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A scalable Digital Fleet Management system can be leveraged by most organizations with high-volume high-value assets. In such scenarios, predictive analytics for tool health becomes central, as it enables decision-making in terms of planning, maintenance, end-of-life replacement, tool selection, etc. End-to-end solutions, spanning all the way from gathering live tool data to the visual representation of tool health, are certainly of major interest. Long-term fleet management can be accomplished through a consistent evaluation of the fleet performance profile. Predictive analysis can anticipate maintenance needs and resultant downtimes, and in turn it helps improve scheduling of procurement and distribution of the fleet. This paper focuses on managing a fleet of thousands of downhole tools based on tool health condition and other variables – a very common use case in Oil & Gas Services.  An end-to-end automated scalable cloud framework is described in detail. This framework integrates failure prediction models for each single asset in the fleet of tools. Based on historical tool data, the models generate tool risk indices (one index per asset) which correlate to the probability of tool failure during near-future field jobs. These risk indices can be used for optimal asset-to-job mapping. They also help in de-risking field operations by identifying tools for overhaul or retirement. The proposed method integrates the tasks of: fetching data from 200,000+ tools, performing feature engineering, modeling via Machine Learning (ML) , and visualizing into a cloud pipeline. Framework scalability becomes a key requirement as fleet size increases or decreases over time to match market demands. The framework also allows for the addition of new ML models to the platform by citizen data scientists, who are not cloud experts. Finally, it is shown who this framework provides systematic steps for sustenance of such large cloud platform.

Introduction: Prosthodontic practice involves procedures in which impressions of the maxillary and mandibular arches are mandatory. Cross infection is one of the major problems that can occur in regular dental practice. Every dentist should take utmost care to prevent cross infection as oral cavity is the source of variety of microorganisms which can often cause diseases that can be fatal. Although precautions, such as wearing of gloves and mask, sterilization of instruments are given importance, the need for disinfection of impressions is often neglected. Hence, the aim of the study was to assess the disinfection potential of radiofrequency glow discharge (RGD) by microbiological studies. Materials and methods:Disinfection potential of RGD on addition silicone (Reprosil, Dentsply, Milford DE, USA) was assessed. Total sample size was 20. Samples were divided into two groups of 10 each. Group I -control group and group II -RGD-treated group. Main groups were subdivided into subgroups A and B. Data collected were analyzed. Results:The RGD-treated samples were found to be culture sterile which meant that there were no signs of growth of any organisms, thus proving the disinfection potential of RGD. Conclusion:From this study, we can conclude that RGD is a very rapid and handy device, which can disinfect saliva contaminated elastomeric impression material surfaces.Clinical significance: When compared with the difficulties and lack of efficiency encountered in disinfecting impressions by immersion and spray atomization, RGD can be very handy in dental clinics, as it is a very rapid and convenient method for infection control.

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