University of Abuja

This study examined corruption and accountability mechanisms within Igbo Eze South Local Government Area of Enugu State. It focused on four major areas: the dominant forms and patterns of corruption; the effectiveness of accountability systems; the roles of traditional institutions in enforcing accountability; and the effects of alleged financial mismanagement on infrastructure and service delivery. Drawing from quantitative survey responses, and framed by the Principal-Agent Theory. The findings shows that; corruption within Igbo Eze South Local Government manifests through contract inflation, nepotism, bribery, and ghost workers, accountability mechanisms in Igbo Eze South Local Government Area are largely ineffective due to weak enforcement and minimal transparency in fund utilization, traditional institutions, such as community elders and village heads, serve as moral watchdogs. It recommends that; The ICPC should establish a permanent local monitoring unit in Igbo Eze South to conduct routine audits, investigate project implementation, and prosecute any identified cases of embezzlement or contract inflation, The Ministry should introduce a quarterly public financial disclosure policy requiring all local government departments in Igbo Eze South to publish budget allocations, expenditures.

The use of electronic examinations (E-examinations) by the Joint Admissions and Matriculation Board (JAMB) in Nigeria signifies a substantial transition towards using technology to improve educational evaluation and human capital advancement. Since its shift to computer-based testing (CBT) in 2015, JAMB's Unified Tertiary Matriculation Examination. This study evaluates the impact of E-examinations on the advancement of human capital development in Nigeria, concentrating on the concerns, effects, obstacles, and enhancement measures related to the implementation of JAMB. The research utilises a mixed-methods approach, integrating primary data from surveys and interviews with JAMB officials, UTME candidates, and stakeholders, as well as an analysis of secondary sources. The research underscores the necessity for strategic measures, such as improving CBT centres, augmenting broadband infrastructure, executing nationwide awareness initiatives, and reinforcing policy enforcement. Rooted in Human Capital Theory, the study emphasises the significance of reliable assessments in promoting skill enhancement and economic output. Recommendations underscore the need to rectify infrastructural deficiencies and enhance stakeholder participation to optimise E-examinations for national advancement. By addressing current deficiencies, JAMB can improve the integrity and inclusiveness of its evaluation system, hence advancing Nigeria's human capital development objectives.

The introduction of significantly shorter, all-oral regimens has significantly shifted the management of drug-resistant tuberculosis (DR-TB) towards a more tolerable and patient-centred therapeutic approach that aims to enhance treatment adherence, clinical outcomes, and quality of life among patients. Nigeria has gradually adopted this all-oral, shorter regimen, but the impact of this regimen in programmatic settings has not yet been studied. In 2022, a longitudinal, two-armed cohort study was conducted to explore the effectiveness, safety, and feasibility of the all-oral shorter regimen in the programmatic management of RR/MDR-TB in Nigeria. Consenting and eligible RR/MDR-TB patients receiving the all-oral regimen (intervention group) in four states were consecutively enrolled and compared to those receiving the standard of care (SOC). Treatment effectiveness, proportion, and 95% confidence intervals of favourable and unfavourable outcomes were measured at the end of treatment and during follow-up (six and 12 months post-treatment). In total 383 Participants were followed monthly throughout the 9-12-month treatment phase and then reassessed at 6 and 12 months after treatment completion, giving a total possible observation period of up to 24 months (185 received the intervention and 198 the standard of care). At the end of follow-up, there was a higher but non-significant proportion of favourable outcomes among the intervention vs. SOC group (80% vs. 69.7%); a higher proportion of favourable outcomes was also noted at the end of treatment among intervention participants (81.1 vs. 76.8%). Around one third of patients reported at least one serious adverse event (SAE), with no significant differences between arms, and none were deemed related to the use of medication. Intervention participants reported greater improvements in health-related quality of life between baseline and four months compared to those receiving the SOC. These findings support the programmatic use of all-oral shorter treatment for RR/MDR-TB as a regimen that is effective, tolerable, safe, and associated with enhanced health-related quality of life for patients in Nigeria.

