سائنس اور روحانیت کی تمثیل: ایک تحقیقی جائزہ

Spirituality is usually understood as a way of being that flows from a certain profound experience of reality, which is known as ‘mystical’, ‘religious’, or ‘spiritual’ experience. There are numerous descriptions of this experience in the literature of the world’s religions, which tend to agree that it is a direct, non-intellectual experience of reality with some fundamental characteristics that are independent of cultural and historical contexts. Spiritual and scientific quests are two complementary inquiries into reality. Any feeling of antagonism between them is a product of a narrow vision. Science deals with what is measurable; religion is the quest for discovering and understanding the immeasurable. A scientist is not intelligent if he denies the existence of the immeasurable. There is nothing that is anti-science but there is a lot that is beyond science. The two quests have to go hand in hand. We not only need to have an understanding of the laws that govern the phenomena occurring in the external world around us but also we need to discover order and harmony in our consciousness. Human understanding is incomplete unless it covers both aspects of reality: matter as well as consciousness. Indeed the division between the scientific and spiritual quests is itself the creation of the human mind. Reality is one undivided whole which includes both matter and consciousness. Our thoughts, being limited by our experience, divide the external world from the inner world of our consciousness, in much the same way as our mind divides time from space though they are both two aspects of a single continuum۔

Regional Scale Sediment Yield Modeling Using Gis and Remote Sensing

A grid based Regional Scale Sediment Yield Model RSSYM was setup different catchments of Indus basin using coarse resolution grid data. When coarse grid data is used, the land slope decreases and sediment delivery is reduced. In this study the slope averaging effect was minimized by using the fractal analysis approach. The fractal constant (α) and fractal dimension (D) equations were correlated with the standard deviation on elevation by Zhang et al. (2000). These equations for fractal constant and fractal dimension were developed using Digital Elevation Model of 1 km2 resolution (DEM) for local topography. The slope was computed for 250 m and 50 m downscaling from 1 km2 grid. A comparison was made between fractal constant equation and fractal dimension to scale down the slope parameter. Both equations were incorporated in RSSYM. The model was applied to Phulra catchment. The results showed that fractal dimension equation gives better results as compared to fractal constant. So the fractal dimension equation was incorporated in RSSYM. The RSSYM was applied on three catchments of Indus basin namely Soan, Gariala and Phulra. For Phulra catchment of Siran river the total observed sediment was 0.304 million tons whereas the simulated sediment was 0.291 million tons. The coefficient of efficiency (COE) was 0.85 and the coefficient of determination was 0.83 which shows that there is a good correlation between the observed and simulated values. In Gariala catchment the observed sediment was 6.01 million tons whereas simulated sediment was 6.80 million tons. The coefficient of efficiency for Gariala catchment was 0.91 and the coefficient of determination observed and simulated hydrograph was 0.81. Similarly for the Soan catchment the observed sediment was 10.61 million tons and simulated sediment was 12.29 million tons. The coefficient of determination and coefficient of efficiency for this catchment was 0.95 and 0.98 respectively. The snowmelt runoff model SRM was applied to Astor catchment to test the applicability of temperature index approach for Indus basin. The terrain is difficult to measure the hydrological and hydraulics data. Most of the data was available at the outlet of the catchments. The input data included daily temperature and precipitation, dividing the catchment in to different zone depending on the elevation difference. The elevation zones were generated from the DEM of the area. The snow depletion curves were made for the snow covered area derived from the satellite data analysis on monthly basis. The model output was discharge hydrograph. The COE was 0.91 which shows that there is a good correlation between the observed and simulated values. The statistical test showed that model performance was good. The results of SRM model encouraged to use temperature index approach for snowmelt runoff estimation in the Indus basin. A snowmelt runoff model was developed using temperature index approach. The model was coupled with regional scale sediment yield model RSSYM. The coupled model was named as modified regional scale sediment yield model (MRSSYM). The MRSSYM model was applied to Astor and Gilgit catchments. For the Astor catchment total observed sediment load was 3.98 million tons whereas the simulated sediment load was 4.34 million tons. The coefficient of the model was 0.89 whereas the coefficient of determination was 0.83. Similarly for Gilgit catchment the measured and simulated sediment loads were 4.50 and 4.48 million tons respectively. The coefficient of efficiency and coefficient of determinations were 0.95 and 0.88 respectively. From the results it can be concluded that MRSSYM can be applied with confidence to various catchments of Indus basin where runoff is due to snowfall and snow melting.