An investigation of the stress-buffering effects of social support in the occupational stress process as a function of team identification

This study explored how the social context influences the stress-buffering effects of social support on employee adjustment. It was anticipated that the positive relationship between support from colleagues and employee adjustment would be more marked for those strongly identifying with their work team. Furthermore, as part of a three-way interactive effect, it was predicted that high identification would increase the efficacy of coworker support as a buffer of two role stressors (role overload and role ambiguity). One hundred and 55 employees recruited from first-year psychology courses enrolled at two Australian universities were surveyed. Hierarchical multiple regression analyses revealed that the negative main effect of role ambiguity on job satisfaction was significant for those employees with low levels of team identification, whereas high team identifiers were buffered from the deleterious effect of role ambiguity on job satisfaction. There also was a significant interaction between coworker support and team identification. The positive effect of coworker support on job satisfaction was significant for high team identifiers, whereas coworker support was not a source of satisfaction for those employees with low levels of team identification. A three-way interaction emerged among the focal variables in the prediction of psychological well-being, suggesting that the combined benefits of coworker support and team identification under conditions of high demand may be limited and are more likely to be observed when demands are low.

Acoustic identification of 12 species of echolocating bat by discriminant function analysis and artificial neural networks

We recorded echolocation calls from 14 sympatric species of bat in Britain. Once digitised, one temporal and four spectral features were measured from each call. The frequency-time course of each call was approximated by fitting eight mathematical functions, and the goodness of fit, represented by the mean-squared error, was calculated. Measurements were taken using an automated process that extracted a single call from background noise and measured all variables without intervention. Two species of Rhinolophus were easily identified from call duration and spectral measurements. For the remaining 12 species, discriminant function analysis and multilayer back-propagation perceptrons were used to classify calls to species level. Analyses were carried out with and without the inclusion of curve-fitting data to evaluate its usefulness in distinguishing among species. Discriminant function analysis achieved an overall correct classification rate of 79% with curve-fitting data included, while an artificial neural network achieved 87%. The removal of curve-fitting data improved the performance of the discriminant function analysis by 2 %, while the performance of a perceptron decreased by 2 %. However, an increase in correct identification rates when curve-fitting information was included was not found for all species. The use of a hierarchical classification system, whereby calls were first classified to genus level and then to species level, had little effect on correct classification rates by discriminant function analysis but did improve rates achieved by perceptrons. This is the first published study to use artificial neural networks to classify the echolocation calls of bats to species level. Our findings are discussed in terms of recent advances in recording and analysis technologies, and are related to factors causing convergence and divergence of echolocation call design in bats.

Acoustic identification of twelve species of echolocating bat by discriminant function analysis and artificial neural networks

We recorded echolocation calls from 14 sympatric species of bat in Britain. Once digitised, one temporal and four spectral features were measured from each call. The frequency-time course of each call was approximated by fitting eight mathematical functions, and the goodness of fit, represented by the mean-squared error, was calculated. Measurements were taken using an automated process that extracted a single call from background noise and measured all variables without intervention. Two species of Rhinolophus were easily identified from call duration and spectral measurements. For the remaining 12 species, discriminant function analysis and multilayer back-propagation perceptrons were used to classify calls to species level. Analyses were carried out with and without the inclusion of curve-fitting data to evaluate its usefulness in distinguishing among species. Discriminant function analysis achieved an overall correct classification rate of 79% with curve-fitting data included, while an artificial neural network achieved 87%. The removal of curve-fitting data improved the performance of the discriminant function analysis by 2 %, while the performance of a perceptron decreased by 2 %. However, an increase in correct identification rates when curve-fitting information was included was not found for all species. The use of a hierarchical classification system, whereby calls were first classified to genus level and then to species level, had little effect on correct classification rates by discriminant function analysis but did improve rates achieved by perceptrons. This is the first published study to use artificial neural networks to classify the echolocation calls of bats to species level. Our findings are discussed in terms of recent advances in recording and analysis technologies, and are related to factors causing convergence and divergence of echolocation call design in bats.

