Bravery as the Virtue of Tamil People as shown in the First Epic

One of the most important human activities is being virtuous. The Tamils, in particular, were seen as the best in the humanistic character of morality. This quality was ingrained in the blood of the Tamil people from the king to the poor. To add to this view, the literature from the Sangam literary period to the present time has been recorded. It can be seen that these exclusions contain four certainties: virtue, wealth, happiness, and home. In them, the Tamils have preserved charity as the object of life of the Tamils, as a bulwark of morality, and as a tool for loving living beings. He who is kind in his mind even when there is not much wealth, and who gives as much as possible without changing his face, is the foremost virtue. Society will prosper only because of the humanists who have a heart that is inferior to the poorer than themselves and to the poor who stand by them. Since then, till today, it is easy to do harm to other beings and it is difficult to do good to others. The individual needs courage when he wants to benefit these creatures. Kovalan is such a leader. He is a person who donates the gold and material he has in order to wipe away the suffering of others and is a humanitarian who follows virtue and life. Virtue is acting for the common good without selfishness. The best thing is to help someone without considering the benefit of what kind of help will be returned back. The message, which can be said to be the pinnacle of Silappathikaram, is that Kannagi did not care when her husband parted ways and returned after losing her gold and wealth. It is said to be the pinnacle of Silapathikaram because the purpose of being born as a human being is to do good deeds to save other beings from suffering. It can be seen that this epic says that he regrets not doing those things in his lifetime. It can be seen from this that the Tamils lived their lives with the fear of virtue and valour

Image Encryption Based on Diffusion and Multiple Chaotic Maps

In the recent world, security is a prime important issue, and encryption is one of the best alternative way to ensure security. More over, there are many image encryption schemes have been proposed, each one of them has its own strength and weakness. This paper presents a new algorithm for the image encryption/decryption scheme. This paper is devoted to provide a secured image encryption technique using multiple chaotic based circular mapping. In this paper, first, a pair of sub keys is given by using chaotic logistic maps. Second, the image is encrypted using logistic map sub key and in its transformation leads to diffusion process. Third, sub keys are generated by four different chaotic maps. Based on the initial conditions, each map may produce various random numbers from various orbits of the maps. Among those random numbers, a particular number and from a particular orbit are selected as a key for the encryption algorithm. Based on the key, a binary sequence is generated to control the encryption algorithm. The input image of 2-D is transformed into a 1- D array by using two different scanning pattern (raster and Zigzag) and then divided into various sub blocks. Then the position permutation and value permutation is applied to each binary matrix based on multiple chaos maps. Finally the receiver uses the same sub keys to decrypt the encrypted images. The salient features of the proposed image encryption method are loss-less, good peak signal-to-noise ratio (PSNR), Symmetric key encryption, less cross correlation, very large number of secret keys, and key-dependent pixel value replacement.Comment: 14 pages,9 figures and 5 tables; http://airccse.org/journal/jnsa11_current.html, 201

Drug Design, Molecular Docking Studies, Microwave Assisted Synthesis and Characterization of Novel Schiff’s Bases of Dibenzosuberenone Derivatives as Anti-Histamine and Anti-Depressant

The project work entitled “Drug Design, Molecular docking studies, Microwave assisted synthesis and Characterization of novel Schiff’s base of Dibenzosuberenone derivatives as antihistamine and anti-Depressant”. The methods proposed have not been reported till date. The above study comprises of the following steps: • Designed the Schiff's base of dibenzosuberenone and assessed ADME property by chemsketch, molinspiration, and swissADME software. • molecular docking studies were performed on the designed compounds • Optimized the method of synthesis for the proposed compounds. • Synthesized the dibenzosuberenone derivatives by using microwave irradiation technique. • The synthesized compounds were purified by recrystallization and identified by TLC. • Determined the physical properties such as solubility, melting point and Rf value. • Characterized the structure of synthesized compounds by • UV SPECTROSCOPY. • IR SPECTROSCOPY. • 1H NMR. • MASS. • The IR spectra showed the relevant functional group for all synthesized compounds. • The 1H NMR showed the relevant proton signals for all synthesized compounds. • The molecular mass was determined by MASS spectroscopy. A new series of novel Schiff base of Dibenzosuberenone derivatives have been prepared and characterized by spectral data. CONCLUSION: Efficient and environmentally benign methodologies for the synthesis of novel schiff’s base of Dibenzosuberenone derivatives using microwave irradiation has been reported. several advantages including percentage yield, reduced time and reduced pollution. So further development of the novel schiff’s base of dibenzosuberenone derivatives may be used to achieve the antihistamine and antidepressant activity in future

ASPERGILLUS FLAVUS MEDIATED SILVER NANOPARTICLES SYNTHESIS AND EVALUATION OF ITS ANTIMICROBIAL ACTIVITY AGAINST DIFFERENT HUMAN PATHOGENS

Objective: Here, we report the extracellular synthesis of silver nanoparticles (AgNPs) using the cell-free extract of fungal isolate Aspergillus flavus and evaluation its inhibitory activity against bacterial pathogens. Methods: Synthesized AgNPs was characterized via high throughput instrumentation such as UV–Visible spectrophotometer (UV-Vis) and Fourier transform infrared spectroscopy, High-resolution transmission electron microscopy (HRTEM), X-ray Diffraction (XRD) and Energy dispersive X-ray spectroscopy (EDAX). Results: Formation of yellowish brown colour clearly indicates the synthesis of AgNPs which produces a SPR peak at 420 nm. Active protein metabolites present in the cell-free extract plays a crucial role in reduction and stabilization of AgNPs. It was clearly observed that synthesized AgNPs were faced-centered cubic crystalline in nature with the mean size of 22±11 nm. Further, synthesized AgNPs capped with protein moieties exhibits excellent inhibitory activity against tested bacterial pathogens. Conclusion: In this study, we have isolated the fungal strain A. flavus from the infected larvae of D. eucharis from the soil. The active metabolites of isolated A. flavus have been successfully used as an eco-friendly reducing agent to generate AgNPs and synthesized particles can be potentially developed as a drug candidature for antimicrobial therapy

