Collage of Pure and Applied Sciences (COPAS)

Identification, Characterization and Evaluation of Potential Vaccine Candidates for the Control and Management of Peste Des Petits Disease in Ruminants

Thu, 06 Aug 2026 00:00:00 GMT

Identification, Characterization and Evaluation of Potential Vaccine Candidates for the Control and Management of Peste Des Petits Disease in Ruminants Adero, Willis Abwao Peste des petits ruminants (PPR) is a highly contagious transboundary viral disease affecting small ruminants and causing major economic losses in sub-Saharan Africa, including Kenya. Current vaccines require strict cold-chain storage, limiting effective disease control in tropical regions. This study aimed to develop and evaluate a thermostable multi-epitope subunit vaccine targeting the Fusion (F) and Hemagglutinin (H) proteins of Peste des petits ruminant virus (PPRV). Bioinformatics tools were used to predict B- and T-cell epitopes from selected immunogenic amino acid sequences. A total of 82 H-protein and 94 F-protein sequences were retrieved from the National Centre for biotechnology information (NCBI) database and analyzed using bioinformatics and immunoinformatics tools. Conserved B- and T-cell epitopes were identified, characterized for antigenicity, allergenicity, toxicity, and immunogenicity, these were assembled into a multi epitope vaccine construct with suitable linkers and adjuvant sequences (Gotsman et al., 2006). The final construct had 620 amino acid residues amongst which 51.6% were predicted to be exposed, 20.7% to be medium exposed while (28.2%) were predicted to be buried. A total of 70 amino acid residues were predicted to be located in disordered regions while 550 were predicted to be located in ordered regions. The Model with the lowest Root-mean-square-deviation of atomic position (RMSD) score of 9.6982, was later chosen for refinement. The predicted vaccine construct had a molecular weight (MW) of 65361.99 Da with a theoretical isoelectric point (pI) of 5.70. The Grand Average of Hydropathy (GRAVY) was predicted to be -0.165 and a half-life of more than >10h in vivo in E. coli and >30h in vitro in mammalian reticulocytes. The vaccine was predicted as stable with instability Index of 21.20, estimated aliphatic index of 92.34 and GDI-HA scores slightly above 0.9, with MolProbability score of 2.619. There was positive correlation between Test vaccine candidate and a Positive (N/75), which was statistically significant (r=+0.95, n=10, p <0.001, (2-tailed). Close similarity was observed between the tested strains. These findings indicate that the vaccine candidate is a promising thermostable subunit vaccine for controlling PPRV in small ruminants. PhD in Medical Biotechnology

Occupational Safety and Health Issues Affecting Air Traffic Controllers in Kenya

Tue, 04 Aug 2026 00:00:00 GMT

Occupational Safety and Health Issues Affecting Air Traffic Controllers in Kenya Maina, Annastacia Waitima Air traffic control (ATC) is central to a safe, efficient, and effective aviation system. It comprises three interdependent components, the human element, namely the air traffic controller (ATCO), operational equipment, both airborne and ground based, and established procedures. These components must function in harmony to prevent collisions and maintain adequate separation between aircraft. Occupational safety and health (OSH) issues in air traffic control (ATC) arise from risks associated with the work environment, work organization, personal factors, and operational procedures. The occupational safety and health act of 2007 requires employers to provide a safe workplace that is free from health risks and supported by adequate welfare arrangements. This study evaluated the occupational safety and health (OSH) issues affecting air traffic controllers (ATCOs) in Kenya and was conducted across seven major manned airports. The research was guided by the Theory of the Risk Management Process as outlined in the International Organization for Standardization (ISO) 31000 principles and guidelines. A quantitative descriptive research design was adopted. From a target population of 172 controllers, a sample of 64 respondents was selected. Data were collected using measurement instruments, structured questionnaires and observation checklists. Statistical analysis was conducted using the Statistical Package for the Social Sciences (SPSS), applying descriptive statistics, Pearson correlation, and chi square tests to examine relationships between workplace factors and health outcomes. The findings established that occupational safety and health (OSH) among air traffic controllers (ATCOs) is significantly influenced by the combined effects of the work environment, work organization, operational procedures, and personal factors. In the work environment, inadequacies in lighting, including glare, equipment serviceability, emergency preparedness infrastructure, and elevated noise levels negatively affected safety, health, and operational efficiency. These conditions contributed to fatigue, stress, and reduced performance. Within work organization, deficiencies in shift scheduling, break allocation, and ergonomic considerations were shown to increase physical strain, reduce alertness, and heighten the likelihood of human error. Headaches were reported by 77.0% of respondents, back pain by 68.8%, and disrupted sleep by 48.4%. A strong positive correlation, r = 0.726, was observed between prolonged sitting and screen time. For operational procedures, although emergency procedures were generally effective, gaps in sensitization and limited controller involvement in procedure design reduced effectiveness in routine operations and increased cognitive workload. Personal factors, including attitude, risk perception, and limited adherence to ergonomic practices such as proper posture and adequate breaks, further influenced health outcomes. Notably, 85.7% of respondents relied on self-medication rather than seeking professional support. Inferential analysis showed statistically significant associations between equipment unserviceability and stress, χ² = 6.24, p < 0.05, stress and help seeking behavior, χ² = 23.3, p < 0.001, and back pain and posture deterioration, χ² = 28.96, p < 0.001. The study concludes that while the occupational safety and health (OSH) challenges affecting air traffic controllers (ATCOs) in Kenya are significant, they are consistent with risks recognized within international aviation safety frameworks. However, gaps remain in implementation, enforcement, and contextual adaptation of these standards. The study therefore recommends improving physical working conditions, optimizing organizational systems, enhancing participatory procedure development, and strengthening individual awareness and training to promote safer, healthier, and more effective air traffic control (ATC) operations. MSc in Occupational Safety and Health