Frequency response function based damage identification using principal component analysis and pattern recognition technique

Pattern recognition is a promising approach for the identification of structural damage using measured dynamic data. Much of the research on pattern recognition has employed artificial neural networks (ANNs) and genetic algorithms as systematic ways of matching pattern features. The selection of a damage-sensitive and noise-insensitive pattern feature is important for all structural damage identification methods. Accordingly, a neural networks-based damage detection method using frequency response function (FRF) data is presented in this paper. This method can effectively consider uncertainties of measured data from which training patterns are generated. The proposed method reduces the dimension of the initial FRF data and transforms it into new damage indices and employs an ANN method for the actual damage localization and quantification using recognized damage patterns from the algorithm. In civil engineering applications, the measurement of dynamic response under field conditions always contains noise components from environmental factors. In order to evaluate the performance of the proposed strategy with noise polluted data, noise contaminated measurements are also introduced to the proposed algorithm. ANNs with optimal architecture give minimum training and testing errors and provide precise damage detection results. In order to maximize damage detection results, the optimal architecture of ANN is identified by defining the number of hidden layers and the number of neurons per hidden layer by a trial and error method. In real testing, the number of measurement points and the measurement locations to obtain the structure response are critical for damage detection. Therefore, optimal sensor placement to improve damage identification is also investigated herein. A finite element model of a two storey framed structure is used to train the neural network. It shows accurate performance and gives low error with simulated and noise-contaminated data for single and multiple damage cases. As a result, the proposed method can be used for structural health monitoring and damage detection, particularly for cases where the measurement data is very large. Furthermore, it is suggested that an optimal ANN architecture can detect damage occurrence with good accuracy and can provide damage quantification with reasonable accuracy under varying levels of damage.

A preliminary investigation of the role of the Phenylalynine: Tyrosine Ratio in children with early and continuously treated phenylketonuria : toward identification of “safe” levels

Children with early and continuously treated phenylketonuria (ECT-PKU) remain at risk of developing executive function (EF) deficits. There is some evidence that a high phenylalanine to tyrosine ratio (phe:tyr) is more strongly associated with impaired EF development than high phenylalanine alone. This study examined EF in a sample of 11 adolescents against concurrent and historical levels of phenylalanine, phe:tyr, and tyrosine. Lifetime measures of phe:tyr were more strongly associated with EF than phenylalanine-only measures. Children with a lifetime phe:tyr less than 6 demonstrated normal EF, whereas children who had a lifetime phe:tyr above 6, on average, demonstrated clinically impaired EF.

BLAST Atlas : a function based multiple genome browser

BLAST Atlas is a visual analysis system for comparative genomics that supports genome-wide gene characterisation, functional assignment and function-based browsing of one or more chromosomes. Inspired by applications such as the WorldWide Telescope, Bing Maps 3D and Google Earth, BLAST Atlas uses novel three-dimensional gene and function views that provide a highly interactive and intuitive way for scientists to navigate, query and compare gene annotations. The system can be used for gene identification and functional assignment or as a function-based multiple genome comparison tool which complements existing position based comparison and alignment viewers.

Frequency response function based structural damage detection using artificial neural networks

Damage detection in structures has become increasingly important in recent years. While a number of damage detection and localization methods have been proposed, few attempts have been made to explore the structure damage with frequency response functions (FRFs). This paper illustrates the damage identification and condition assessment of a beam structure using a new frequency response functions (FRFs) based damage index and Artificial Neural Networks (ANNs). In practice, usage of all available FRF data as an input to artificial neural networks makes the training and convergence impossible. Therefore one of the data reduction techniques Principal Component Analysis (PCA) is introduced in the algorithm. In the proposed procedure, a large set of FRFs are divided into sub-sets in order to find the damage indices for different frequency points of different damage scenarios. The basic idea of this method is to establish features of damaged structure using FRFs from different measurement points of different sub-sets of intact structure. Then using these features, damage indices of different damage cases of the structure are identified after reconstructing of available FRF data using PCA. The obtained damage indices corresponding to different damage locations and severities are introduced as input variable to developed artificial neural networks. Finally, the effectiveness of the proposed method is illustrated and validated by using the finite element modal of a beam structure. The illustrated results show that the PCA based damage index is suitable and effective for structural damage detection and condition assessment of building structures.

