In this paper, we introduce the notation of the soft bornological group to solve the problem of boundedness for the soft group. We combine soft set theory with bornology space to produce a new structure which is called soft bornological group. So that both the product and inverse maps are soft bounded. As well as, we study the actions of the soft bornological group on the soft bornological sets. The aim soft bornological set is to partition into orbital classes by acting soft bornological group on the soft bornological set. In addition, we explain the centralizer, normalizer, and stabilizer in details. The main important results are to prove that the product of soft bornological groups is soft bornological group and the action for different elements are the same actions.
SM ADAI, BN RASHID, Journal of Current Researches on Social Sciences, 2023
DBN Rashid, International Journal of Development in Social Sciences and Humanities, 2020
HM Al-Dabbas, RA Azeez, AE Ali, IRAQI JOURNAL OF COMPUTERS, COMMUNICATIONS, CONTROL AND SYSTEMS ENGINEERING, 2023
n this work, the effect of gamma rays on blood thinning drugs was studied using the drug (Aspirin), where gamma rays were spread with the drug using a radioactive source (Co60), and 15,000 grams of Aspirin were placed in the device (gamma chamber 900). The drug was subjected to different irradiation doses (5 KGy, 10 KGy, 15 KGy) and the amount of absorption of the drug was observed in the gamma for different doses and the study of x-rays. After confirming the absorption of the drug to radiation, the effect of the drug on blood thinning was calculated using the rat model and compared with the same drug and the same dose but without exposing the drug to radiation and comparing all results with the control group. The way drugs absorbed radiati
... Show MoreEstimating the semantic similarity between short texts plays an increasingly prominent role in many fields related to text mining and natural language processing applications, especially with the large increase in the volume of textual data that is produced daily. Traditional approaches for calculating the degree of similarity between two texts, based on the words they share, do not perform well with short texts because two similar texts may be written in different terms by employing synonyms. As a result, short texts should be semantically compared. In this paper, a semantic similarity measurement method between texts is presented which combines knowledge-based and corpus-based semantic information to build a semantic network that repre
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