Visualizing the core-periphery distinction in theory domains

As specific parts of a theory are refined over time, the aggregated set of variables and associations of multiple theory instances provide the identity of a theory domain. This research applies a meta-theoretical analysis to the problem of theory identity and the core-periphery distinction. The theoretico-empirical network for quantitative publications over a 20 year span of two top Information Systems journals is analysed and visualized to illustrate these aspects of theory. The analysis provides insight into the density of research in specific theory domains, the verisimilitude and explanatory ubiquity of core versus peripheral postulates, and suggests opportunities for increasing explanatory depth and integration in select theory domains.

An evolutionary-based similarity reasoning scheme for monotonic multi-input fuzzy inference systems

In this paper, an Evolutionary-based Similarity Reasoning (ESR) scheme for preserving the monotonicity property of the multi-input Fuzzy Inference System (FIS) is proposed. Similarity reasoning (SR) is a useful solution for undertaking the incomplete rule base problem in FIS modeling. However, SR may not be a direct solution to designing monotonic multi-input FIS models, owing to the difficulty in getting a set of monotonically-ordered conclusions. The proposed ESR scheme, which is a synthesis of evolutionary computing, sufficient conditions, and SR, provides a useful solution to modeling and preserving the monotonicity property of multi-input FIS models. A case study on Failure Mode and Effect Analysis (FMEA) is used to demonstrate the effectiveness of the proposed ESR scheme in undertaking real world problems that require the monotonicity property of FIS models.

Rule learning and extraction using a hybrid neural network : a case study on fault detection and diagnosis

A hybrid network, based on the integration of Fuzzy ARTMAP (FAM) and the Rectangular Basis Function Network (RecBFN), is proposed for rule learning and extraction problems. The underlying idea for such integration is that FAM operates as a classifier to cluster data samples based on similarity, while the RecBFN acts as a “compressor” to extract and refine knowledge learned by the trained FAM network. The hybrid network is capable of classifying data samples incrementally as well as of acquiring rules directly from data samples for explaining its predictions. To evaluate the effectiveness of the hybrid network, it is applied to a fault detection and diagnosis task by using a set of real sensor data collected from a Circulating Water (CW) system in a power generation plant. The rules extracted from the network are analyzed and discussed, and are found to be in agreement with experts’ opinions used in maintaining the CW system.

Application of fuzzy inference techniques to FMEA

In traditional Failure Mode and Effect Analysis (FMEA), the Risk Priority Number (RPN) ranking system is used to evaluate the risk level of failures, to rank failures, and to prioritize actions. This approach is simple but it suffers from several weaknesses. In an attempt to overcome the weaknesses associated with the traditional RPN ranking system, several fuzzy inference techniques for RPN determination are investigated in this paper. A generic Fuzzy RPN approach is described, and its performance is evaluated using a case study relating to a semiconductor manufacturing process. In addition, enhancements for the fuzzy RPN approach are proposed by refining the weights of the fuzzy production rules.

The electrochemical surface forces apparatus : the effect of surface roughness, electrostatic surface potentials, and anodic oxide growth on interaction forces, and friction between dissimilar surfaces in aqueous solutions

We present a newly designed electrochemical surface forces apparatus (EC-SFA) that allows control and measurement of surface potentials and interfacial electrochemical reactions with simultaneous measurement of normal interaction forces (with nN resolution), friction forces (with μN resolution), and distances (with Å resolution) between apposing surfaces. We describe three applications of the developed EC-SFA and discuss the wide-range of potential other applications. In particular, we describe measurements of (1) force–distance profiles between smooth and rough gold surfaces and apposing self-assembled monolayer-covered smooth mica surfaces; (2) the effective changing thickness of anodically growing oxide layers with Å-accuracy on rough and smooth surfaces; and (3) friction forces evolving at a metal–ceramic contact, all as a function of the applied electrochemical potential. Interaction forces between atomically smooth surfaces are well-described using DLVO theory and the Hogg–Healy–Fuerstenau approximation for electric double layer interactions between dissimilar surfaces, which unintuitively predicts the possibility of attractive double layer forces between dissimilar surfaces whose surface potentials have similar sign, and repulsive forces between surfaces whose surface potentials have opposite sign. Surface roughness of the gold electrodes leads to an additional exponentially repulsive force in the force–distance profiles that is qualitatively well described by an extended DLVO model that includes repulsive hydration and steric forces. Comparing the measured thickness of the anodic gold oxide layer and the charge consumed for generating this layer allowed the identification of its chemical structure as a hydrated Au(OH)3 phase formed at the gold surface at high positive potentials. The EC-SFA allows, for the first time, one to look at complex long-term transient effects of dynamic processes (e.g., relaxation times), which are also reflected in friction forces while tuning electrochemical surface potentials.

