, mental, or sexual violence, making violence against women (VAW) a serious public health emergency. One of the main issues in educational institutions is violence against women. With the introduction of smart campuses and smart technologies, educational institutions are doing everything within their power to avert these kinds of incidents. Recent developments in computer science, such as artificial intelligence (AI), Internet of Things (IoT), and pattern recognition, have been essential in creating solutions meant to stop and react to VAW. This study presents a thorough systematic review from academic digital libraries from 2010-2023 of some of the initiatives that have been used to address the issue of violence against women. The state-of-the-art for these contributions is currently described in this document along with trends, architectures, technologies, and open problems. It highlights how these technological interventions are utilized for early detection, prevention, and response to incidents of VAW. The findings suggest a growing reliance on technology to create safer educational environments, but also emphasize the need for continued research, particularly in developing inclusive, ethical, and effective technological solutions. This review aims to inform stakeholders in the education and technology sectors about the current state of computer science applications in the fight against VAW, providing insights into best practices and areas for future development. 10.7753/IJCATR1307.1001 Mon, 01 Jan 2024 00:00:00 GMT http://ir.tum.ac.ke/handle/123456789/17645 2024-01-01T00:00:00Z