Internet of Things Platform for Smart Farming

Novel Coronavirus (2019-nCoV) or Human Corona-virus (HCoV) could be a new coronavirus that belongs to Be-tacoronaviruses type Human Coronaviruses, just like the Middle East Respiratory Syndrome (MERS) coronavirus and Severe Acute Respiratory Syndrome (SARS) coronavirus. Wu-han, the capital city of Hubei province, China recorded the initial cases of this virus in December 2019. Until 2 May 2020, 10:00 CET, near 23,8287 deaths were reported out of 3.34 million confirmed cases recorded across the world. By the tip of January 2020, China confirmed that the COVID-19 transmitted from human to human. This study aims to check a completely unique medicament called "Echinacea purpurea" against the crystal structure of 2019-nCoV Main Protease. This study presents an ideal model for Echinacea purpurea to be tested in silico against 2019-nCoV Main Protease.

In the era of IoT, the world of connected experiences is created by the convergence of multiple technologies including real-time analytics, machine learning, and commodity sensors and embedded systems. However, with the proliferation of these IoT technologies and devices, there are challenges in integrating, indexing and managing time-series data from multiple sources to optimize the storage of those data and/or retrieve the information from them in real-time. Many researchers have addressed the data integration issue through developing time-series data compression techniques; however, they focused mainly on the application of integer value compression to IoT data. Moreover, existing work does not focus on the issues of data and information retrieval without decompression. In this paper, we solve these issues by constructing an indexing framework within a lossless compression for floating point time-series data, where an index is based on the time-stamp from the compressed data that facilitates the search for data without full decompression. We conduct several sets of experiments and quantify the performance of our proposed approach. The experimental results, performed on IoT datasets, show a reduction in storage compared with existing compression techniques. The experimental study also demonstrates the capability of time-series data indexing and integration in real-time.

Human Coronavirus (HCoV) or Novel Coronavirus (2019-nCoV) is probably a brand new version of coronavirus that belongs to Betacoronaviruses kind Human Coronaviruses, similar to the Severe Acute Respiratory Syndrome (SARS) coronavirus and Middle-East Respiratory Syndrome (MERS) coronavirus. China recorded the number one case of this virus in December 2019 at Wuhan, the capital town of Hubei province. By 27 March 2020, 10:00 CET, nearly 23,335 humans died out of 509,164 showed instances recorded throughout the world. By the give up of January 2020, China showed that the Novel Coronavirus (2019-nCoV) transmitted from one human to another. This studies pursuits to research a completely specific medicament called “Hinokitiol Copper Chelate” towards the large quantity 2019-nCoV Spike Glycoprotein with a unmarried receptor binding domain. This take a look at gives a super version for Hinokitiol Copper Chelate to be examined in silico towards 2019-nCoV Main Protease.KeywordsHinokitiol Copper ChelateCOVID-192019-nCoVHuman coronavirus

Internet of things has evolved due to convergence of multiple technologies, including wireless communication, real-time analytics, machine learning, commodity sensors, and embedded systems. Traditional fields of embedded systems, WSN, control systems, automation. A research article is a smart 2 way architecture of IoT that will use not only the sensors of IoT but also the cloud technology for the collection of information, alerting techniques for information and also tracking information. For the patients and older people this method will be beneficial for the assist systems enhancements for an independent living environment. Clinical fee will be reduced and anomaly detection is early with the use of this architecture. The architecture represented in this research article is the SMMC. At the point of care the accumulation of data from the IoT sensors takes place at first. Micro controller thereafter processes the analogue signal gathered from the sensors. The scientific support algorithms for selection are performed on the statistical data received by the cloud for alarming anomalies in the records. Prompting for sensors to get feedback or alerts is done using machine – machine protocols. © 2018, Institute of Advanced Scientific Research, Inc. All rights reserved.