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Arabic lacks a specific form for progressive tenses and instead uses the imperfective form ‘jafʕal’ to express habitual and progressive aspects. Arabic also uses an active participle form (AP) to express progressiveness. This paper addresses the effect of transitivity, futurity, and aspectuality on the translation of English present progressive (PP) into Arabic verbal and active participle counterparts. To investigate which of the two forms is used to translate English PP into Arabic, data were collected from 100 students who were studying an elective ‘translation’ course at Princess Sumaya University for Technology (PSUT). The researchers built a questionnaire of 38 English sentences each of which has two main translations: one that uses the imperfective form ‘ja-fʕal’ and another that has an (AP) form, mainly ‘fa:ʕil’ or ‘mu-fʕil’. The participants were asked to rate the acceptability of each sentence on a scale of 0-2. The findings reveal that transitivity and the future reading of the progressive verb affect the translatability of the progressive tenses as imperfective or (AP) form. Transitive verbs are more likely to be translated as imperfective verbs than transitive APs because (AP) does not have as strong verbal properties as lexical verbs. On the other hand, translocative verbs accept (AP) translations fairly enough to refer to future. The findings also reveal that the aspectuality of the verb affects its translation in one of the two main forms mentioned above. (AP) translations of English (PP) become more acceptable when the root of the verb indicates state-of-affair actions, achievements or accomplishments.

Construction sector is considered as one of the most important and rapidly growing sectors that form the basis of the economy in United Arab Emirates. Lots of construction projects in United Arab Emirates are experiencing cost overrun due to many factors, which in some cases leads to shutting down of construction companies and delay projects for several years. The need of a statistical analysis of the factors causing cost overrun and to create a clear image of how the industry professionals view and understand the problem are highly crucial. Using a selected set of 38 factors classified into five categories (cost estimating factors, construction parties factors, field factors, financing factors and construction items factors), these factors will be evaluated by the main two parties in any construction project, the contractor and the consultant. this research first identifies the main factors leading to cost overrun in UAE construction industry and then evaluate these factors from the contractor's and the consultant's perspective using a questionnaire survey that has been formed previously. Finally, a relationship between cost overrun percentage and factors will be established to create prediction models to evaluate the impact of these factors on cost overrun. Factor analysis and regression modeling are used to examine the effect of these factors on the cost overrun.

We consider the problem of micro-aggregation in secure statistical databases, by enhancing the primitive Micro-Aggregation Technique (MAT), which integrates proximity information. This problem is known to be NP-hard and has been tackled using many heuristic solutions. In this paper, we report a new two-stage multi-variate micro-aggregation technique so-called Enhanced Genetic Multi-variate Micro-Aggregation Technique (EGMMAT). It utilizes a genetic algorithm to aggregate the multi-variate micro-data. In the first stage, the micro-data file is split into a number of various domes under the flavour of Maximum Distance Average Vector (MDAV). Each dome has pre-defined size set by administrator. It is worth mentioning that the genetic operations (crossover & mutation) are continuously applied on each dome separately until the convergences condition is satisfied. The second stage of EGMMAT starts with combining all sub-groups in all domes into one huge dome, then the refinement process is achieved by applying the genetic operations in all micro-data to enhance the final results. Our experimental results, which are done on the benchmark data sets for real-life data, demonstrate that the newly proposed method is superior to the state-of-the-art by as much as 8.5%.