University of Port Harcourt

Failures of flexible pavements have been attributed to the indiscriminate use of lateritic soils without prior characterisation. This study investigates the compaction behaviour of lateritic soils at Ife-Sekona Road, southwestern Nigeria. The suitability of base learners and ensemble machine-learning (ML) algorithms for predicting lateritic soils-soaked California Bearing ratio (CBR) were also tested. The Optimum Moisture Content (OMC), Maximum Dry Density (MDD) and CBR of soil samples were determined and compared using the Standard Proctor, West African Compaction and the modified AASHTO tests. Statistical comparisons were conducted using one way analysis of variance (ANOVA) and Least Significant Difference (LSD) post-hoc test at 0.05 level of significance. Base learners ML including Random Forest (RF), Elastic Net Regression (ENR), Gradient Boosted Tree (Xgboost), Support Vector Regression (SVR) and their ensemble were also employed to predict soaked CBR using 160 sample data and 10-fold cross validation. Results showed that differences in OMC and MDD between Standard Proctor and both the WACT and Modified AASHTO were significant, while differences between the latter two were not. Soaked CBR differ significantly between the three methods. The Modified AASHTO resulted in the best compaction characteristics with the highest MDD and lowest OMC. The best-performing stacked models were those combining RF and ENR, and those integrating SVM and ENR with R² = 0.80 and NSE = 0.72. MDD was the most important feature for CBR prediction from all base learners. Findings from this study provides practical guidance and data-driven decision making for improved pavement durability in tropical environments.

Juvenile red mangroves (Rhizophora spp) play a critical role in early-stage biomass accumulation, which directly contributes to carbon sequestration. However, species- and age-specific allometric models for this growth phase are scarce. This paper developed site- and age specific allometric equations to estimate aboveground biomass (AGB) and belowground biomass (BGB) of approximately 1-year old Rhizophora mangle. Sixty representative seedlings were destructively sampled randomly based on the mean size-class distribution within the revegetated area. Due to observed structural variability, segmented regression was fitted; and thresholds of height and collar diameter were used to stratify the dataset into two subsets. The seedlings were carefully uprooted, separated into AGB and BGB components; freshly weighed, and oven-dried until a constant weight was achieved, to obtain dry biomass. Candidate equations were fitted for AGB and BGB, utilizing power-law models and log-transformed variables. The models were validated with leave-one-out cross-validation (LOOCV). The best fit equations utilized both height and diameter as predictor variables, and the predictive power of the equations were high (R2 > 0.80, RMSE < 0.39) with no significant difference between the observed and predicted AGB and BGB. The developed models offer an efficient and non-destructive method of estimating total biomass of juvenile mangroves, which has a direct effect on the restoration monitoring, blue carbon evaluation, and ecological surveillance of mangrove ecosystems. This enables performance evaluation of restoration initiatives and provide baseline for long-term monitoring and understanding of biomass accumulation and dynamics in red mangrove ecosystem.

The increasingly scarce world water resources require proper planning and management. This study was designed to estimate total crop water requirements (CWR), gross and net irrigation water requirements (GIWR and NIWR), actual irrigation water requirements (AIWR) and the moisture deficit at harvest (MDH) for 27 irrigation scenarios under three cropping systems using CROPWAT 8.0 and surrogate machine learning (ML) algorithms as an alternative. CROPWAT software was employed to simulate CWR, GIWR, NIWR, AIWR and MDH for maize, soybeans, and sweet potatoes. Simulations were based on 27 irrigation scenarios; formed by combining different irrigation water application levels (FC)), allowed water depletion levels (Depletion), and irrigation efficiencies (Eff). Estimated CWRs were cross checked against potential evapotranspiration (PET) data from the Food and Agricultural Organization Water Productivity Open Access Portal ((FAO WAPOR). Five surrogate linear ML models were also used to estimate irrigation metrics from CROPWAT simulations across all scenarios. Estimated CWR values ranged from 378.7 to 396.2 mm for soybeans, 508.8 to 528.3 mm for maize, and 583.6 to 619.5 mm for sweet potatoes, while NIWR varied between 93.5 and 654.2 mm across cropping systems. Varieties of linear ML models captured the simple relationship between scenario parameters and IWRs or MDHs across cropping systems, with NSE ranging from 0.62 to 0.99. Findings from this study highlight the importance of efficient irrigation practices to optimise water use, reduce resource strain, and also support sustainable agriculture, especially in regions experiencing water shortages.

Many countries have implemented policies designed to mitigate group-based or horizontal inequalities, with affirmative action programs being the most prominent examples. However, research examining the extent and motivations behind public support for such redistribution policies outside the United States remains limited. This study addresses this gap by investigating attitudes toward affirmative action in Nigeria - a nation characterised by pronounced political and economic disparities across ethnic groups and regions. Affirmative action in Nigeria is institutionalised through the Federal Character Principle (FCP) and operationally administered by the Federal Character Commission (FCC). Utilising original survey data from over 2000 Nigerian respondents, this paper analyses support for the FCP and FCC, as well as its underlying drivers. Our findings reveal that perceived societal benefits, especially the maintenance of political stability and the alleviation of ethnic inequalities, constitute significantly more influential factors shaping public endorsement of affirmative action than potential individual or group-level material gains.

The roles of pharmacists have changed in recent times, with pharmacists advancing public health through immunization. This study aimed to evaluate community pharmacists’ knowledge, attitude and role in Hepatitis B immunization in Nigeria. A cross-sectional descriptive study was conducted among 49 registered community pharmacists in Anambra state. Convenience sampling technique was employed. A structured questionnaire was adapted as instrument for data collection. Data were analyzed using SPSS version 23 and summarized using descriptive statistics: frequency, percentage, and mean. Scores obtained were compared using ANOVA and Independent Student’s t-test. The relationship between the sociodemographic characteristics of the respondents and their role in Hepatitis B vaccination was assessed using the Chi-square test. Statistical significance was established at p < 0.05. More females, 31 (63.3