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Using machine learning models to predict the effects of seasonal fluxes on Plesiomonas shigelloides population density
(2023) Ekundayo, T. C.; Ijabadeniyi, O. A.; Igbinosa, E. O.; Okoh, A. I.
Seasonal variations (SVs) affect the population density (PD), fate, and fitness of pathogens in environmental water resources and the public health impacts. Therefore, this study is aimed at applying machine learning intelligence (MLI) to predict the impacts of SVs on P. shigelloides population density (PDP) in the aquatic milieu. Physicochemical events (PEs) and PDP from three rivers acquired via standard microbiological and instrumental techniques across seasons were fitted to MLI algorithms (linear regression (LR), multiple linear regression (MR), random forest (RF), gradient boosted machine (GBM), neural network (NN), K-nearest neighbour (KNN), boosted regression tree (BRT), extreme gradient boosting (XGB) regression, support vector regression (SVR), decision tree regression (DTR), M5 pruned regression (M5P), artificial neural network (ANN) regression (with one 10-node hidden layer (ANN10), two 6- and 4-node hidden layers (ANN64), and two 5- and 5-node hidden layers (ANN55)), and elastic net regression (ENR)) to assess the implications of the SVs of PEs on aquatic PDP. The results showed that SVs significantly influenced PDP and PEs in the water (p < 0.0001), exhibiting a site-specific pattern. While MLI algorithms predicted PDP with differing absolute flux magnitudes for the contributing variables, DTR predicted the highest PDP value of 1.707 log unit, followed by XGB (1.637 log unit), but XGB (mean-squared-error (MSE) = 0.0025; root-mean-squared-error (RMSE) = 0.0501; R2 = 0.998; medium absolute deviation (MAD) = 0.0275) outperformed other models in terms of regression metrics. Temperature and total suspended solids (TSS) ranked first and second as significant factors in predicting PDP in 53.3% (8/15) and 40% (6/15), respectively, of the models, based on the RMSE loss after permutations. Additionally, season ranked third among the 7 models, and turbidity (TBS) ranked fourth at 26.7% (4/15), as the primary significant factor for predicting PDP in the aquatic milieu. The results of this investigation demonstrated that MLI predictive modelling techniques can promisingly be exploited to complement the repetitive laboratory-based monitoring of PDP and other pathogens, especially in low-resource settings, in response to seasonal fluxes and can provide insights into the potential public health risks of emerging pathogens and TSS pollution (e.g., nanoparticles and micro- and nanoplastics) in the aquatic milieu. The model outputs provide low-cost and effective early warning information to assist watershed managers and fish farmers in making appropriate decisions about water resource protection, aquaculture management, and sustainable public health protection.
First-year Students’ University and Programme Selection at a South African University: Choice or Compulsion?
Dube, N.; Nyambo, S.; Kanjiri, N. K.; Ruzungunde, V. S.
This article examines factors influencing university and programme selection among first-year students at a historically disadvantaged university in the Eastern Cape, South Africa. Qualitative focus groups and convenience sampling were used. Residential proximity, institutional reputation, programme image, entry requirements, affordability and funding influenced university selection. Programme selection was shaped by background dynamics, subjects, unmet requirements, family and peer influence, not being selected for first-choice programmes, limited career guidance and funding availability. University selection was principally informed by choice, while programme selection was largely influenced by external factors.
Decolourization of synthetic dyes by laccase produced from Bacillus sp. NU2
(2022) Edoamodu, C. E.; Nwodo, U. U.
This study evaluated laccase synthesized by Bacillus sp. NU2 for degradation of five synthetic dyes. Sawdust, wheat bran, banana peel and tangerine peel were used as carbon sources for bacterial growth and laccase production. Tangerine peel yielded the most laccase. The purified enzyme decolourized Congo Red, Methyl Orange, Remazol Brilliant Blue R, Reactive Blue 4 and Malachite Green by 87%, 70%, 65%, 63% and 51%, respectively. The laccase showed high catalytic potential for detoxifying dye effluents in environmental systems.
Hepatoprotective activities of polyherbal formulations: A systematic review
(2023) Aladejana, E. B.; Aladejana, A. E.
Liver diseases pose a substantial global public health challenge, including liver failure, hepatitis, cirrhosis and associated complications. This systematic review examined the hepatoprotective activity of polyherbal formulations and their mechanisms of action. Electronic databases were searched for studies published between January 2010 and April 2023. Sixty-one articles showed that polyherbal formulations have significant activity against various hepatotoxic agents, including antioxidant, anti-inflammatory, antifibrotic and antiapoptotic effects. Some formulations also stimulated liver regeneration, enhanced bile secretion and promoted detoxification. Polyherbal formulations are a promising alternative to conventional treatments, but further work is needed to identify active compounds and clarify pharmacokinetics and pharmacodynamics.
Experiences of B. Ed Students on the use of E-Learning as a Vehicle to Learning during the COVID-19 Pandemic
(2022) Duku, N.; Makeleni, S.; Mkhomi, M. S.; Mavuso, M. P.
This qualitative study explores Bachelor of Education students’ experiences of e-learning at a South African university during the COVID-19 pandemic. Telephonic assisted open-ended interviews, framed by Engeström Activity Theory, showed varied experiences even among students at the same institution. Internet connectivity and access to devices such as laptops and smartphones shaped participation. The study concludes that universities should respond to students’ contextual factors and develop sustainable teaching and learning plans for the post-COVID-19 era.