University of Ibadan Journals
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Design and Fabrication of a Briquette-Fueled Stove for Integration with Fish Smoking Kilns
No full textThe transition toward clean, sustainable biomass energy systems is critical to mitigating theenvironmental and health impacts of traditional fish smoking practices. Briquettes, derivedfrom renewable agricultural residues, offer a low-emission alternative with promising thermalperformance in artisanal and semi-industrial settings. This study presents the design,fabrication, and performance evaluation of a briquette-fueled stove developed for integrationwith fish smoking kilns. The stove was constructed using locally available materialsand featured thermally insulated walls, an ash collection chamber, and a variable-speedair blower for controlled combustion. Three insulation materials—clay, fiber, and sawdust—were comparatively tested under a range of blower speeds using the standardizedWater Boiling Test (WBT) protocol. An expanded experimental matrix was implementedwith blower speeds of 50, 75, and 100 rpm applied to each insulation type to examine heatretention and combustion dynamics. Boiling time data were statistically analyzed usingANOVA and standard deviation metrics to validate performance differences. The fiberinsulatedvariant consistently exhibited the highest thermal efficiency, achieving boilingin 15.0 ± 0.6 minutes at 100 rpm. In contrast, clay insulation required up to 30.1 ± 0.9minutes, and sawdust insulation yielded intermediate results. Lifecycle cost and materialdurability assessments indicate that fiber-based insulation, despite slightly higher upfrontcost, offers long-term savings due to reduced heat loss and better structural resilience.Detailed schematics are provided to ensure reproducibility and technical transferability.Overall, the briquette-fueled stove provides an efficient and scalable solution for sustainablefish smoking operations, particularly in rural and resource-constrained environment
Effect of Extraction Solvents on Yield, Mineral Composition, Phytochemical Constituents, Antioxidant and Antimicrobial Properties of Chrysophyllum albidum (African Star Apple) Leaf
No full textChrysophyllum albidum is a tropical plant that belongs to the family Sapotaceae with about 800 species that make up almost half of the order Ericales The yield, mineral, phytochemical constituents, antioxidant and antimicrobial properties of different solvent extracts of Chrysophyllum albidum leaves were investigated. The leaves of C. albidum were successively extracted in acetone, ethyl acetate, petroleum ether, ethanol and distilled water. All obtained crude extracts were evaluated for yield, minerals, phytochemical constituents, free radical scavenging (2, 2-diphenyl-1- picryl hydrazyl - DPPH and Ferric Reducing Antioxidant Power Assay-FRAP) activities and antimicrobial properties. The ethanol extract has the best yield (10.41%) while petroleum ether has the least (2.22%). Phytochemical screening of the crude extracts showed the highest presence of tannin (4.25±0.01), total phenolic content (77.48±0.02) and flavonoid (18.39±0.06) in acetone extract, saponin (1.43±0.30) and alkaloid (3.76±0.10) were highest in aqueous extract while all were absent in petroleum ether extract. Aqueous extract showed a higher presence of magnesium (6.103±0.00), potassium 27.578±0.00), sodium (2.969±0.00), zinc (0.036±0.00) and manganese (0.082±0.00) except calcium (1.001±0.00) that was higher in ethyl acetate extract and iron (0.161±0.00) in acetone extract. The antioxidant activity of Acetone (IC = 83.10±0.52 ?g mL) and ethanol (IC = 86.71±0.06 ?g mL) extract showed 2, 2-diphenyl-1- picryl hydrazyl (DPPH) scavenging activity which was comparable with that of standard ascorbic acid (IC = 95.14±0.51 ?g mL). Ferric Reducing Antioxidant Power Assay (FRAP) scavenging activity showed a maximum effect in acetone extract while the least was observed in pet ether extract. The in vitro antimicrobial activity was done by agar disc diffusion method against Staphylococcus aureus, Aeromonas hydrophila, E. coli, Pseudomonas aeruginosa, Salmonella spp, and Bacillus spp. Maximum -antibacterial activity (zone of inhibition) was shown by acetone and ethanol extracts against all the tested organisms whereas ethyl acetate extract showed no activity. This study has revealed that the leaf extracts of C. albidum possess potent phytonutrients, antioxidants and free radical scavenging activity which may be due to the presence of flavonoids and total phenolic content (TPC) as well as antimicrobial effects against some of the tested bacteria
Design and Assessment of Heat Exchanger Parameters for Thermal Efficiency in Fish Smoking Kiln
No full textEfficient thermal management in fish smoking kilns is essential for enhancing product quality,optimizing fuel consumption, and promoting operational sustainability. Conventionalkiln systems frequently suffer from inconsistent heat retention and poor energy utilizationdue to suboptimal heat exchanger configurations. This study evaluates the influenceof three key parameters—blower speed (650, 725, and 800 rpm), number of open pipeinlets (5, 10, and 15), and insulation material (sawdust, clay, and fiber) on the thermalperformance of a locally fabricated heat exchanger integrated into a fish smoking kiln. AResponse Surface Methodology (RSM)-based central composite design was employed tostructure a 20-run experimental matrix, allowing for systematic interaction analysis betweendesign variables. Airflow rate measurements were taken at the exhaust outlet ofthe heat exchanger to ensure consistent thermal load evaluation. Statistical analysis usingANOVA revealed significant effects (p < 0.01) of blower speed and insulation type on outletair temperature, with the maximum temperature of 96?C achieved at 800 rpm, 15 openpipes, and fiber insulation. Additionally, airflow rate was strongly influenced by both linearand quadratic terms of blower speed and pipe configuration, with adjusted R2 = 0.995and Adequate Precision = 79.48, indicating model robustness. This work distinguishesitself from previous studies by integrating cost-effective materials, a scalable exchangerdesign, and a quantitative optimization framework for kiln retrofitting. Its adaptability todiverse kiln geometries and biomass fuel types underscores its potential for widespreadapplication across artisanal and semi-industrial fish processing facilities. These findingsoffer a replicable pathway for transitioning traditional fish smoking systems toward higherthermal efficiency and environmental compliance
