Elizade University

Rabbit reproductive and growth characteristics are significantly influenced by various factors including breed, sex, parity, and diet. Understanding these impacts is essential to enhance production efficiency. This study examined the effects of varying inclusion levels of a tiger nut-based diet on reproductive parameters, breed effect, sex effect, and interactions. Three rabbit breeds: Dutch Belted (DUT), New Zealand White (NZW), and Hyla Max were used for this breeding study, involving 60 growing rabbits as parents stock, comprising 48 does and 12 bucks over a period of 52 weeks. The analysis revealed significant differences (p < 0.05) in mortality rates at day 7, with Hyla Max experiencing the highest mortality followed by Dutch Belted and NZW. Weight measurements indicated that male rabbits were heavier than females at both day 7 (0.11 ± 0 kg vs. 0.09 ± 0 kg) and day 14 (0.16 ± 0 kg vs. 0.15 ± 0 kg; p < 0.05). Parity significantly impacted weight gain at day 21 and mortality rates at birth and day 7 (p < 0.05). A diet containing 30 g tiger nut inclusion resulted in higher weights at day 14 (0.16 ± 0.00 kg) and day 28 (0.29 ± 0.1 kg), while the 10 g inclusion level achieved the greatest weight gain at day 28 (1.36 ± 1.14 kg; p < 0.05). Additionally, NZW rabbits exhibited the highest individual kit birth weight (0.05 ± 0 kg), while Dutch Belted rabbits had the highest pre-weaning mortality rate (15.97 ± 2.85%; p < 0.05). Noteworthy interactions among breed, sex, parity, and diet were identified. The findings underscore the significant influences of breed, sex, parity, and diet on reproductive and growth traits in rabbits, paving the way for optimized management practices aimed at improving productivity.

Ethnoveterinary practices represent an essential component of indigenous knowledge systems that contribute significantly to animal healthcare in rural communities. However, documented evidence on such practices, particularly those related to the treatment of dogs, remains limited in Ondo State, Nigeria. This study was conducted to identify, document, and analyse the botanical resources and traditional methods employed by local dog owners in Ilara-Mokin, Ondo State. Data were collected through open-ended interviews and structured questionnaires administered to knowledgeable respondents. The study revealed a range of ethnoveterinary practices that utilise various medicinal plants and manual techniques, such as hand-picking of ectoparasites, for the management of canine ailments, including tick infestation, diarrhoea, mastitis, lameness, and intestinal worms. The findings highlight the depth of indigenous knowledge applied to animal healthcare within the community and underscore its continued relevance in the absence of readily available modern veterinary services. Given the threat of cultural erosion and biodiversity loss, there is an urgent need to systematically preserve, validate, and integrate this traditional knowledge into modern veterinary and conservation frameworks to ensure its sustainability and potential contribution to scientific research and rural development.

Background: Topography-sensitive design is essential in the creation of sustainable and practical landscapes within academic institutions, especially in areas where sloped topography presents both challenges and opportunities. This paper explores aspects of sustainable landscape design that enable slope stability and land use in a campus location. Methodology: This study adopted a qualitative multiple–case study approach using criterion-based purposive sampling. Six case studies were examined, comprising three Nigerian university campuses (OAU, CURID, and JABU) and three international residential projects (Spain, Greece, and Brazil). Data were collected from archival sources, published drawings, photographs, and site descriptions. Analysis was conducted using a standardized comparative matrix supported by deductive coding across morphological adaptation, ecological performance, and spatial organization. Key parameters such as slope condition, stabilization strategies, drainage approaches, and land-use efficiency were qualitatively classified to ensure analytical consistency and reproducibility. Results: Findings reveal that effective slope stabilization extends beyond structural interventions to include terrain-responsive spatial zoning, vegetative reinforcement, and integrated drainage systems. Institutional cases emphasized land usability, accessibility, and circulation efficiency, while residential precedents demonstrated advanced ecological integration, passive design, and minimal land disturbance. The synthesis highlights the value of combining macro-scale campus planning strategies with micro-scale architectural adaptations. Conclusion: The study concludes that sustainable slope management in campus environments requires an integrated landscape–architecture approach that treats topography as a design asset rather than a constraint. This research contributes a transferable, landscape-based analytical framework for slope-sensitive campus planning, bridging architectural design and environmental engineering at the conceptual and planning scale, with relevance to tropical and developing contexts.

