The correlation between stacking fault energy and the work hardening behaviour of high-mn twinning induced plasticity steel tested at various temperatures

High-Mn Twinning Induced Plasticity (TWIP) steels have superior mechanical properties, which make them promising materials in automotive industry to improve the passenger safety and the fuel consumption. The TWIP steels are characterized by high work hardening rates due to continuous mechanical twin formation during the deformation. Mechanical twinning is a unique deformation mode, which is highly governed by the stacking fault energy (SFE). The composition of steel alloy was Fe-18Mn-0.6C-1Al (wt.%) with SFE of about 25-30 mJ/m2 at room temperature. The SFE ensures the mechanical twinning to be the main deformation mechanism at room temperature. The microstructure, mechanical properties, work hardening behaviour and SFE of the steel was studied at the temperature range of ambient ≤T[°C]≤ 400°C. The mechanical properties were determined using Instron tensile testing machine with 30kN load cell and strain rate of 10-3s-1 and the work hardening behaviour curves were generated using true stress and true strain data. The microstructure after deformation at different temperatures was examined using Zeiss Supra 55VP SEM. It was found that an increase in the deformation temperature raised the SFE resulting in the deterioration of the mechanical twinning that led to decrease not only in the strength but also in the total strain of the steel. A correlation between the temperature, the SFE, the mechanical twinning, the mechanical properties and the work hardening rate was also found. © (2014) Trans Tech Publications, Switzerland.

A two-stage dynamic group decision making method for processing ordinal information

In group decision making (GDM) problems, ordinal data provide a convenient way of articulating preferences from decision makers (DMs). A number of GDM models have been proposed to aggregate such kind of preferences in the literature. However, most of the GDM models that handle ordinal preferences suffer from two drawbacks: (1) it is difficult for the GDM models to manage conflicting opinions, especially with a large number of DMs; and (2) the relationships between the preferences provided by the DMs are neglected, and all DMs are assumed to be of equal importance, therefore causing the aggregated collective preference not an ideal representative of the group's decision. In order to overcome these problems, a two-stage dynamic group decision making method for aggregating ordinal preferences is proposed in this paper. The method consists of two main processes: (i) a data cleansing process, which aims to reduce the influence of conflicting opinions pertaining to the collective decision prior to the aggregation process; as such an effective solution for undertaking large-scale GDM problems is formulated; and (ii) a support degree oriented consensus-reaching process, where the collective preference is aggregated by using the Power Average (PA) operator; as such, the relationships of the arguments being aggregated are taken into consideration (i.e., allowing the values being aggregated to support each other). A new support function for the PA operator to deal with ordinal information is defined based on the dominance-based rough set approach. The proposed GDM model is compared with the models presented by Herrera-Viedma et al. An application related to controlling the degradation of the hydrographic basin of a river in Brazil is evaluated. The results demonstrate the usefulness of the proposed method in handling GDM problems with ordinal information.

Ordinal random fields for recommender systems

Recommender Systems heavily rely on numerical preferences, whereas the importance of ordinal preferences has only been recognised in recent works of Ordinal Matrix Factorisation (OMF). Although the OMF can effectively exploit ordinal properties, it captures only the higher-order interactions among users and items, without considering the localised interactions properly. This paper employs Markov Random Fields (MRF) to investigate the localised interactions, and proposes a unified model called Ordinal Random Fields (ORF) to take advantages of both the representational power of the MRF and the ease of modelling ordinal preferences by the OMF. Experimental result on public datasets demonstrates that the proposed ORF model can capture both types of interactions, resulting in improved recommendation accuracy.

Testing slope homogeneity in large panels with serial correlation

Pesaran and Yamagata (Pesaran, M.H., Yamagata, T., Testing slope homogeneity in large panels, Journal of Econometrics 142, 50-93, 2008) propose a test for slope homogeneity in large panels, which has become very popular in the literature. However, the test cannot deal with the practically relevant case of heteroskedastic and/serially correlated errors. The present note proposes a generalized test that accommodates both features. © 2013 Elsevier B.V.