The global rise of antimicrobial resistance underscores the urgent need for novel therapeutic agents, with medicinal aromatic plants representing a promising source of bioactive compounds. Citrus medica L. cv. ‘Liscia’ has traditionally been used for its pharmacological properties; however, its essential oil remains insufficiently characterized. This study provides the first integrated evaluation of C. medica cv. ‘Liscia’ essential oil (CMLEO) for its antimicrobial, antibiofilm, and anti-quorum-sensing activities using complementary in vitro and in silico approaches. GC–MS analysis identified limonene as the main constituent (86.9%), followed by myrcene (2.9%), β -bisabolene (1.25%), nerol (1.11%), geraniol (1.10%), and aromadendrene (0.94%). CMLEO exhibited strong broad-spectrum antibacterial activity, with inhibition zones of 6.67–33.00 mm, outperforming limonene and ampicillin against most tested strains, particularly Photobacterium damselae and Vibrio fluvialis , whereas Pseudomonas aeruginosa showed reduced sensitivity. Against fungal pathogens, CMLEO demonstrated moderate antifungal activity (11.67–14.00 mm), comparable to amphotericin B. Antibiofilm activity peaked at the MIC, notably against Bacillus subtilis ATCC 6051 (86.20±0.61%), and declined at sub-MIC levels, becoming negligible at MIC/8, especially for Escherichia coli ATCC 25922 and P. aeruginosa ATCC 27853. CMLEO also inhibited violacein production in C. violaceum CV12472 completely at MIC (100±0%) and progressively at MIC/2 and MIC/8 (51.29–9.04%), with QS halos decreasing from 24.00±1.63 mm to 7.00±0.00 mm. Swarming motility of P. aeruginosa PAO1 was suppressed dose-dependently (72.83–40.22%), indicating interference with virulence. In silico analyses, including molecular docking, 200 ns MD simulations, DFT, and ADMET profiling, predicted strong ligand–target interactions, stable complex dynamics, favorable electronic properties, and predicted safe pharmacokinetics. These results demonstrate the promising efficacy of CMLEO and its major constituents against multidrug-resistant pathogens and highlight its potential as a natural, multi-target antimicrobial agent.
South Africa’s transition toward smart-grid electricity systems is reshaping tariff structures, metering practices, grid management, and utility revenue generation, yet limited empirical evidence links energy-policy interventions with revenue outcomes across distinct stages of this transition. This study addresses this gap by examining the relationship between energy policy, smart-grid development, electricity pricing, and Eskom’s revenue performance across three phases: Pre-Smart Grid (2000–2007), Transition (2008–2020), and Smart Grid Integration (2021–2024). Annual Eskom electricity data for 2000–2024, covering electricity prices, energy demand, energy generation, peak demand, and revenue, were analysed using phase-based regression models, complemented by an assessment of tariff structures, incentives, subsidies, and regulatory support. The results show substantial revenue growth across all phases: Eskom revenue increased by 67.11% in the Pre-Smart Grid phase, 372.57% during the Transition phase, and 68.27% during the Smart Grid Integration phase, while electricity tariffs increased by 42.85%, 336%, and 65.08%, respectively. Overall, tariffs increased approximately 15-fold compared with a 14.5-fold increase in revenue. The regression models were statistically significant overall for the Pre-Smart Grid (R² = 93.57%, p = 0.008) and Transition (R² = 75.53%, p = 0.004) phases, whereas insufficient observations prevented reliable inference for the Smart Grid Integration phase. The findings indicate that improved revenue performance occurred alongside smart metering, digital billing, tariff reforms, and broader sector interventions, but do not establish that smart-grid deployment independently caused revenue growth. The study concludes that cost-reflective and time-of-use tariffs, advanced metering, improved collection systems, and targeted policy and financial support are important for strengthening revenue outcomes during South Africa’s smart-grid transition.
Pesticide residues in fruits may persist through the food chain and contribute to human exposure, yet the long-term population-level implications of such exposure remain insufficiently characterized mathematically. This study develops a novel mathematical modeling framework to investigate the dynamics of pesticide contamination in tomato fruits and its potential effects on human populations. The model incorporates pesticide influx, residue uptake and depletion, human health transitions, and the beneficial contribution of tomato consumption. Positivity, boundedness, equilibrium, local and global stability, and Hopf bifurcation analyses were performed to characterize the system dynamics. Sensitivity analysis identified pesticide influx as a key influential parameter. Numerical simulations showed that increasing pesticide influx decreases the healthy human population while increasing pesticide burden and the affected population, with effects emerging gradually under sustained exposure. An optimal control model incorporating pesticide-prevention and residue-removal interventions was formulated using Pontryagin’s Maximum Principle. Both controls reduced pesticide residues and the affected human population while improving the healthy population dynamics across the considered scenarios. The study recommends prioritizing prevention of excessive pesticide entry into food systems, complemented by effective residue-removal practices before consumption. The model provides a theoretical framework for assessing pesticide-exposure dynamics and intervention strategies, while its predicted human-health effects require validation through experimental and epidemiological studies.