Customer churn is the loss of business clients to a competitor. Since keeping current clients is more economical than finding new ones, customer retention measures such as churn detection are now essential aspects of modern banking strategy. However, many existing studies rely heavily on conventional machine learning approaches such as Support Vector Machines, Logistic Regression , Random Forest, etc., often neglecting the deeper learning capabilities of neural networks. Also, the repeated use of the same small dataset by the banking studies may limit the improvement of the models’ generalisation. To address these gaps, this study presents a method that integrates deep learning for customer churn detection and a soft and hard voting classifier ensemble embedded with the best performing models over the years for results comparison, supported by a synthetic data augmentation method for model improvement. The study utilised a secondary banking churn dataset from Kaggle, which contained 10,000 unique customer records. To address the dataset limitations, a Conditional Tabular Generative Adversarial Network (CTGAN) model was used to generate an additional 10,000 records, expanding the dataset used for the study to 20,000 rows. Data preprocessing steps were done before training, including oversampling using Synthetic Minority Oversampling Technique (SMOTE). Model development and analysis processes were implemented using Python programming language with prominent libraries and frameworks on Google Colab. In this study, a Feedforward neural network and a soft and hard voting classifier were developed. The voting classifier ensembles integrated three prominent classifiers: Random Forest, XGBoost, and Logistic Regression. The performances were evaluated using Accuracy, F1 Score, and Area Under ROC Curve as metrics. Results show that while the Feedforward Neural Network achieved strong predictive performance with an accuracy of 88.23%, an F1 Score of 87.83% and an AUC of 94.73%, the ensemble approaches performed slightly better as the soft voting classifier delivered the best results, obtaining an accuracy of 89.46%, F1 Score of 88.92% and AUC of 95.40% showing the advantage of combining multiple models to leverage complementary strengths. After comparison with past studies, the proposed models did not surpass the very best outcomes. However, they remain highly competitive, achieving performance levels that are on par with or exceed many earlier works. The contribution of this work is to show how synthetic data augmentation, enhanced preprocessing, deep learning techniques, and machine learning ensembles can improve churn detection in banking studies. Banking institutions can utilise the results from this study to accurately detect churn, supporting proactive customer retention strategies, targeted marketing, and personalised financial services, thereby reducing revenue losses.

s study explores the impact of social media monetization on youth employment and economic productivity in Nigeria’s labor market. As digital platforms like Instagram, YouTube, and TikTok offer youngNigerians opportunities for income generation, they also reshape traditional employment patterns. Theresearch examines the dual-edged nature of social media monetization, balancing the potential for eco-nomic empowerment against the challenges of job security, income volatility, and productivity. UtilizingMultinomial Logistic Regression, the study analyzes survey data from Nigerian youth engaged in digi-tal entrepreneurship to determine the effects on employment outcomes and economic productivity. Keyfindings highlight that while social media provides flexible employment opportunities, it may also diverttime from other productive activities, with implications for long-term economic growth. The study con-cludes with policy recommendations to optimize the benefits of digital entrepreneurship while addressingits challenges, aiming to integrate these emerging work patterns into Nigeria’s broader economic framework.

