The climate change has become an important issue through the last years due to its accelerated effect that have been taken place all over the world. Global climate change has a serious sub sequences all over the world as a result of greenhouse gas emission and solar radiation activity especially in the coastal zone area, climate change includes many parameters such as rising temperature, rain and dust storms, floods and drought. These parameters have negative significant effect on urban and rural area along any coastal area that contributes continuous threats on population, shore line erosion, sea water intrusion, coastal water resources, economy, tourism, land use, pollution and the surrounding environment. The SLR is a major impact of the climate change scenarios due to the global warming and the melting of earth poles glaciers that will sink large part of the coastal cultivated land, the reduction of the cultivated land will cause the needs for finding alternative source of protein. In this paper we established an algae-brine system adjacent to the aquaponic system inside Heliopolis university campus, by the help of the brine that resulted from the RO unit inside the aquaponic system we were able to breed a spicy of algae that can tolerate high rate of salinity, The name of the algae  spicy is  Donaliella salina which is a rich source of protein and can be used a source of fish food inside the aquaponics system, At the end of this study a group of conclusions were deduced for its optimum breeding environment, Also EIA study was achieved to find out its effect on the surrounding environment.

Analysis of jointly censoring data has received considerable attention in the last few years. In this paper, the estimation problem of life time for two populations follow Rayleigh distribution based on joint progressive type-II censored sample (JPC-II) scheme is developed. We obtained the maximum likelihood estimators (MLEs) of the unknown parameters when it exists. Bayes estimators of the unknown parameters using squared error (SE), linear-exponential (LINEX) and general entropy (GE) loss functions are considered. Further, we obtain both approximate and Bayes credible confidence intervals. The theoretical results of point and interval estimation obtained are assessment and compared through illustrative example and simulation studies.

Estimating Gas condensate reserves and predicting their performance has been an area to reconcile with in the industry today. The general method of estimating the volume of gas (reserves) in a gas reservoir is the Gas Material Balance (P/Z against Gp plot). This method basically involves computing gas compressibility factors (z) at different pressures during production from the reservoir. Condensate reservoirs exhibit two phase occurrence as reservoir pressure is depleted below dew point, hence posing a challenge to accurately estimating the compressibility factor (z factor) to be used for the plot. This may ultimately result in underestimating or overestimating the Gas-Initially-In-Place in such reservoirs. The reservoir fluid acquired from the field is used in experimental Constant Volume Depletion simulation to acquire data representing the actual reservoir depletion which is validated with observed minimal deviations and presented in the PVT reports. To replace the need for time consuming and complicated experiments, it was attempted to develop a relationship or model to determine the best estimates of the two-phase compressibility factors at different pressures. In this paper, the Peng-Robinson's equation of state is employed to determine the different phase compressibility factors and combined in proportions with respect to the overall mole fractions of the two phases as a weighting factor to obtain a general relationship for the two-phase compressibility factor of the reservoir fluid. The reserve estimates from this method were compared with the field data and other methods for possible deviations and corrections. It is conclusive that this project results in the highest accuracy in condensate reserves estimation with an average error of about 1%, whereas obtainable error using the Standing-Katz single phase z factor could be up to 8%.