Scientific Records in Sangam Literature

The Tamil language is more than 2,000 years old, age-old, and has dialects. The Tamil language has the nature of being a mother tongue and has the ability to mix with other languages. Classical literature, which encompasses all the characteristics, serves as a mirror of time that describes the life of man. Ancient Tamils did not explore science to the extent of exploring love and heroism, the gifts of fame and justice, which were two eyes in life. If the news is examined scientifically, one can learn the deep scientific knowledge of ancient Tamils. Tolkappiyam, one of the grammar books, looks at the distinctions between life and the five land distinctions, the measurement names mentioned by them, the characteristics of the planets, etc., with a scientific eye. It is also aimed at examining various scientific aspects of the solar system in the classical literature, such as the movements of the solar system, the calculations of the time, the observations on the disk, the rotation of the air, the rotation of the air, the space travel of the ancient Tamils, the use of aircraft, the knowledge of astronomy, etc

Virtues in Viveka Chintamani

Viveka Chintamani brings out many wise ideas. In this worldly life, man has to deal with the four essentials in life virtue, wealth, pleasure, and home. Tamil people followed good principles in their life and gained enough wealth through these virtues. When a man leads a guilt free life. It is called virtue. Even a man cannot do good things to others, it does not matter but he should not do evil things. A man should cultivate good humane character and be grateful. Humans must be kind to all living things. Love is considered as the main characteristic of all virtues. One should live like a musk deer. The ancient Tamil sages gave their own life for pride and praises. Those people always welcomed and entertained their guests. It is also known that the king's people followed great moral values in their life. They followed the principles of Periyar. This article examines the virtues mentioned in Viveka Chintamani

Biosorption of Arsenic (III) by Using Lemon Peel Powder as Low Cost Effective Biosorbent

The use of lemon peel powder, a novel, low-cost, and sustainable biosorbent derived from food waste, to remove arsenic has largely gone unexplored. The feasibility and viability of the As (III) biosorption abilities of lemon peel powder are compared in this study. The parameters such as contact time, pH, the amount of lemon peels used, the initial arsenic concentration, and temperature all had an effect on the sorption process. Thermodynamic, kinetic, and equilibrium were all evaluated. The optimal pH was 6.0, and it lasted until pH 8 with 72.34% removal efficiency. Lemon peel (LP) has a pH PZC value of 7 and a surface pH of 7. The analysis of kinetics revealed that the biosorption was regulated by a second-order reaction, as well as the fact that the catalytic region of the biosorbent was heterogeneous; however, the biosorption process was better defined by the Freundlich and Temkin isotherms. Finally, it is possible to remove arsenic (III) using waste content. Thermodynamic and equilibrium analysis have shown that sorption is a natural process that is spontaneous, beneficial, and endothermic. In addition, Fourier Transfer Infrared Spectroscopy (FTIR) research shows that arsenic reacts with metal oxides and the -OH functional group in lemon peel. These findings indicate that this peel can be used to remove arsenic from a simulated aqueous solution as a valuable, low-cost sorbent. This research lays the groundwork for the potential production of an effective filtration device that uses citrus peel powder as a low-cost, innovative, and long-lasting biosorbent to treat water polluted with arsenic (III). Keywords: Arsenic, Equilibrium, FTIR, Isotherms, Kinetic, PZC, Thermodynami DOI: 10.7176/JNSR/12-14-01 Publication date:July 31st 202

Anomaly Intrusion Detection based on Concept Drift

Nowadays, security on the internet is a vital issue and therefore, intrusion detection is one of the major research problems for networks that defend external attacks. Intrusion detection is a new approach for providing security in existing computers and data networks. An Intrusion Detection System is a software application that monitors the system for malicious activities and unauthorized access to the system. An easy accessibility condition causes computer networks vulnerable against the attack and several threats from attackers. Intrusion Detection System is used to analyze a network of interconnected systems for avoiding uncommon intrusion or chaos. The intrusion detection problem is becoming a challenging task due to the increase in computer networks since the increased connectivity of computer systems gives access to all and makes it easier for hackers to avoid their traces and identification. The goal of intrusion detection is to identify unauthorized use, misuse and abuse of computer systems. This project focuses on algorithms: (i) Concept Drift based ensemble Incremental Learning approach for anomaly intrusion detection, and (ii) Diversity and Transfer-based Ensemble Learning. These are highly ranked anomaly detection models. We study and compare both learning models. The Network Security Laboratory-Knowledge Discovery and Data Mining (NSL-KDD99) dataset have been used for training and to detect the misuse activities

