Objective: A descriptive design, using the methodological approach, is carried throughout the present
study from April 1st 2012 to May 20th 2013 to construct the school physical environment standardized
features tool.
Methodology: An instrument of (141) item is constructed for the purpose of the study. A purposive
sample of (44) school; (22) public and (22) private ones is selected. Content Validity of the instrument is
determined through the use of panel of (11) expert who are specialists in Community Health Nursing and
Community Medicine. Internal consistency reliability, using the split-half technique, is employed through
the computation of Cronbach alpha correlation coefficient of (0.93) for internal scale. Data were collected
through the use of the instrument and the schools' visits as means of data collection. Data are analyzed
through the application of the inferential statistical data analysis procedure of simple Pearson’s
correlation coefficient and factor analysis (principle component) method.
Results: Findings of the study reveal that the features are presented, post their rearrangement, under five
factors that include school services, emergency and school sanitation, food and protection services, safe
school environment, and school environment. So, the new tool can be structured, tested and used as
guide for new investigations. Such presentation of factors reflects the actual model by which the school's
physical environment features can be considered as essential elements for future evaluation through the
utilization of the constructed tool.
Recommendations: The study recommends that the new discovered tool can be used as measure for
future work, and further studies can be carried out on large sample size and nation-wide base.
Some of the main challenges in developing an effective network-based intrusion detection system (IDS) include analyzing large network traffic volumes and realizing the decision boundaries between normal and abnormal behaviors. Deploying feature selection together with efficient classifiers in the detection system can overcome these problems. Feature selection finds the most relevant features, thus reduces the dimensionality and complexity to analyze the network traffic. Moreover, using the most relevant features to build the predictive model, reduces the complexity of the developed model, thus reducing the building classifier model time and consequently improves the detection performance. In this study, two different sets of select
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In this study, the effect of location of construction joints on the performance of reinforced concrete structural elements is experimentally investigated.
Nineteen beam specimens with dimensions of 200×200×950 mm were tested. The variables investigated are the location of the construction joints
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