In this paper we present the first ever measured experimental electron momentum density of Cu2Sb at an intermediate resolution (0.6 a.u.) using 59.54 keV 241Am Compton spectrometer. The measurements are compared with the theoretical Compton profiles using density function theory (DFT) within a linear combination of an atomic orbitals (LCAO) method. In DFT calculation, Perdew-Burke-Ernzerhof (PBE) scheme is employed to treat correlation whereas exchange is included by following the Becke scheme. It is seen that various approximations within LCAO-DFT show relatively better agreement with the experimental Compton data. Ionic model calculations for a number of configurations (Cu+x/2)2(Sb-x) (0.0≤x≤2.0) are also performed utilizing free a

This research include the designation of newly instrument (Turbidmeter) depending on using photo voltaic detector (8.5mm.*8.5mm.).These dimensions have large area which increases the scattering rays with a variable intensity. The properties of this design are local mode and the used tools are a available in the local markets as well as its less cost light weight system. It is worth mentioning that the possibility of its application in many fields such as: Clinical, Laboratory, Industrial and Fuel fields. This designation, applied to estimate Barium Sulphate in turbidity method. The analytical results show high accuracy and repetition, also the linearity ranges from (4-180) ppm. At the detection limit (0.05) ppm. With correlation coefficient

This study aims to study argumentation in political debates by figuring out the logical fallacies employed in the debates of Clinton and Trump, the presidential nominees of the 2016 elections, and Biden and Trump, the leading contenders in the 2020 United States presidential election. The study attempts to answer the questions: (1) What relevance fallacies are adopted in the debate between Trump and Clinton? (2) What rhetorical devices are used to influence the audience and gain voters besides fallacies in the debates selected? The study analyses two texts from two arguments using Damer's (2009) taxonomy of relevance fallacy and rhetorical devices based on Perrine’s (1969) model of communication and interpersonal rhetoric to answe

Changing oil-wet surfaces toward higher water wettability is of key importance in subsurface engineering applications. This includes petroleum recovery from fractured limestone reservoirs, which are typically mixed or oil-wet, resulting in poor productivity as conventional waterflooding techniques are inefficient. A wettability change toward more water-wet would significantly improve oil displacement efficiency, and thus productivity. Another area where such a wettability shift would be highly beneficial is carbon geo-sequestration, where compressed CO2 is pumped underground for storage. It has recently been identified that more water-wet formations can store more CO2. We thus examined how silica based nanofluids can induce such a wettabil

This research suggests a robust and systematic way for Arabic Sentiment Analysis using a vast dataset of 66,666 text reviews. One of the main advantages of this study is that the dataset was perfectly balanced (33,333 positive samples and 33,333 negative samples). In machine learning, this 50/50 split is important because it eliminates class bias and enables the predictive model to treat both sentiment classes equally. As shown in the values of the metrics — overall accuracy, weighted precision, weighted recall, and F1 score — there is great similarity among them, indicating a stable and reliable assessment of the model's real potential throughout the Arabic dataset. Based on data profile, the average word count per review is 42.3

The speech recognition system has been widely used by many researchers using different
methods to fulfill a fast and accurate system. Speech signal recognition is a typical
classification problem, which generally includes two main parts: feature extraction and
classification. In this paper, a new approach to achieve speech recognition task is proposed by
using transformation techniques for feature extraction methods; namely, slantlet transform
(SLT), discrete wavelet transforms (DWT) type Daubechies Db1 and Db4. Furthermore, a
modified artificial neural network (ANN) with dynamic time warping (DTW) algorithm is
developed to train a speech recognition system to be used for classification and recognition
purposes. T