No abstract

Gasoline is a petroleum-derived liquid that is used primarily as a fuel in internal combustion engines (ICE), particularly spark ignition Otto Engine. Gasoline is a blend of hydrocarbons with some contaminants, including sulfur, nitrogen, oxygen, and certain metals. The four major constituent groups of gasoline are olefins, aromatics, paraffins, and napthenes. Octane number (ON) is measure of the ignition quality or flammability of gasoline. The ONs are Research Octane Number (RON) and Motor Octane Number (MON). RON is measured relative to a mixture of isooctane and n-heptane. Antiknock Index (AKI) is a measure of a fuel's ability to resist engine knock or octane quality. The AKI is an arithmetic average of RON and MON. The ON decreases with an increase chain length in the hydrocarbon molecule. The ONs increase with carbon chain branching. Another way of increasing the ON is used gasoline octane boosters as additives, such as tetraethyl lead (TEL), methyl tertiary-butyl ether (MTBE), and ferrocene. Aromatic alcohols, ethanol, and methanol also increase the ON of gasoline. The advantage to adding oxygenates, such as MTBE, methanol, and ethanol, to gasoline is that they cause very little pollution when they burn and are cleaner fuels.

One of the key activities of any client is contractor selection. Without a suitable and precise method for selecting the best contractor, the completion of a project will likely be affected. In this study, we examine the use of the analytical hierarchy process (AHP) as a decision-support model for contractor selection. This model can assist project management teams in identifying contractors who are most likely to deliver satisfactory outcomes in a selection process that is not based simply on the lowest bid. In this study, an AHP-based model is tested using a hypothetical scenario in which candidate contractors are evaluated. Six criteria for the primary objective are evaluated. The criteria used for contractor selection in the model are identified, and the significance of each criterion is determined using a questionnaire. Comparisons are made by ranking the aggregate score of each candidate based on each criterion, and the candidate with the highest score is deemed the best. This study contributes to the construction sector in two ways: first, it extends the understanding of selection criteria to include degrees of importance, and second, it implements a multi-criteria AHP approach, which is a new method for analyzing and selecting the best contractor.

No abstract

Abstract-Images / Videos are major source of content on the internet and the content is increasing rapidly due to the advancement in this area. Image analysis and retrieval is one of the active research field and researchers from the last decade have proposed many efficient approaches for the same. Semantic technologies like ontology offers promising approach to image retrieval as it tries to map the low level image features to high level ontology concepts. In this paper, we have proposed Semantic Image Retrieval: An Ontology based Approach which uses domain specific ontology for image retrieval relevant to the user query. The user can give concept / keyword as text input or can input the image itself. Semantic Image Retrieval is based on hybrid approach and uses shape, color and texture based approaches for classification purpose. Mammals domain is used as a test case and its ontology is developed. The proposed system is trained on Mammals dataset and tested on large number of test cases related to this domain. Experimental results show the efficiency / accuracy of the proposed system and support the implementation of the same.