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The purpose of this paper is to study a new method with which multi‐walled carbon nanotubes (MWNTs) can be dispersed and aligned in low density polyethylene (LDPE) for improving its mechanical properties.

Dispersion and alignment of MWNTs in LDPE matrix are enhanced by ultrasonic vibration, solution casting and melt mixing and flow moulding method. The properties of the composite are characterised using scanning electron microscopy, tensile testing machine and the Izod impact testing machine.

It is found that MWNTs in LDPE achieve some dispersion and alignment resulting in improvement in LDPE's strength and toughness.

Polymer/CNTs nanocomposites are expected to have good process ability of the polymers and high mechanical and functional properties of the CNTs. Enhancing dispersion and alignment of MWNTs in the polymer matrix will promote and expand the applications and development of polymer/MWNTs nanocomposites.

The method that enhances MWNTs dispersion and alignment in LDPE matrix provides a new way for alignment of other CNTs in polymer matrix.

We make use of the Volunteered Geographic Information (VGI) data to extract the total extent of the roads using remote sensing images. VGI data is often provided only as vector data represented by lines and not as full extent. Also, high geolocation accuracy is not guaranteed and it is common to observe misalignment with the target road segments by several pixels on the images. In this work, we use the prior information provided by the VGI and extract the full road extent even if there is significant mis-registration between the VGI and the image. The method consists of image segmentation and traversal of multiple agents along available VGI information. First, we perform image segmentation, and then we traverse through the fragmented road segments using autonomous agents to obtain a complete road map in a semi-automatic way once the seed-points are defined. The road center-line in the VGI guides the process and allows us to discover and extract the full extent of the road network based on the image data. The results demonstrate the validity and good performance of the proposed method for road extraction that reflects the actual road width despite the presence of disturbances such as shadows, cars and trees which shows the efficiency of the fusion of the VGI and satellite images.