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.
How to publish in Muni Repository
- Contact the library through email: libsupport@muni.ac.ug
Communities in MR
Select a community to browse its collections.
Recent Submissions
Model predictive control based on single-phase shift modulation for triple active bridge DC-DC converter
(Springer Nature, 2024-12-05) Adam, Ahmed Hamed Ahmed; Chen, Jiawei; Xu, Minghan; Kamel, Salah; Ali, Guma
The triple-active bridge (TAB) converter is widely used in various applications due to its high efficiency and power density. However, the high-frequency (HF) transformer coupling between the ports presents challenges for controller design. This article presents a model predictive control (MPC) approach based on single-phase shift modulation for the TAB converter. The developed MPC offers improved transient performance, control flexibility, and precision, ensuring compliance with DC voltage regulations and achieving optimal solutions for port decoupling. The MPC utilizes a cost function to provide robust voltage regulation, and an algorithm based on Karush-Kuhn-Tucker (KKT) conditions is developed to derive closed-form solutions for optimal control parameters. To validate the performance of the TAB converter with the proposed MPC control, Typhoon 602 hardware-in-loop (HIL) experimental case study is conducted. Additionally, a comparison with previous works is carried out to confirm the effectiveness of the proposed method. The results of the HIL experimental setup and the comparative analysis demonstrate that the developed method is effective, providing faster dynamic characteristics and port power decoupling operation capability.
A secure and efficient blockchain and distributed ledger technology-based optimal resource management in digital twin beyond 5G networks using hybrid energy valley and levy flight distributer optimization algorithm.
(IEEE, 2024-08-19) Kumar, K. Suresh; Alzubi, Jafar A.; Sarhan, Nadia M.; Awwad, E. M.; Kandasamy, V.; Ali, Guma
This paper aims to establish a virtual object management system, as well as optimal task scheduling using the foundation of Digital Twins (DT), to improve the user’s experience with management and to accomplish the task efficiently. On the other hand, offloading tasks using IoT gadgets to edge computing, fails to speed up control by users. The capabilities of the DT are provided by executing processes such as visualization, virtualization, synchronization, and simulation. The optimal selection of the virtual objects for the DT is done by utilizing the implemented Hybrid Energy Valley with Lévy Flight Distribution Optimization (HEV-LFDO) in order to optimally offload the task by the edge devices. The optimal selection of the virtual objects is done with the aid of the HEV-LFDO in the DT by considering the total cost of executing all tasks using the selected virtual objects and the decision variables to determine whether a virtual object is taken for executing a task or not as the constraint. The data for performing resource management is secured using the blockchain or distributed ledger technology. This accounts for the minimization of the local loss function. Finally, the secured data is considered for optimal resource management tasks. The optimal resource management is done using the same HEV-LFDO. This optimal resource management is carried out by considering the constraints like the cost of assigning a virtual object for the task to the edge device, and the cost of assigning the task to the edge device. These two costs are analyzed by taking the network’s bandwidth, energy consumption, and computational resources into consideration. Experimental verifications are conducted on the executed optimal resource management scheme to prove the ability of the implemented model to be integrated with the edge computing network. The overall processing time as well as the latency
are also minimized by executing the optimal resource management scheme.
