The dynamic development of computer and software technology in recent years was accompanied by the expansion and widespread implementation of artificial intelligence (AI) based methods in many aspects of human life. A prominent field where rapid progress was observed are high‐throughput methods in biology that generate big amounts of data that need to be processed and analyzed. Therefore, AI methods are more and more applied in the biomedical field, among others for RNA‐protein binding sites prediction, DNA sequence function prediction, protein‐protein interaction prediction, or biomedical image classification. Stem cells are widely used in biomedical research, e.g., leukemia or other disease studies. Our proposed approach of

It is well known that drilling fluid is a key parameter for optimizing drilling operations, cleaning the hole, and managing the rig hydraulics and margins of surge and swab pressures. Although the experimental works represent valid and reliable results, they are expensive and time consuming. In contrast, continuous and regular determination of the rheological fluid properties can perform its essential functions during good construction. The aim of this study is to develop empirical models to estimate the drilling mud rheological properties of water-based fluids with less need for lab measurements. This study provides two predictive techniques, multiple regression analysis and artificial neural networks, to determine the rheological

Objective Advantageous properties of silicone elastomer made it one of the favorable materials in maxillofacial prosthesis construction, but these properties may change after months of usage or after pigments addition. This study aimed to define the optimum concentration for a mixture of two types of intrinsic pigments that added to VST-50 maxillofacial silicone material and study their effects on mechanical properties before and after artificial aging. Methods After the pilot study was conducted, 0.1% by weight of rayon flocking and 0. 2% by weight of burnt sienna intrinsic pigment concentration was selected because of improvement in tested mechanical properties of VST-50 maxillofacial silicone. A total of one hundred and eighty samples we

With the fast-growing of neural machine translation (NMT), there is still a lack of insight into the performance of these models on semantically and culturally rich texts, especially between linguistically distant languages like Arabic and English. In this paper, we investigate the performance of two state-of-the-art AI translation systems (ChatGPT, DeepSeek) when translating Arabic texts to English in three different genres: journalistic, literary, and technical. The study utilizes a mixed-method evaluation methodology based on a balanced corpus of 60 Arabic source texts from the three genres. Objective measures, including BLEU and TER, and subjective evaluations from human translators were employed to determine the semantic, contextual an

The adsorption behavior of Bismarck brown (BB) dye from aqueous solutions onto graphene oxide GO and graphene oxide-g-poly (n-butyl methacrylate-co-methacrylic acid) GO-g-pBCM as adsorbents was investigated. The prepared GO and GO-g-pBCM were characterized by Fourier transform infrared spectroscopy FTIR, which confirmed the compositions of the prepared adsorbents. Adsorption of BB dye onto GO and GO-g-pBCM was explored in a series of batch experiments under various conditions. The data were examined utilizing Langmuir and Freundlich isotherms. The Langmuir isotherm was seen as increasingly reasonable from the experimental information of dye on formulating adsorbents. Kinetic investigations showed that the experimental data were fitted ve

The distribution of the expanded exponentiated power function EEPF with four parameters, was presented by the exponentiated expanded method using the expanded distribution of the power function, This method is characterized by obtaining a new distribution belonging to the exponential family, as we obtained the survival rate and failure rate function for this distribution, Some mathematical properties were found, then we used the developed least squares method to estimate the parameters using the genetic algorithm, and a Monte Carlo simulation study was conducted to evaluate the performance of estimations of possibility using the Genetic algorithm GA.

Reservoir permeability plays a crucial role in characterizing reservoirs and predicting the present and future production of hydrocarbon reservoirs. Data logging is a good tool for assessing the entire oil well section's continuous permeability curve. Nuclear magnetic resonance logging measurements are minimally influenced by lithology and offer significant benefits in interpreting permeability. The Schlumberger-Doll-Research model utilizes nuclear magnetic resonance logging, which accurately estimates permeability values. The approach of this investigation is to apply artificial neural networks and core data to predict permeability in wells without a nuclear magnetic resonance log. The Schlumberger-Doll-Research permeability is use