Muni Repository (MR)
This repository contains open access publications of Muni University Library.
Objectives:
- To digitally collect, preserve and provide electronic access to scholarly works and research output of Muni University.
- Increase the visibility and impact of our research, making it easy for researchers, students, policymakers and journalists to reference, replicate, and re-use the work.
- Issue permanent, unique and trustworthy identifiers when creating URLs to access the resource without concern that the location of the resource may change.
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- Contact the library through email: libsupport@muni.ac.ug
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Recent Submissions
Wearable Nanogenerators power health monitors in off-grid regions
(IEEE, 2025-12-29) Lamba, Akshit; Shamya, A; Fallah, Mohammed H.; Bahodirkhonugli, Sayfiddinov Izzatullakhon; Nallakumar, R.; Ali, Guma
Health monitoring devices in remote areas often don’t have reliable power, making it hard for healthcare staff to help these patients promptly. Because some regions lack reliable electricity, healthcare workers usually struggle to monitor heart rate, blood pressure, and temperature. We suggest combining wearable nanogenerators with health monitoring systems so that the user's motion powers them. Combining triboelectric and piezoelectric principles, the tiny devices can provide the electrical power needed to operate health monitors when people walk or move their arms. This system's algorithm for managing energy permits the devices to operate longer. Simulations of different motions confirm that the proposed system can provide sufficient electricity for the health monitors to run independently. This solution works best for hard-to-reach or underserved areas, providing a more sustainable and affordable alternative to standard power-dependent health devices. The novelty of this study lies in the integrated approach of coupling hybrid piezoelectric– triboelectric nanogenerators with an adaptive energy management algorithm designed specifically for wearable healthcare devices. Unlike prior works that focus primarily on material enhancement or single-source energy harvesting, this research emphasises a co-optimised framework that integrates motion-based energy conversion, storage regulation, and power utilisation control. The contribution of this work is the development of a self-sustaining, algorithm-governed wearable system capable of reliable health data monitoring in off-grid and energy-scarce environments.
Real-time power analytics prevent blackouts in overloaded urban grids
(IEEE, 2025-12-29) Balassem, Zaid Ajzan; Vij, Priya; Kumar, S. Senthil; Rakhmanovich, Ibragimov Ulmas; Pushpalatha, A.; Ali, Guma; Karimova, Farida; Arnav, Jain
Increased demand for energy, factors that change load, and aging infrastructure are putting tremendous pressure on urban power grids. Because of these problems, regular blackouts and trouble keeping the power on occur in large, crowded cities during high-demand times. Typical grid management systems react to events, as they depend on delayed data and mostly manual actions to avoid cascading failures after an overload occurs. To manage these real-time threats, we present in this paper a unique Real-Time Power Analytics Framework (RTPAF) that continuously observes the grid with smart meters and edge computing, uses LSTM neural networks to forecast possible overloads, and sets automatic load redistribution actions using intelligent controllers. A multi-staged framework connects fast data capture, noise reduction before analysis, predictive tools, and critical system response to prioritize hospitals and transport networks. A simulation of an urban grid with 500 nodes, built in GridLAB-D and MATLAB Simulink, was performed to check how the system operated. The simulation found that RTPAF brought down the number of typical blackouts by over 90%, and because its reaction was less than 500 milliseconds, it quickly mitigated possible overload situations. As a result, the model's forecasting accuracy of 94.3% significantly improved the grid’s ability to plan and make decisions. Using this approach in real time strongly supports energy security, minimizes cases where power is interrupted, and can meet the high reliability requirements for future smart cities. The solution suggested is a significant achievement for the preemptive management of urban energy.
Agricultural waste to biofuel: Transforming crop residue into next-generation aviation fuel.
