Simple method for the rapid simultaneous screening of photocatalytic activity over multiple positions of self-cleaning films

An intelligent ink, previously shown to be capable of rapidly assessing photocatalytic activity, was simply applied via a felt-pen onto a commercially available piece of Activ (TM) self-cleaning glass. The ink, comprising of redox dye resazurin and the sacrificial electron donor glycerol within an aqueous hydroxy ethyl cellulose (HEC) polymer media, was photocatalytically degraded in a two-step process. The key initial stage was the photo-reductive conversion of resazurin to resorufin, whereby a colour change from blue to pink occurred. The latter stage was the subsequent photo-reduction of the resorufin, where a slower change from pink to colourless was seen. Red and green components of red-green-blue colour extracted from flat-bed scanner digital images of resazurin ink coated photocatalytic films at intervals during the photocatalysis reaction were inversely proportional to the changes seen via UV-visible absorption spectroscopy and indicative of reaction kinetics. A 3 x 3 grid of intelligent ink was drawn onto a piece of Activ (TM) and a glass blank. The photocatalysis reaction was monitored solely by flat-bed digital scanning. Red-green-blue values of respective positions on the grid were extracted using a custom-built program entitled RGB Extractor (c). The program was capable of extracting a number of 5 x 5 pixel averages of red-green-blue components simultaneously. Allocation of merely three coordinates allowed for the automatic generation of a grid, with scroll-bars controlling the number of positions to be extracted on the grid formed. No significant change in red and green components for any position on the glass blank was observed; however, the Activ (TM) film displayed a homogenous photo-reduction of the dye, reaching maxima in red and minima in green components in 23 +/- 3 and 14 +/- 2 min, respectively. A compositionally graded N-doped titania film synthesised in house via a combinatorial APCVD reaction was also photocatalytically tested by this method where 247 positions on a 13 x 19 grid were simultaneously analysed. The dramatic variation in photocatalysis observed was rapidly quantified for all positions (2-3 hours) allowing for correlations to be made between thicknesses and N : Ti% compositions attained from Swanepoel and WDX analysis, respectively. N incorporation within this system was found to be detrimental to film activity for the photocatalysis reaction of intelligent ink under 365 nm light.

Structural damage identification based on self-fitting ARMAX model and multi-sensor data fusion

Statistical time series methods have proven to be a promising technique in structural health monitoring, since it provides a direct form of data analysis and eliminates the requirement for domain transformation. Latest research in structural health monitoring presents a number of statistical models that have been successfully used to construct quantified models of vibration response signals. Although a majority of these studies present viable results, the aspects of practical implementation, statistical model construction and decision-making procedures are often vaguely defined or omitted from presented work. In this article, a comprehensive methodology is developed, which essentially utilizes an auto-regressive moving average with exogenous input model to create quantified model estimates of experimentally acquired response signals. An iterative self-fitting algorithm is proposed to construct and fit the auto-regressive moving average with exogenous input model, which is capable of integrally finding an optimum set of auto-regressive moving average with exogenous input model parameters. After creating a dataset of quantified response signals, an unlabelled response signal can be identified according to a 'closest-fit' available in the dataset. A unique averaging method is proposed and implemented for multi-sensor data fusion to decrease the margin of error with sensors, thus increasing the reliability of global damage identification. To demonstrate the effectiveness of the developed methodology, a steel frame structure subjected to various bolt-connection damage scenarios is tested. Damage identification results from the experimental study suggest that the proposed methodology can be employed as an efficient and functional damage identification tool. © The Author(s) 2014.

The effects of cocaine self-administration on dendritic spine density in the rat hippocampus are dependent on genetic background

Chronic exposure to cocaine induces modifications to neurons in the brain regions involved in addiction. Hence, we evaluated cocaine-induced changes in the hippocampal CA1 field in Fischer 344 (F344) and Lewis (LEW) rats, 2 strains that have been widely used to study genetic predisposition to drug addiction, by combining intracellular Lucifer yellow injection with confocal microscopy reconstruction of labeled neurons. Specifically, we examined the effects of cocaine self-administration on the structure, size, and branching complexity of the apical dendrites of CA1 pyramidal neurons. In addition, we quantified spine density in the collaterals of the apical dendritic arbors of these neurons. We found differences between these strains in several morphological parameters. For example, CA1 apical dendrites were more branched and complex in LEW than in F344 rats, while the spine density in the collateral dendrites of the apical dendritic arbors was greater in F344 rats. Interestingly, cocaine self-administration in LEW rats augmented the spine density, an effect that was not observed in the F344 strain. These results reveal significant structural differences in CA1 pyramidal cells between these strains and indicate that cocaine self-administration has a distinct effect on neuron morphology in the hippocampus of rats with different genetic backgrounds.

The role of fear of self and responsibility in obsessional doubt processes: a bayesian hierarchical model

The literature concerning obsessive-compulsive disorder (OCD) indicates that obsessions frequently imply negative evaluative beliefs regarding the self. The construct of the feared self has been used to describe the set of harmful attributes an individual worries they may possess. This study aimed to partially replicate previous research that demonstrated a relationship between feared-self beliefs and obsessional doubt in OCD-relevant contexts. The relationship between perceptions of personal responsibility and associated levels of doubt was also examined. Nonclinical participants (N = 221; 155 female; Mage = 26.4, SD = 9.2) were presented with vignettes related to checking and non OCD-relevant themes, which quantified doubt through the presentation of alternating reality-based (i.e., sensory) and possibility-based information. Of the total sample, 112 participants were randomly allocated to a personally relevant condition (in which the action implied in the vignettes was completed by the reader), and 109 were allocated to a second, other-relevant, condition (in which the action implied in the vignettes was completed by a proximal other). The results provided support for reasoning processes implicated in OCD, suggesting that feared-self beliefs may partially contribute to heightened levels of doubt in response to possibility vs. reality-based information in OCD-relevant contexts. Personal relevance contributed to greater baseline levels of doubt, but not to greater responses to the reality- and possibility-based statements accompanying the OCD-relevant vignette. Implications for theory and future research are discussed.

Toward Self-Organising Service Communities

This paper discusses a framework in which catalog service communities are built, linked for interaction, and constantly monitored and adapted over time. A catalog service community (represented as a peer node in a peer-to-peer network) in our system can be viewed as domain specific data integration mediators representing the domain knowledge and the registry information. The query routing among communities is performed to identify a set of data sources that are relevant to answering a given query. The system monitors the interactions between the communities to discover patterns that may lead to restructuring of the network (e.g., irrelevant peers removed, new relationships created, etc.).