Reliable estimation of critical parameters such as hydrocarbon pore volume, water saturation, and recovery factor are essential for accurate reserve assessment. The inherent uncertainties associated with these parameters encompass a reasonable range of estimated recoverable volumes for single accumulations or projects. Incorporating this uncertainty range allows for a comprehensive understanding of potential outcomes and associated risks. In this study, we focus on the oil field located in the northern part of Iraq and employ a Monte Carlo based petrophysical uncertainty modeling approach. This method systematically considers various sources of error and utilizes effective interpretation techniques. Leveraging the current state of a

The study aimed to prepare a nanocapsules formulation from the acetonic extract of Moringa oleifera leaves, using polymeric capsules, and test its toxicity against the third instar larvae of Culex pipiens mosquitoes. The leaf extract was prepared using acetone as a solvent, and the nano polymeric capsules were prepared using the synthetic polymer polyethylene glycol 4000. The results showed the successful preparation of nano polymeric capsules from the leaf extract, with an average particle size of 259.2 nm, and a nanocapsule diameter of 263.83 nm, as determined by DLS and SEM analysis, respectively. The toxicity results indicated that the nano polymeric capsules of the leaf extract exhibited higher mortality rates, reaching 97.6% a

The term ‘photometry’ refers to the accurate determination of the apparent brightness of an astronomical object. Until roughly 1980, nearly all astronomical photometry was done by means of analog measurements of photographic plates, or by analog or digital (photon-counting) techniques with photomultipliers. These photometers produced brightness readings which were typically displayed on dials, plotted on strip charts or printed on strips of paper, and it was often quite practical to analyse these raw data with pencil, paper and a slide rule or table of logarithms. However, during the late 1970s electronic area detectors for astronomy became more advanced: first, for a brief period, television-type cameras were employed, but these were s

Due to its importance in physics and applied mathematics, the non-linear Sturm-Liouville problems
witnessed massive attention since 1960. A powerful Mathematical technique called the Newton-Kantorovich
method is applied in this work to one of the non-linear Sturm-Liouville problems. To the best of the authors’
knowledge, this technique of Newton-Kantorovich has never been applied before to solve the non-linear
Sturm-Liouville problems under consideration. Accordingly, the purpose of this work is to show that this
important specific kind of non-linear Sturm-Liouville differential equations problems can be solved by
applying the well-known Newton-Kantorovich method. Also, to show the efficiency of appl

Diesel engine oil was subjected to thermal oxidization (TO) for six periods of time (0 h, 24 h, 48 h, 72 h, 96 h, and 120 h) and was subsequently characterized by terahertz time domain spectroscopy (THz-TDS). The THz refractive index generally increased with oxidation time. The measurement method illustrated the potential of THz-TDS when a fixed setup with a single cuvette is used. A future miniaturized setup installed in an engine would be an example of a fixed setup. For the refractive index, there were highly significant differences among the oxidation times across most of the 0.3–1.7 THz range.

Image compression is a serious issue in computer storage and transmission,  that simply makes efficient use of redundancy embedded within an image itself; in addition, it may exploit human vision or perception limitations to reduce the imperceivable information Polynomial coding is a modern image compression technique based on modelling concept to remove the spatial redundancy embedded within the image effectively that composed of two parts, the  mathematical model and the residual. In this paper, two stages proposed technqies adopted, that starts by utilizing the lossy predictor model along with multiresolution base and thresholding techniques corresponding to first stage. Latter by incorporating the near lossless com

Image classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven class