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The political power of Ghurids is recognized during the governance era of Shansabaneyah dynasty. They have ruled from 401 to 612 AH after which their successors ruled in Indian subcontinent for long years. The current study conducts a descriptive-analytical investigation in the field of historical literature in order to assess and evaluate the causes of longevity in political and military power of Ghurids and their political establishment in the eastern lands of the Islamic Caliphate. Results of this study demonstrated that these factors in turn contributed to the long-term sustainability Ghurids. Offensive and defensive power of Ghurids, geographic spread, the relative popularity of Shansabaneyah among Ghurids, financial and economic power of Ghurids, their conversion to Islam, the support from Abbasid Caliphate, the governance of the family, the presence of multiple centers and the circumstances surrounding were among the most important factors.

Background.Chronic kidney disease (CKD) is a covert disease. Accurate prediction of CKD progression over time is necessary for reducing its costs and mortality rates. The present study proposes an adaptive neurofuzzy inference system (ANFIS) for predicting the renal failure timeframe of CKD based on real clinical data.Methods.This study used 10-year clinical records of newly diagnosed CKD patients. The threshold value of 15 cc/kg/min/1.73 m2of glomerular filtration rate (GFR) was used as the marker of renal failure. A Takagi-Sugeno type ANFIS model was used to predict GFR values. Variables of age, sex, weight, underlying diseases, diastolic blood pressure, creatinine, calcium, phosphorus, uric acid, and GFR were initially selected for the predicting model.Results.Weight, diastolic blood pressure, diabetes mellitus as underlying disease, and currentGFR(t)showed significant correlation with GFRs and were selected as the inputs of model. The comparisons of the predicted values with the real data showed that the ANFIS model could accurately estimate GFR variations in all sequential periods (Normalized Mean Absolute Error lower than 5%).Conclusions.Despite the high uncertainties of human body and dynamic nature of CKD progression, our model can accurately predict the GFR variations at long future periods.