(IEEE, 2025-12-29) Maury, Shyam; Mangaiyarkarasi, V.; Madaminjonugli, Bakhriddinov Makhamadali; MuhamedAle, Hasssan; Arunkumar, E.; Ali, Guma; Rakhimov, Navruzbek; Shetty, Chinmai
The rapid expansion of international aviation has significantly contributed to greenhouse gas emissions. The existing type of jet fuel consumes a considerable amount of crude oil, which has compounded the demand. As a result, the industry accounts for a carbon footprint that reduces the pressure on alternative energy sources with lower carbon emissions. Agricultural waste comprises straw, husks, and stalks, which denotes one such available lignocellulosic feedstock that is not exhaustively utilised to offer a solution to environmental and economic dilemmas in the aviation energy production. The given research paper proposes an integrated solution to transform agricultural waste materials into high-energy-density biofuels, which can be utilised in the aviation industry through a two-stage biochemical and thermochemical treatment, followed by subsequent fuel upgrading to produce a high-quality product. Pre-treatment options, which were attempted on a lab scale, included steam explosion, dilute acid hydrolysis, enzyme saccharification, fermentation, pyrolysis, and Fischer-Tropsch catalytic upgrading. The parameters of the process conditions were optimised to achieve a high yield and minimise energy consumption. Results were statistically analysed to ensure reproducibility, and fuel properties were compared to ASTM D7566 standards to verify that they conformed to conventional jet fuel specifications. The results show that biofuels produced from agricultural waste have an energy density similar to that of Jet A fuel, with notable reductions in carbon and particulate emissions, making them a viable option for mitigating greenhouse gas emissions caused by aviation. The techno-economic analysis also demonstrates the viability of large-scale implementation, based on the availability of feedstock, process effectiveness, and compliance with regulations. Twith regulations. The practice is also compatible with the principles of the circular economy , which emphasises the value of agricultural residues, agrarian eco nomies, and sustainable waste management. Moreover, it is possible to optimise it with AI, emulate the use of blockchains to track feedstock, and adopt the concept of hybrid biofuel electric to make the future of biofuel work more efficiently and easily tr acked. Comprehensively, the paper demonstrates that agricultural waste can be a feasible and sustainable aviation biofeedstock of the next generation, as it can help minimise carbon footprints, make biofuels economically viable, and promote the current trend of carbon neutral aircraft in the global community.
AIoT-driven smart agri-grid (ASAG) for sustainable precision agriculture
(IEEE, 2025-12-29) Sundaram, N. Kalyana; Rajendran, Megala; Ehssan, Muhamed; Soy, Aakansha; Anandhi, K.; Begum, T Ummal Sariba; Ali, Guma; Dhananjaya, B
By advising and teaching farmers on how to apply modern farm practices that embrace Artificial Intelligence (AI) and the Internet of Things (IoT), precision agriculture is revolutionising sustainable farming by optimising for usages that are as much as possible and waste as little as can be afforded. In this research, we propose an AIoT-driven Smart Agri Grid (ASAG) framework that integrates real-time nanosensor networks, an AI-operational control microclimate, an autonomous decision-support system, and secure data sharing via a blockchain using encrypted statistical data. To achieve real-time analytics, edge computing is used in the framework for real-time data analytics, predictive algorithms for dynamic irrigation & nutrient management, and federated learning for distributed AI training, which maintains privacy and scalability. In addition, the system uses AI-based waste-minimisation techniques, such as predictive harvest timing and the conversion of bio-waste into organic fertilisers, thereby reducing post-harvest losses. Experimental results show that ASAG can improve crop yield by 20 to 30%, reduce water waste by up to 50%, and reduce chemical overuse by up to 30%, with its economic and environmental benefits. The feasibility of such deployment on a large scale in precision agriculture is further confirmed by a cost-benefit analysis. The results reinforce the power of AI and IoT in transforming contemporary farming into a self-optimising, climate-resilient system. For long-term sustainability in global agriculture, quantum AI will be used to predict soil health, monitor AI-assisted carbon sequestration, and enable genomic AI for climate-resistant crops.
Inter-simple sequence repeat markers reveal a moderate genetic diversity among fusarium species causing common bean root rot in Uganda
(Journal of Advances in Biology & Biotechnology, 2026-02-14) Erima, Samuel; Nyine, Moses; Edema, Richard; Nkuboye, Allan; Orodriyo, Harriet; Candiru, Agnes; Otim, Michael Hilary; Paparu, Pamela
Aims: The present study aimed to determine the genetic diversity and population structure of Fusarium species causing common bean root rot in Uganda
Study Design: The study used isolates from a previous disease survey in Uganda
Place and Duration of Study: The isolates were collected from 6 different agro-ecological zones of Uganda. Isolation was conducted at the legumes pathology laboratory of the National Agricultural Research Organization at Namulonge, Kampala. The isolates were collected in 2019.
Methodology: DNA was extracted from 101 Fusarium species isolates using a modification of the cetyltrimethylammonium bromide protocol. Seventeen inter-simple sequence repeat primers were used in the polymerase chain reaction. The bands were scored for presence and absence using 1 and 0, respectively. The genetic diversity and population structure were determined using parameters such as polymorphic information content, allele divergence frequency, Principal component analysis, and admixture analysis. Analysis of molecular variance was also conducted.
Results: The average polymorphic information content of the isolates was 84%. The average Wright's fixation index (Fst) and expected heterozygosity were 0.2. The result of the analysis of molecular variance revealed that 0.2% of the variation was between the agro-ecological zones, while 99.8% of the variation was within agro-ecological zones. Admixture analysis showed that the isolates have an admixed ancestry.
Conclusion: Since the isolates from the different agro-ecological zones were similar, released varieties may not face extreme variants when they are planted in agro-ecological zones where they were not screened.