In this study, Mentha pulegium leaves and flowers harvested in three different Sicilian areas were investigated from a micromorphological, phytochemical and biological point of view. Light and scanning electron microscopy showed the presence of spherocrystalline masses of diosmin both in the leaf epidermal cells and in thin flower petals. Two different chemotypes were identified (I, kaempferide/rosmarinic acid; II, jaceidin isomer A). Phytochemical screening identified plant from collection site II as the richest in total phenolics (16.74 g GAE/100 g DE) and that from collection site I as the richest in flavonoids (46.56 g RE/100 g DE). Seventy-seven metabolites were identified both in flower and leaf extracts. Plant from site II showed the best antioxidant (0.90–83.72 µg/mL) and anti-inflammatory (27.44–196.31 µg/mL) activity expressed as half-maximal inhibitory concentration (IC50) evaluated by DPPH, TEAC, FRAP, ORAC, BSA denaturation and protease inhibition assays. These data were also corroborated by in vitro cell-based assays on lymphocytes and erythrocytes. Moreover, plant of site II showed the best antiangiogenic properties (IC50 33.43–33.60 µg/mL) in vivo on a chick chorioallantoic membrane. In conclusion, pedoclimatic conditions influence the chemotype and the biological activity of M. pulegium, with chemotype I showing the most promising biological properties.
The real challenge in Human-Robot Interaction (HRI) is to build machines capable of perceiving human emotions so that robots can interact with humans in a proper manner. Emotion varies accordingly to many factors, and gender represents one of the most influential ones: an appropriate gender-dependent emotion recognition system is recommended indeed. In this article, we propose a Gender Recognition (GR) module for the gender identification of the speaker, as a preliminary step for the final development of a Speech Emotion Recognition (SER) system. The system was designed to be installed on social robots for hospitalized and living at home patients monitoring. Hence, the importance of reducing the software computational effort of the architecture also minimizing the hardware bulkiness, in order for the system to be suitable for social robots. The algorithm was executed on the Raspberry Pi hardware. For the training, the Italian emotional database EMOVO was used. Results show a GR accuracy value of 97.8%, comparable with the ones found in the literature.
The real challenge in Human Robot Interaction (HRI) is to build machines capable of perceiving human emotions so that robots can interact with humans in a proper manner. It is well known from the literature that emotion varies accordingly to many factors. Among these, gender represents one of the most influencing one, and so an appropriate gender-dependent emotion recognition system is recommended. In this paper, a two-level hierarchical Speech Emotion Recognition (SER) system is proposed: the first level is represented by the Gender Recognition (GR) module for the speaker’s gender identification; the second is a gender-specific SER block. Specifically for this work, the attention was focused on the optimisation of the first level of the proposed architecture. The system was designed to be installed on social robots for hospitalised and living at home elderly patients monitoring. Hence, the importance of reducing the software computational effort of the architecture also minimizing the hardware bulkiness, in order for the system to be suitable for social robots. The algorithm was executed on the Raspberry Pi hardware. For the training, the Italian emotional database EMOVO was used. Results show a GR accuracy value of 97.8%, comparable with the ones found in literature.
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