Harnessing the potential of artificial intelligence in managing viral hepatitis
(Mesopotamian journal of Big Data, 2024-08-15) Ali, Guma; Mijwil, Maad M.; Adamopoulos, Ioannis; Buruga, Bosco Apparatus; Gök, Murat; Sallam, Malik
Viral hepatitis continues to be a serious global health concern, impacting millions of people, putting a strain on healthcare systems across the world, and causing significant morbidity and mortality. Traditional diagnostic, prognostic, and therapeutic procedures to address viral hepatitis are successful but have limits in accuracy, speed, and accessibility. Artificial intelligence (AI) advancement provides substantial opportunities to overcome these challenges. This study investigates the role of AI in revolutionizing viral hepatitis care, from early detection to therapy optimization and epidemiological surveillance. A comprehensive literature review was conducted using predefined keywords in the Nature, PLOS ONE, PubMed, Frontiers, Wiley Online Library, BMC, Taylor & Francis, Springer, ScienceDirect, MDPI, IEEE Xplore Digital Library, and Google Scholar databases. Peer-reviewed publications written in English between January 2019 and August 2024 were examined. The data of the selected research papers were synthesized and analyzed using thematic and narrative analysis techniques. The use of AI-driven algorithms in viral hepatitis control involves many significant aspects. AI improves diagnostic accuracy by integrating machine learning (ML) models with serological, genomic, and imaging data. It enables tailored treatment plans by assessing patient-specific characteristics and predicting therapy responses. AI-powered technologies aid in epidemiological modeling, and AI-powered systems effectively track treatment adherence, identify medication resistance, and control complications associated with chronic hepatitis infections. It is vital in identifying new antiviral medicines and vaccines, speeding the development pipeline through high-throughput screening and predictive modeling. Despite its transformational promise, using AI in viral hepatitis care presents various challenges, including data privacy concerns, the necessity for extensive and varied datasets, and the possibility of algorithmic biases. Ethical considerations, legal frameworks, and multidisciplinary collaboration are required to resolve these issues and ensure AI technology’s safe and successful use in clinical practice. Exploiting the full AI’s potential for viral hepatitis management provides unparalleled prospects to improve patient outcomes, optimize public health policies, and, eventually, and alleviate the disease’s negative impact worldwide. This study seeks to provide academics, medics, and policymakers with the fundamental knowledge they need to harness AI’s potential in the fight against viral hepatitis.
Legacy and emerging organic pollutants in indoor and outdoor environments in africa: contamination levels, health risks, and analytical techniques
(Elsevier, 2025-01-02) Muhwezi, Godfrey; Kyarimpa, Christine; Gumula, Ivan; Nagawa, Christine Betty; Omwoma, Solomon; Matovu, Henry; Matsiko, Julius; Odongo, Silver; Sifuna, Douglas; Špánik, Ivan; Kato, Charles Drago; Ssebugere, Patrick
The World Health Organisation reported that over 20% of the global disease burden and deaths are caused by environmental factors such as air and dust pollution. Legacy organic pollutants (LOPs) and emerging organic pollutants (EOPs) are among the biggest contributors to this burden. Herein we reviewed literature published for the period 2000-2023 on selected LOPs and EOPs in indoor dust and outdoor air in Africa to understand the occurrence, associated human health risks and common analytical techniques used to identify and quantify these contaminants. The findings revealed generally lower contamination levels for most pollutants than that in most regions outside Africa except for PCBs in outdoor air and OCPs in indoor settled dust. Apart from a few studies for polychlorinated biphenyls (PCBs), most studies reported hazard index (HI) values less than 1 for outdoor air and indoor dust exposure, which suggested negligible non-carcinogenic risks in children and adults. Gas Chromatography and mass spectrometry were the most frequently used analytical techniques for both air and dust probably due to their sensitivity and selectivity in detecting chemical traces at the pg m-3 and ng g−1 levels. Although the current contamination levels are generally low, their synergistic and cumulative effects may in the long run affect the environment and human health. There is also a paucity of data on most contaminants, especially for PCNs, and PFASs in both outdoor air and dust. Coordinated efforts are needed to limit the trade, importation, and disposal of products containing LOPs and EOPs in Africa.
Modules with reduced endomorphism rings
(World Scientific Publishing, 2024-10-17) Kimuli, Philly Ivan; Ssevviiri, David
In this paper, we study endo-reduced modules as modules whose endomorphism rings have no nonzero nilpotent elements. We characterize their properties for different classes of modules, including K-non-singular modules, multiplication modules and finitely generated modules over commutative Dedekind domains. In the subcategory of finitely generated modules, it is shown that the class of rings R for which every faithful multiplication
R-module is endo-reduced is precisely that of reduced rings; while the class of rings R for which every multiplication R-module is endo-reduced is precisely that of von Neumann regular rings. Characterizations of when an endo-reduced module will be a reduced module are given. We prove that a finitely generated module over a principal ideal domain (PID) is endo-reduced exactly if it is either a semisimple module with pair-wise non-isomorphic submodules or a torsion-free module which is isomorphic to the underlying ring.