Influence of particle morphology on nanostructural feature and conducting property in Sm-doped CeO2 sintered body

Doped ceria (CeO2,) compounds are fluorite type oxides, which show oxide ionic conductivity higher than yttria stabilized zirconia (YSZ), in oxidizing atmospheres. As a consequence of this, considerable interest has been shown in application of these materials for 'low (500-650 degreesC)' or 'intermediate (650-800 degreesC)' temperature operation, solid oxide fuel cells (SOFCs). In this study, the authors prepared two kinds of nanosize Sm-doped CeO2 particles with different morphologies: one type was round and the other was elongated. Processing these powders with different morphology produced dense materials with very different ionic conducting properties and different nanoscale microstructures. Since both particles are very fine and well dispersed, sintered bodies with high density (relative density >95% of theoretical) could be prepared using both types of powder particles. The electrical conductivity of sintered bodies prepared from these powders with different starting morphologies was very different. Materials prepared from particles having a round shape were much higher than those produced using powders with an elongated morphology. Measured activation energies of the corresponding sintered samples showed a similar trend; round particles (60 kJ/mol), elongated particles (74 kJ/mol). While X-ray diffraction (XRD) profiles of these sintered materials were identical, diffuse scatter was observed in the back.-round of selected area electron diffraction pattern recorded from both sintered bodies. This indicated an underlying structure that appeared to have been influenced by the processing technology. Detailed observation using high-resolution transmission electron microscopy (HR-TEM) revealed that the size of microdomain with ordering of cations in the sintered body made from round shape particles was much smaller than that of the sintered body made from elongated particles. Accordingly, it is concluded that the morphology of doped CeO2 powders strongly influenced the microdomain size and electrolytic properties in the doped CeO2 sintered body. (C) 2004 Elsevier B.V. All rights reserved.

Widespread sensory hypersensitivity is a feature of chronic whiplash-associated disorder but not chronic idiopathic neck pain

Objectives: To investigate sensory changes present in patients with chronic whiplash-associated disorders and chronic idiopathic neck pain using a variety of quantitative sensory tests to better understand the pain processing mechanisms underlying persistent symptoms. Methods: A case control study was used with 29 subjects with chronic whiplash-associated disorders, 20 subjects with chronic idiopathic neck pain, and 20 pain-free volunteers. Pressure pain thresholds were measured over the articular pillars of C2-C3, C5-C6, the median, radial, and ulnar nerve trunks in the arm and over a remote site, the muscle belly of tibialis anterior. Heat pain thresholds, cold pain thresholds, and von Frey hair sensibility were measured over the cervical spine, tibialis anterior, and deltoid insertion. Anxiety was measured with the Short-Form of the Spielberger State Anxiety Inventory. Results: Pressure pain thresholds were decreased over cervical spine sites in both subject groups when compared with controls (P < 0.05). In the chronic whiplash-associated disorders group, pressure pain thresholds were also decreased over the tibialis anterior, median, and radial nerve trunks (P < 0.001). Heat pain thresholds were decreased and cold pain thresholds increased at all sites (P < 0.03). No differences in heat pain thresholds or cold pain thresholds were evident in the idiopathic neck pain group at any site compared with the control group (P > 0.27). No abnormalities in von Frey hair sensibility were evident in either neck pain group (P > 0.28). Discussion: Both chronic whiplash-associated disorders and idiopathic neck pain groups were characterized by mechanical hyperalgesia over the cervical spine. Whiplash subjects showed additional widespread hypersensitivity to mechanical pressure and thermal stimuli, which was independent of state anxiety and may represent changes in central pain processing mechanisms. This may have implications for future treatment approaches.

Normalized Gaussian Networks with Mixed Feature Data

With mixed feature data, problems are induced in modeling the gating network of normalized Gaussian (NG) networks as the assumption of multivariate Gaussian becomes invalid. In this paper, we propose an independence model to handle mixed feature data within the framework of NG networks. The method is illustrated using a real example of breast cancer data.

Customer information system data pre-processing with feature selection techniques for non-technical losses prediction in an electricity market

Non-technical losses (NTL) identification and prediction are important tasks for many utilities. Data from customer information system (CIS) can be used for NTL analysis. However, in order to accurately and efficiently perform NTL analysis, the original data from CIS need to be pre-processed before any detailed NTL analysis can be carried out. In this paper, we propose a feature selection based method for CIS data pre-processing in order to extract the most relevant information for further analysis such as clustering and classifications. By removing irrelevant and redundant features, feature selection is an essential step in data mining process in finding optimal subset of features to improve the quality of result by giving faster time processing, higher accuracy and simpler results with fewer features. Detailed feature selection analysis is presented in the paper. Both time-domain and load shape data are compared based on the accuracy, consistency and statistical dependencies between features.

Mutation analysis to verify feature matrices for isolating errors in simulation models

Software simulation models are computer programs that need to be verified and debugged like any other software. In previous work, a method for error isolation in simulation models has been proposed. The method relies on a set of feature matrices that can be used to determine which part of the model implementation is responsible for deviations in the output of the model. Currrently these feature matrices have to be generated by hand from the model implementation, which is a tedious and error-prone task. In this paper, a method based on mutation analysis, as well as prototype tool support for the verification of the manually generated feature matrices is presented. The application of the method and tool to a model for wastewater treatment shows that the feature matrices can be verified effectively using a minimal number of mutants.

Critical feature analysis of a radiotherapy machine

The software implementation of the emergency shutdown feature in a major radiotherapy system was analyzed, using a directed form of code review based on module dependences. Dependences between modules are labelled by particular assumptions; this allows one to trace through the code, and identify those fragments responsible for critical features. An `assumption tree' is constructed in parallel, showing the assumptions which each module makes about others. The root of the assumption tree is the critical feature of interest, and its leaves represent assumptions which, if not valid, might cause the critical feature to fail. The analysis revealed some unexpected assumptions that motivated improvements to the code.