A Framework for Distributed Denial-of-Service Attack Detection in Internet of Things Environments

Thu, 11 Jun 2026 00:00:00 GMT

A Framework for Distributed Denial-of-Service Attack Detection in Internet of Things Environments Wawire, Silas Amisi Internet of Things (IoT) networks are ubiquitous across industries, homes, and critical infrastructure due to their automation capabilities. However, IoT devices remain highly vulnerable to Distributed Denial-of-Service (DDoS) attacks owing to their limited computational power and inherent heterogeneity. Also, IoT systems often favor usability over security. While deep learning has shown promise for detecting such attacks, existing approaches have largely been validated on conventional network datasets using centralized architectures that create single points of failure. Furthermore, the centralized approaches raise privacy concerns by requiring raw data transmission to the cloud and lack preprocessing tailored to IoT-specific protocols such as MQTT. This study aimed to develop a distributed deep learning framework for DDoS detection in IoT environments. The specific objectives were to design and implement a distributed detection framework based on deep learning, and to evaluate and compare its performance against a centralized paradigm using accuracy, precision, recall, and F1-score. A quantitative experimental research design using the DoS/DDoS-MQTT-IoT dataset was employed. The proposed framework integrated three components: a CNN-BiLSTM ensemble model, a three-tier edge-fog-cloud architecture incorporating federated learning where edge devices performed preprocessing and fog nodes conducted local training while raw data remained on premises, and preprocessing adapted to MQTT's publish-subscribe semantics. Model hyperparameters were held constant across both experimental conditions, and ten-fold cross-validation was applied. Statistical significance was assessed using a paired t-test with an alpha level of 0.05. The distributed detection framework achieved 99.68% accuracy, 99.02% precision, 99.28% recall, and 99.10% F1-score. The distributed framework demonstrated a 64% lower error rate than the centralized approach (1.35% versus 3.76%). This improvement was statistically significant as confirmed by a paired t test over ten-fold cross-validation (p < 0.001). The distributed framework also outperformed traditional machine learning methods and standalone deep learning models including CNN alone, BiLSTM alone, and RNN alone. Overall, the distributed approach exhibits superior accuracy and adaptability compared to centralized detection. It also provides privacy benefits through federated learning. Future work should validate the framework on additional IoT protocols and datasets and explore model compression techniques to further reduce computational requirements on resource-constrained edge devices. MSc in Computer Systems

Agronomic activities and Seasonal Variations on Abundance and Diversity of bee Species and the state of knowledge of pollinator importance in Loitokitok Sub-County, Kenya

Thu, 28 May 2026 00:00:00 GMT

Agronomic activities and Seasonal Variations on Abundance and Diversity of bee Species and the state of knowledge of pollinator importance in Loitokitok Sub-County, Kenya Aika, Charles Omari Honey bees are prone to agronomic activities such as misuse of agrochemicals, land fragmentation, alteration of natural habitats and change in land use patterns. Understanding how bee species respond to habitat destruction is significant towards development of effective measures to ensure that the environment is protected and conserved. The study sought to assess the effects of agronomic activities and seasonal variations on abundance and diversity of bee species and the state of knowledge of pollinator importance in Loitokitok sub-county, Kenya. Experimental research design comprising of three different habitats was conducted in order to establish effects of agronomic activities on bee abundance and diversity and state of knowledge of pollinator importance in Loitokitok Sub-County, Kenya. The study aimed at evaluating the effect of agronomic practices on the diversity and abundance of bee species, determining the impact of seasonal weather variations on abundance and diversity of bee species and assessing the state of knowledge of pollinator importance among small-holder farmers in Loitokitok sub county, Kenya. The study area was stratified into three habitats (1) cultivated farm, (2) rangeland (3) natural forest. A survey of the study area was done and the habitats identified. A sample area of 1 × 1 km square was picked at random from each of the three study areas. The selected areas were further sub-divided into 0.5 × 0.5 km smaller study areas and a total of 3 belts were laid down randomly within the small study areas. Sampling of the bees was done for 3 months using a sweep net and pan traps to collect the bee species. Shannon Weiner diversity index was used to compute diversity and richness of honey bee species. One way ANOVA was used to compute the statistical significance of bee species abundance across the three habitats. A total of 1,106 bee specimens from 2 families and 7 species were collected from the three study habitats. Apis mellifera, was the most abundant bee species followed by Pseudapis spp., Lasioglossum spp., Xylocopa spp., Braunsapis spp., Ceratina spp. while Heriades spp. was the least abundant bee species. Natural edge habitat had the highest bee species abundance followed by rangeland while cultivated habitat had the least bee species abundance. Cultivated habitat recorded highest diversity index, H/= 1.511 followed by rangeland with H/= 1.424 while the natural habitat had the least at H/= 1.351. However, the overall diversity index was H/= 1.43. There was a statistical significance (p<0.05) between cultivated habitat & rangeland, cultivated habitat & natural forest edge and also between rangeland & natural forest edge respectively. Seasonal weather changes influenced bee species abundance and diversity in the study area. The bee species abundance and diversity were greater during the rainy season as compared to the dry season. This study reveals that agronomic activities had an influence on bee species abundance and diversity. Farmers demonstrated substantial knowledge of bees, with about 90% identifying different species and recognizing nest sites and food resources. More than 86% had experience in honey harvesting, while most understood the importance of bee visits to crops and live fence flowers. Notably, 90.9% expressed willingness to promote bee populations on their farmlands. Therefore, the local community, farmers and other stakeholders should be sensitized on the importance of bee conservation and its contribution to their welfare and on utilization of cost-effective approaches towards bee management and conservation. Master of Science in Zoology (Conservation Biology)