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Gupta, R. N.
- Analysis of Rainfall Data for Soil Conservation and Crop Planning
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Indian Forester, Vol 101, No 4 (1975), Pagination: 238-248Abstract
Twenty years rainfall data of Rehmankhera (Lucknow) have been analysed in this paper. Mean monthly, percent distribution, expected rainfall at different percent chance, frequency of continuous dry days, daily maximum rainfall, variability of annual rainfall, its distribution and trend have been presented. Monthly data showed a great variability in rainfall. About 76% of rainfall was concentrated in monsoon season. Rabi season received only 9.9% rainfall which will cause failure in Rabi season crop without adequate measures for conserving moisture and without providing irrigation facilities in the region. Weekly rainfall has great importance in crop planning than monthly seasonal or annual rainfall. Probability and frequency analysis of rainfall data have been done which provides more useful tool for application of such data. One year out of every ten years has been found to be drought year.
- Rules for Germination Test of Tree Seeds for Certification
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Indian Forester, Vol 101, No 6 (1975), Pagination: 320-327Abstract
In order that results may be reproduceable, it is essential that germination tests for certification of seeds are standardised. The International Seed Testing Association has laid down guide lines for these tests in 1966, and based on these lines it is necessary to standardize the procedures for each species separately. In this connection, rules for germination tests of 55 Indian tree species have been worked out and tabulated.- Preparation of Diclofenac Diethylamine Nanoemulsions by Ultrasonication-Stability and Process Parameter Evaluation Under Various Conditions
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1 Agra Public Institute of Technology and Computer Education, Agra, IN
2 Department of Pharmaceutics, IIT-B.H.U., Varanasi, IN
1 Agra Public Institute of Technology and Computer Education, Agra, IN
2 Department of Pharmaceutics, IIT-B.H.U., Varanasi, IN
Source
Research Journal of Pharmaceutical Dosage Form and Technology, Vol 3, No 6 (2011), Pagination: 285-293Abstract
In this study, oil-in-water nanoemulsions of Diclofenac Diethylamine were produced by Ultrasonication. The influence of emulsifying conditions including emulsifier type and concentration, homogenization pressure, temperature, cycle, on time, off time and total time on the properties and stability of the nanoemulsions were investigated using a Zetasizer. The mean diameters (z-average) of the dispersed particles containing Diclofenac Diethylamine ranged from 50.57 to 154.9 nm and the polydispersity index ranged from 0.318 to 0.719 and the zeta potential ranged from19.2 to35.3. The nanoemulsions produced with Tween-80 had the smallest particle sizes and narrowest size distribution. The particle sizes decreased with increases in homogenization pressure and cycle, and also with temperature up to 40ºC. The physical stability of the nanoemulsions increased with the elevation of temperature up to 40º C, with pressure up to 200 MPa and homogenization cycle (up to three cycles).Keywords
Nanoemulsion, Diclofenac Diethylamine, Ultrasonication, Particle size, Zetasizer.- Artificial Intelligence Model for Prediction of Local and Main FALL in caving Panel of Bord and Pillar Method of Mining
Abstract Views :89 |
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Authors
Affiliations
1 Department of Mining Engineering, Indian Institute of Technology (ISM), Dhanbad – 826004, Jharkhand, IN
2 National Institute of Rock Mechanics, Bangalore – 560070, Karnataka, IN
3 All India Institute of Medical Sciences, Patna – 801507, Bihar, IN
1 Department of Mining Engineering, Indian Institute of Technology (ISM), Dhanbad – 826004, Jharkhand, IN
2 National Institute of Rock Mechanics, Bangalore – 560070, Karnataka, IN
3 All India Institute of Medical Sciences, Patna – 801507, Bihar, IN
Source
Journal of Mines, Metals and Fuels, Vol 70, No 4 (2022), Pagination: 171-181Abstract
Depillaring with caving method of mining is a common practice in Indian coalfields and so is the occurrence of fall in goaf area, which can be considered as a boon in disguise as it allows wining of coal from large reserves but this becomes a curse just because of its unpredicted occurrence. Various empirical and statistical models are developed after idealization of several complicated mechanisms but they are not able to predict roof fall accurately especially in caving panels. Therefore, a new approach based on Artificial Intelligence is used to predict the sequence of local and main fall in caving panel taking into account a host of geotechnical and mining parameters of the mine. Mathematical equations and hidden calculations of artificial neural networks are known to have the capability of learning and analyzing records endlessly. Two different models have been deployed after optimal hyper parameter optimization to predict the occurrence of fall and to characterize the nature of fall (local or main) with considerable and reliable accuracy.Keywords
Bord and Pillar, Caving, Deep Learning Algorithm, Deep Neural Network, Hyper Parameter Optimization, Local Fall, Main Fall, TalosReferences
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