CHARACTERIZATION OF FILLING MATERIALS FOR EASEL PAINTINGS

Developing appropriate treatments for easel paintings can be complex, as many works are composed of various materials that respond in different ways. When selecting a filling material for these artworks, several properties are investigated including: the need for the infill to react to environmental conditions in a similar manner as the original material; the need for the infill to have good handling properties, adhesion to the original support, and cohesion within the filling material; the ability for the infill to withstand the stress of the surrounding material and; be as flexible as the original material to not cause further damage. Also, changes in colour or mechanical properties should not occur as part of the ageing process. Studies are needed on acrylic-based materials used as infills in conservation treatments. This research examines some of the chemical, physical, and optical changes of eleven filling materials before and after ageing, with the aim to evaluate the overall appropriateness of these materials as infills for easel paintings. The materials examined were three rabbit skin glue (RSG) gessoes, and seven commercially prepared acrylic materials, all easily acquired in North America. Chemical analysis was carried out with Fourier transform infrared (FTIR) spectroscopy and X-ray fluorescence (XRF), pyrolysis gas chromatography-mass spectroscopy (Py-GC/MS), and differential scanning calorimetry (DSC). Overall the compositions of the various materials examined were found to be in agreement with the available literature and previous research. This study also examined characteristics of these materials not described in previous works and, additionally, presented the compositions and behaviour of several commonly used materials with little literature description. After application of an ageing regimen, most naturally aged and artificially aged samples displayed small changes in gloss, colour, thickness, and diffusive behaviour; however, to evaluate these materials fully mechanical testing and environmental studies should be carried out.

Assisting End-users in Filling Out Web Services

With the quick advance of web service technologies, end-users can conduct various on-line tasks, such as shopping on-line. Usually, end-users compose a set of services to accomplish a task, and need to enter values to services to invoke the composite services. Quite often, users re-visit websites and use services to perform re-occurring tasks. The users are required to enter the same information into various web services to accomplish such re-occurring tasks. However, repetitively typing the same information into services is a tedious job for end-users. It can negatively impact user experience when an end-user needs to type the re-occurring information repetitively into web services. Recent studies have proposed several approaches to help users fill in values to services automatically. However, prior studies mainly suffer the following drawbacks: (1) limited support of collecting and analyzing user inputs; (2) poor accuracy of filling values to services; (3) not designed for service composition. To overcome the aforementioned drawbacks, we need maximize the reuse of previous user inputs across services and end-users. In this thesis, we introduce our approaches that prevent end-users from entering the same information into repetitive on-line tasks. More specifically, we improve the process of filling out services in the following 4 aspects: First, we investigate the characteristics of input parameters. We propose an ontology-based approach to automatically categorize parameters and fill values to the categorized input parameters. Second, we propose a comprehensive framework that leverages user contexts and usage patterns into the process of filling values to services. Third, we propose an approach for maximizing the value propagation among services and end-users by linking a set of semantically related parameters together and similar end-users. Last, we propose a ranking-based framework that ranks a list of previous user inputs for an input parameter to save a user from unnecessary data entries. Our framework learns and analyzes interactions of user inputs and input parameters to rank user inputs for input parameters under different contexts.