Federal University of Technology Owerri

Antimicrobial resistance (AMR) is a critical public health challenge in sub-Saharan Africa (SSA), driven by unregulated antibiotic access, self-medication and weak health systems. Community-based interventions, including education, awareness campaigns, stewardship programmes and regulatory measures, are increasingly used to address inappropriate antibiotic use, yet evidence of their effectiveness in SSA remains fragmented. This systematic review aims to identify and synthesise studies conducted between 2015 and 2025 that evaluate public health interventions in SSA community settings aimed at reducing antibiotic misuse or AMR. Following preferred reporting items for systematic reviews and meta-analyses protocols (PRISMA-P) guidelines, eligibility criteria will be structured using the patient, intervention, comparison and outcome framework. Searches will be conducted in PubMed/MEDLINE, Scopus, Web of Science and Google Scholar. Studies published in English between 2015 and 2025 will be eligible. Two reviewers will independently conduct screening, data extraction and risk-of-bias (RoB) assessment (using Cochrane RoB V.2.0, ROBINS-I or Joanna Briggs Institute (JBI) tools). This systematic review will synthesise evidence from previously published studies and therefore does not require formal ethical approval. The review will be conducted and reported in accordance with the methodological guidance of the JBI and the PRISMA 2020 statement for systematic reviews. The findings will be disseminated through publication in a peer-reviewed journal and presentation at national and international conferences focusing on AMR and community-based health approaches. CRD420251160424.

This study evaluated the functional properties of fiber-enriched breakfast cereals formulated from millet, malted mungbean and tigernut flours using quadratic response surface models. The data obtained were subjected to regression/statistical analysis as prescribed by the RSM. The regression coefficients were used to test the models. The linear, binary and ternary blends were generated and further highlighted by the response surface plot. The blend ratio ‘31.16g millet, 21.72g mungbean, 47.11g tigernut’ with high desirability of 0.951 was selected after optimization and were validated. The blend will be suitable for production of high gluten-free fiber breakfast cereal and other food products such as bread, cake, sausage and biscuit. The developed regression models will enable food industries maximized/optimized the blending of these flours in production of aforementioned food products so as to increase the product quality. The results showed that blend composition significantly influenced bulk density (BD), water absorption capacity (WAC), oil absorption capacity (OAC), solubility (S), emulsification capacity (EC), and foam stability (FS) (p<0.05), while wettability, viscosity, swelling index, gelatinization temperature, and foam capacity were not significantly affected. Malted mungbean flour played a key role in reducing BD and improving WAC and OAC, indicating its suitability for producing lighter cereals with improved hydration and flavor retention. Significant (p<0.05) interaction effects among the flours, particularly for WAC and EC, highlighted the importance of optimized blend combinations. Foam stability exhibited the highest model predictability (Adj. R² = 0.8596), mainly enhanced by millet inclusion. However, these findings support the development of nutritionally enhanced, consumer-acceptable breakfast cereals with improved reconstitution, texture, and stability, suitable for children, adults, and the elderly, and valuable for commercial cereal formulation and product optimization.

Gas condensate banking can significantly reduce near-well gas productivity by as much as ~60% in tight gas reservoirs. Existing treatment techniques are resource demanding and could alter the reservoir structure permanently. This study investigates the effectiveness of enhanced electrokinetic oil recovery (EK-EOR) as a low-impact alternative for treating condensate banks. Using compositional reservoir simulation (CMG GEM), the influence of key reservoir and operational parameters—porosity, permeability, producer well location (i, j), injection rate, and injection pressure—on cumulative gas production (CGP) was examined. A Box–Behnken design of experiments was employed to generate 62 simulation runs, and a proxy model was developed to approximate full-field responses. Statistical validation showed strong model fidelity (R2 = 0.99, AAPE = 2.2%). The proxy was then optimized using a genetic algorithm (GA) to identify conditions that maximize gas recovery. Results indicate that lower injection rates and lower injection pressures maximize CGP through enhanced electro-osmotic flow and reduced water blocking, achieving a peak cumulative gas of 4.06 × 108 ft3. A secondary optimum at high injection pressure could be attributed to re-pressurization and partial re-vaporization of condensate near the wellbore. Reservoir quality also exerted a strong control: higher permeability and moderate porosity favoured gas yield, while optimal producer placement near the reservoir boundary increased drainage efficiency. This study demonstrates a systematic optimization framework combining design of experiments, proxy modelling, and evolutionary algorithms to evaluate EK-EOR performance.

The study presents the simulation-based method for evaluating transformer core materials by considering the spatial nature of magnetic flux lines. The aim is to broaden the design space of materials compared with those conventionally characterised solely by relative permeability and core loss by introducing a pair of field-distribution indices; the compactness index (CI) and symmetry index (SI). Five fundamental materials, Metglas, Silicon Steel, Ferrite, Soft Iron and Solid Iron, were investigated through MATLAB simulation with the basis of a simplified excitation model. Vector field analysis, flux density surface maps, radial flux profile, and angular flux responses were described for each material magnetic features. The simulation results revealed that the best efficiency obtained was Metglas with a compactness index of 99.17, then silicon steel, soft iron, ferrite and solid iron with 44.10, 41.30, 30.08, and 21.74 respectively. The peak value of the flux density for Metglas was about 0.30 T measured just outside the inner radius at r = 0.03 m under a 1 A excitation, and about 0.18 T at r = 0.05 m used for the angular profile. The symmetry index was unity for all materials, ensuring the magnetic field distribution was perfectly balanced in the symmetric excitation geometry. These results demonstrate that while magnetic symmetry is preserved across materials in this geometry, the CI yields a relevant quantitative difference in field performance. The study therefore offers a refined basis for evaluating transformer core materials through a geometry oriented framework that can support compact and thermally stable transformer designs. The results are consistent with analytical expectations and recent FEA studies, highlighting the relevance of CI and SI for modern power systems where magnetic performance must be maximised in compact designs.

