Pozzuolo del Friuli, Italy
Pozzuolo del Friuli, Italy

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News Article | May 15, 2017
Site: www.prweb.com

Kopis Mobile announces they will be conducting demonstrations of their latest product at the 2017 SOFIC (Special Operations Forces Industry Conference) in Tampa, FL from May 15-18. Environmental Reachback Situational Awareness (ERSA) is a long-range Internet of Things (IoT) communication system that enables CBRNE sensors to send real-time readings to provide situational awareness to incident commanders and HAZMAT team leaders. ERSA also has GPS capability so both sensor readings and locations are transmitted to a predetermined operations center. This new product and others will be showcased at the show. For more information, visit Kopis Mobile at booth #454 at the Special Operations Forces Industry Conference (SOFIC), or check out the company’s website at http://www.kopismobile.com About Kopis Mobile: Kopis Mobile specializes in designing and manufacturing custom apps and app-enabled electronics, backed with a thorough understanding of current threats to military and law enforcement operations. Kopis Mobile has driven the creation of multiple custom apps and app-enabled electronics equipment for the Department of Defense, law enforcement and private security markets since 2013. A former Naval Special Warfare Operator and a group of talented engineers founded Kopis Mobile with in-depth knowledge of tactical operations, mobile devices, electronics, software and mechanical engineering. The founders all share a focused vision of enabling technology that directly impacts the warfighter and first responder on the ground by making them more efficient in a cost-effective way.


Firrao G.,University of Udine | Firrao G.,Italian National Institute of Biosystems and Biostructures | Torelli E.,University of Udine | Gobbi E.,University of Udine | And 3 more authors.
Journal of Cereal Science | Year: 2010

Mycotoxin contamination is a major concern to the maize industry worldwide. Despite the several strategies that have been exploited in an attempt to reduce the severity of this problem, during conducive years, severely contaminated lots are still introduced in the maize processing chain affecting the general quality and safety of the product. As chemical analysis is laborious, time consuming and equipment dependent, more convenient methods are needed for the early identification of contaminated lots. Here a novel approach based on image analysis that provides fast response with minimal equipment and effort is presented. Maize samples were grounded and imaged under 10 different LED lights with emission centered at wavelengths ranging from 720 to 940 nm. The digital images were converted into matrices of data to compute comparative indexes. A three layers feed-forward neural network was trained to predict mycotoxin content from the calculated indexes. The results showed a significant correlation between predictions from image analysis and the concentration of the mycotoxin fumonisin as determined by chemical analysis. The technique developed produces reliable contamination estimates within few minutes and can be readily used to assist lot selection in various steps of the maize processing chain. © 2010 Elsevier Ltd.


Del Gatto A.,Italian Agricultural Research Council | Pieri S.,Italian Agricultural Research Council | Mangoni L.,Italian Agricultural Research Council | Raccuia S.A.,CNR Institute for Agricultural and Forest Systems In the Mediterranean | And 6 more authors.
Acta Horticulturae | Year: 2013

Despite the potential offered by the rape cultivation as biofuels crop, in Italy its cultivation is still almost absent. A further reason for a widerspread of this crop might arise from the exploitation of secondary products (like flour and cake) useful in various application fields. In 2010-2011, thanks to the Project EXTRAVALORE the agronomic evaluations were carried out on 38 cultivars of rape. The screening of the genotypes was performed in three different localities of Italy: in the North (Palazzolo - UD), Centre (Osimo - AN) and South Italy (Cassibile - SR). The environments significantly influenced the morpho-phenological traits and the production of the studied varieties. The best performances were obtained in Central Italy, where the rape, on average of genotypes, flowered before than in North and South Italy; moreover the plant height resulted higher. These results confirm the good adaptability to this environment for this crop. Among the cultivars, Zoom and Albatross showed yields close to 4.8 t ha-1; other fifteen varieties have exceeded the average value. In regards to the seed oil content, two cultivars ('Katabatic' and 'Adriana') showed an average value of 48%. In northern Italy it has been obtained intermediate yields, with a more marked delay in flowering. DK Expower Hornet, Hybriswing and Fregat were the most productive (3.0 t ha-1). The highest oil content was recorded in Albatross and Primus, (48%). In Sicily, because of the weather condition some cultivars ('Kutiba', 'Ilia', 'Tassilo', 'Adriana' and 'Anaconda') have not been able to flower, probably for the lack of low temperatures. The seed yields and the seed oil content, lower than 45%, resulted the lowest compared to the other environments.


Torelli E.,University of Udine | Firrao G.,University of Udine | Firrao G.,Italian National Institute of Biosystems and Biostructures | Bianchi G.,ERSA | And 2 more authors.
Journal of the Science of Food and Agriculture | Year: 2012

Background: Contamination by mycotoxins is a major concern to the maize industry in north-east Italy where maize grain is often spoiled by Fusarium spp. In this work, fumonisins, deoxynivalenol and zearalenone were determined and an artificial neural network (ANN) model suitable for predicting mycotoxin contamination of maize at harvest time was developed. Results: The occurrence of deoxynivalenol and zearalenone was very limited, while fumonisins concentration ranged from 163 and to 3663 μg kg -1 in 2007, and from 333 to 11473 μg kg -1 in 2008. Statistical data analysis of factors affecting fumonisins concentration revealed that irrigation, chemical treatment against the European corn borer and harvest date significantly affected the level of contamination (P < 0.05), although the relevance of the factors was different in 2007 and 2008. The neural network approach showed a significant correlation between ascertained values and predictions based on agronomic data. Conclusion: This is the first time that an artificial neural network has been used to predict fumonisin accumulation in maize: the prediction has been shown to have the potential for the development of a new approach for the rapid cataloging of grain lots. © 2012 Society of Chemical Industry.


Bianchi G.L.,ERSA | De Amicis F.,ERSA | De Sabbata L.,ERSA | Di Bernardo N.,ERSA | And 6 more authors.
EPPO Bulletin | Year: 2015

Since 2003 the presence of a new syndrome characterized by symptoms of stunting, chlorotic mottling, leaf deformation, reduced yields and quality has been reported in some white berry varieties of Vitis vinifera in Trentino-Alto Adige and Friuli Venezia Giulia vineyards. The identification of a new virus, provisionally called Grapevine Pinot gris virus (GPGV), in a cv. Pinot gris vine suggested an association between this new syndrome and the virus presence (Giampetruzzi et al., 2012), however the contemporary presence of GPGV in both symptomatic and asymptomatic plants has still to be explained. In this work, a large-scale monitoring over a 3-year period (2012-14) of Friuli Venezia Giulia vineyards and nurseries has shown a widespread presence of GPGV in symptomatic plants and also in asymptomatic vines, even if at a slightly lower percentage. Quantitative analyses of the virus titer revealed a great variability in the viral content of both symptomatic and asymptomatic plants but the mean GPGV quantity in symptomatic vines was significantly higher than in asymptomatic plants. © 2015 OEPP/EPPO.

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