Structural damage identification with noise polluted Frequency Response Functions (FRFs)

Damage detection in structures has become increasingly important in recent years. While a number of damage detection and localization methods have been proposed, very few attempts have been made to explore the structure damage with noise polluted data which is unavoidable effect in real world. The measurement data are contaminated by noise because of test environment as well as electronic devices and this noise tend to give error results with structural damage identification methods. Therefore it is important to investigate a method which can perform better with noise polluted data. This paper introduces a new damage index using principal component analysis (PCA) for damage detection of building structures being able to accept noise polluted frequency response functions (FRFs) as input. The FRF data are obtained from the function datagen of MATLAB program which is available on the web site of the IASC-ASCE (International Association for Structural Control– American Society of Civil Engineers) Structural Health Monitoring (SHM) Task Group. The proposed method involves a five-stage process: calculation of FRFs, calculation of damage index values using proposed algorithm, development of the artificial neural networks and introducing damage indices as input parameters and damage detection of the structure. This paper briefly describes the methodology and the results obtained in detecting damage in all six cases of the benchmark study with different noise levels. The proposed method is applied to a benchmark problem sponsored by the IASC-ASCE Task Group on Structural Health Monitoring, which was developed in order to facilitate the comparison of various damage identification methods. The illustrated results show that the PCA-based algorithm is effective for structural health monitoring with noise polluted FRFs which is of common occurrence when dealing with industrial structures.

Person re-identification using group information

After first observing a person, the task of person re-identification involves recognising an individual at different locations across a network of cameras at a later time. Traditionally, this task has been performed by first extracting appearance features of an individual and then matching these features to the previous observation. However, identifying an individual based solely on appearance can be ambiguous, particularly when people wear similar clothing (i.e. people dressed in uniforms in sporting and school settings). This task is made more difficult when the resolution of the input image is small as is typically the case in multi-camera networks. To circumvent these issues, we need to use other contextual cues. In this paper, we use "group" information as our contextual feature to aid in the re-identification of a person, which is heavily motivated by the fact that people generally move together as a collective group. To encode group context, we learn a linear mapping function to assign each person to a "role" or position within the group structure. We then combine the appearance and group context cues using a weighted summation. We demonstrate how this improves performance of person re-identification in a sports environment over appearance based-features.

Identification of a serine protease inhibitor which causes inclusion vacuole reduction and is lethal to Chlamydia trachomatis

The mechanistic details of the pathogenesis of Chlamydia, an obligate intracellular pathogen of global importance, have eluded scientists due to the scarcity of traditional molecular genetic tools to investigate this organism. Here we report a chemical biology strategy that has uncovered the first essential protease for this organism. Identification and application of a unique CtHtrA inhibitor (JO146) to cultures of Chlamydia resulted in a complete loss of viable elementary body formation. JO146 treatment during the replicative phase of development resulted in a loss of Chlamydia cell morphology, diminishing inclusion size, and ultimate loss of inclusions from the host cells. This completely prevented the formation of viable Chlamydia elementary bodies. In addition to its effect on the human C. trachomatis strain, JO146 inhibited the viability of the mouse strain, Chlamydia muridarum, both in vitro and in vivo. Thus, we report a chemical biology approach to establish an essential role for Chlamydia CtHtrA. The function of CtHtrA for Chlamydia appears to be essential for maintenance of cell morphology during replicative the phase and these findings provide proof of concept that proteases can be targetted for anti-microbial therapy for intracellular pathogens.

Identification of a long non-coding RNA gene, GHSROS (growth hormone secretagogue receptor opposite strand), which stimulates cell migration in non-small cell lung cancer cell lines

The molecular mechanisms involved in non‑small cell lung cancer tumourigenesis are largely unknown; however, recent studies have suggested that long non-coding RNAs (lncRNAs) are likely to play a role. In this study, we used public databases to identify an mRNA-like, candidate long non-coding RNA, GHSROS (GHSR opposite strand), transcribed from the antisense strand of the ghrelin receptor gene, growth hormone secretagogue receptor (GHSR). Quantitative real-time RT-PCR revealed higher expression of GHSROS in lung cancer tissue compared to adjacent, non-tumour lung tissue. In common with many long non-coding RNAs, GHSROS is 5' capped and 3' polyadenylated (mRNA-like), lacks an extensive open reading frame and harbours a transposable element. Engineered overexpression of GHSROS stimulated cell migration in the A549 and NCI-H1299 non-small cell lung cancer cell lines, but suppressed cell migration in the Beas-2B normal lung-derived bronchoepithelial cell line. This suggests that GHSROS function may be dependent on the oncogenic context. The identification of GHSROS, which is expressed in lung cancer and stimulates cell migration in lung cancer cell lines, contributes to the growing number of non-coding RNAs that play a role in the regulation of tumourigenesis and metastatic cancer progression.