Myeloid cell leukemia 1 (MCL-1), a crucial anti-apoptotic member of the B-cell lymphoma 2 (BCL-2) family, has been extensively documented to be overexpressed in a variety of cancers, where it is essential for fostering cancer cell survival and treatment resistance. Elevated levels of MCL-1 have been observed in hematological cancers, such as acute myeloid leukemia and multiple myeloma, as well as in multiple solid tumors. We therefore examined the potential inhibitory effects of Centella asiatica compounds on MCL-1 using a computational drug discovery approach. Molecular docking analyses, including Glide XP (extra precision) were performed to evaluate the binding affinities of the compounds against the prepared crystal structure of MCL-1 (PDB ID: 6FS1) within the Schrödinger Suites. Additionally, the binding free energies of the compounds were computed to assess their thermodynamic stability within the binding pocket. The physicochemical and pharmacokinetic properties of the identified compounds were analyzed based on Lipinski's Rule of Five (RO5), electrostatic potential distribution, and ADME predictions. Predictive models for MCL-1 inhibitors were developed using AutoQSAR to examine the drug-likeness and biological activity of the screened compounds. We found 12 hit compounds, most of which met the RO5 criteria and were in the suggested ADME parameter range. Additionally, the predicted pIC50 values for these compounds were promising, suggesting their potential as MCL-1 inhibitors. The results of this study offer insightful analysis for the rational design of new anticancer treatments aimed at MCL-1.

The metaverse is an emerging field of immersive digital environments made possible by virtual reality (VR), augmented reality (AR), and artificial intelligence (AI). It has the potential to completely transform the way healthcare is delivered in nations of the world. Africa, a continent that struggles with health issues, has unique chances for innovation courtesy of the metaverse. This article explores how the metaverse can change healthcare in Africa, focusing on telemedicine, medical education, mental health care, and patient involvement. Noteworthy applications are AR-assisted remote surgeries, VR-based medical training simulations, and virtual platforms that make it easier to obtain healthcare, particularly in rural areas. Although this technology has a lot of potential, however, there are currently several obstacles that prevent the use of the metaverse for health delivery in Africa from being widely accepted. The paper ends with a discussion of policies that encourage the use of the metaverse, including funding digital infrastructure, building public-private partnerships, and creating legal frameworks that guarantee the safe and fair delivery of healthcare. In Africa's ongoing digital transformation, the metaverse will be essential to changing the healthcare landscape in a way that is humanistic, efficient, and accessible.

This article examines the evolving location patterns of African migrants to answer the question: What determines African migrants’ destination choices? Analysing regular migration between 10 African source countries and 10 OECD destinations from 1990 to 2017, it highlights how barriers such as distance, policy restrictions and historical ties shape migration distribution. Findings reveal that while productivity drives spatial concentration for countries such as Egypt, migration costs and location-specific effects are critical in perpetuating migration patterns. The article distinguishes between traditional and emerging destinations, using advances in modelling increasing returns to scale to capture the geographical concentration and dispersion of African migrants.

Background: Congenital malaria is thought to be a relatively rare condition in which malaria parasite is transmitted from the mother to the baby during pregnancy or at childbirth. This is so either because the babies were not routinely screened or due to difficulty in detecting the relatively scanty parasite in the newborn period. The study set out to document malaria parasitemia among the neonates in Akure, Ondo state. Methods: Febrile babies admitted into the special care baby unit of University of Medical Sciences Teaching Hospital and Mother and Child Hospital, Akure were tested for malaria parasite using microscopy method and confirmed by nested PCR. Data were analysed using excel and SPSS version 25.0 Results: Nineteen percent of the babies were preterm, 81% were full term and the overall prevalence of malaria among them was 74.7% while prevalence of congenital malaria was 92.9% and prevalence of neonatal malaria was 54.1%. Plasmodium falciparum was the most prevalent infecting species (98.3%), first born babies and male children were significantly more infected (p=0.001). Conclusion: Prevalence of malaria was very high in this study. Malaria infection in the neonates can be difficult to diagnose because the clinical features are nonspecific and parasitaemia often scanty. Screening for malaria parasite should therefore be done as part of routing tests for the neonates.