Framework for a Stimulated Predictive Distributed Learning Method
Get PDFDue to the intrinsic properties of high-dimensional microarray datasets, most feature selection approaches do not
scale well, which makes these models inapplicable and impairs the performance of most classifiers. This study
used data complexity and stability measures to maintain class distribution and reduce features variability while
proposing a novel predictive distributed FS model through horizontal partitioning. Brain tumour microarray
benchmark was employed for implementation. Six classifiers as well as feature selection methods were
employed along with their ensemble learning techniques. The study observed the proposed distributed model
with an average accuracy of 98.54% and 99.67% obtained from both the single and ensemble
models respectively
Predicting Students’ Academic Performance in Virtual Learning Environment Using Pearson Correlation Coefficient
Get PDFFeature Selection involves selecting the most relevant features from a dataset during the prediction process. The
selection method of features greatly influences how accurate, understandable, and effective predictive models
are. Predicting students' academic success or struggle in a Virtual Learning Environment (VLE) is limited.
Students who drop out of online courses are substantially more numerous than those who drop out of traditional
courses [1,2]. The methodology followed in the study involved the use of two approaches: training and testing
machine learning models with features selected from the dataset, and the second approach involved training and
testing the machine models using all features in the dataset without feature selection. The Pearson Correlation
Coefficient (PCC) feature selection method is used to select the features used for prediction. The two
approaches were compared in terms of their impacts on the performance of the machine learning algorithms.
The study was carried using nine classification models, which include Logistic Regression, K-Nearest
Neighbour (KNN), Support Vector Machine (SVM), Random Forest, Gradient Boosting, XGBoosting,
LightGBM, MLP classifier (Neural Network) and Naïve Bayes. The result of the study showed that logistic
Regression show highest accuracy mean of 0.7333 with feature selection and reduced accuracy mean of 0.7188
when all features were used in the prediction process. Without feature selection, the accuracy mean of Random
Forest is 0.6813 and applying PCC feature selection to select the features for prediction, the accuracy mean of
Random Forrest increased to 0.7333 revealing that feature selection method such as PCC is important for
improving model performance
Development of a Knowledge Management System to Support Intelligent Rice Farming
Get PDFRice is a staple food worldwide. It is most common in Asia, Africa and Latin America. In Ghana, the annual rice consumption is about 1.5 million metric tons. However, about 60% of this demand is imported majorly from Asia. This high reliance on imported rice is one of the contributory factors that weaken Ghana’s foreign exchange reserves. Hence, there is a need to encourage local rice production. However, the efforts by local farmers are thwarted by many challenges, including pest infestations, bird interference, insufficient technology for efficient fertiliser and herbicide applications, and the absence of reliable systems for predicting rainfall patterns. Additionally, inadequate access to modern agricultural extension services and the lack of advanced storage facilities exacerbate these difficulties. Although numerous intelligent agriculture systems exist that could address these issues in an environmentally sustainable manner, farmers in this region remain largely unaware of such technologies and persist with outdated and inefficient methods. This study sought to address these challenges by developing a customised Knowledge Management System (KMS) aimed at facilitating knowledge dissemination and supporting intelligent agricultural practices in rice farming. The research used a system prototyping methodology to produce a prototype KMS, which the System Usability Scale (SUS) evaluated for usability. The system achieved an average score of 70.025, surpassing the threshold for "Acceptability Usability," which denotes that the KMS meets the minimum standards for practical application. This result highlights the potential for the KMS to enhance agricultural practices and improve productivity within the community
Evaluation of a Counter Current Cooling System of a Fish Feed Extrusion
No full textThis study explores the application of a counter-current cooling system to manage the excessheat generated during fish feed extrusion, a process critical for producing high-quality,nutritionally balanced feed. During extrusion, elevated temperatures caused by friction candegrade nutrients, damage product structure, and lead to economic losses. To address this,the research employed a counterflow cooling mechanism designed to enhance thermal regulationand product quality. An optimal experimental design approach was adopted usingResponse Surface Methodology (RSM) to analyze the influence of three key process variables:screw speed (150, 200, 250, and 300 rpm), die size (4, 6, and 8 mm), and water flowrate (25, 50, 75, and 100 Lmin¹). Response parameters included extrudate temperature,cooling efficiency, and bulk density. The study incorporated replication and error analysisto improve the reliability of optimization results. Maximum extrudate temperature reached319°C at the highest screw speed, while the optimal conditions were at screw speed of254.97 rpm, 5 mm die size, and 100 Lmin¹ water flow resulted in effective heat reductionand desirable product characteristics with a desirability score of 0.633. The study alsohighlights the adaptability of the proposed cooling system for various food matrices, includinghigh-protein and fortified blends. Additionally, it considers scalability, operationalcost, and maintenance aspects, suggesting that the system is practical for both small- andlarge-scale aquafeed production. Overall, the counter-current cooling approach demonstrateda significant improvement in process control, product safety, and energy efficiency,offering a sustainable solution for modern aquafeed manufacturing
