A Cloud-to-Edge Approach to Support Predictive Analytics in Robotics Industry

Data management and processing to enable predictive analytics in cyber physical systems holds the promise of creating insight over underlying processes, discovering anomalous behaviours and predicting imminent failures threatening a normal and smooth production process. In this context, proactive strategies can be adopted, as enabled by predictive analytics. Predictive analytics in turn can make a shift in traditional maintenance approaches to more effective optimising their cost and transforming maintenance from a necessary evil to a strategic business factor. Empowered by the aforementioned points, this paper discusses a novel methodology for remaining useful life (RUL) estimation enabling predictive maintenance of industrial equipment using partial knowledge over its degradation function and the parameters that are affecting it. Moreover, the design and prototype implementation of a plug-n-play end-to-end cloud architecture, supporting predictive maintenance of industrial equipment is presented integrating the aforementioned concept as a service. This is achieved by integrating edge gateways, data stores at both the edge and the cloud, and various applications, such as predictive analytics, visualization and scheduling, integrated as services in the cloud system. The proposed approach has been implemented into a prototype and tested in an industrial use case related to the maintenance of a robotic arm. Obtained results show the effectiveness and the efficiency of the proposed methodology in supporting predictive analytics in the era of Industry 4.0

HIGH-PERFORMANCE TYRE

The present invention relates to a tyre for the wheels of vehicles, in particular motor vehicles, characterized by a tread comprising at least one copolymer of (iso-styrene/trans-butadiene):diene-terminated copolymers. Said tread is characterized by less sensitivity to the effects of temperature, with less weakness, better consistency of the performances, and improved characteristics in terms of grip and/or resistance to laceration

HIGH PERFORMANCE TYRE

The present invention relates to a tyre for the wheels of vehicles, in particular motor vehicles, characterized by a tread comprising at least one iso-styrene/trans-butadiene elastomeric copolymer with a styrene content higher than 30% by weight with respect to the total weight of copolymer and a molecular weight higher than 500,000. Said tread is characterized by improved dynamic-mechanical behaviour at high temperatures, with higher dynamic modulus and hysteresis values, and at the same time better properties at break, optimum elasticity at low temperature and higher abrasion resistance

PREMISES, a scalable data-driven service to predict alarms in slowly-degrading multi-cycle industrial processes

In recent years, the number of Industry 4.0 enabled manufacturing sites has been continuously growing, and both the quantity and variety of signals and data collected in plants are increasing at an unprecedented rate. At the same time, the demand of Big Data processing platforms and analytical tools tailored to manufacturing environments has become more and more prominent. Manufacturing companies are collecting huge amounts of information during the production process through a plethora of sensors and networks. To extract value and actionable knowledge from such precious repositories, suitable data-driven approaches are required. They are expected to improve the production processes by reducing maintenance costs, reliably predicting equipment failures, and avoiding quality degradation. To this aim, machine learning techniques tailored for predictive maintenance analysis have been adopted in PREMISES (PREdictive Maintenance service for Industrial procesSES), an innovative framework providing a scalable Big Data service able to predict alarming conditions in slowly-degrading processes characterized by cyclic procedures. PREMISES has been experimentally tested and validated on a real industrial use case, resulting efficient and effective in predicting alarms. The framework has been designed to address the main Big Data and industrial requirements, by being developed on a solid and scalable processing framework, Apache Spark, and supporting the deployment on modularized containers, specifically upon the Docker technology stack

Plasma activated water as resistance inducer against bacterial leaf spot of tomato.

Plant bacterial diseases are routinely managed with scheduled treatments based on heavy metal compounds or on antibiotics; to reduce the negative environmental impact due to the use of such chemical compounds, as pollution or selection of antibiotic resistant pathogens, the integrated control management is required. In the frame of a sustainable agriculture the use of bacterial antagonists, biological agents, plant defence response elicitors or resistant host plant genotypes are the most effective approaches. In this work, cold atmospheric pressure plasma (CAP) was applied to sterile distilled water, inducing the production of a hydrogen peroxide, nitrite and nitrate, and a pH reduction. In particular, an atmospheric pressure dielectric barrier discharge (DBD) has been used to produce plasma activated water (PAW), that was firstly assayed in in vitro experiments and then in planta through application at the root apparatus of tomato plants, against Xanthomonas vesicatoria (Xv), the etiological agent of bacterial leaf spot. Moreover, the transcription abundance of five genes related to the plant defense was investigated in response to PAW treatment. PAW did not show direct antimicrobial activity against Xv in in vitro experiments, but it enhanced the tomato plants defenses. It was effective in reducing the disease severity by giving relative protections of ca. 61, 51 and 38% when applied 1 h, 24 h and 6 days before the experimental inoculation, respectively. In addition, the experiments highlighted the pal gene involvement in response to the PAW treatments and against the pathogen; its transcription levels resulted significantly high from 1 to 48 h until their decrease 192 h after PAW application

Linee Guida per la Sorveglianza Sanitaria degli operatori dei Corpi di Polizia.

Si forniscono le linee guida della sorveglianza sanitaria per le forze di polizi