Assessing groundwater recharge is essential for managing water resources sustainably, especially in semi-arid regions. In order to assess groundwater reserves for residential and agricultural use, this study examines the spatial and temporal distribution of recharge in the Kabago watershed in Southern Zamfara State, Nigeria. The watershed, which is roughly 5.97 km² in size and is composed of migmatite, gneiss, schist, and granitic rocks, has a Sudan savannah climate with high evapotranspiration and little annual rainfall (1164 mm). Hydrological processes during a 21-year period (1990–2010) were simulated using the Soil and Water Assessment Tool (SWAT 2012) linked with ArcGIS. SUFI-2 in SWAT-CUP was used for calibration and validation utilizing streamflow data from the Maru gauging station. With NSE values of 0.85 and 0.75, R2 values of 0.85 and 0.75, PBIAS of 2.5% and –1.3%, and RSR values of 0.43 and 0.52 for calibration and validation, respectively, the model performed satisfactorily. Soil water capacity, groundwater delay, baseflow coefficient, and surface runoff factors were among the sensitive characteristics. The results indicated that model-derived potential recharge values, which represent roughly 49% of annual precipitation, ranged from 379.93 to 678.88 mm/year with an average of 545.69 mm/year. Runoff and evapotranspiration were responsible for 15.35% (178.65 mm/year) and 33.24% (386.93 mm/year) of the water loss, respectively. Recharge variability was largely influenced by rainfall distribution, soil type, slope, and land use, with agricultural land promoting infiltration. Overall, the Kabago watershed functions as a significant recharge zone, though high evapotranspiration highlights vulnerability to land use and climate change. These findings provide a sound basis for groundwater management and policy in Zamfara and comparable semi-arid regions.

This study analyzed the profitability of taro farming in North Central, Nigeria. A simple random sampling design was used to select 220 taro farmers based on a well-structured questionnaire. The data were analyzed using descriptive statistics, farm budgetary technique, and depreciation of assets, financial analysis, multiple regression analysis, and Kendall’s coefficient of concordance. The average age and farm experience of taro farmers were 42 and 10 years, respectively. The results show that the mean total cost of taro production per hectare was 410.442$, while the mean total revenue was 1066.67$ indicating a substantial positive margin. The calculated t-value of (112.14) was greater than the tabulated t-value (1.96) at 5% level of significance, this implies that taro production was profitable in the study area. The profitability analysis further shows a gross margin of 827.25$ and a net farm income of 656.22$, reflecting a rate of return on investment (ROI) of 1.596. Despite the high profitability, variation in returns SD = 87,602 relative to cost SD = 42,594 suggest disparities in the efficiency and access to productive resources. Age, experience, education, and farm size were significant factors influencing the net farm income of taro farmers. These findings underscore the economic potential of taro as a food security and income generating crop in Nigeria. The study recommends strengthening agricultural input support programs through access to inputs, use of resistant varieties, enhancing extension service delivery system and improving market infrastructure, value addition to sustain profitability among taro farmers.

Majority of existing studies have a country-specific, cross-country, and regional focus, but none of these studies have examined the determinants of global financial inclusion using world average data. This study examines the determinants of global financial inclusion from 2005 to 2023 using world average data obtained from the world development indicators. The study used multiple regression estimation methods namely the fully modified ordinary least squares (FOLS) regression method, the generalized linear model (GLM) regression method, and the quantile regression method. The findings reveal that global indebtedness has an adverse effect on global financial inclusion while adult literacy rate is beneficial for global financial inclusion. Inflation, unemployment, and economic growth do not have a significant effect on global financial inclusion.

