Objectives: To evaluate the families’ attitudes toward environment pollution, and determine the relationship
between families’ attitudes towards environment pollution and their demographic characteristics of age,
education, type of family, and socioeconomic status.
Methodology: A descriptive design is carried throughout the present study to evaluate families’ attitudes toward
environment pollution for the period of October 5th2013 to May 7th2014. A non-probability "purposive" sample of
(110) families’ is selected. The sample is comprised of two groups; (75) urban families’ and (35) rural ones. An
evaluation tool is designed and constructed for the purpose of the study. It is consisted of (4) main parts;
demographic data, attitudes toward environment pollution, attitudes toward producing factors for house
environment pollution, and method of house environment arrangement. Content validity and internal consistency
reliability are determined for the study instrument through a pilot study. Data are collected through the use of the
study instrument and structured interview as means of data collection. Data are analyzed through the application
of descriptive data analysis approach, which includes frequency, percent and mean of scores.
Results: The results of the study indicate that the vast majority of families’ has experienced high level of attitudes
toward environment pollution, producing factors for house environment pollution, and method of house
environment arrangement.
Recommendations: The study recommends that education program by mass media can be forwarded to families’
with regard to issues related to environment pollution in order to increase their awareness, improves their
attitudes, and positively change their behaviors
A common field development task is the object of the present research by specifying the best location of new horizontal re-entry wells within AB unit of South Rumaila Oil Field. One of the key parameters in the success of a new well is the well location in the reservoir, especially when there are several wells are planned to be drilled from the existing wells. This paper demonstrates an application of neural network with reservoir simulation technique as decision tool. A fully trained predictive artificial feed forward neural network (FFNNW) with efficient selection of horizontal re-entry wells location in AB unit has been carried out with maintaining a reasonable accuracy. Sets of available input data were collected from the exploited g
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