To finalize any construction investment project, it would be necessary to identify the most significant problems and obstacles that lead to project reluctance and stalling. Unexpected events and conflicts may have disrupted these strategies and impacted project development. Due to the high initial investment costs of construction projects, crises can have an immediate impact, resulting in significant financial losses. The 2014 financial crisis was one of the most prominent crises that Iraq faced, which prompted the researcher to identify and evaluate those obstacles through this research and questionnaires using Pareto scientific theory to exclude factors that do not contribute to project lag. It was discovered that 28 o

Database is characterized as an arrangement of data that is sorted out and disseminated in a way that allows the client to get to the data being put away in a simple and more helpful way. However, in the era of big-data the traditional methods of data analytics may not be able to manage and process the large amount of data. In order to develop an efficient way of handling big-data, this work studies the use of Map-Reduce technique to handle big-data distributed on the cloud. This approach was evaluated using Hadoop server and applied on EEG Big-data as a case study. The proposed approach showed clear enhancement for managing and processing the EEG Big-data with average of 50% reduction on response time. The obtained results provide EEG r

Adsorption studies were performed at different initial Tetracycline (TC) and Amoxicillin (AMO) concentration, different biomass dosage and type, contact time, agitation speed, and initial pH.  In the batch mode were investigated. The optimum pH of solutions is 6.5 for TC and 5 for AMO, agitation speed 200 rpm and concentration 50 ppm. The results in FTIR showed that there were -OH and amides (N-H) and other functional groups on the surface of Cladophora and Spirulina algae. The equilibrium isotherm data were modeled with Freundlich, Temkin, and Langmuir isotherm models. The data best fitted with the Langmuir model. The maximal adsorption capacity from the Langmuir model was (9.86, 20. 5 mg/g) for TC and (7.89, 17.4 mg/g) for AMO on

BN Rashid