ABSTRACT
The phytochemical profile of essential oils is influenced by genetic and paragenetic factors. In this research, we studied the essential oils of Lavandula angustifolia and Lavandula x intermedia cultivated in Lebanon. The latter is a cross hybrid between Lavandula angustifolia and Lavandula latifolia and is also known as lavandin and Lavandula hybrida. Specifically, the chemical composition and biological activities (antibacterial, antioxidant, anticancer, and hemolytic) of the essential oils were assessed. GC-MS results showed marked differences in the chemical compositions of the oils. For example, linalool was more abundant in L. x intermedia (44.15%) than in L. angustifolia (32%), while an opposite trend was observed for the percentages of 1,8-cineole (8.6% in L. angustifolia and 4.0% in L. x intermedia). FTIR analysis confirmed the richness of both oils in monoterpenes and sesquiterpenes. In terms of antioxidant activity, L. angustifolia essential oil demonstrated significantly better activity (IC50= 5.24 ± 1.20 mg/mL) compared to L. x intermedia oil in the DPPH radical scavenging assay. MTT cell viability assays revealed that L. angustifolia essential oil was a slightly more potent antiproliferative agent than L. x intermedia oil on human colorectal (HCT-116) and human breast (MCF-7) cancer cells. The antibacterial activity of the essential oils was tested against Staphylococcus aureus, Staphylococcus epidermidis, Enterococcus faecalis, Escherichia coli, and Serratia marcescens. Both oils showed good antibacterial activities with MIC values of 0.174 and 0.169 mg/mL for L. angustifolia and L. x intermedia oils, respectively. MBC determinations revealed that the antibacterial activity was bactericidal against all bacteria, except Staphylococcus aureus. Furthermore, both essential oils did not exhibit notable hemolytic activity on red blood cells. Overall, Lebanese L. angustifolia and L. x intermedia essential oils have promising industrial and medicinal values.
ABSTRACT
Internet of Things technologies are spurring new types of instructional games, namely reality-enhanced serious games (RESGs), that support training directly in the field. This paper investigates a key feature of RESGs, i.e., user performance evaluation using real data, and studies an application of RESGs for promoting fuel-efficient driving, using fuel consumption as an indicator of driver performance. In particular, we propose a reference model for supporting a novel smart sensing dataflow involving the combination of two modules, based on machine learning, to be employed in RESGs in parallel and in real-time. The first module concerns quantitative performance assessment, while the second one targets verbal recommendation. For the assessment module, we compared the performance of three well-established machine learning algorithms: support vector regression, random forest and artificial neural networks. The experiments show that random forest achieves a slightly better performance assessment correlation than the others but requires a higher inference time. The instant recommendation module, implemented using fuzzy logic, triggers advice when inefficient driving patterns are detected. The dataflow has been tested with data from the enviroCar public dataset, exploiting on board diagnostic II (OBD II) standard vehicular interface information. The data covers various driving environments and vehicle models, which makes the system robust for real-world conditions. The results show the feasibility and effectiveness of the proposed approach, attaining a high estimation correlation (R2 = 0.99, with random forest) and punctual verbal feedback to the driver. An important word of caution concerns users' privacy, as the modules rely on sensitive personal data, and provide information that by no means should be misused.
