The aim of this work was to establish the deformation behavior of two vanadium mic-roalloyed medium carbon steels with different contents of carbon and titanium by tensile testing at 77 K. Samples were reheated at 1250 C for 30 min and continuously cooled in still air. Beside acicular ferrite as the dominant morphology in both microstructures, the steel with lower content of carbon and negligible amount of titanium contains consi-derable fraction of grain boundary ferrite and pearlite. It was found that Ti-free steel exhibits a higher strain hardening rate and significantly lower elongation at 77 K than the fully acicular ferrite steel. The difference in tensile behavior at 77 K of the two steels has been associated with the influence of the pearlite, together with higher dislocation density of acicular ferrite.

 In this paper, English language will be used as an instance of natural languages as we will be concerned with the set of all natural language texts. this research tries to employ a set of all synonyms as a way to hide secret message inside a natural language text. 

The aim of this work was to determine TTT diagram of medium carbon V-N micro-alloyed steel with emphasis on the

development of intragranular ferrite morphologies. The isothermal treatment was carried out at 350, 400, 450, 500, 550

and 600 οC. These treatments were interrupted at different times in order to analyze the evolution of the microstructure.

Metallographic evaluation was done using optical and scanning electron microscopy (SEM). The results show that at high

temperatures (≥ 500 ○C) polygonal intragranulary nucleated ferrite idiomorphs, combined with grain boundary ferrite and

pearlite were produced and followed by an incomplete transformation phenomenon. At intermediate temperatures (450, 500

○C) an interloced acicular ferrite (AF) microstructure is produced, and at low temperatures (400, 350 ○C) the sheave of

parallel acicular ferrite plates, similar to bainitic sheaves but intragranularly nucleated were observed. In addition to sheaf

type acicular ferrite, the grain boundary nucleated bainitic sheaves are observed.

This paper presents empirical methods for enhancing the accuracy of inductive learning systems. It addresses the problems of: learning propositional production rules in multi-class classification tasks in noisy domains, maintaining continuous learning when confronted with new situation after the initial learning phase is completed, and classifying an object when no rule is satisfied for it.

It is shown that interleaving the learning and performance-evaluation process allows accurate classifications to be made on real-world data sets. The paper presents the system ARIS which implements this approach, and it is shown that the resulting classifications are often more accurate than those made by the non-refined knowledge bases.

The core design decision that lies behind ARIS is that it employs an ordering of the rules according to their weight. A rule’s weight is learned by using Bayes’ theorem to calculate weights for the rule’s conditions and combining them. This model focuses the analyses of the knowledge base and assists the refinement process significantly.

The system is non-interactive, it relies on heuristics to focus the refinement on those experiments that appear to be most consistent with the refinement data set. The design framework of ARIS consists of tabular model for expressing rule weights, and the relationship between refinement cases and the rules satisfied for each case to focus the refinement process. The system has been used to refine knowledge bases created by ARIS itself, as well as to refine knowledge bases created by the RIPPER and C4.5 systems in ten selected domains.

Problem statement: In the construction industry, success of building projects greatly depends on the client’s performance. The performance of clients is crucial since decisions made will influence the overall project performance. Typically, clients are represented by managerial and technical representatives during the design process. They play major roles in conveying required information to the design team regarding their future project. The quality of clients’ representatives has potential influence on design team performance. In designing green buildings client’s attributes are critical factors to high performance of building projects. Attributes such as knowledge and experience on green buildings, commitment level to green and clients’ ability to participate and manage design process are the key factors to produce green design. The propose of this study to identify current performance level of clients of building projects and establish key clients’ attributes influencing green design performance. Approach: To achieve mentioned aim a questionnaire survey was conducted to collect dada required. A sample of 274 respondents has been covered under the study, including architects and engineers practicing design and consultancy building sectors. Prior to analysis of data WINSTEPS software were used for Rasch modeling to determine validity and reliability of date. Descriptive analysis data includes quantitative and qualitative. Results and Conclusion: Client qualities in general were low, client communication with design team; client involvement and ability to coordinate design process were moderate. However, Knowledge and experience on green …

The polymerization mechanism of methylol-functional benzoxazine monomers is reported using a series of monofunctional benzoxazine monomers synthesized via a condensation reaction of ortho-, meta-, or para-methylol–phenol, aniline, and paraformaldehyde following the traditional route of benzoxazine synthesis. A phenol/aniline-type monofunctional benzoxazine monomer has been synthesized as a control. The structures of the synthesized monomers have been confirmed by ¹H NMR and FT-IR. The polymerization behavior of methylol monomers is studied by DSC and shows an exothermic peak associated with condensation reaction of methylol groups and ring-opening polymerization of benzoxazine at a lower temperature range than the control monomer. The presence of methylol group accelerates the ring-opening polymerization to give the ascending order of para-, meta-, and ortho-positions in …

Fuzzy decision forests aim to improve the predictive power of single fuzzy decision trees by allowing multiple views of the same domain to be modelled. Such forests have been successfully created for classification problems where the outcome field is discrete; however predicting a continuous output value is more challenging in combining the output from multiple fuzzy decision trees. This paper presents a new approach to creating fuzzy regression tree forests based upon the induction of multiple fuzzy regression decision trees from one training sample, where each tree will represent a different view of the data domain. The singular fuzzy regression trees are induced using a proven algorithm known as Elgasir which fuzzifies crisp CHAID decision trees using trapezoidal membership functions for fuzzification and applies Takagi-Sugeno inference to obtain the final predicted values. A modified version of Artificial Immune System Network model (opt-aiNet) is then used for the simultaneous optimization of the membership functions across all trees within the forest. A strength of the proposed method is that data does not require fuzzification before forest induction this reducing pre-processing time and the need for subjective human experts. Five problem sets from the UCI repository and KEEL repository are used to evaluate the approach. The experimental results have shown that fuzzy regression tree forests reduce the error rate compared with single fuzzy regression trees.