Markov CA,Multi Regression,and Multiple Decision Making for Modeling Historical Changes in Kirkuk City,Iraq

The cellular automata (CA) model is an important tool in land use change studies. Swift increases in population and long-term expectations of rapid urbanization have led to extensive land use change, and normal living conditions have affected the natural resources of the land. This paper highlights and analyzes the historical urban changes in Kirkuk City, Iraq, considering repeated changes undergone by the state such change as government infrastructures, wars, and economic blockade. In this paper, an integrated model, built-in multi regression model, and multi-criteria evaluation were considered to improve the representation of CA transition rules. Environmental and socioeconomic factors were used to produce Suitable Maps (SMs). These SMs were practicalities to create factor layers and weight usage, rating method process for variance expert decision-making groups, and geographic information systems for the periods 1984, 1990, 2000, and 2010. The roots of the equation (R²) values are compared and these values are chosen to produce a good model of suitable maps. The approach used in this study provides a mechanism for monitoring suitability maps in Kirkuk. Furthermore, the model Markov CA is implemented and evaluated. The results indicate that the model, its related concepts performs sufficiency     

Gravitational clustering of galaxies in an expanding universe

We inquire the phenomena of clustering of galaxies in an expanding universe from a theoretical point of view on the basis of thermodynamics and correlation functions. The partial differential equation is developed both for the point mass and extended mass structures of a two-point correlation function by using thermodynamic equations in combination with the equation of state taking gravitational interaction between particles into consideration. The unique solution physically satisfies a set of boundary conditions for correlated systems and provides a new insight into the gravitational clustering problem.     

Gulmarg,Kashmir, India: Potential Site for Optical Astronomical Observations

The site characteristics of Gulmarg, Kashmir at an altitude of about 2743.2 m above sea level is based on analysis of meteorological conditions, cloud cover, temperature, wind speed, wind direction, relative humidity and atmospheric pressure, etc. Analysis and characterization of meteorological conditions suggest that Gulmarg, Kashmir is a potential site for carrying out photometric as well as spectroscopic observations of celestial objects.     

In situ bioremediation of trichloroethylene-contaminated water by a resting-cell methanotrophic microbial filter

Abstract

An in situ microbial filter technology is being tested and developed for remediating migrating subsurface plumes contaminated with low concentrations of trichloroethylene (TCE). The current focus is the establishment of a replenishable bioactive zone (catalytic filter) along expanding plume boundaries by the injection of a representative methanotrophic bacterium, Methylosinus trichosporium OB3b. This microbial filter strategy has been successfully demonstrated using emplaced, attached resting cells (no methane additions) in a 1.1 m flow-through test bed loaded with water-saturated sand. Two separate 24 h pulses of TCE (109 ppb and 85 ppb), one week apart, were pumped through the system at a flow velocity of 15 mm h^?1; no TCE (< 0.5 ppb) was detected on the downstream side of the microbial filter. Subsequent excavation of the wet sand confirmed the existence of a TCE-bioactive zone 21 days after it had been created. An enhanced longevity of the cellular, soluble-form methane monooxygenase produced by this methanotroph is a result of the laboratory bioreactor culturing conditions. Additional experiments with cells in sealed vials and emplaced in the 1.1 m test bed yielded a high resting-cell finite TCE biotransformation capacity of about 0.25 mg per mg of bacteria; this is suitable for a planned sand-filled trench field demonstration at a Lawrence Livermore National Laboratory site.     

Yield Forecasting of Spring Maize Using Remote Sensing and Crop Modeling in Faisalabad-Punjab Pakistan

Real time, accurate and reliable estimation of maize yield is valuable to policy makers in decision making. The current study was planned for yield estimation of spring maize using remote sensing and crop modeling. In crop modeling, the CERES-Maize model was calibrated and evaluated with the field experiment data and after calibration and evaluation, this model was used to forecast maize yield. A Field survey of 64 farm was also conducted in Faisalabad to collect data on initial field conditions and crop management data. These data were used to forecast maize yield using crop model at farmers’ field. While in remote sensing, peak season Landsat 8 images were classified for landcover classification using machine learning algorithm. After classification, time series normalized difference vegetation index (NDVI) and land surface temperature (LST) of the surveyed 64 farms were calculated. Principle component analysis were run to correlate the indicators with maize yield. The selected LSTs and NDVIs were used to develop yield forecasting equations using least absolute shrinkage and selection operator (LASSO) regression. Calibrated and evaluated results of CERES-Maize showed the mean absolute % error (MAPE) of 0.35–6.71% for all recorded variables. In remote sensing all machine learning algorithms showed the accuracy greater the 90%, however support vector machine (SVM-radial basis) showed the higher accuracy of 97%, that was used for classification of maize area. The accuracy of area estimated through SVM-radial basis was 91%, when validated with crop reporting service. Yield forecasting results of crop model were precise with RMSE of 255 kg ha^?1, while remote sensing showed the RMSE of 397 kg ha^?1. Overall strength of relationship between estimated and actual grain yields were good with R² of 0.94 in both techniques. For regional yield forecasting remote sensing could be used due greater advantages of less input dataset and if focus is to assess specific stress, and interaction of plant genetics to soil and environmental conditions than crop model is very useful tool.     

Peculiar motions of galaxy clusters: correlation function approach

The correlation function theory on the basis of prescribed boundary conditions provides a deeper understanding in studying the dynamical parameters of galaxy clusters. The approach approximates that the moderate dense systems discussed by a two point correlation function is helpful for describing the dynamical nature of galaxy clusters. The projected theory of two point correlation function for point mass and extended mass structures can be used an alternative tool in measuring the average peculiar motion and temperature profile of galaxy clusters.     

Coronal holes: Mass loss driven by magnetic reconnection

We propose that bubbles of matter ejected from magnetic reconnection sites in polar plumes drive the solar wind in coronal holes.