Formulation and evaluation of once a day matrix tablets of Valsartan

The objective of the research investigation was to design and evaluate the oral once a day matrix tablets of valsartan by using various natural matrix former gums such as separately. Initially natural gums are extracted and purified and their extracts are evaluated for their proximate phyto-chemical studies. Tablets were prepared by wet granulation method. The prepared tablets were further evaluated for physical parameters, In-vitro dissolution, and drug-excipients interactions are also carried out. The FT-IR studies revealed that there was no chemical interaction between drug and excipients. Among all prepared formulations, formulation F-8 exhibited precise controlled release of drug over a prolonged period of 24 hrs. The in- vitro dissolution data obtained for various formulations were fitted into zero order, first order, Higuchi’s and Korsymeyer - Peppas kinetic models. The optimized formulation displayed zero order release kinetics and Korsmeyer and Peppas equation give release pattern with values of (n = 0.9094) indicating non-fickian (or) Anomalous types of diffusion takes place through matrix of Gum Kondagogu. The optimized formulations F-8 was subjected to stability studies for three months at  400C/75%RH as per ICH guidelines and result does not shows any change in physical parameters and in-vitro release studies.

Supply and demand for cereals in Nepal, 2010–2030:

This paper attempts to estimate the future supply and demand for cereals in Nepal. While there has been considerable research in the past examining the agricultural sector in Nepal, to the best of our knowledge there has been no analysis of the supply-demand scenario for food grains in the country. The analysis undertaken in this paper attempts to bridge this gap in the literature by estimating supply and demand models for the three most important cereals in Nepal's food basket: rice, wheat, and maize. The supply projections have been carried out on the basis of a single-crop production function model using data for the period 1995–2008. For estimating the demand function and projecting future demand, data from the Nepal Living Standards Survey II (NLSS II), undertaken in the year 2003/04, are used.cereal supply, cereal demand,

Reforms in Indian agro-processing and agriculture sectors in the context of unilateral and multilateral trade agreements

In this paper, we explore the potential impacts of trade and investment-related policy reforms on India's agro-processing sector. We consider the direct effects of policy reforms within the processing sector, and the indirect effects on agro-processing of policy reforms in the primary agriculture sector, in the Indian economy as a whole, and in a multilateral framework. Towards this, we develop a 22-sector, 16-region version of the GTAP computable general equilibrium (CGE), global model for our analysis. We find that trade and investment-related reforms in agro-processing together can help the sector to grow. Policy reforms that stimulate investment and help to improve productivity will be crucial in offsetting the contractionary pressures of trade reform alone on the production of processed agricultural products. We also find that indirect effects on agroprocessing from India's policy reforms in other sectors are more important than reforms in agro-processing itself. Our findings argue for an economy-wide perspective when targeting reform or development of the agro-processing sector in India. Compared to trade reform, comprehensive domestic reforms in the agro-processing and agriculture sectors relating to investment are critical for achieving growth in agro-processing. However, while the impacts of trade reform per se seem to be small, trade reform - by ushering in a higher degree of competition - could itself be a stimulus for investment and productivity gains in India. At present, unilateral reforms, especially those that improve productivity in agroprocessing and in primary agriculture, are more important to agro-processing than multilateral trade reforms. Nevertheless, our findings also suggest the importance of pursuing a domestic reform agenda within a multilateral trading strategy that can accommodate the expected economic growth of India and its future role in global markets, with general equilibrium effects on agro-processing.Agriculture, Agro-processing, Trade agreements, CGE models

Bio-inspired Algorithms for the Design of Multiple Optimal Power System Stabilizers: SPPSO and BFA

Power System Stabilizers (PSSs) provide stabilizing control signals to excitation systems to damp out inter-area and intra-area oscillations. The PSS must be optimally tuned to accommodate the variations in the system dynamics. Designing multiple optimal PSSs is a challenging task for researchers. This paper presents the comparison between two bio-inspired algorithms: a Small Population based Particle Swarm Optimization (SPPSO) and the Bacterial Foraging Algorithm (BFA) for the simultaneous tuning of a number of PSSs in a multi-machine power system. The cost function to be optimized by both algorithms takes into consideration the time domain transient responses. The effectiveness of the algorithms is evaluated and compared for damping the system oscillations during small and large disturbances. The robustness of the optimized PSSs in terms of damping is shown using the Matrix Pencil analysis

