Kampala International University

The increasing digitization of academic environments has made campus networks essential for learning, research, and administration; however, these networks are highly vulnerable to evolving cyber threats. Among these threats, Distributed Denial of Service (DDoS) and Address Resolution Protocol (ARP) spoofing attacks pose significant risks to network availability, integrity, and confidentiality. Traditional rule-based and signature-based intrusion detection systems are limited by their static nature and poor adaptability to the dynamic traffic patterns typical of campus networks. This review systematically examines Machine Learning (ML) techniques for detecting and preventing DDoS and ARP attacks. Guided by the PRISMA framework, a total of 110 studies were selected from 206 peer-reviewed papers published from 2020 to 2025. Supervised, unsupervised, and hybrid ML models utilizing algorithms such as Random Forests, Support Vector Machines, Convolutional Neural Networks, Long Short-Term Memory networks, and CNN-LSTM ensembles demonstrate high detection accuracy and robust anomaly modeling under real-time, resource-constrained conditions. Integration with Software-Defined Networking (SDN), federated learning, and edge computing enhances scalability, privacy preservation, and low-latency mitigation. Despite these advances, challenges remain in dataset representativeness, model generalization, interpretability, computational overhead, and the development of unified frameworks capable of simultaneously mitigating volumetric DDoS, application-layer attacks, and Layer-2 ARP spoofing. Emerging approaches, such as reinforcement learning and explainable AI, offer promise for adaptive, zero-day, and adversarial threat mitigation. The review highlights the need for modular, context-aware, and scalable ML frameworks validated on real campus traffic to enable resilient and operationally feasible intrusion detection and prevention. These insights provide a foundation for designing secure, adaptive, and intelligent network defense strategies in modern campus environments.

Persistent toxic substances (PTS), including heavy metals, persistent organic pollutants (POPs), and persistent, mobile, and toxic/very persistent and very mobile (PMT/vPvM) substances present an increasing menace to soil health, alimentary systems, atmospheric cleanliness as well as human health. Despite the large amount of literature on each of the individual groups of contaminants, there is still no unified model that connects the dynamics of the soil-atmosphere environment, bioaccumulation in the food chain, new detection techniques, and policy measures. This review presents an interdisciplinary synthesis of dynamics in the PTS in the agricultural environment, explicitly incorporating (i) historic contaminants and emerging PMT/vPvM chemicals, (ii) soil-crop-livestock-human transfer pathways, and (iii) the state-of-the-art remediation and monitoring technologies into a single management framework. We critically evaluated conventional remediation methods alongside next-generation methods, such as engineered consortia of microorganisms, synergistic phytotransformation of plants and microbes, biochar-assisted immobilization, nanosensor-based detection, IoT-based soil sensing, precision agriculture, machine-learning-driven risk prediction, and blockchain-based traceability. Contrary to the previous reviews, which only take into account the remediation, detection, and policy separately, this study presents a systems-based approach, which integrates technological innovation, sustainable agronomic practices, and multilayered governance tools (such as the Stockholm Convention, REACH, and national soil action plans). We highlight the fact that the combination of smart agricultural technology and regenerative land management will help reduce the accumulation of PTS and maintain productivity, especially in resource-scarcity settings. The review outlines the research gaps, including contaminant-microbiome interactions, longitudinal deterioration of ecosystem services, and socioeconomic barriers to technology adoption. We propose a transdisciplinary roadmap that aligns environmental toxicology, soil science, public health, and policy innovation to mitigate PTS and safeguard food security. This integrative approach provides a strategic framework for advancing sustainable management of persistent toxic substances in agricultural systems. This study looks at persistent toxic substances (PTS), harmful chemicals like some pesticides, industrial pollutants, and heavy metals that do not easily break down in the environment. Because they linger in soil, water, air, and food, they can move through the food chain and affect both ecosystems and people. In this study, the authors reviewed recent research and real-world cases to explain where PTS come from (e.g., farming chemicals, industrial waste, plastics), how they spread (air, water, and soil), and what health problems they can cause (such as hormone disruption, breathing issues, nerve damage, and cancer). They also examined solutions, from traditional cleanup methods to newer, nature-based options. Created in https://BioRender.com

