Every day, huge numbers of instant tweets (messages) are published on Twitter as it is one of the massive social media for e-learners interactions. The options regarding various interesting topics to be studied are discussed among the learners and teachers through the capture of ideal sources in Twitter. The common sentiment behavior towards these topics is received through the massive number of instant messages about them. In this paper, rather than using the opinion polarity of each message relevant to the topic, authors focus on sentence level opinion classification upon using the unsupervised algorithm named bigram item response theory (BIRT). It differs from the traditional classification and document level classification algorithm. The investigation illustrated in this paper is of threefold which are listed as follows: (1) lexicon based sentiment polarity of tweet messages; (2) the bigram cooccurrence relationship using naïve Bayesian; (3) the bigram item response theory (BIRT) on various topics. It has been proposed that a model using item response theory is constructed for topical classification inference. The performance has been improved remarkably using this bigram item response theory when compared with other supervised algorithms. The experiment has been conducted on a real life dataset containing different set of tweets and topics.

No abstract

No abstract

No abstract

Material selection is one of the key steps in the concept design phase. It may be challenging due to circular dependencies. An optimal choice of material depends on the yet-to-bedetermined geometry and vice versa. Commonly used material selection methods like Ashby's chart (M.F.Ashby 1992) may not be applicable since it considers only one requirement at a time. Using the properties of materials as design variables and solving it as an optimization problem may lead to solutions that are practically infeasible materials. This paper proposes a novel method to select the material and size for a design problem using design space projection. The method involves formulating a mathematical model to evaluate the quantities of interest as a function of materials properties and key geometrical parameters. The quantities of interest are used to determine whether the thermal and mechanical requirements are satisfied. They are used in the method to create projected design spaces with good designs and are used as to tool to evaluate whether a given material and corresponding size would satisfy the requirements. The method is applied to the thermo-structural problem of target wheel design for an ultra-high power x-ray source with multiple requirements. The results indicate that the existing design configuration and materials of common x-ray tube targets could be potentially used for an ultrahigh-power specification of 1.5 MW, with only the size and angular speed being scaled up. The paper also presents results from the application of the method to specific common materials to arrive at the smallest possible sizing of the target wheel. The results produced using the method indicate the theoretical feasibility of an x-ray target wheel design with MW scale power specifications and common materials, for novel applications like microbeam cancer therapy.