King Faisal University (KFU) (Arabic: جامعة الملك فيصل Ğāmiʿat al-Malak Fayṣal) is a public university with the main campus in the city of Hofuf in Al Ahsa, Saudi Arabia founded in 1975. KFU was initially established with four colleges: two in Dammam and the other two in Al-Ahsa. Later, the colleges in Dammam have been segregated to become the Imam Abdulrahman Bin Faisal University.
Microwave-assisted hydrodistillation (MAHD) has been considered as a green extraction process for the recovery of essential oils from medicinal and aromatic plants. The present study deals with extraction kinetics, mass-transfer behaviour, process optimisation and chemical characterisation of Hibiscus rosa-sinensis flower oil using MAHD. Response Surface Methodology (RSM) employing a Central Composite Design (CCD) was applied to investigate the effect of extraction time, microwave power and methanol-to-plant material ratio on the oil recovery. The best conditions for operation, determined by RSM optimisation were: microwave power 300 W, methanol to plant material ratio 8:1 and time of extraction 120 min, which gave oil yield of 1.25%. The kinetic studies were then prolonged up to 180 min under the optimised condition to further probe the extraction behaviour with the greatest cumulative oil yield of 1.48%. The extraction profile showed the typical three-step behaviour of a fast-washing step, a diffusion-controlled extraction step, and an equilibrium step. roughly 56.1% of maximum extraction yield was attained within the first 30 min whereas roughly 83.8% of final yield was reached within 120 min. Gas Chromatography–Mass Spectrometry (GC–MS) analysis showed a complex mixture of volatile chemicals where Ethyl ethanimidate was the major ingredient. The kinetic study demonstrated that the extraction rate was strongly influenced by the mass-transfer behaviour, and the observed extraction profile was consistent with the principles of Fick’s second law of diffusion. Among the kinetic models studied, the Peleg model had the best fit to the experimental data, highlighting the significance of mass-transfer processes during extraction. Microwave power was shown to have a substantial effect on the extraction efficiency, and the maximum oil recovery was achieved at 300 W, however the extraction performance decreased slightly at higher power levels. Similarly, the ratio of methanol to plant material was found to be very important in maximising the extraction yield and an optimum ratio of 8:1 was obtained. The results demonstrate the potential of MAHD for the extraction of Hibiscus rosa-sinensis flower oil under the investigated laboratory conditions and provide useful information for process optimization, kinetic modelling, mass-transfer interpretation, and future scale-up evaluation.
The objective of the current study was to assess the hepatoprotective properties of nano-phytosomes (CRV-PNPs) loaded with carvacrol (CRV) in response to lead (Pb)- triggered liver injury. Male Wistar rats were assigned to six groups: Control, CRV, CRV-PNPs, Pb, Pb/CRV, and Pb/CRV-PNPs. Pb intoxication led to considerable blood profile disruption; in addition, it lowered serum total protein and its fractions. Pb also provoked an oxidative stress cascade, indicated by reductions in catalase (CAT), superoxide dismutase (SOD), and glutathione peroxidase (GPx) activities and glutathione (GSH) levels, coinciding with a marked increase in malondialdehyde (MDA) concentrations. Pb also increased the inflammatory mediators (NF-kappa B, TNF-alpha, IL-1 beta, IL-6, NLRP3). Compared to crude CRV, CRV-PNPs provided significantly better protection against Pb-induced hepatotoxicity by restoring hematological indices and liver function, correcting dyslipidemia, and attenuating histopathological alterations. This protective efficacy was associated with improved antioxidant enzyme activity, diminished oxidative damage, and reduced inflammatory response by downregulating key mediators. Molecular docking revealed that CRV has a strong affinity for antioxidant and inflammatory proteins. The CRV-PNPs exhibited a better protective effect against Pb-induced hepatic damage than crude CRV, which might be attributed to improved stability, solubility, and bioavailability, as well as multiple protective mechanisms.
