Jordan University of Science and Technology

About: Jordan University of Science and Technology is a education organization based out in Irbid, Irbid, Jordan. It is known for research contribution in the topics: Population & Medicine. The organization has 7582 authors who have published 13166 publications receiving 298158 citations. The organization is also known as: JUST.

Size Distribution and Mineral Nutrients of Soybean Seeds in Response to Drought Stress

Abstract: Accumulation of mineral nutrients in soybean seeds grown under drought stress may have a role in drought tolerance. Because drought stress reduces dry matter accumulation and seed size, drought stress may increase the concentration of mineral nutrients in soybean seeds and may decrease the content of nutrient per seed. The objective was to study the effect of drought on seed size distribution and whether different drought-stress patterns increase mineral nutrient concentration in soybean seeds similar in size. Two experiments were conducted, one in the greenhouse and one in the field. In the greenhouse experiment, three-drought stress treatments were imposed on soybean plants at beginning seed fill (R5) for 23–26 days: well-watered (WW), sudden severe stress (SS), and gradual stress (GS) imposed prior to severe stress. In the field experiment, irrigation and nonirrigation (rainfed) conditions were imposed on soybean plants at R5 for the entire seed filling period. Drought stress decreased seed si...

Radiographic evaluation of mandibular third molar eruption space.

Abstract: Objective. The purpose of this study was to investigate variables in third molar and arch dimensions among subjects with impacted and erupted mandibular third molars. Study Design. Standardized panoramic radiographs were taken for 134 subjects (60 males and 74 females with 213 third molars) with an average age of 19.8 years. For the impacted group, only those who had mesioangular inclination of the third molars were recorded. The radiographic features studied were angulation of tooth, level of eruption, development of root, mesiodistal crown width, retromolar space, and gonial angle. Results. Third molar angulation in the impacted group (69 subjects with 101 third molars) averaged 16.28 degrees (SD ± 11.88 degrees), with no significant gender differences. The retromolar space was significantly smaller in the impacted group than in the erupted group (P < .001). This space was also the only significant variable (P < .05) associated with lateral asymmetry in the eruption/impaction status of third molars. In the impacted group, 83% of subjects had third molar space/crown width ratios less than 1, whereas in the erupted group 69% had ratios of at least 1. The average space/crown width ratios in the impacted and erupted groups were 0.78 and 1.06, respectively (P < .001). The impacted and erupted groups did not differ significantly with respect to mesiodistal crown width and gonial angle. Approximately 76% of impacted third molars had complete root formation, 20% had two thirds root formation, and only 4% displayed one third root formation. Of the impacted third molars, 66% were partially erupted and the rest were unerupted. There was a strong positive relationship between frequency of third molar impaction and degree of angulation (P < .001), but the former was inversely associated with retromolar space (P < .001). Conclusions. The most significant variable associated with third molar impaction is inadequate space. The vast majority of impacted third molars had space/crown width ratios of less than 1. (Oral Surg Oral Med Oral Pathol Oral Radiol Endod 1999;88:285-91)

Enhancing Aspect-Based Sentiment Analysis of Arabic Hotels’ reviews using morphological, syntactic and semantic features

Abstract: This research presents an enhanced approach for Aspect-Based Sentiment Analysis (ABSA) of Hotels’ Arabic reviews using supervised machine learning. The proposed approach employs a state-of-the-art research of training a set of classifiers with morphological, syntactic, and semantic features to address the research tasks namely: (a) T1:Aspect Category Identification, (b) T2:Opinion Target Expression (OTE) Extraction, and (c) T3: Sentiment Polarity Identification. Employed classifiers include Naive Bayes, Bayes Networks, Decision Tree, K-Nearest Neighbor (K-NN), and Support-Vector Machine (SVM).The approach was evaluated using a reference dataset based on Semantic Evaluation 2016 workshop (SemEval-2016: Task-5). Results show that the supervised learning approach outperforms related work evaluated using the same dataset. More precisely, evaluation results show that all classifiers in the proposed approach outperform the baseline approach, and the overall enhancement for the best performing classifier (SVM) is around 53% for T1, around 59% for T2, and around 19% in T3.