Rapid identification of cytochrome P450cam variants by in vivo screening of active site libraries

The selection of cytochrome P450 enzymes from large variant libraries, and the subsequent use of these enzymes in preparative scale biotransformations, remains a formidable challenge due to the complexities of the associated electron transport systems. Here, a powerful approach for the generation and screening of P450cam libraries for new function is presented that is both flexible and robust. A targeted library was generated wherein only the P450cam active-site amino acids Y96 and F98 were fully randomized and biotransformations, using a novel P450cam whole-cell system, were screened by GC–MS for the hydroxylation of diphenylmethane. One in 50 of the reactions screened, including 16 different variants, produced 4-hydroxydiphenylmethane with up to 92% conversion observed in the case of the Y96A variant. These results demonstrate a primary example of the screening of P450cam libraries in a format that is compatible with extension to preparative scale reactions.

Domain-specific application analysis for customized instruction identification

With the increasing importance of Application Domain Specific Processor (ADSP) design, a significant challenge is to identify special-purpose operations for implementation as a customized instruction. While many methodologies have been proposed for this purpose, they all work for a single algorithm chosen from the target application domain. Such algorithm-specific approaches are not suitable for designing instruction sets applicable to a whole family of related algorithms. For an entire range of related algorithms, this paper develops a methodology for identifying compound operations, as a basis for designing “domain-specific” Instruction Set Architectures (ISAs) that can efficiently run most of the algorithms in a given domain. Our methodology combines three different static analysis techniques to identify instruction sequences common to several related algorithms: identification of (non-branching) instruction sequences that occur commonly across the algorithms; identification of instruction sequences nested within iterative constructs that are thus executed frequently; and identification of commonly-occurring instruction sequences that span basic blocks. Choosing different combinations of these results enables us to design domain-specific special operations with different desired characteristics, such as performance or suitability as a library function. To demonstrate our approach, case studies are carried out for a family of thirteen string matching algorithms. Finally, the validity of our static analysis results is confirmed through independent dynamic analysis experiments and performance improvement measurements.

IT consumerization and its effects on IT business value, IT capabilities, and the IT function

IT consumerization is both a major opportunity and significant challenge for organizations. However, IS research has hardly discussed the implications for IT management so far. In this paper we address this topic by empirically identifying organizational themes for IT consumerization and conceptually exploring the direct and indirect effects on the business value of IT, IT capabilities, and the IT function. More specifically, based on two case studies, we identify eight organizational themes: consumer IT strategy, policy development and responsibilities, consideration of private life of employees, user involvement into IT-related processes, individualization, updated IT infrastructure, end user support, and data and system security. The contributions of this paper are: (1) the identification of organizational themes for IT consumerization; (2) the proposed effects on the business value of IT, IT capabilities and the IT function, and; (3) combining empirical insights into IT consumerization with managerial theories in the IS discipline.

Structural identification of a laboratory-based steel through truss cantilevered bridge with suspended span using a layered genetic algorithm with patternsearch step (GAPS)

Structural identification (St-Id) can be considered as the process of updating a finite element (FE) model of a structural system to match the measured response of the structure. This paper presents the St-Id of a laboratory-based steel through-truss cantilevered bridge with suspended span. There are a total of 600 degrees of freedom (DOFs) in the superstructure plus additional DOFs in the substructure. The St-Id of the bridge model used the modal parameters from a preliminary modal test in the objective function of a global optimisation technique using a layered genetic algorithm with patternsearch step (GAPS). Each layer of the St-Id process involved grouping of the structural parameters into a number of updating parameters and running parallel optimisations. The number of updating parameters was increased at each layer of the process. In order to accelerate the optimisation and ensure improved diversity within the population, a patternsearch step was applied to the fittest individuals at the end of each generation of the GA. The GAPS process was able to replicate the mode shapes for the first two lateral sway modes and the first vertical bending mode to a high degree of accuracy and, to a lesser degree, the mode shape of the first lateral bending mode. The mode shape and frequency of the torsional mode did not match very well. The frequencies of the first lateral bending mode, the first longitudinal mode and the first vertical mode matched very well. The frequency of the first sway mode was lower and that of the second sway mode was higher than the true values, indicating a possible problem with the FE model. Improvements to the model and the St-Id process will be presented at the upcoming conference and compared to the results presented in this paper. These improvements will include the use of multiple FE models in a multi-layered, multi-solution, GAPS St-Id approach.