It is not often easy to identify a certain group of words as a lexical bundle, since the same set of words can be, in different situations, recognized as idiom, a collocation, a lexical phrase or a lexical bundle. That is, there are many cases where the overlap among the four types is plausible. Thus, it is important to extract the most identifiable and distinguishable characteristics with which a certain group of words, under certain conditions, can be recognized as a lexical bundle, and this is the task of this paper.
As the word can fluctuate between central and peripheral dlalten it represents the first lexical meaning and the second represents the meaning that gain by the context in which it appear so this research came to show signs that can be acquired the root ( GUM ) and its derivatives from the contexts in which it is mentioned in the Koran as well as siqnificant Lexical if this research is divided on two first is eating Lexical semantics , the second handled connotations in the Koran was based on what the commentators and concemed with the meanings of the Koran and the owners Almagamat in astatement that the semantic .
Words in a language do not exist in isolation but in close connection with each other ,teaming up in one way or another known to the Russian semasiology M. M. Pokrovsky , one of the first to realize the systematic nature of the lexicon, wrote about the second half of the nineteenth century : „the Words and their meanings do not live separate from each other life, but are joined together in our minds), regardless of our consciousness to different groups , and the basis for grouping is the similarity or direct contrast in the main value.
Samples of gasoline engine oil (SAE 5W20) that had been exposed to various oxidation times were inspected with a UV-Visible (UV-Vis) spectrophotometer to select the best wavelengths and wavelength ranges for distinguishing oxidation times. Engine oil samples were subjected to different thermal oxidation periods of 0, 24, 48, 72, 96, 120, and 144 hours, resulting in a range of total base number (TBN) levels. Each wavelength (190.5 – 849.5 nm) and selected wavelength ranges were evaluated to determine the wavelength or wavelength ranges that could best distinguish among all oxidation times. The best wavelengths and wavelength ranges were analyzed with linear regression to determine the best wavelength or range to predict oxidation t
... Show MoreWorldwide, there is an increased reliance on COVID-19-related health messages to curb the COVID-19 outbreak. Therefore, it is vital to provide a well-prepared and authentic translation of English-language messages to reach culturally and linguistically diverse audiences. However, few studies, if any, focus on how non-English-speaking readers receive and linguistically accept the lexical choices in the messages translated into their language. The present study tested a sample of translated Arabic COVID-19-related texts that were obtained from the World Health Organization and Australian New South Wales Health websites. This study investigated to that extent Arabic readers would receive translated COVID-19 health messages and whether the t
... Show MoreMillions of lives might be saved if stained tissues could be detected quickly. Image classification algorithms may be used to detect the shape of cancerous cells, which is crucial in determining the severity of the disease. With the rapid advancement of digital technology, digital images now play a critical role in the current day, with rapid applications in the medical and visualization fields. Tissue segmentation in whole-slide photographs is a crucial task in digital pathology, as it is necessary for fast and accurate computer-aided diagnoses. When a tissue picture is stained with eosin and hematoxylin, precise tissue segmentation is especially important for a successful diagnosis. This kind of staining aids pathologists in disti
... Show MoreThe last few years witnessed great and increasing use in the field of medical image analysis. These tools helped the Radiologists and Doctors to consult while making a particular diagnosis. In this study, we used the relationship between statistical measurements, computer vision, and medical images, along with a logistic regression model to extract breast cancer imaging features. These features were used to tell the difference between the shape of a mass (Fibroid vs. Fatty) by looking at the regions of interest (ROI) of the mass. The final fit of the logistic regression model showed that the most important variables that clearly affect breast cancer shape images are Skewness, Kurtosis, Center of mass, and Angle, with an AUCROC of
... Show More