Gumbel distribution was dealt with great care by researchers and statisticians. There are traditional methods to estimate two parameters of Gumbel distribution known as Maximum Likelihood, the Method of Moments and recently the method of re-sampling called (Jackknife). However, these methods suffer from some mathematical difficulties in solving them analytically. Accordingly, there are other non-traditional methods, like the principle of the nearest neighbors, used in computer science especially, artificial intelligence algorithms, including the genetic algorithm, the artificial neural network algorithm, and others that may to be classified as meta-heuristic methods. Moreover, this principle of nearest neighbors has useful statistical featu

 Data Driven Requirement Engineering (DDRE) represents a vision for a shift from the static traditional methods of doing requirements engineering to dynamic data-driven user-centered methods. Data available and the increasingly complex requirements of system software whose functions can adapt to changing needs to gain the trust of its users, an approach is needed in a continuous software engineering process. This need drives the emergence of new challenges in the discipline of requirements engineering to meet the required changes. The problem in this study was the method in data discrepancies which resulted in the needs elicitation process being hampered and in the end software development found discrepancies and could not meet the need

ObjectivePlatanus (plane tree) flowers are rich in condensed tannins (CTs), which have been reported to possess various bioactive properties. However, their specific anti-cancer effects on triple-negative breast cancer cells remain poorly characterized. This study aimed to evaluate the dose-dependent effects of purified Platanus flower CTs on cellular proliferation, migration, and the expression of key malignant-related genes (PCNA and MMP9) in MDA-MB-231 breast cancer cells. Materials and methodsMDA-MB-231 cells were treated with serial concentrations of purified Platanus flower CTs (1.25, 2.5, and 5 mg/mL). Cellular proliferation was assessed by calculating doubling time using time-lapse microscopy and ImageJ single-cell tracking. Migrati

Advanced strategies for production forecasting, operational optimization, and decision-making enhancement have been employed through reservoir management and machine learning (ML) techniques. A hybrid model is established to predict future gas output in a gas reservoir through historical production data, including reservoir pressure, cumulative gas production, and cumulative water production for 67 months. The procedure starts with data preprocessing and applies seasonal exponential smoothing (SES) to capture seasonality and trends in production data, while an Artificial Neural Network (ANN) captures complicated spatiotemporal connections. The history replication in the models is quantified for accuracy through metric keys such as m