Abstract: In this study, the different approaches currently followed by design codes to assess the stability of buildings utilizing concrete moment resisting frames structural system are evaluated. For such purpose, a parametric study was performed. It involved analyzing group of concrete moment resisting frames having different slenderness ratios (height/width ratios), designed for different lateral loads to vertical loads ratios and constructed using ordinary reinforced concrete and high strength concrete for stability check and overall buckling using code approaches and computer buckling analysis. The objectives were to examine the influence of such parameters that directly linked to frames’ lateral stiffness on the buildings’ stability and evaluates the code approach in view of buckling analysis results. Based on this study, it was concluded that, the most susceptible buildings to instability and magnification of second order effects are buildings having high aspect ratios (height/width ratio), having low lateral to vertical loads ratio and utilizing construction materials of high strength. In addition, the study showed that the instability limits imposed by codes are mainly mathematical to ensure reliable analysis not a physical ones and that they are in general conservative. Also, it has been shown that the upper limit set by one of the codes that second order moment for structural elements should be limited to 1.4 the first order moment is not justified, instead, the overall story check is more reliable.
Abstract: Social networking sites such as Twitter and Facebook
attracts over 500 million users across the world, for those users, their
social life, even their practical life, has become interrelated. Their
interaction with social networking has affected their life forever.
Accordingly, social networking sites have become among the main
channels that are responsible for vast dissemination of different kinds
of information during real time events. This popularity in Social
networking has led to different problems including the possibility of
exposing incorrect information to their users through fake accounts
which results to the spread of malicious content during life events.
This situation can result to a huge damage in the real world to the
society in general including citizens, business entities, and others. In this paper, we present a classification method for detecting the
fake accounts on Twitter. The study determines the minimized set of
the main factors that influence the detection of the fake accounts on
Twitter, and then the determined factors are applied using different
classification techniques. A comparison of the results of these
techniques has been performed and the most accurate algorithm is
selected according to the accuracy of the results. The study has been
compared with different recent researches in the same area; this
comparison has proved the accuracy of the proposed study. We claim
that this study can be continuously applied on Twitter social network
to automatically detect the fake accounts; moreover, the study can be
applied on different social network sites such as Facebook with minor
changes according to the nature of the social network which are
discussed in this paper.