The method of lowest-order constrained variational, which predicts reasonably the nuclear matter semi-empirical data is used to calculate the equation of state of beta-stable matter at finite temperature. The Reid soft-core with and without the N-∆ interactions which fits the N-N scattering data as well as the U V 14 potential plus the three-nucleon interaction are considered in the nuclear many-body Hamiltonian. The electron and muon are treated relativistically in the total Hamiltonian at given temperature, to make the fluid electrically neutral and stable against beta decay. The calculation is performed for a wide range of baryon density and temperature which are of interest in the astrophysics. The free energy, entropy, proton abundance, etc. of nuclear beta-stable matter are calculated. It is shown that by increasing the temperature, the maximum proton abundance is pushed to the lower density while the maximum itself increases as we increase the temperature.The proton fraction is not enough to see any gas-liquid phase transition. Finally we get an overall agreement with other many-body techniques, which are available only at zero temperature.
Water quality (WQ) plays a crucial role in management of water resources. Water quality index (WQI) is frequently used methods to assess of water quality for drinking purposes. WQI can be predicted using chemical analysis which might not, however, be viable for a longer period in all the country-scale rivers. Thus, in this investigation, two neural-based soft computing techniques-artificial neural network (ANN), generalized neural network (GRNN)- and one hybrid soft computing techniques- adaptive neuro-fuzzy interference system (ANFIS) with four membership function- were used to predict WQI in Khorramabad, Biranshahr and Alashtar sub-watersheds in Iran. Ten distinct physiochemical parameters were used as input variables and WQI as output. Simultaneously, a correlation plot and pairs were used to ascertain the relation of input and output variables. The soft computing techniques were compared using six fitness criteria: NSE, MAE, LMI, RMSE, MAPE, CC. Results indicated that ANN better predicted WQI than did GRNN and ANFIS. Among the different membership functions of ANFIS, ANFIS_trimf was far better than were other membership functions. Thus, it was concluded that ANN was a viable tool for the prediction of WQI.
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