A STABLE CLUSTERING SCHEME WITH NODE PREDICTION IN MANET

The main concern in MANET is increasing network lifetime and security. Clustering is one of the approaches that help in maintaining network stability. Electing an efficient and reliable Cluster Head (CH) is a challenging task. Many approaches are proposed for efficient clustering, weight-based clustering is one among them. This paper proposes a stable clustering scheme which provides network stability and energy efficiency. Proposed Stable Clustering Algorithm with Node Prediction (SCA-NP) computes the weight of the node using a combination of node metrics. Among these metrics, Direct Trust (DT) of the node provides a secure choice of CH and Node Prediction metric based on the minimum estimated time that node stay in the cluster provides the stable clustering. Mobility prediction is considered as the probability that a node stays in the network. This metric helps in electing CH which is available in the network for a longer time. Simulation is done in NS3 to evaluate the performance of SCA-NP in terms of clusters formed, network lifetime, efficiency in packet delivery, detecting malicious nodes and avoiding them in communication

Uniqueness of qq-shift difference polynomials of meromorphic functions sharing a small function

summary:We investigate the uniqueness of a $q$-shift difference polynomial of meromorphic functions sharing a small function which extend the results of N. V. Thin (2017) to $q$-difference operators

Advancing sustainable agriculture: a critical review of smart and eco-friendly nanomaterial applications

Undoubtedly, nanoparticles are one of the ideal choices for achieving challenges related to bio sensing, drug delivery, and biotechnological tools. After gaining success in biomedical research, scientists are exploring various types of nanoparticles for achieving sustainable agriculture. The active nanoparticles can be used as a direct source of micronutrients or as a delivery platform for delivering the bioactive agrochemicals to improve crop growth, crop yield, and crop quality. Till date, several reports have been published showing applications of nanotechnology in agriculture. For instance, several methods have been employed for application of nanoparticles; especially metal nanoparticles to improve agriculture. The physicochemical properties of nanoparticles such as core metal used to synthesize the nanoparticles, their size, shape, surface chemistry, and surface coatings affect crops, soil health, and crop-associated ecosystem. Therefore, selecting nanoparticles with appropriate physicochemical properties and applying them to agriculture via suitable method stands as smart option to achieve sustainable agriculture and improved plant performance. In presented review, we have compared various methods of nanoparticle application in plants and critically interpreted the significant differences to find out relatively safe and specific method for sustainable agricultural practice. Further, we have critically analyzed and discussed the different physicochemical properties of nanoparticles that have direct influence on plants in terms of nano safety and nanotoxicity. From literature review, we would like to point out that the implementation of smaller sized metal nanoparticles in low concentration via seed priming and foliar spray methods could be safer method for minimizing nanotoxicity, and for exhibiting better plant performance during stress and non-stressed conditions. Moreover, using nanomaterials for delivery of bioactive agrochemicals could pose as a smart alternative for conventional chemical fertilizers for achieving the safer and cleaner technology in sustainable agriculture. While reviewing all the available literature, we came across some serious drawbacks such as the lack of proper regulatory bodies to control the usage of nanomaterials and poor knowledge of the long-term impact on the ecosystem which need to be addressed in near future for comprehensive knowledge of applicability of green nanotechnology in agriculture

THREE-DIMENSIONAL QUANTITATIVE STRUCTURE–ACTIVITY RELATIONSHIPS MODELING STUDIES OF PHYTOCHEMICALS FROM BRASSICACEAE AS POTENT INHIBITORS AGAINST TUMOR INFLAMMATION

Objective: The aim of this study was to develop a three-dimensional quantitative structure–activity relationships (3D-QSARs) model for studying theinteraction of different phytochemicals with nuclear factor kappa B (NF-ĸB) inducing kinase, a major regulator in tumor inflammation.Methods: Different phytochemicals (ligands) from Brassicaceae were selected and tested for Lipinski's rule of five and further analyzed using interaction studies (docking) to identify the binding site in the target protein. Ligands with best fit were made to pass through ADMET filter, and the nontoxic ligands were selected based on the pIC50 values.Results: The 3D-QSARs of the ligands were designed using comparative molecular field analysis, and glucoraphanin was found to be stable and fit after subjecting for molecular dynamics simulation with annealing studies.Conclusion: Thus, the model may be prospectively used in drug design to find possible inhibitors of NF-ĸB, which plays a key prominent role in cancer inflammation.Keywords: Three-dimensional quantitative structure–activity relationships, Brassica oleracea, Simulation, Annealing, Nuclear factor kappa B kinase

IoT and image processing Techniques-Based Smart Sericulture Nature System

Silkworms are reared for the production of raw silk in sericulture. Sericulture’s main tasks include growing food plants to feed silkworms, spinning silk cocoons, and reeling the cocoons to unwind the silk thread for value-added services such processing and weaving. The Smart Sericulture paper provides a clear picture of technical innovation in underperforming agricultural branches such as sericulture, as well as an explanation of the system and strategies that can be used to improve sericulture quality and production. The intricate procedure of monitoring environmental indicators such as humidity, temperature, and rain, as well as the health of the silkworm, will be presented in this brief communication

Identification and characterization of Tobacco streak virus, as a new causative agent of necrosis disease of Anthurium

194-200Tobacco streak virus (TSV) was detected from Anthurium samples collected from Anthurium variety Medioria Red from College of Horticulture, Bangalore, India grown under polyhouse cultivation. Sap inoculation of the samples expressing positive reaction through direct antigen coating-enzyme linked immunosorbant assay (DAC-ELISA) produced both localized and systemic infection. The localized infection produced circular necrotic lesions on the leaves of cowpea cv. C152 plants. The samples were also confirmed through dot blot immunibinding assay (DIBA), which showed positive reaction by producing circular, purple coloured spot against TSV specific antibody. Further molecular characterization of TSV through RT-PCR with coat protein gene specific primers produced amplicon size of 929 bp pertaining to RNA3 region. The basic local alignment search tool (BLAST) analysis of coat protein gene through National Centre for Biotechnology Information (NCBI) database had nucleotide and amino acid sequence similarity of 97.6 to 98.8 % and 94.9 to 97.4 %, respectively with known strains of TSV from India. Phylogenetic analysis of the nucleotide sequences Confirmed the Anthurium strain of TSV forms single subgroup with other crop of Indian isolates. This study provides important clues about spread of TSV. To the best of our knowledge it is the first detailed study of TSV infecting Anthurium

Strengthening of Nevanlinna's Five-Value Theorem

[[abstract]]In this paper, we generalise Nevanlinna's ve-value theorem for derivatives of meromorphic functions by considering weaker assump- tions of sharing ve values to partially sharing k( 5) values. As a particular cases of our results, we deduce earlier results of C.-C. Yang [8, Theorem 3.2] and T.-G. Chen, K.-Y. Chen and Y.-L. Tsai [1]