Predicting Delta Ferrite Content in Stainless Steel Castings

The distribution of delta ferrite fraction was measured with the magnetic method in specimens of different stainless steel compositions cast by the investment casting (lost wax) process. Ferrite fraction measurements published in the literature for stainless steel cast samples were added to the present work data, enabling an extensive analysis about practical methods to calculate delta ferrite fractions in stainless steel castings. Nineteen different versions of practical methods were formed using Schaeffler, DeLong, and Siewert diagrams and the nickel and chromium equivalent indexes suggested by several authors. These methods were evaluated by a detailed statistical analysis, showing that the Siewert diagram, including its equivalent indexes and iso-ferrite lines, gives the lowest relative errors between calculated and measured delta ferrite fractions. Although originally created for stainless steel welds, this diagram gives relative errors lower than those for the current ASTM standard method (800/A 800M-01), developed to predict ferrite fractions in stainless steel castings. Practical methods originated from a combination of different chromium/nickel equivalent indexes and the iso-ferrite lines from Schaeffler diagram give the lowest relative errors when compared with combinations using other iso-ferrite line diagrams. For the samples cast in the present work, an increase in cooling rate from 0.78 to 2.7 K/s caused a decrease in the delta ferrite fraction, but a statistical hypothesis test revealed that this effect is significant in only 50% of the samples that have ferrite in their microstructures.

Towards a hybrid approach for adapting web graphical user interfaces to heterogeneous devices using context

Ubiquitous Computing promises seamless access to a wide range of applications and Internet based services from anywhere, at anytime, and using any device. In this scenario, new challenges for the practice of software development arise: Applications and services must keep a coherent behavior, a proper appearance, and must adapt to a plenty of contextual usage requirements and hardware aspects. Especially, due to its interactive nature, the interface content of Web applications must adapt to a large diversity of devices and contexts. In order to overcome such obstacles, this work introduces an innovative methodology for content adaptation of Web 2.0 interfaces. The basis of our work is to combine static adaption - the implementation of static Web interfaces; and dynamic adaptation - the alteration, during execution time, of static interfaces so as for adapting to different contexts of use. In hybrid fashion, our methodology benefits from the advantages of both adaptation strategies - static and dynamic. In this line, we designed and implemented UbiCon, a framework over which we tested our concepts through a case study and through a development experiment. Our results show that the hybrid methodology over UbiCon leads to broader and more accessible interfaces, and to faster and less costly software development. We believe that the UbiCon hybrid methodology can foster more efficient and accurate interface engineering in the industry and in the academy.

Discrimination of media moments and media intervals: sticker-based watch-and-comment annotation

In this paper we discuss the problem of how to discriminate moments of interest on videos or live broadcast shows. The primary contribution is a system which allows users to personalize their programs with previously created media stickers-pieces of content that may be temporarily attached to the original video. We present the system's architecture and implementation, which offer users operators to transparently annotate videos while watching them. We offered a soccer fan the opportunity to add stickers to the video while watching a live match: the user reported both enjoying and being comfortable using the stickers during the match-relevant results even though the experience was not fully representative.

PRoSPer: perceptual similarity queries in medical CBIR systems through user profiles

In this paper, we present a novel approach to perform similarity queries over medical images, maintaining the semantics of a given query posted by the user. Content-based image retrieval systems relying on relevance feedback techniques usually request the users to label relevant/irrelevant images. Thus, we present a highly effective strategy to survey user profiles, taking advantage of such labeling to implicitly gather the user perceptual similarity. The profiles maintain the settings desired for each user, allowing tuning of the similarity assessment, which encompasses the dynamic change of the distance function employed through an interactive process. Experiments on medical images show that the method is effective and can improve the decision making process during analysis.