This paper identifies and describes the textual densities of ideational metaphors through the application of GM theory (Halliday, 1994) to the textual analysis of two twentieth century English short stories: one American (The Mansion (1910-11), by Henry Jackson van Dyke Jr.), and one British (Home (1951), by William Somerset Maugham). One aim is to get at textually verifiable statistical evidence that attests to the observed dominance of GM nominalization in academic and scientific texts, rather than to fiction (e.g. Halliday and Martin (1993). Another aim is to explore any significant differentiation in GM’s us by the two short- story writers. The research has been carried out by identifying, describing, and statistically analysing the frequencies of ideational GM structures in both fiction texts to get at their comparative textual densities in terms of word-counts. The obtained results have shown that GM structures – though used in both the American and British short stories – are statistically quite infrequent in both texts, accounting for a tiny (0.0064%) of the total text-wording in T1. against (0.0137%) for T2. Such very low rates of frequency (well below the threshold of even 1% of each text volume) corroborates the previously observed dominance of GM nominalization in academic and scientific texts, rather than in fiction. These same low densities of use does not allow drawing significant inference differentials in GM’s use by the two writers.
Statistical methods of forecasting have applied with the intention of constructing a model to predict the number of the old aged people in retirement homes in Iraq. They were based on the monthly data of old aged people in Baghdad and the governorates except for the Kurdistan region from 2016 to 2019. Using BoxJenkins methodology, the stationarity of the series was examined. The appropriate model order was determined, the parameters were estimated, the significance was tested, adequacy of the model was checked, and then the best model of prediction was used. The best model for forecasting according to criteria of (Normalized BIC, MAPE, RMSE) is ARIMA (0, 1, 2)
KE Sharquie, AA Noaimi, E Abdulqader, WK Al-Janabi, J Dermatol Venereol, 2020 - Cited by 6
The paper examines key aspects of the use of phraseologi-cal units related to colors in Russian culture and speech. It explores their role in shaping cultural identity, reflecting national characteristics and men-tality. The study analyzes the frequency and contexts of the use of color-related phraseological units in contemporary speech, as well as the influ-ence of media and literature on their popularization. The author highlights the significance of phraseological units in preserving cultural heritage and fostering a deeper understanding of language and culture.
Statistical methods of forecasting have applied with the intention of constructing a model to predict the number of the old aged people in retirement homes in Iraq. They were based on the monthly data of old aged people in Baghdad and the governorates except for the Kurdistan region from 2016 to 2019. Using Box-Jenkins methodology, the stationarity of the series was examined. The appropriate model order was determined, the parameters were estimated, the significance was tested, adequacy of the model was checked, and then the best model of prediction was used. The best model for forecasting according to criteria of (Normalized BIC, MAPE, RMSE) is ARIMA (0, 1, 2).
Dried imported blood worms Chironomus reparius was used to motivate the growth of young carp Cyprinus carpio L ., as fish powder was partial and total replaced by blood worms which is a component of the fodder of the common carp fish. Results have shown that blood worm partial replacement treatment surpasses the imported fish powder. Rates of growth motivation of this treatment have been higher than both the control and total replacement processes. Results have shown significant differences in the weight of the fish in the partial replacement of the fish powder by the blood worms.
Summary The aim of this study is the evaluation the resistance of S. marcescence obtained from soil and water to metals chlorides (Zn+2, Hg+2, Fe+2, Al+3, and Pb+2). Four isolates, identified as Serratia marcescence and S. marcescena (S4) were selected for this study according to their resistance to five heavy metals. The ability of S. marcescena (S4) to grow in different concentrations of metals chloride (200-1200 µg/ml) was tested, the highest concentration that S. marcescence (S4) tolerate was 1000 µg/ml for Zn+2, Hg+2, Fe+2, AL+3, pb+2 and 300 µg/ml for Hg+2 through 24 hrs incubation at 37 Co. The effects of temperature and pH on bacteria growth during 72 hrs were also studied. S. marcescence (S4) was affected by ZnCl2, PbCl2, FeC12
... Show MoreCurrently and under the COVID-19 which is considered as a kind of disaster or even any other natural or manmade disasters, this study was confirmed to be important especially when the society is proceeding to recover and reduce the risks of as possible as injuries. These disasters are leading somehow to paralyze the activities of society as what happened in the period of COVID-19, therefore, more efforts were to be focused for the management of disasters in different ways to reduce their risks such as working from distance or planning solutions digitally and send them to the source of control and hence how most countries overcame this stage of disaster (COVID-19) and collapse. Artificial intelligence should be used when there is no practica
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