Glycated hemoglobin (HbA1c) is a well-established biomarker reflecting chronic glycemic control in diabetes. It accumulates through non-enzymatic glycation of hemoglobin under sustained hyperglycemia and serves as a surrogate of metabolic memory. Emerging in parallel, glycosylated RNA (glycoRNA), small noncoding RNAs bearing covalently attached N-linked glycans, has revealed unexpected roles in immune signaling and glycoimmunomodulation. While glycation and glycosylation represent distinct biochemical processes, both are modulated by glucose availability and cellular stress. This opinion paper aimed to explore the conceptual parallels between HbA1c and glycoRNA, proposing that hyperglycemia-induced metabolic changes may simultaneously influence both processes. In light of these, glycoRNA represents an emerging biomarker as a functional effector in metabolic disease, mirroring the hyperglycemia-driven immunological dimensions of HbA1c with a further advantage of a defined metabolic sequence, which can deepen diagnostics towards disease progression patterns. Though direct experimental evidence is currently limited, we outline plausible mechanistic intersections and suggest methodological frameworks for future research. Therefore, this perspective aims to stimulate interdisciplinary investigation into glycoRNA biology within the broader context of glycemic dysregulation and immune modulation in diabetes.

Digitalisation of the power grid, through advanced metering, wide-area measurement, and IP-based control has improved visibility and automation, but it has also widened the cyber-attack surface. This review examines how machine learning is being applied to protect grid operational technology (OT) and the associated information and communication technology (ICT) stack. Most studies focus on intrusion/anomaly detection for supervisory control and data acquisition (SCADA) and industrial control systems (ICS) traffic and logs, measurement-integrity protection (including false-data-injection attacks), and detection of malware, botnets, or access abuse. Supervised and ensemble methods remain common where reliable labels exist, while unsupervised detectors and deep sequence/graph models are used to capture temporal behaviour and rare events. Across the literature, higher detection accuracy often comes with practical costs: false alarms, detection latency, compute limits in the field, and limited interpretability, constraints that matter in real-time control environments. These trade-offs into a conceptual taxonomy, an evaluation/reporting checklist, and a deployment-oriented roadmap that emphasises benchmarks, stress testing, and operator-facing explainability. The study is close by outlining open problems in data availability, reproducibility, adversarial robustness, and secure deployment that must be addressed to move from promising prototypes to reliable, field-ready systems.

This study evaluated the radiological quality and associated health risks of water from Lake Edward, located in Rukungiri District, Uganda. Water samples were collected from sixteen locations grouped into four zones representing fishing, farming, domestic/livestock, and control areas. Gross alpha and gross beta activities were measured as preliminary indicators of radiological water quality, while activity concentrations of naturally occurring radionuclides (226Ra, 228Ra, and 40K) were determined using gamma-ray spectrometry. Radiological health risks were assessed by estimating the annual effective dose (AED) from water ingestion for adults and children, excess lifetime cancer risk (ELCR), and radiological hazard index (HI) in accordance with guidelines recommended by the International Commission on Radiological Protection and the World Health Organization. The mean gross alpha activity ranged from 0.16 ± 0.07 Bq L⁻1 in the fishing zone to 0.66 ± 0.05 Bq L⁻1 in the farming zone, while gross beta activity ranged from 0.22 ± 0.03 to 0.75 ± 0.05 Bq L⁻1 across the study area. Most samples complied with WHO guideline limits for drinking water, although a few locations in the farming zone showed elevated alpha and beta activities. The activity concentrations of 226Ra, 228Ra, and 40K ranged from below detection limit to 0.28 ± 0.03 Bq L⁻1, 0.21 ± 0.03 Bq L⁻1, and 0.44 ± 0.06 Bq L⁻1, respectively, with higher concentrations observed in agriculturally influenced areas. The calculated AED ranged from 0.022 to 0.128 mSv y⁻1 for adults and from 0.010 to 0.065 mSv y⁻1 for children. Two locations within the farming zone exceeded the WHO screening level of 0.1 mSv y⁻1 for adults. The estimated ELCR values were within internationally acceptable limits, although the hazard index exceeded unity at selected sampling points, indicating potential long-term radiological concern. Overall, the results suggest that water from Lake Edward generally presents low radiological risk for consumption; however, localized elevations in radionuclide concentrations associated with agricultural activities were observed. This study provides essential baseline radiological data for Lake Edward and highlights the need for routine monitoring and improved land-use management to ensure the long-term radiological safety of water resources in the region.