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    Alginate Films Encapsulating Lemongrass Essential Oil as Affected by Spray Calcium Application

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    [EN] The necessity of producing innovative packaging systems has directed the attention of food industries towards the use of biodegradable polymers for developing new films able to protect foods and to extend their shelf-life, with lower environmental impact. In particular, edible films combining hydrophilic and hydrophobic ingredients could retard moisture loss, gas migration and ensure food integrity, reducing the necessity of using synthetic plastics. Alginate-based films obtained from emulsions of lemongrass essential oil (at 0.1% and 0.5%) in aqueous alginate solutions (1%), with Tween 80 as surfactant (0.3%), were obtained by casting and characterized as to microstructure and thermal behavior, as well as tensile, barrier and optical properties. Films were also crosslinked through spraying calcium chloride onto the film surface and the influence of oil emulsification and the crosslinking effect on the final film properties were evaluated. The film microstructure, analyzed through Field Emission Scanning Electron Microscopy (FESEM) revealed discontinuities in films containing essential oil associated with droplet flocculation and coalescence during drying, while calcium diffusion into the matrix was enhanced. The presence of essential oil reduced the film stiffness whereas calcium addition lowered the filmÂżs water solubility, increasing tensile strength and reducing the extensibility coherent with its crosslinking effect.This research was funded by the Ministerio de Economia y Competitividad (MINECO) of Spain, through the project AGL2016-76699-R.Cofelice, M.; Cuomo, F.; Chiralt, A. (2019). Alginate Films Encapsulating Lemongrass Essential Oil as Affected by Spray Calcium Application. Colloids and Interfaces. 3(3):1-15. https://doi.org/10.3390/colloids3030058S11533Rossi, M., Passeri, D., Sinibaldi, A., Angjellari, M., Tamburri, E., Sorbo, A., 
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    Epidemic and timer-based message dissemination in VANETs: A performance comparison

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    Saccharomyces boulardii: a summary of the evidence for gastroenterology clinical practice in adults and children.

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    Probiotics are viable, nonpathogenic microorganisms (bacteria or yeast) which when administered in adequate amounts, confer a health benefit on the host. At this time, Saccharomyces boulardii is the only yeast commonly used in clinical practice. Literature on this probiotic is wide and even more data become available each year. Thus, it could be problematic for a physician summarize all the best information deriving from basic research and clinical studies. With the aim to help physicians in the use of Saccharomyces boulardii, this paper focuses on the available evidences for its efficacy and safety in different diseases in adult and pediatric patients in order to provide a practical guidance for gastroenterology clinical practice. Indications and dosage for several gastrointestinal diseases for a correct use of this probiotic are provided, and recent insights on its mechanisms of action and possible future clinical application are also discussed

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    Get PDF
    Large scale deployments of Internet of Things (IoT) networks are becoming reality. From a technology perspective, a lot of information related to device parameters, channel states, network and application data are stored in databases and can be used for an extensive analysis to improve the functionality of IoT systems in terms of network performance and user services. LoRaWAN (Long Range Wide Area Network) is one of the emerging IoT technologies, with a simple protocol based on LoRa modulation. In this work, we discuss how machine learning approaches can be used to improve network performance (and if and how they can help). To this aim, we describe a methodology to process LoRaWAN packets and apply a machine learning pipeline to: (i) perform device profiling, and (ii) predict the inter-arrival of IoT packets. This latter analysis is very related to the channel and network usage and can be leveraged in the future for system performance enhancements. Our analysis mainly focuses on the use of k-means, Long Short-Term Memory Neural Networks and Decision Trees. We test these approaches on a real large-scale LoRaWAN network where the overall captured traffic is stored in a proprietary database. Our study shows how profiling techniques enable a machine learning prediction algorithm even when training is not possible because of high error rates perceived by some devices. In this challenging case, the prediction of the inter-arrival time of packets has an error of about 3.5% for 77% of real sequence cases

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