Ayurveda, the oldest paradigm of health and healing known to the world, grew out of the Rishi tradition. The medical and research community is constantly seeking for new natural agents. Sphaeranthus indicus Linn. (Gorakhmundi, Family: Asteraceae), is widely used in Ayurvedic system of medicine to treat various diseases. The present investigation was designed to perform physicochemical and phytochemical analysis with HPTLC fingerprints of Sphaeranthus indicus (leave, flower, and stem) to establish the standard parameters of this herb. Different analytical parameters like extractive values, total ash, acid insoluble ash and water-soluble ash, moisture content, pH values of different parts of the drug were performed. Preliminary phytochemical screening was done to detect and quantify different active constituents such as alkaloids, flavanoids, proteins and total poly phenol using various analytical methods. Our preliminary data of phytochemical screening of the extracts revealed the presence of steroids, carbohydrates, proteins, terpenoid, flavonoid, tannins, alkaloids, resin and saponin. In the current study we established the HPTLC fingerprint of the extract using phytochemical standards. We believe that our study provides a substantial data for identification, purification, standardization, and phytochemical characterization of novel therapeutic compounds that can be used potential drug to combat various diseases for betterment of mankind. Keywords: Sphaeranthus indicus, Ayurveda, physicochemical, UV- Spectrophotometer, HPTLC
Connected Autonomous Vehicles (CAVs) are widely expected to improve traffic safety and efficiency by exploiting information from surrounding vehicles via V2V communication. A CAV typically adapts its speed based on information from the vehicle it follows. CAVs can also use information from vehicles further ahead within their communication range, and this results in improved traffic safety and efficiency. In mixed traffic scenarios, however, this may not always be possible due to the presence of human-driven vehicles that do not have communication capabilities. Furthermore, as wireless vehicular networks are unreliable, information from other vehicles can be delayed or lost, which brings more challenges for CAVs in utilizing information from multiple leading vehicles. A few studies have investigated the impact of CAVs where they use information from multiple leading vehicles on traffic safety and efficiency, but only in very limited scenarios (i.e., with a very small number of vehicles).In contrast, this paper investigates the impact of CAV carfollowing control based on multiple leading vehicles information on both mixed traffic safety and efficiency in realistic scenarios in terms of imperfect communication, vehicle modelling, and traffic scenario. Results show that exploiting information from multiple, rather than a single, leading vehicles in CAV controller design further improves both traffic safety and efficiency especially at high penetration rates. In addition to proper tuning of CAV controller parameters (control gains and time headways), the scale of the improvement depends on both market penetration rate (MPR) and communication reliability. A packet error rate (PER) of 70% leads to an increase in traffic efficiency by 4.18% (at 40% MPR) and 12.19% (at 70% MPR), compared to the simple single leading vehicle information based controller.
In Distributed Real Time System (DRTS), systematic allocation of the tasks among processors is one of the important parameter, which determine the optimal utilization of available resources. If this step is not performed properly, an increase in the number of processing nodes results in decreasing the total system throughput. The Inter-Task Communication (ITC) is always the most costly and the least reliable parameter in the loosely coupled DRTS. In this paper an efficient task allocation algorithm is discussed, which performs a static allocation of a set of "m" tasks T = {t 1 ,t 2 ,…t m } of a program to a set of "n" processors P = {p 1 ,p 2 ,….p n }, (where, m >> n) to minimize the application program's Parallel Processing Cost(PPC) with the goal to maximize the overall throughput of the system through and allocated load on all the processors should be approximately balanced. While designing the algorithm the Execution Cost (EC) and Inter Task Communication Cost (ITCC) have been taken into consideration.
Despite extensive research on Byzantine Fault Tolerant (BFT) systems, overheads associated with such solutions preclude widespread adoption. Past efforts such as the Cross Fault Tolerance (XFT) model address this problem by making a weaker assumption that a majority of replicates are correct and communicate synchronously. Although XPaxos of Liu et al. (applying the XFT model) achieves similar performance as Paxos, it does not scale with the number of faults. Also, its reliance on a single leader introduces considerable downtime in case of failures. This thesis presents Elpis, the first multi-leader XFT consensus protocol. By adopting the Generalized Consensus specification from the Crash Fault Tolerance model, we were able to devise a multi-leader protocol that exploits the commutativity property inherent in the commands ordered by the system. Elpis maps accessed objects to non-faulty processes during periods of synchrony. Subsequently, these processes order all commands which access these objects. Experimental evaluation confirms the effectiveness of this approach: Elpis achieves up to 2x speedup over XPaxos and up to 3.5x speedup over state-of-the-art Byzantine Fault-Tolerant Consensus Protocols
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