V2O5·nH2O crystalline nanosheets : hydrothermal fabrication and structure evolution

V2O5·nH2O nanosheets are fabricated hydrothermally with the acidified peroxovanadate solution at 200 °C for 12 h. The X-ray diffraction suggests that V2O5·nH2O nanosheets display lamellar ordering along c-axis direction. Transmission electron microscopy, field-emission scanning electron microscopy, and selected area electron diffraction indicate that V2O5·nH2O nanosheets are very thin in thickness and micron-sized in lateral dimension, and they are two-dimensional crystallites. X-ray photoelectron spectroscopy and thermogravimetric analysis are utilized to confirm the elemental composition of nanosheets. The formation process of nanosheets is also discussed in terms of time- and temperature-controlled experiments.

Comparative study on MoO3 and HxMoO3 nanobelts : structure and electric transport

In this study, the suspension of MoO3 nanobelts was first prepared in a hydrothermal way from Mo powders and H2O2 solution, which could be transformed into the suspension of HxMoO3 nanobelts under an acidic condition using N2H4 ·H2O as the reducing agent. Three paper-form samples made from MoO3 and HxMoO3 nanobelts (low or high hydrogen content) were then fabricated via a vacuum filtration method, followed by their structural comparative analysis such as FESEM, XRD, Raman spectra, and XPS, etc. The measurement of electric resistances at room temperature shows that the conductance of HxMoO3 nanobelts is greatly improved because of hydrogen doping. The temperature-dependent resistances of HxMoO3 nanobelts agree with the exponential correlation, supporting that the conducting carriers are the quasi-free electrons released from Mo5+. In addition, the formation process of HxMoO3 nanobelts from MoO3 nanobelts is also discussed.

A new dempster-shafer theory-based method with fuzzy targets for fuzzy sets ranking

In this paper, a new Fuzzy Set (FS) ranking method (for type-1 and interval type-2 FSs), which is based on the Dempster-Shafer Theory (DST) of evidence with fuzzy targets, is investigated. Fuzzy targets are adopted to reflect human viewpoints on fuzzy ranking. Two important measures in DST, i.e., the belief and plausibility measures, are used to rank FSs. The proposed approach is evaluated with several benchmark examples. The use of the belief and plausibility measures in fuzzy ranking are discussed and compared. We further analyze the capability of the proposed approach in fulfilling six reasonable fuzzy ordering properties as discussed in [9]-[11].

Enhancing an evolving tree-based text document visualization model with fuzzy c-Means clustering

An improved evolving model, i.e., Evolving Tree (ETree) with Fuzzy c-Means (FCM), is proposed for undertaking text document visualization problems in this study. ETree forms a hierarchical tree structure in which nodes (i.e., trunks) are allowed to grow and split into child nodes (i.e., leaves), and each node represents a cluster of documents. However, ETree adopts a relatively simple approach to split its nodes. Thus, FCM is adopted as an alternative to perform node splitting in ETree. An experimental study using articles from a flagship conference of Universiti Malaysia Sarawak (UNIMAS), i.e., Engineering Conference (ENCON), is conducted. The experimental results are analyzed and discussed, and the outcome shows that the proposed ETree-FCM model is effective for undertaking text document clustering and visualization problems.