Identification and Distributions of Parasites on Developmental Stages of Clarias gariepinus Reared in Different Water Renewal Culture Systems
No full textThe intensification and commercialization of fish production often cause an imbalance in the water environment thereby exposing them to stress and biological pathogens – parasites, bacteria, fungi and viruses. Parasites are the primary causative agent of infections forming pathways for secondary infections whereas the knowledge about identification and distribution of parasites is vague to most farmers which prompted this study. The population size was 3% of functioning farms where five live fish were randomly collected from water renewal culture systems (Daily (DWR), Weekly (WWR) and Bi-weekly BWR)) for parasitological examination. Relevant keys were used for parasite identifications. Water parameters were measured for the community of parasites using standard methods. Descriptive statistics (percentages and mean) were used for analysis. The parasites observed across the culture systems in this study were categorized into three groups – protozoans (Trichodina spp., Vorticella spp., Tetrahymena spp., Chilodonella spp., Ichthyobodo spp., Piscinoodinium spp., and Ambiphyra sp); helminths (Dactylogyrus spp., Gyrodactylus spp., suspected Salmonichus spp., and unidentified nematode spp.,) and crustacean (Argulus sp.). Trichodina spp., Vorticella spp., and Dactylogyrus spp., parasitized all developmental stages (fry, fingerlings, juveniles and adults) collected from DWR and WWR. Trichodina spp.was highly distributed on the skin (66%) and gills (84.5%) in BWR; Vorticella spp.on the skin (29.4%) and predominantly dominated the intestine (100%) in WWR; Dactylogyrus spp.was on the skin (2.5%) and gills (36.8%) in DWR. No Vorticella spp.and Dactylogyrus spp., were recorded on gills and intestine respectively across the culture systems but nematode spp was predominantly found in the intestine. Therefore, the presence of parasites in all the culture systems and developmental stages indicates that neither a system nor developmental stage is exempted thereby more attention should be given to fish hygiene, especially with the awareness of different species of parasites in fish farms.  
Advancing Integrated Fish Farming in Nigeria: Climate-Smart Innovations and Policy Strategies
No full textNigeria faces a significant gap between fish demand and domestic supply, with imports covering nearly half of consumption. Integrated fish farming offers a pathway to expand production while addressing resource and environmental challenges. This study applies an evidence-based policy analysis of scholarly, institutional, and grey literature published up to 2022. Using the Climate-Smart Aquaculture (CSAq) framework, supported by institutional and diffusion of innovation theories, it assesses both the benefits of emerging technologies and the conditions influencing their adoption. Results show that solar-powered aeration, biofloc systems, and aquaponics can lower costs, improve resilience to climate variability, and reduce environmental impacts. Yet uptake is constrained by limited finance, weak extension support, and inadequate regulatory incentives. The study concludes that targeted reforms in credit access, extension training, and sustainability-focused regulations are essential to scale these innovations. Strengthening such enablers would reduce Nigeria’s fish deficit, enhance food security, and align aquaculture with climate adaptation priorities
A Comparative Evaluation of Embedding Techniques from Language Models for Automatic Grading of Short Answer Questions
Get PDFAn automatic grading system of short answer questions on an e-learning platform can help reduce stress, save time, increase the productivity of instructors and help provide feedback to students in record time. However, the success of automatic grading of short answer questions (open-ended questions) depends on the ability of the computer to adequately capture the semantic similarity between students’ answers and the reference answer provided by the examiner. This paper presents a comparative study of some embedding techniques from language models for automatic grading of short answer questions in order to address the longstanding challenge of automating the assessment of students' responses to open-ended questions. It studies five embedding techniques such as Word2vec, Bi-LSTM, BERT, SBERT, and OpenAI on four datasets (SemEval, Texas, ASAG, and MIT) to find the best method among them for Automatic Short Answer Grading (ASAG). Experiments include regression tasks and classification tasks using Mean Squared Error (MSE), Pearson Correlation, and accuracy as metrics for evaluation. The results indicate that fine-tuned BERT achieved the highest accuracy of 75% on SemEval dataset in classification tasks, while OpenAI performed better in the regression tasks with a MSE of 0.57 on the Texas dataset. The research highlights automated grading as a means to reduce instructors' workload while enhancing the quality of feedback provided to learners. Future studies will focus on extending the experiments to include both domain-specific and non-domain-specific