This study examined the impact of camel’s foot essential oil, also known as Piliostigma thonningii seed essential oil (Milne–Rech seed essential oil, MSEO), on the reproductive performance of Bunaji bulls. Piliostigma thonningii was selected because its seeds are rich in bioactive phytochemicals, including phenolics, flavonoids, and terpenoids, which have demonstrated antioxidant, antimicrobial, and endocrine-modulating properties capable of enhancing testicular function, spermatogenesis, and semen quality. In addition, the plant is widely available in tropical savanna ecosystems, making it a cost-effective and sustainable phytogenic alternative to synthetic reproductive enhancers. A completely randomized design was used to allocate fifteen clinically healthy bulls (aged 22–24 months; average body weight: 160.55 ± 0.60 kg) to three dietary treatments. For 16 weeks, bulls were fed a common basal diet supplemented with 0 ml/kg MSEO (T1, control), 10 ml/kg MSEO (T2), or 20 ml/kg MSEO (T3). Dietary treatments did not affect semen pH or its creamy white coloration (P > 0.05). However, MSEO supplementation significantly increased (P < 0.05) sperm concentration and ejaculate volume, with T3 recording the highest values. Sperm motility, percentage of live sperm, and live-to-dead sperm ratio were significantly improved (P < 0.05) in T2 and T3 compared with the control. Conversely, bulls in T3 exhibited the lowest proportions of dead and morphologically abnormal sperm cells (P < 0.05). Percentages of normal sperm cells and certain sperm abnormalities, including midpiece droplet, coiled tail, detached head, free tail, and bent tail, were not significantly affected by dietary treatment (P > 0.05). Scrotal length and circumference increased significantly (P < 0.05) with MSEO inclusion, with the highest values observed in T3. In conclusion, dietary supplementation with 20 ml/kg MSEO enhanced testicular development and semen quality without adverse effects, supporting its potential use as a natural, sustainable phytogenic alternative to synthetic reproductive enhancers in bull production systems.

Accurate prediction of effluent quality remains a significant challenge in wastewater treatment due to fluctuating influent loads, sensor noise, and non-linear system behaviour. This work evaluates three machine-learning models, Decision Tree, Random Forest, and XGBoost, using a five-year dataset (1825days) comprising daily influent and effluent measurements of pH, TDS, TSS, BOD, COD, and microbial indicators. A comprehensive preprocessing framework, including noise reduction and smoothing, reduced parameter variability by 28–51% and revealed clearer long-term patterns essential for modelling. Comparative analysis showed that Random Forest provided the most reliable generalisation, achieving test R² values of 0.414 (pH), 0.317 (TDS), 0.221 (BOD), 0.200 (COD), and 0.133 (TSS), outperforming XGBoost, which despite high training accuracy (e.g., R² = 0.93 for pH), suffered substantial overfitting with test R² dropping to as low as 0.046 for E. coli. Decision Tree performed the weakest overall, with consistently low predictive accuracy across all parameters. Effluent trend analysis indicated strong operational performance, including high removal efficiencies for BOD (92.7%), COD (89.1%), TSS (91.1%), and microbial indicators (~99%), while TDS removal remained limited (29.4%). All models struggled to predict microbial parameters due to their episodic variability and weak correlation with physicochemical inputs. Overall, Random Forest emerged as the most robust algorithm for effluent forecasting. At the same time, results highlight the need for advanced feature engineering, additional process-level data, and specialised modelling approaches to improve microbial prediction. These findings demonstrate the potential of machine-learning tools to enhance real-time monitoring, early anomaly detection, and decision support in wastewater treatment plants.

Advanced Persistent Threats (APTs) pose significant challenges to cybersecurity due to their stealthy, multi-stage nature. Traditional detection methods struggle to identify such complex behaviors. This study investigates the effectiveness of deep learning (DL) techniques in detecting APTs. Two research questions guide the study: (i) How effective is a custom Transformer-based model compared to existing DL models in detecting APTs? (ii) To what extent have DL models contributed to APT detection between 2020 and 2025? A two-fold methodology was adopted: a meta-analysis of 30 peer-reviewed studies and an empirical evaluation of a Transformer model trained on the CIC-IDS2018 dataset. The model achieved 99.71% accuracy with macro-averaged F1-score, precision, and recall values above 0.70, demonstrating strong overall performance but revealing challenges in classifying rare attack types. The meta-analysis further identified key research gaps, including limited use of multi-class classification, underutilization of Transformers, and a need for real-world datasets. Findings show that Transformer-based architectures are not only viable but good at modeling complex feature dependencies and detecting sophisticated APT behaviours. This research highlights both the strengths and limitations of DL for cybersecurity and suggests future directions for improving robustness and scalability in real-world deployment.