Background:Oil pulling or oil swishing therapy is a traditional procedure in which the practitioners rinse or swish oil in their mouth. It is supposed to cure oral and systemic diseases but the evidence is minimal. Oil pulling with sesame oil and sunflower oil was found to reduce plaque related gingivitis. Coconut oil is an easily available edible oil. It is unique because it contains predominantly medium chain fatty acids of which 45-50 percent is lauric acid. Lauric acid has proven anti inflammatory and antimicrobial effects. No studies have been done on the benefits of oil pulling using coconut oil to date. So a pilot study was planned to assess the effect of coconut oil pulling on plaque induced gingivitis.Materials and Methods:The aim of the study was to evaluate the effect of coconut oil pulling/oil swishing on plaque formation and plaque induced gingivitis. A prospective interventional study was carried out. 60 age matched adolescent boys and girls in the age-group of 16-18 years with plaque induced gingivitis were included in the study and oil pulling was included in their oral hygiene routine. The study period was 30 days. Plaque and gingival indices of the subjects were assessed at baseline days 1,7,15 and 30. The data was analyzed using paired t test.Results:A statistically significant decrease in the plaque and gingival indices was noticed from day 7 and the scores continued to decrease during the period of study.Conclusion:Oil pulling using coconut oil could be an effective adjuvant procedure in decreasing plaque formation and plaque induced gingivitis.

This article surveys emerging technologies related to pervasive edge computing (PEC) for industrial internet-of-things (IIoT) enabled by fifth-generation (5G) and beyond communication networks. PEC encompasses all devices that are capable of performing computational tasks locally, including those at the edge of the core network (edge servers co-located with 5G base stations) and in the radio access network (sensors, actuators, etc.). The main advantages of this paradigm are core network offloading (and benefits therefrom) and low latency for delay-sensitive applications (e.g., automatic control). We have reviewed the state-of-the-art in the PEC paradigm and its applications to the IIoT domain, which have been enabled by the recent developments in 5G technology. We have classified and described three important research areas related to PEC-distributed artificial intelligence methods, energy efficiency, and cyber security. We have also identified the main open challenges that must be solved to have a scalable PEC-based IIoT network that operates efficiently under different conditions. By explaining the applications, challenges, and opportunities, our paper reinforces the perspective that the PEC paradigm is an extremely suitable and important deployment model for industrial communication networks, considering the modern trend toward private industrial 5G networks with local operations and flexible management.

The climate change crisis, exacerbated by the global dependency of fossil fuels, has brought significant challenges. In the medium to long term, extensive renewable-energy-based electrification is considered to be one of the most promising development paths to address these challenges. However, this is tangible only if the energy infrastructure can accommodate renewable energy sources and distributed energy resources, such as batteries and heat pumps, without adversely affecting power grid operations. To realize renewable-energy-based electrification goals, a new concept-the Energy Internet (EI)-has been proposed, inspired by the most recent advances in information and telecommunication network technologies. Recently, many measures have also been taken to practically implement the EI. Although these EI models share many ideas, a definitive universal definition of the EI is yet to be agreed. Additionally, some studies have proposed protocols and architectures, but a generalized technological overview is still missing. An understanding of the technologies that underpin and encompass the current and future EI is very important to push toward a standardized version of the EI that will eventually make it easier to implement it across the world. In this paper, we first examine and analyze the typical popular definitions of the EI in scientific literature. Based on definitions, assumptions, scope, and application areas, the scientific literature is then classified into four different groups representing the way in which the papers have approached the EI. Then, we synthesize these definitions and concepts, and keeping in mind the future smart grid, we propose a new universal definition of the EI. We also identify the underlying key technologies for managing, coordinating, and controlling the multiple (distributed or not) subsystems with their own particular challenges. The survey concludes by highlighting the main challenges facing a future EI-based energy system and indicating core requirements in terms of system complexity, security, standardization, energy trading and business models and social acceptance.

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This paper proposes a cyber-physical system to manage flexible residential loads based on virtualized energy packets. Before being used, flexible loads need to request packets to an energy server, which may be granted or not. If granted, the energy server guarantees that the request will be fulfilled. Each different load has a specific consumption profile and user requirement. In the proposed case study, the residential consumers share a pool of energy resources that need to be allocated by the energy server whose aim is to minimize the imports related to such a group. The proposed solution shows qualitative advantages compared to the existing approaches in relation to computational complexity, fairness of the resource allocation outcomes and effectiveness in peak reduction. We demonstrate our solution based on three different representative flexible loads; namely, electric vehicles, saunas and dishwashers. The numerical results show the efficacy of the proposed solution for three different representative examples, demonstrating the advantages and drawbacks of different allocation rules.