Technique for Producing Mullite and Other Mixed-Oxide Systems

Mixed-oxide systems are of interest in a variety of technical ceramic applications. The key in many systems is to achieve a high degree of homogeneity, approaching the molecular level. A technique for producing mixed oxides via direct oxidation of mixtures of organometallic compounds is discussed as a potential route. Preliminary work performed on producing mullite is discussed.     

Bioprocess applications of model-based estimation techniques

The last decade has seen the development of a number of approaches for estimating those variables which are difficult to measure on-line in industrial process situations. Whilst a range of techniques is available, a common element is the use of process knowledge in the form of a system model. In the case of bioprocess systems, although a large range of models has been presented in the literature, their use in estimation schemes on an industrial scale has been limited. A number of reasons can be identified for their low level of utilisation. Of particular significance is the uncertainty which exists in quantifying system performance and the process-model mismatch which inevitably results. The level of ‘pre-defined model’ uncertainty, together with the knowledge gained during the course of the fermentation, serves to dictate estimator structure. The paper considers a range of estimation strategies and contrasts, through industrial applications, their performance characteristics and utility.     

Cognitive categorization and quality of performance ratings.

The effects of cognitive categorization of raters on accuracy, leniency, and halo of performance evaluations were investigated in a field setting. One hundered seventy-four subordinates evaluated the performance of their managers on three performance dimensions. Managers were categorized as congruent or incongruent based on subordinates' perceptions of the extent to which the manager's behavior met the subordinates' expectations. The results indicated that the quality of ratings assigned by subordinates was related to the cognitive categories used. As hypothesized, ratings of managers who were categorized as congruent were found to be more accurate and also to contain more leniency and halo tendency than the ratings of managers who were categorized as incongruent. Implications of these findings for performance-appraisal research are discussed. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Photodegradation of ethylene–propylene copolymer and ethylene–propylene–ethylidenenorbornene terpolymer

Significant progress has been made in recent years regarding the photooxidation of olefin copolymers, but questions still remain. This paper reviews the progress and probes the photooxidative chemistry of ethylene–propylene (EP) and ethylene/propylene/diene monomer (EPDM) copolymers. Both stabilized and unstabilized polymer plaques were irradiated in a xenon are and the surface chemistry followed using infra-red spectroscopy. Model compounds were used to help elucidate the chemistry caused by unique structural features present in the copolymers. Volatile products evolved during photooxidation were determined giving valuable insight into the degradation chemistry.     

Inductive transfer with context-sensitive neural networks

Context-sensitive Multiple Task Learning, or csMTL, is presented as a method of inductive transfer which uses a single output neural network and additional contextual inputs for learning multiple tasks. Motivated by problems with the application of MTL networks to machine lifelong learning systems, csMTL encoding of multiple task examples was developed and found to improve predictive performance. As evidence, the csMTL method is tested on seven task domains and shown to produce hypotheses for primary tasks that are often better than standard MTL hypotheses when learning in the presence of related and unrelated tasks. We argue that the reason for this performance improvement is a reduction in the number of effective free parameters in the csMTL network brought about by the shared output node and weight update constraints due to the context inputs. An examination of IDT and SVM models developed from csMTL encoded data provides initial evidence that this improvement is not shared across all machine learning models.     

Computing linear approximations to nonlinear neuronal response

We present an approach to obtain nonlinear information about neuronal response by computing multiple linear approximations. By calculating local linear approximations centered around particular stimuli, one can obtain insight into stimulus features that drive the response of highly nonlinear neurons, such as neurons highly selective to a small set of stimuli. We implement this approach based on stimulus-spike correlation (i.e., reverse correlation or spike-triggered average) methods. We illustrate the benefits of these linear approximations with a simplified two-dimensional model and a model of an auditory neuron that is highly selective to particular features of a song.     