Chemical profiling of fern Cheilosoria mysurensis (Wall. ex Hook.) Ching & Shing and its biological activity

Cheilosoria mysurensis (Wall. ex Hook.) Ching & Shing, a medicinal fern traditionally used to treat burns, throat pain and bone fracture. There is no any scientific report regarding anticancer studies of this species. The aim of the study was to find out the chemical components through GC/MS analysis with their antioxidant and cytotoxic activity. GC-MS analysis shows primary ingredients viz, Cis-9, 10-epoxyoctadecan-1-ol and Oxiraneundecanoic acid, 3-pentyl-, methyl ester, trans were present. High free-radical scavenging activity has been discovered in 2, 2-diphenyl-1-picrylhydrazyl (DPPH) and showed the IC50 value of 52.13±0.33. Furthermore, C. mysurensis also showed good cytotoxic effects against DLA and EAC cell lines with values of 72.9% and 79.5% at 200 µg/ml dose respectively. Overall findings suggested that the identified chemical compounds proved to be rich in antioxidant property. Further, this fern can be a good resource for pharma industry to produce novel anti-cancer drugs

Estimating Waterbird Abundance on Catfish Aquaculture Ponds Using an Unmanned Aerial System

In this study, we examined the use of an unmanned aerial system (UAS) to monitor fish-eating birds on catfish (Ictalurus spp.) aquaculture facilities in Mississippi, USA. We tested 2 automated computer algorithms to identify bird species using mosaicked imagery taken from a UAS platform. One algorithm identified birds based on color alone (color segmentation), and the other algorithm used shape recognition (template matching), and the results of each algorithm were compared directly to manual counts of the same imagery. We captured digital imagery of great egrets (Ardea alba), great blue herons (A. herodias), and double-crested cormorants (Phalacrocorax auritus) on aquaculture facilities in Mississippi. When all species were combined, template matching algorithm produced an average accuracy of 0.80 (SD = 0.58), and color segmentation algorithm produced an average accuracy of 0.67 (SD = 0.67), but each was highly dependent on weather, image quality, habitat characteristics, and characteristics of the birds themselves. Egrets were successfully counted using both color segmentation and template matching. Template matching performed best for great blue herons compared to color segmentation, and neither algorithm performed well for cormorants. Although the computer-guided identification in this study was highly variable, UAS show promise as an alternative monitoring tool for birds at aquaculture facilities

Novel Proposed Work for Empirical Word Searching in Cloud Environment

People's lives have become much more convenient as a result of the development of cloud storage. The third-party server has received a lot of data from many people and businesses for storage. Therefore, it is necessary to ensure that the user's data is protected from prying eyes. In the cloud environment, searchable encryption technology is used to protect user information when retrieving data. The versatility of the scheme is, however, constrained by the fact that the majority of them only offer single-keyword searches and do not permit file changes.A novel empirical multi-keyword search in the cloud environment technique is offered as a solution to these issues. Additionally, it prevents the involvement of a third party in the transaction between data holder and user and guarantees integrity. Our system achieves authenticity at the data storage stage by numbering the files, verifying that the user receives a complete ciphertext. Our technique outperforms previous analogous schemes in terms of security and performance and is resistant to inside keyword guessing attacks.The server cannot detect if the same set of keywords is being looked for by several queries because our system generates randomized search queries. Both the number of keywords in a search query and the number of keywords in an encrypted document can be hidden. Our searchable encryption method is effective and protected from the adaptive chosen keywords threat at the same time

Improving animal monitoring using small unmanned aircraft systems (sUAS) and deep learning networks

In recent years, small unmanned aircraft systems (sUAS) have been used widely to monitor animals because of their customizability, ease of operating, ability to access difficult to navigate places, and potential to minimize disturbance to animals. Automatic identification and classification of animals through images acquired using a sUAS may solve critical problems such as monitoring large areas with high vehicle traffic for animals to prevent collisions, such as animal-aircraft collisions on airports. In this research we demonstrate automated identification of four animal species using deep learning animal classification models trained on sUAS collected images. We used a sUAS mounted with visible spectrum cameras to capture 1288 images of four different animal species: cattle (Bos taurus), horses (Equus caballus), Canada Geese (Branta canadensis), and white-tailed deer (Odocoileus virginianus). We chose these animals because they were readily accessible and whitetailed deer and Canada Geese are considered aviation hazards, as well as being easily identifiable within aerial imagery. A four-class classification problem involving these species was developed from the acquired data using deep learning neural networks. We studied the performance of two deep neural network models, convolutional neural networks (CNN) and deep residual networks (ResNet). Results indicate that the ResNet model with 18 layers, ResNet 18, may be an effective algorithm at classifying between animals while using a relatively small number of training samples. The best ResNet architecture produced a 99.18% overall accuracy (OA) in animal identification and a Kappa statistic of 0.98. The highest OA and Kappa produced by CNN were 84.55% and 0.79 respectively. These findings suggest that ResNet is effective at distinguishing among the four species tested and shows promise for classifying larger datasets of more diverse animals