The present study was designed at to ascertain the plausible bioactive compounds of the aerial methanolic extract of Barleria buxifolia via GC-MS analysis which is used as a noteworthy ethnomedicinal plant for treating various diseases. The peaks perceived in the mass spectra were identified as compounds and were matched with the National Institute of Standards and Technology and Wiley library. Identified compounds were predicted for its activity using PASS software. Interestingly, about 30 compounds were scrutinized with their retention time, molecular formula, molecular weight, peak area (%). Based on structure, activities were predicted. The GC-MS analysis unveiled the different kinds of bioactive compounds such as alkaloids, terpenoids, triterpenoids, esters, aliphatic ketones, β-carotene etc. In bioinformatics approach, using the software, Prediction Activity Spectra for Substances (PASS), pharmacological effects and drug likeness were determined for all the compounds precisely which endorse the traditional usage of B. buxifolia for the treatment of various kinds of diseases such as anti-inflammatory, antiulcer, antihypertensive, antiviral, antiobesity, antidiabetic, caridioprotectant, vasoprotector, spasmolytic, respiratory analeptic, carminative etc. It is inferred that the putative hits obtained from B. buxifolia could potentially serve as a launching pad for a hit-to-lead a novel drug development. Barleria buxifolia, Acanthaceae, GC-MS analysis, PASS prediction, Drug likeness, Activity.
The Internet of Things (IoT) is inter communication of embedded devices using networking technologies. The IoT will be one of the important trends in future; can affect the networking, business and communication. In this paper, proposing a remote sensing parameter of the human body which consists of pulse and temperature. The parameters that are used for sensing and monitoring will send the data through wireless sensors. Adding a web based observing helps to keep track of the regular status of patient. The sensing data will be continuously collected in a database and will be used to inform patient to any unseen problems to undergo possible diagnosis. Experimental results prove the proposed system is user friendly, reliable, economical. IoT typically expected to propose the advanced high bandwidth connectivity of embedded devices, systems and services which goes beyond machine –to – machine (M2M) context. The advanced connectivity of devices aide in automation is possible in nearly all field. Everyone today is so busy in their lives; even they forget to take care of their health. By keeping all these things in minds, technology really proves to be an asset for an individual. With the advancement in technology, lots of smart or medical sensors came into existence that continuously analyzes individual patient activity and automatically predicts a heart attack before the patient feels sick.
Physical layer network coding (PNC) is a promising strategy that aims to improve the spectral efficiency in relay based wireless transmission. In this paper, lattice based channel coding is employed at the source nodes for better performance during Multiple Access Channel (MAC) phase transmission. The achievable transmission rate in various PNC relaying such as Decode and Forward (DF), Amplify and Forward (AF) and Compute and Forward (CF) for the Two Way Relay Network model (TWRN) is derived and simulated through MATLAB software and analyzed. The analysis shows that rate performance of CF relaying outperforms other relaying techniques in Additive White Gaussian Noise (AWGN) channel, but it degrades for Rayleigh fading channel. To mitigate the effects of fading, channel aware precoding technique is employed at source nodes and Bit Error Rate (BER), symbol error rate performance are analyzed for CF relaying and Monte Carlo simulations are used to demonstrate the theoretical results. Channel precoding performance of CF relaying is compared with DF relaying.
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