Crop yield variability is a vital issue for farmers, policymakers, and researchers. This study investigate the application of machine learning (ML) techniques to design and predict crop yield variability. We exploit a dataset containing historical climate, soil, and management factors to train and evaluate several ML models, including random forest, support vector machines, and neural networks. Our results show that ML models can effectively capture complex relationships between input features and crop yield, outperforming traditional linear regression models. The best-performing model, a random forest regressor, achieves a mean absolute error (MAE) of 10.2% and R-squared value of 0.85. We identify key factors influencing crop yield variability, including temperature, precipitation, and soil organic carbon content. Our findings demonstrate the potential of ML for improving crop yield prediction and informing decision-making in precision agriculture

Mobility modelling plays a pivotal role in evaluating the performance of Mobile Ad Hoc Networks (MANETs), as the movement patterns of nodes strongly influence routing efficiency, connectivity, and network stability. Existing mobility models, particularly entity and group-based frameworks, have proven useful in simulating various MANET applications but remain limited in capturing the complex movement behaviour characteristic of Search-and-Rescue (SAR) operations in disaster scenarios. This study proposes a realistic hybrid mobility model that integrates the essential features of both entity and group mobility paradigms to better represent the coordinated yet flexible dynamics of SAR teams. Three sets of simulations were performed using the Network Simulator 2 (NS2). The first and second simulations focused on generating and combining existing entity and group mobility patterns using the BonnMotion tool, resulting in two composite models (Real1 and Real2). The third simulation compared these models using AODV and DSDV routing protocols under varying node mobility levels and traffic conditions. Key performance metrics, including relative mobility, node degree, partition count, link duration, packet delivery ratio (PDR), throughput, and average end-to-end delay, were analysed. Results demonstrate that it is feasible to concatenate existing mobility models into a coherent hybrid framework. Among the developed models, Real2 exhibited superior performance in most test conditions, yielding higher PDR, lower delay, and more stable connectivity than its constituent models. These findings confirm that the performance of MANETs is highly dependent on mobility realism. Consequently, selecting a model that accurately reflects the behavioural characteristics of the target scenario is essential for credible MANET performance evaluation in emergency response contexts.

This study explores how Monte Carlo simulation and artificial intelligence (AI) tools can be applied to identify, assess, and mitigate risks in green public procurement for energy transition projects. Through a systematic literature review of recent peer-reviewed studies, the research develops a structured framework that integrates risk management into sustainable procurement. The findings emphasize the importance of auditability, adaptability to regulatory shifts, and stakeholder engagement in ensuring resilient and transparent procurement practices. By incorporating AI-based monitoring and Monte Carlo simulation, the framework enables proactive risk mitigation, greater accountability, and improved decision-making in energy transition initiatives.

<strong class="journal-contentHeaderColor">Abstract.</strong> This paper investigates the prevalence and identification of spurious correlations within space weather datasets, a critical concern given the complex inter-dependencies of nature of geophysical phenomena. This is carried out using daily-averaged galactic cosmic ray (GCR) datasets from MOSC and OULU neutron monitor (NM) stations analyzed separately, the large Forbush Decrease (FD) (FD &gt; 3 %) and the small FD (FD &le; 3 %) in each station, to account for the effects of 11-year solar cycle oscillations. For the first time, a statistical analytical method was employed to test the link between FD amplitudes and solar-geomagnetic variables in each dataset after the effects of 11-year solar cycle oscillations are filtered. We demonstrate that, while significant correlations between various space-weather indices and Forbush Decrease events are empirically observable, a meticulous analysis reveals that a subset of these relationships may not reflect true physical causality but rather arise from statistical artifacts or confounding factors inherent in the data. Specifically, analyses of Forbush Decreases often reveal varying correlation coefficients with geomagnetic and solar wind parameters, which can fluctuate significantly across time periods and cosmic-ray stations. For instance, correlations between Forbush Decrease amplitudes and interplanetary magnetic field strength, solar wind speed, and geomagnetic indices like Kp and Dst have been observed to exhibit both negative and positive trends, depending on the specific dataset and analytical approach employed. The results obviously show inconsistencies in the datasets for both MOSC and OULU stations for the large and small FDs, respectively &ndash; specifically, strong correlations were noticed for the parameters&rsquo; regression analyses after the effects of 11-year solar cycle oscillations were removed for both big and small Fds. These inconsistencies strongly suggest the influence of 11-year solar cycle oscillations on the FDs counted on both stations, thereby affecting the relationships between the FDs and the geomagnetic tested variables, echoing concerns about "spurious regression" in the stationary time series. Most of the results are statistically significant at a 95 % confidence level. The results obtained here imply that 11-year solar cycle oscillations have impacts on the GCR flux intensity.