Ahmadu Bello University Zaria is a federal government research university in Zaria, Kaduna State, Nigeria. ABU was founded on 4 October 1962, as the University of Northern Nigeria.The university operates two campuses: Samaru (main) and Kongo in Zaria. There is a pre-degree school in Funtua a few kilometres from the main campus owned by the university. The Samaru campus houses the administrative offices and the faculties of physical sciences, life sciences, social sciences, arts and languages, education, environmental design, engineering, medical sciences, agricultural sciences and research facilities. The Kongo campus hosts the faculties of Law and Administration. The Faculty of Administration consists of Accounting, Business Administration, Local Government and Development Studies and Public Administration Departments. Additionally, the university is responsible for other institutions and programmes at other locations.It is named after the Sardauna of Sokoto, Alhaji Sir Ahmadu Bello, the first premier of Northern Nigeria.The university runs a wide variety of undergraduate and graduate programmes (and offers associate degrees and vocational and remedial programmes). It has a large medical programme with its own ABU Teaching Hospital, one of the largest teaching hospitals in Nigeria and Africa.In September 2021, the University moved the departments of Insurance and Actuarial science, Accounting and Business Administration from Kongo to Samaru (Business school)..
Vancomycin-resistant enterococci (VRE) are high-priority pathogens; however, most healthcare providers in resource-limited settings do not consider enterococci in the differential diagnosis of clinical bacterial infections. This cross-sectional surveillance study determined the antimicrobial resistance (AMR) profile of Enterococcus and factors associated with their presence in patients attending the Ahmadu Bello University Teaching Hospital (ABUTH), Zaria, Nigeria. Non-repetitive clinical samples from 180 patients were processed for the isolation, identification, and antimicrobial susceptibility testing of enterococci in Slanetz-Bartley agar and the Vitek® 2 Compact system. Structured questionnaires were used to collect participants’ sociodemographic and risk factor data. The prevalence of enterococcal infection was 23.3% (42/180), comprising 57.1% Enterococcus faecalis and 42.9% Enterococcus faecium. Subgroup analysis showed no significant association between the isolation rates and sample type (p=0.379). The frequency of enterococcal infection was significantly highest among patients aged 31–40 years (p<0.05), with a higher frequency among older patients. Moreover, unemployed patients (OR=1.8, p=0.0195) and those using pit latrines (OR=2.1, p=0.034) had significantly increased the odds of enterococcal infection. Enterococci resistance was highest against ampicillin (100%), tetracycline (90.5%), and ciprofloxacin (69%), with 83.3% exhibiting a multidrug-resistant (MDR) phenotype. Remarkably, 7.1% of all isolates were VR-E. faecium. Moreover, enterococci with a multiple antimicrobial resistance (MAR) index of 0.3 (45.2%) were predominant. A high level of MDR enterococci (including VRE) was identified in the clinical samples from patients initially managed for non-enterococcal infections, underscoring the need to use a selective bacteriological medium to minimize microbiological misdiagnosis in resource-limited healthcare settings. This study provides valuable local surveillance data indicating gaps in the integration of appropriate diagnostic bacteriology into clinical infectious disease practice.
Commercial cold storage systems are energy-intensive thermal processes where fluctuating ambient temperatures often drive internal heat gains, leading to compressor short-cycling and reduced efficiency. To address these challenges, this study develops a Multiple Model Predictive Control (MMPC) strategy based on a data-driven state-space architecture. Utilizing a modified high-fidelity benchmark model from Aalborg University, subspace system identification was employed to characterize the system dynamics across a 14-day tropical temperature profile (8.9 to 32.8°C). The model incorporates ambient temperature and heat transfer rates as exogenous inputs, alongside suction pressure, expansion valve and compressor capacity, to predict air and goods temperatures based on expert knowledge. The optimal 7th-order model achieved a goodness-of-fit of 98.7% and 90.4% for the respective outputs, with a minimal MSE of 56.6e-05. Bayesian optimization was implemented to tune the MPC prediction and control horizons across varying operating conditions. Simulation results demonstrate that the MMPC framework performed better than both a single MPC and the benchmark PI-deadband controller. The MMPC reduced compressor switching by 38.5% (72 vs. 117 cycles) and achieved energy savings of 25.4% over the benchmark and 65.3% over the single MPC. Furthermore, the MMPC maintained food temperatures strictly below 5°C, ensuring food safety for perishable goods, whereas the single MPC failed to maintain regulation (5.9°C). All simulations were performed using the software package MATLAB R2025b and Simulink R2025b.