AN EFFICIENT ALGORITHM FOR SEQUENCE GENERATION IN DATA MINING

ABSTRAC

Genotypic and Pathotypic Characterization of Newcastle Disease Viruses from India

Newcastle disease virus (NDV) is an avian paramyxovirus that causes significant economic losses to the poultry industry in most parts of the world. The susceptibility of a wide variety of avian species coupled with synanthropic bird reservoirs has contributed to the vast genomic diversity of this virus as well as diagnostic failures. Since the first panzootic in 1926, Newcastle disease (ND) became enzootic in India with recurrent outbreaks in multiple avian species. The genetic characteristics of circulating strains in India, however, are largely unknown. To understand the nature of NDV genotypes in India, we characterized two representative strains isolated 13 years apart from a chicken and a pigeon by complete genome sequence analysis and pathotyping. The viruses were characterized as velogenic by pathogenicity indices devised to distinguish these strains. The genome length was 15,186 nucleotides (nt) and consisted of six non-overlapping genes, with conserved and complementary 3′ leader and 5′ trailer regions, conserved gene starts, gene stops, and intergenic sequences similar to those in avian paramyxovirus 1 (APMV-1) strains. Matrix gene sequence analysis grouped the pigeon isolate with APMV-1 strains. Phylogeny based on the fusion (F), and hemagglutinin (HN) genes and complete genome sequence grouped these viruses into genotype IV. Genotype IV strains are considered to have “died out” after the first panzootic (1926–1960) of ND. But, our results suggest that there is persistence of genotype IV strains in India

Machine learning in incident categorization automation

IT incident management process requires a correct categorization to attribute incident tickets to the right resolution group and obtain an operational system as quickly as possible, having the lowest possible impact on the business and costumers. In this work, we introduce a module to automatically categorize incident tickets, turning the responsible teams for incident management more productive. This module can be integrated as an extension into an incident ticket system (ITS), which contributes to reduce the time wasted on incident ticket route and reduce the amount of errors on incident categorization. To automate the classification, we use a support vector machine (SVM), obtaining an accuracy of 89%, approximately, on a dataset of real-world incident tickets.info:eu-repo/semantics/acceptedVersio

A biosensor assay for the detection of Mycobacterium avium subsp. paratuberculosis in fecal samples

A simple, membrane-strip-based lateral-flow (LF) biosensor assay and a high-throughput microtiter plate assay have been combined with a reverse transcriptase polymerase chain reaction (RT-PCR) for the detection of a small number (ten) of viable Mycobacterium (M.) avium subsp. paratuberculosis (MAP) cells in fecal samples. The assays are based on the identification of the RNA of the IS900 element of MAP. For the assay, RNA was extracted from fecal samples spiked with a known quantity of (101 to 106) MAP cells and amplified using RT-PCR and identified by the LF biosensor and the microtiter plate assay. While the LF biosensor assay requires only 30 min of assay time, the overall process took 10 h for the detection of 10 viable cells. The assays are based on an oligonucleotide sandwich hybridization assay format and use either a membrane flow through system with an immobilized DNA probe that hybridizes with the target sequence or a microtiter plate well. Signal amplification is provided when the target sequence hybridizes to a second DNA probe that has been coupled to liposomes encapsulating the dye, sulforhodamine B. The dye in the liposomes provides a signal that can be read visually, quantified with a hand-held reflectometer, or with a fluorescence reader. Specificity analysis of the assays revealed no cross reactivity with other mycobacteria, such as M. avium complex, M. ulcerans, M. marium, M. kansasii, M. abscessus, M. asiaticum, M. phlei, M. fortuitum, M. scrofulaceum, M. intracellulare, M. smegmatis, and M. bovis. The overall assay for the detection of live MAP organisms is comparatively less expensive and quick, especially in comparison to standard MAP detection using a culture method requiring 6-8 weeks of incubation time, and is significantly less expensive than real-time PCR