BallotMaps: detecting name bias in alphabetically ordered ballot papers

The relationship between candidates' position on a ballot paper and vote rank is explored in the case of 5000 candidates for the UK 2010 local government elections in the Greater London area. This design study uses hierarchical spatially arranged graphics to represent two locations that affect candidates at very different scales: the geographical areas for which they seek election and the spatial location of their names on the ballot paper. This approach allows the effect of position bias to be assessed; that is, the degree to which the position of a candidate's name on the ballot paper influences the number of votes received by the candidate, and whether this varies geographically. Results show that position bias was significant enough to influence rank order of candidates, and in the case of many marginal electoral wards, to influence who was elected to government. Position bias was observed most strongly for Liberal Democrat candidates but present for all major political parties. Visual analysis of classification of candidate names by ethnicity suggests that this too had an effect on votes received by candidates, in some cases overcoming alphabetic name bias. The results found contradict some earlier research suggesting that alphabetic name bias was not sufficiently significant to affect electoral outcome and add new evidence for the geographic and ethnicity influences on voting behaviour. The visual approach proposed here can be applied to a wider range of electoral data and the patterns identified and hypotheses derived from them could have significant implications for the design of ballot papers and the conduct of fair elections.     

Managing empowerment and control in an intranet environment

Abstract. An intranet increases in sophistication and complexity as it evolves. This evolution leads to an increasing need for control over the intranet. However, this is a contentious issue, as an intranet is deemed to be an empowering technology. Consequently, intranet control systems must balance empowerment and control so as not to negate each other. This paper investigates intranet control activities and their effect on users' perceptions of empowerment throughout the evolution of an intranet in Hewlett Packard (Ireland). The growth of the intranet is charted as a six-stage model that illustrates an evolution of purpose, control and empowerment. The control strategies for managing the intranet implemented at each stage are investigated, and their resultant effects on empowerment are evaluated. The study reveals the importance of balancing control strategies with empowerment initiatives in managing intranet environments. Based on the evidence available, the study recommends the implementation of specific controls at particular stages in the evolution of an intranet in order to achieve control systems that balance empowerment and control.     

Visual analysis of social networks in space and time using smartphone logs

We designed and applied interactive visualisation techniques for investigating how social networks are embedded in time and space, using data collected from smartphone logs. Our interest in spatial aspects of social networks is that they may reveal associations between participants missed by simply making contact through smartphone devices. Four linked and co-ordinated views of spatial, temporal, individual and social network aspects of the data, along with demographic and attitudinal variables, helped add context to the behaviours we observed. Using these techniques, we were able to characterise spatial and temporal aspects of participants’ social networks and suggest explanations for some of them. This provides some validation of our techniques.Unexpected deficiencies in the data that became apparent prompted us to evaluate the dataset in more detail. Contrary to what we expected, we found significant gaps in participant records, particularly in terms of location, a poorly connected sample of participants and asymmetries in reciprocal call logs. Although the data captured are of high quality, deficiencies such as these remain and are likely to have a significant impact on interpretations relating to spatial aspects of the social network. We argue that appropriately-designed interactive visualisation techniques–afforded by our flexible prototyping approach–are effective in identifying and characterising data inconsistencies. Such deficiencies are likely to exist in other similar datasets, and although the visual approaches we discuss for identifying data problems may not be scalable, the categories of problems we identify may be used to inform attempts to systematically account for errors in larger smartphone datasets.     

Collective Organization Behaviors of Multi-Cell Systems Induced by Engineered ECM-Cell Mechanical Coupling

Cells in vivo are surrounded by fibrous extracellular matrix (ECM), which can mediate the propagation of active cellular forces through stressed fiber bundles and regulate various biological processes. However, the mechanisms for multi-cellular organization and collective dynamics induced by cell-ECM mechanical couplings, which are crucial for the development of novel ECM-based biomaterial for cell manipulation and biomechanical applications, remain poorly understood. Herein, the authors design an in vitro quasi-3D experimental system and demonstrate a transition between spreading and aggregating in collective organizational behaviors of discrete multi-cellular systems, induced by engineered ECM-cell mechanical coupling, with the observed phenomena and underlying mechanisms differing fundamentally from those of cell monolayers. During the process of collective cell organization, the collagen substrate undergoes reconstruction into a dense fiber network structure, which is correlated with local cellular density and consistent with observed enhanced cells' motility; and the weakening of fiber bundle formation within the hydrogel reduces cells’ movement. Moreover, cells can respond to the curvature and shape of the original cell population and form different aggregation patterns. These results elucidate important physical factors involved in collective cell organization and provide important references for potential applications of biomaterials in new therapies and tissue engineering.