Perfluorooctanesulfonic acid (PFOS), a persistent and bioaccumulative substance, has emerged as a major environmental contaminant of global concern. PFOS, widely used in industry and consumer products, is common in aquatic ecosystems and may harm primary producers like phytoplankton, though its effects on their community structure and physiology remain unclear. Here, we investigated the impact of PFOS on freshwater physicochemical parameters, phytoplankton community dynamics, biochemical composition, and oxidative stress responses in a mesocosm experiment over a 28-day period. The water quality indicators generally were unaltered by PFOS exposure, but phosphate levels decreased initially in the control. Phytoplankton biomass (cell density) decreased at 10 µg L−1 and 10 mg L−1 PFOS exposures. Phytoplankton species richness, Shannon, and Simpson diversity indices declined at 10 mg L−1, with communities at this concentration becoming distinctly different from the control and lower PFOS treatments by day 28. This shift in community structure was primarily driven by taxa such as Chlorogonium sp. and Microcystis aeruginosa, which contributed strongly to the observed compositional dissimilarity. Proteins content in the phytoplankton increased under PFOS exposure, peaking on day 21. In contrast, carbohydrate and lipid contents were reduced under PFOS relative to the control. PFOS exposure elevated glutathione-S-transferase activity, malondialdehyde, and hydrogen peroxide levels, whereas peroxidase activity was reduced. The phytoplankton biomass and lipids were positively associated, while oxidative stress markers were closely linked with high PFOS concentrations. Overall, these results highlight that PFOS exposure suppressed phytoplankton biomass, altered their community composition, and induced oxidative stress and metabolic shift at higher concentrations, highlighting its potential ecological risk in freshwater systems.
Residual byproducts and waste substances now serve as the predominant sources of pollution across ecosystems. Periwinkle shell waste is a widely produced, calcium-rich marine byproduct often piled in open dumps, resulting in foul odours and visual pollution. This study seeks to optimise ash content derived from periwinkle shell waste as a modifier of bitumen for hot-mix asphalt production, while assessing the environmental and cost implications. The optimisation was executed using the Design Expert 13 Software and a custom design approach, which involves the importation of an already structured experimental design. The models developed for the asphalt-mx responses showed good data fit, which was confirmed by good R2 values and a difference between the Adjusted R2 value and Predicted R2 value. Also, the normal plot of Residuals and the plot of Residuals versus the Predicted showed evidence of good data fit. The optimum bitumen content for the control mix and modified mix was 6.086
Multimodal multiobjective optimization problems (MMOPs) frequently arise in real-world applications, where widely separated solutions in the decision space may yield similar objective values. The principal challenge in solving MMOPs lies in identifying all equivalent Pareto-optimal solutions while maintaining an appropriate balance between diversity and convergence in both decision and objective spaces. This paper proposes a novel algorithm, Dynamic Topology Multiobjective Particle Swarm Optimization with Adaptive Lévy Flight (DT-MMPSO-Levy), to address these challenges. DT-MMPSO-Levy incorporates a dynamic small-world topology, enabling adaptive re-wiring of particles throughout generations. This facilitates both local and global information exchange, enhancing decision space diversity. To balance exploration and exploitation across the optimization process, a phase-based adaptive velocity update mechanism is employed. During early generations, Lévy flight-induced heavy-tailed jumps promote global exploration for discovering diverse Pareto-optimal sets. In mid generations, an adaptive control parameter, Omega, modulates the exploration-exploitation trade-off based on population diversity. In later stages, the algorithm shifts focus toward convergence by emphasizing local exploitation. The performance of DT-MMPSO-Levy is benchmarked against seven state-of-the-art MMOP algorithms on twelve standard test problems from the CEC 2019 suite. Experimental results show that DT-MMPSO-Levy achieves superior performance, securing the best scores in 8 out of 12 cases for Inverted Generation Distance in Decision Space (IGDX), 7 out of 12 for Pareto Sets Proximity (PSP), and 7 out of 12 for Inverted Generation Distance in Objective Space (IGDF) metrics, thereby demonstrating its effectiveness and robustness.
