ANTIBACTERIAL AND ANTIFUNGAL ACTIVITIES OF OCIMUM GRATISSIMUM L.

The development of antibiotic resistance in microorganisms is a global challenge for the clinicians, pharmacist and research scientists leading to development of new medicinal formulations that are effective and easily consumable. The plant yielding essential oil with chief constituent as eugenol has been identified as an important compound with strong inhibition of bacteria, and storage fungi. Ocimum gratissimum is an aromatic shrub occurring in warm tropical regions has been used in traditional medicine in India to cure various ailments in general and as an antimicrobial agent in particular. The literature surveyed reveals that the plant oil exhibit strong potentiality against gram negative and gram positive bacteria along with diverse human and plant fungi. As a natural product the plant oil is safer as food preservatives and stored grain protectant and has wide application in cosmetics and perfumery industries. This comprehensive review is attempted to provide the valuable information on antimicrobial activities of the plant essential oil that will be used to explore and develop a standard therapeutics system for the management of clinical and multiple drug resistant microorganisms

EFFECT OF BOTANICALS FOR MANAGEMENT OF LEAF BLAST AND ENHANCING YIELD TRAITS IN RICE

Field experiments were undertaken during kharif 2012 and 2013 under irrigated ecosystem to evaluate the efficacy of botanicals along with standard fungicides for assessing per cent disease incidence, plant height, number of tiller per plant, number of spikelet per panicle, panicle length, 100-grain weight and grain yield against blast of rice. Pooled data of two years suggest that neem based commercial biopesticides with azadiractin as active ingredients were found effective in reducing disease severity and improving the yield attribute of the crop and proves promising products when compared to standard fungicides. Among the botanicals the spraying of Achook, Neem Azal T/S, Neem gold and Tricure shows significant reduction in disease severity, along with improving yield attributes, increasing the 100-grain weight and grain yield

Stability and activity of Enzymes from Halophilic and Haloalkaliphilic Bacteria in the Presence of Organic Solvents

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Formation of double ring patterns on Co2MnSi Heusler alloy thin film by anodic oxidation under scanning probe microscope

Double ring formation on Co2MnSi (CMS) films is observed at electrical breakdown voltage during local anodic oxidation (LAO) using atomic force microscope (AFM). Corona effect and segregation of cobalt in the vicinity of the rings is studied using magnetic force microscopy and energy dispersive spectroscopy. Double ring forma-tion is attributed to the interaction of ablated material with the induced magnetic field during LAO. Steepness of forward bias transport characteristics from the unperturbed region of the CMS film suggest a non equilibrium spin contribution. Such mesoscopic textures in magnetic films by AFM tip can be potentially used for memory storage applications.Comment: 7 pages, 5 figure

Design and Implementation of PRBS Generator using VHDL

Pseudo random binary sequence is essentially a random sequence of binary numbers. So PRBS generator is nothing but random binary number generator. It is ‘random’ in a sense that the value of an element of the sequence is independent of the values of any of the other elements. It is 'pseudo' because it is deterministic and after N elements it starts to repeat itself, unlike real random sequences. The implementation of PRBS generator is based on the linear feedback shift register (LFSR). The PRBS generator produces a predefined sequence of 1's and 0's, with 1 and 0 occurring with the same probability. A sequence of consecutive n*(2^n -1) bits comprise one data pattern, and this pattern will repeat itself over time

Reservoir computing model of two-dimensional turbulent convection

Reservoir computing is applied to model the large-scale evolution and the resulting low-order turbulence statistics of a two-dimensional turbulent Rayleigh-B\'{e}nard convection flow at a Rayleigh number ${\rm Ra}=10^7$ and a Prandtl number ${\rm Pr}=7$ in an extended domain with an aspect ratio of 6. Our data-driven approach which is based on a long-term direct numerical simulation of the convection flow comprises a two-step procedure. (1) Reduction of the original simulation data by a Proper Orthogonal Decomposition (POD) snapshot analysis and subsequent truncation to the first 150 POD modes which are associated with the largest total energy amplitudes. (2) Setup and optimization of a reservoir computing model to describe the dynamical evolution of these 150 degrees of freedom and thus the large-scale evolution of the convection flow. The quality of the prediction of the reservoir computing model is comprehensively tested. At the core of the model is the reservoir, a very large sparse random network charcterized by the spectral radius of the corresponding adjacency matrix and a few further hyperparameters which are varied to investigate the quality of the prediction. Our work demonstrates that the reservoir computing model is capable to model the large-scale structure and low-order statistics of turbulent convection which can open new avenues for modeling mesoscale convection processes in larger circulation models.Comment: 16 pages, 12 figure

HIGH PERFORMANCE COMPUTING AND PROCESS CONTROL OF ADDITIVE LAYER MANUFACTURING METHODS FOR POLYMER PRODUCT METAL TOOLS PRODUCTION

Purpose of the study: Additive layer manufacturing is basically different from the traditional formative manufacturing process where a complete structure can be constructed into designed shape from layer to layer manufacturing rather than other methods or casting, forming or other machining processes. Additive layer manufacturing is a highly versatile, flexible, and customizable. Methodology: In this paper, we discussed high-performance computing and process control of AM methods by using different parameters. The significant interest in making complex, innovative and robust products by using AM methods to great extent to deal with work is needed in AM challenges relevant to key enabling technologies namely different materials and metrology to achieve functionally and reproductive ways. Main Findings: In this paper, we discussed major processes that highly accurate and the key applications, challenges and recent developments of future additive Am processes. Applications of this study: Additive layer manufacturing methods to develop the most highly and controlled methods for producing a variety of complex shapes and structures. The significant role of AM layer technology is to make produce the most economical and highly effective methods. In this study, we compared different AM methods for achieving the most highly and controlled methods of AM technology. Novelty/Originality of this study: Today manufacturing trends are very highly impacted by technologies globalizations. Various manufactures are using layer manufacturing into their best practices so that they can be changes in the global economy and manufacturing

Non-intrusive, transferable model for coupled turbulent channel-porous media flow based upon neural networks

Turbulent flow over permeable interface is omnipresent featuring complex flow topology. In this work, a data driven, end to end machine learning model has been developed to model the turbulent flow in porous media. For the same, we have derived a non linear reduced order model with a deep convolution autoencoder network. This model can reduce highly resolved spatial dimensions, which is a prerequisite for direct numerical simulation. A downstream recurrent neural network has been trained to capture the temporal trend of reduced modes, thus it is able to provide future evolution of modes. We further evaluate the trained model s capability on a newer dataset with a different porosity. In such cases, fine tuning could reduce the efforts (up to two order of magnitude) to train a model with limited dataset and knowledge and still show a good agreement on the mean velocity profile. Leveraging the current model, we find that even quick fine tuning achieving an impressive order of magnitude reduction in training time by approximately still results in effective flow predictions. This promising discovery encourages the fast development of a substantial amount of data-driven models tailored for various types of porous media. The diminished training time substantially lowers the computational cost when dealing with changing porous topologies, making it feasible to systematically explore interface engineering with different types of porous media. Overall, the data driven model shows a good agreement, especially for the porous media which can aid the DNS and reduce the burden to resolve this complex domain during the simulations. The fine tuning is able to reduce the training cost significantly and maintain an acceptable accuracy when a new flow condition comes into play