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The "actor network theory" approach in dealing with landscapes in historical centers
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The historical center's landscape suffers from neglect, despite their importance and broad capabilities in enhancing the cultural value of the historical center, as landscape includes many heterogeneous human and non-human components, material and immaterial, natural and manufactured, also different historical layers, ancient, modern and contemporary. Due to the difference in these components and layers, it has become difficult for the designer to deal with it. Therefore, the research was directed by following a methodology of actor-network theory as it deals with such a complex system and concerned with an advanced method to connect the various components of considering landscape as a ground that can include various elements and deal with them to produce all homogenized to enhance the value of the historic center.  

         The research problem represented by the lack of a comprehensive presentation of the effective network theory approach in dealing with landscapes, so the goal of the research became, to build a comprehensive framework for the effective network theory approach in dealing with the historical center landscape, which is represented by two components, actors and the concept of networking actors.

           Finally, the research has taken the landscape of AlQushla building to illustrate how the actors and the concept of networking actors was been achieved, and who they worked to enhance the cultural value of the historical center.

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Publication Date
Sat Dec 01 2012
Journal Name
Journal Of Economics And Administrative Sciences
Comparing Several Nonlinear Estimators for Regression Function
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The aim of this paper is to estimate a nonlinear regression function of the Export of the crude oil Saudi (in Million Barrels) as a function of the number of discovered fields.

 Through studying the behavior of the data we show that its behavior was not followed a linear pattern or can put it in a known form so far there was no possibility to see a general trend resulting from such exports.

We use different nonlinear estimators to estimate a regression function, Local linear estimator, Semi-parametric as well as an artificial neural network estimator (ANN).

The results proved that the (ANN) estimator is the best nonlinear estimator am

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