This paper presents a five-level switched-capacitor multilevel inverter (SCMLI) with dual boosting and common-ground (CG) capability for standalone and grid-connected applications. The proposed converter employs two switched capacitors (SCs), six switches, and two diodes while charging both capacitors only to the input voltage, resulting in reduced capacitor rating and cost. Unlike existing five-level converters, the proposed topology can continue operating as a three-level inverter under the failure of either switched capacitor resulting in improved inverter reliability. To mitigate the large charging-current spikes commonly encountered in SCMLIs, a quasi-soft charging technique using a small charging inductor is introduced, and a practical design methodology is presented. The charging inductor significantly reduces peak capacitor charging current, lowers current stress on semiconductor devices, and improves converter efficiency. The proposed converter is further analyzed in terms of leakage-current mitigation, voltage and current stresses, power loss, efficiency, scalability, and cost, and is quantitatively compared with recently reported common-ground five-level inverters. Experimental results obtained from a 1-kW prototype validate the converter under different operating conditions, including load and modulation variations, while hardware-in-the-loop results demonstrate successful grid-connected operation with effective active and reactive power control.
Generating faithful and semantically meaningful explanations for knowledge graph (KG) query results remains a fundamental challenge, due to the disconnect between execution-level provenance and ontology-level semantics. Provenance-based approaches ensure faithfulness to query evaluation but lack semantic abstraction, while ontology-mediated methods provide formal justifications without reflecting actual execution behavior. We introduce Onto-Explain, a unified framework for explainable KG reasoning that bridges this gap by jointly modeling how results are derived and why they hold. The framework integrates provenance tracing, ontology lifting, attribution-based prioritization, and contrastive reasoning within a single execution-consistent workflow. Unlike prior approaches that treat these components in isolation, Onto-Explain enables their coordinated operation over shared representations under bounded reasoning and tractable attribution. Technically, the framework combines provenance semiring models with lightweight Description Logics and is realized through a set of interacting modules that support provenance factorization, ontology lifting, attribution scoring, contrastive reasoning, and memoized reuse. This design produces ontology-grounded minimal or near-minimal explanations with ranked elements and contrastive insights. We evaluate the framework on LUBM, DBpedia, Wikidata, FedBench, and HPO using a layered protocol that combines execution-consistent validation at scale with pseudooracle evaluation on tractable subsets. Results show high execution faithfulness (0.98 on structured datasets and 0.97 on heterogeneous graphs) and strong semantic grounding (0.95 and 0.83, respectively). Attributionbased ranking improves nDCG@10 from 0.69 to 0.73 to 0.80-0.83, while contrastive explanations achieve up to 92% coverage with compact explanation sets (2-3 elements). The framework scales near-linearly up to 100 M triples. Overall, the results show that combining provenance and ontology-based reasoning yields explanations that are both faithful and scalable, while preserving semantic grounding.
Hydrogen enrichment can improve compression-ignition (CI) engine fuel-conversion performance, but its practical use is constrained by pressure-rise, peak-pressure, emissions, and component-temperature limits. This study develops a thermally constrained reduced-order digital-twin framework for identifying safe-beneficial hydrogen utilization in a mildly boosted direct-injection CI engine. A crank-angle thermo-combustion model with hydrogen-dependent burn phasing and duration modifiers is coupled to a transient six-node thermal network. Four calibration variables-injection-timing shift, air-fuel ratio (AFR), hydrogen blend fraction, and base load-are sampled at 1600, 2000, and 2400 rpm to train speed-specific surrogates for brake thermal efficiency, brake specific fuel consumption (BSFC), nitrogen oxides (NOx) proxy, peak pressure, peak wall temperature, and peak pressure-rise rate. Feasibility is imposed using limits of 10.8 MPa, 4.9 bar/deg, and 660 K for peak pressure, pressure-rise rate, and peak wall temperature, respectively. Principal thermo-performance surrogates achieved R2 = 0.992-0.998, while pressure-rise-rate and gated NOx models achieved R2 = 0.972-0.975 and 0.945-0.976, respectively. Hydrogen enrichment increased brake thermal efficiency and reduced BSFC, but intensified NOx tendency, pressure-rise, and wall-temperature loading, especially under richer/high-load operation. The safe-beneficial envelope expanded under leaner operation and compressed rapidly at lower AFR and high load. The selected 2000 rpm balanced point was recovered by the full model with brake thermal efficiency of 0.436, BSFC of 196 g/kWh, peak pressure of 9.70 MPa, pressure-rise rate of 4.05 bar/deg, and peak wall temperature of 597 K, while retaining positive local robustness margins. The framework supports thermally constrained hydrogen-assisted CI pre-calibration.