Optimal Design of Power System Stabilizers using a Small Population Based PSO

Power system stabilizers (PSSs) are used to generate supplementary control signals to excitation systems in order to damp out local and inter-area oscillations. In this paper, a modified particle swarm optimization (PSO) algorithm with a small population is presented for the design of optimal PSSs. The small population based PSO (SPPSO) is used to determine the optimal parameters of several PSSs simultaneously in a multi-machine power system. In order to maintain a dynamic search process, the idea of particle regeneration in the population is also proposed. Optimal PSS parameters are determined for the power system subjected to small and large disturbances. The effectiveness of the PSSs parameters determined by the SPPSO algorithm is observed in damping out the power system oscillations fast after a disturbance. The advantage of the proposed approach is its convergence in fewer evaluations and lesser computations are required per evaluation. Results obtained with the SPPSO optimized PSSs parameters are compared against published PSS parameters for the Kundur\u27\u27s two area power system

Nonlinear Radiative Heat Transfer of Cu-Water Nanoparticles over an Unsteady Rotating Flow under the Influence of Particle Shape

A 3D study on Cu-water-rotating nanofluid over a permeable surface in the presence of nonlinear radiation is presented. Particle shape and thermophysical properties are considered in this study. The governing equations in partial forms are reduced to a system of nonlinear ordinary differential equations using suitable similarity transformations. An effective Runge-Kutta-Fehlberg fourth-fifth order method along with shooting technique is applied to attain the solution. The effects of flow parameters on the flow field and heat transfer characteristics were obtained and are tabulated. Useful discussions were carried out with the help of plotted graphs and tables. It is found that the rate of heat transfer is more enhanced in column-shaped nanoparticles when compared to tetrahedron- and sphere-shaped nanoparticles. Higher values of rotating parameter enhance the velocity profile and corresponding boundary layer thickness. It has quite the opposite behavior in angular velocity profile. Further, unsteady parameter increases the velocity profile and corresponding boundary layer thickness

Efficacy of Colletotrichum gloeosporioides, potential fungi for bio control of Echinochloa crus-galli (Barnyard grass)

A systematic field study was conducted in agricultural fields such as food crops, pulses, vegetable crops, oil crops and commercial crops to estimate infestation of Echinochloa crus – galli (barnyard grass), a common terrestrial weed belonging to family poaceae. The in vitro pathogenicity studies on barnyard grass were conducted using spore inoculum (8X107/ml) of an indigenous fungus, Colletotrichum gloeosporioides. The pathogen was re-isolated from inoculated plants to fulfill Koch’s postulates and confirmed its host specificity on barnyard grass. The disease by the isolate was critically analyzed and the results revealed that Colletotrichum gloeosporioides is a potential agent to biological control of barnyard grass. The results revealed that the pathogen causes significantly (P < 0.05) severe infection on host weed and destructs the weed population by leaf spot diseases. The findings of the research suggested that the isolate Colletotrichum gloeosporioides is highly virulent and host-specific, and recommended for further studies as a promising biocontrol agent against barnyard grass weed. Keywords: Mycoherbicides, Barnyard grass, Colletotrichum gloeosporioides, Koch’s postulate

An Efficient Comparative Analysis of CNN-based Image Classification in the Jupyter Tool Using Multi-Stage Techniques

The main process of this image classification with a convolution neural network using deep learning model was performed in the programming language Python code in the Jupyter tool, mainly using the data set of IRS P-6 LISS IV from an Indian remote sensing satellite with a high resolution multi-spectral camera with around 5.8m from an 817 km altitude Delhi image. To classify the areas within the cropped image required to apply enhancement techniques, the image size was 1000 mb. To view this image file required high-end software for opening. For that, initially, ERDAS imaging software viewer was used for cropping into correct resolution pixels. based on that cropped image used for image classification with preprocessing for applying filters for enhancement. And with the convolution neural network model, required to train the sample images of the same pixels, was collected from the group of objects that were cropped. Then we needed to use image sample areas to train the model with learning rate and epoch rate to improve object detection accuracy using the Jupyter notebook tool with tensorflow and machine learning model produce the accuracy rate of 90.78%