With the rapid growth of World Wide Web and Internet, a problem of improving web server’s performance is having primary importance. When a web user requests any resource from web server, he/she experiences some amount of delay which is known as User Perceived Latency. Web caching is an effective and economical technique to reduce UPL. Performance of web server can still be improved by predicting a future request from the current page and then move it to the web server’s cache. This concept is known as web cache pre-fetching. Web cache pre-fetching using markov tree and multidimensional matrix have limitations regarding to time complexity and memory, respectively. In this paper, an attempt has been made to use markov model and concept of stationary distribution for web cache pre-fetching. A comparison has been presented for proposed algorithms with Least Recently Used (LRU). Empirical results based on implementation confirm that proposed algorithms are better than LRU.
To change the gathering of circulated data in worldwide manufacturing services, sharing and managing plenty of information across many participants utilizing a fitting information system plan. Even the forced "trust tax" on manufacturers during their uncountable efforts with clients, providers, merchants, governments, specialist organizations, and other manufacturers tremendously increased. In the information and programming, recollecting can apply some strategies like processing some information with security and privacy; this thing comes under IoT with blockchain technologies. Furthermore, with support to data integration and data handling, blockchain technologies are eager to manage transaction data concerning IoT technologies. In addition to this, blockchain allows a massive "trust tax" using small and medium scale businesses while minimizing the "trust tax" comprehensively compared to accepted manufacturers. This book chapter will investigate the blockchain-based trust mechanism and security. In addition to this, it will also involve blockchain quality assurance, which is an essential part of intelligent manufacturing.
The stock market is very volatile and non-stationary and generates huge volumes of data in every second. In this article, the existing machine learning algorithms are analyzed for stock market forecasting and also a new pattern-finding algorithm for forecasting stock trend is developed. Three approaches can be used to solve the problem: fundamental analysis, technical analysis, and the machine learning. Experimental analysis done in this article shows that the machine learning could be useful for investors to make profitable decisions. In order to conduct these processes, a real-time dataset has been obtained from the Indian stock market. This article learns the model from Indian National Stock Exchange (NSE) data obtained from Yahoo API to forecast stock prices and targets to make a profit over time. In this article, two separate algorithms and methodologies are analyzed to forecast stock market trends and iteratively improve the model to achieve higher accuracy. Results are showing that the proposed pattern-based customized algorithm is more accurate (10 to 15%) as compared to other two machine learning techniques, which are also increased as the time window increases.
A 4-nitrophenol-degrading bacterial strain PNP was isolated from pesticide-contaminated soil collected from Lucknow. Strain PNP utilized 0.5 mM 4-nitrophenol as its carbon source and degraded it completely within 24 h with stoichiometric release of nitrite ions. Strain PNP was associated with the genus Pseudomonas in a phylogentic tree and exhibited highest 16S rRNA gene sequence similarity to Pseudomonas juntendi BML3 (99.79%) and Pseudomonas inefficax JV551A3 (99.79%). Based on values of average nucleotide identity and digital DNA-DNA hybridization among strain PNP and its closely related type strains, it concluded that strain PNP belongs to Pseudomonas alloputida. The Illumina HiSeq platform was used to sequence the PNP genome. The draft genome sequence of Pseudomonas alloputida PNP was presented here. The total size of the draft assembly was 6,087,340 bp, distributed into 87 contigs with N50 value of 139502. The genome has an average GC content of 61.7% and contains 5461 coding sequences and 77 putative RNA genes. This Whole Genome Shotgun project has been submitted at DDBJ/ENA/GenBank under the accession JAGKJH000000000.
An Investigation was carried out to study the level of some inorganic pollutant i.e. nitrate (NO 3 - ), nitrite (NO 2 - ), ammonium (NH 4 + ) and phosphates (PO 4 3- ) in surface and drinking water of U.P and M.P province. Nitrate level in all the surface water samples ranged between 4-25 mg L -1 . Only one site i.e. Nariwari Surval, showed NO 3 - content 4 -1 which was below the Maximum Acceptable Limit (MAL) i.e. 13 mg L -1 . Nitrite was found 2-11 folds higher than the MAL (0.06 mg L -1 ). Highest ammonium content was observed in Gomti river (3.20 mg L -1 ) and lowest (0.29 mg L -1 ) in Bihad river. However, NH 4 + level in all the samples were below the MAL (5 mg L -1 ). Phosphate content was more than their MAL (0.1 mg L -1 ) in all the surface water samples. Their concentration varied from 1-3 mg L -1 respectively. In drinking water samples NO 3 - content ranged between 44-83 mg L -1 . All the samples contained NO 3 - above the MAL (45 mg L -1 ). Highest NO 2 - content i.e. 4 mg L -1 which was very close to the MAL (3.29 mg L -1 ) was observed at Avadhesh Pratap Singh (A.P.S.) University and lowest i.e. 0.19 mg L -1 at Lucknow University. Ammonium in all the samples was above the MAL (0.5 mg L -1 ) however, highest value i.e. 6 mg L -1 was detected at A.P.S. University. Phosphate was observed around 2 mg L -1 in all the samples. Existing study revealed that water quality of all the sites of both the provinces is deteriorated.
Single-camera, single-view videogrammetry has been used to determine static aeroelastic deformation of a slotted flap configuration on a semispan model at the National Transonic Facility (NTF). Deformation was determined by comparing wind-off to wind-on spatial data from targets placed on the main element, shroud, and flap of the model. Digitized video images from a camera were recorded and processed to automatically determine target image plane locations that were then corrected for sensor, lens, and frame grabber spatial errors. The videogrammetric technique has been established at NASA facilities as the technique of choice when high-volume static aeroelastic data with minimum impact on data taking is required. The primary measurement at the NTF with this technique in the past has been the measurement of static aeroelastic wing twist on full span models. The first results using the videogrammetric technique for the measurement of component deformation during semispan testing at the NTF are presented.
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