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.

Multi-shot person re-identification via relational stein divergence

Person re-identification is particularly challenging due to significant appearance changes across separate camera views. In order to re-identify people, a representative human signature should effectively handle differences in illumination, pose and camera parameters. While general appearance-based methods are modelled in Euclidean spaces, it has been argued that some applications in image and video analysis are better modelled via non-Euclidean manifold geometry. To this end, recent approaches represent images as covariance matrices, and interpret such matrices as points on Riemannian manifolds. As direct classification on such manifolds can be difficult, in this paper we propose to represent each manifold point as a vector of similarities to class representers, via a recently introduced form of Bregman matrix divergence known as the Stein divergence. This is followed by using a discriminative mapping of similarity vectors for final classification. The use of similarity vectors is in contrast to the traditional approach of embedding manifolds into tangent spaces, which can suffer from representing the manifold structure inaccurately. Comparative evaluations on benchmark ETHZ and iLIDS datasets for the person re-identification task show that the proposed approach obtains better performance than recent techniques such as Histogram Plus Epitome, Partial Least Squares, and Symmetry-Driven Accumulation of Local Features.

Structural identification of hindered amine light stabilisers in coil coatings using electrospray ionisation tandem mass spectrometry

Hindered amine light stabilisers (HALS) are the most effective antioxidants currently available for polymer systems in post-production, in-service applications, yet the mechanism of their action is still not fully understood. Structural characterisation of HALS in polymer matrices, particularly the identification of structural modifications brought about by oxidative conditions, is critical to aid mechanistic understanding of the prophylactic effects of these molecules. In this work, electrospray ionisation tandem mass spectrometry (ESI-MS/MS) was applied to the analysis of a suite of commercially available 2,2,6,6-tetramethylpiperidine-based HALS. Fragmentation mechanisms for the \[M + H](+) ions are proposed, which provide a rationale for the product ions observed in the MS/MS and MS(3) mass spectra of N-H, N-CH(3), N-C(O)CH(3) and N-OR containing HALS (where R is an alkyl substituent). A common product ion at m/z 123 was identified for the group of antioxidants containing N-H, N-CH3 or N-C(0)CH3 functionality, and this product ion was employed in precursor ion scans on a triple quadrupole mass spectrometer to identify the HALS species present in a crude extract from of a polyester-based coil coating. Using MS/MS, two degradation products were unambiguously identified. This technique provides a simple and selective approach to monitoring HALS structures within complex matrices. Copyright (C) 2010 John Wiley & Sons, Ltd.

Identification of double bond position in lipids : From GC to OzID

Recent developments in mass spectrometry and chromatography provide new possibilities for the identification and in some instances quantification of a wide range of lipids in complex matrices. These advances in analytical technologies have provided a tantalizing glimpse of the true structural diversity of lipids in nature and have reinvigorated interest in the role of lipids in biology. While technological advances have been impressive, difficulties in the ready identification of sites of unsaturation (i.e., double bond position) within these molecules presents a significant impediment to understanding lipid biochemistry. This is of particular importance given the growing body of literature suggesting that the presence of naturally occurring lipid double bond isomers can have a significant influence, both positive and negative, on the development of pathologies such as cancer, cardiovascular disease and type 2 diabetes. This article provides a critical review of the Current suite of analytical approaches to the challenge of identification of the position of carbon-carbon double bonds in intact lipids. Crown Copyright (C) 2009 Published by Elsevier B.V. All rights reserved.

Identification and Validity of Accelerometer Cut-Points for Toddlers

The purpose of this study was to derive ActiGraph cut-points for sedentary (SED), light-intensity physical activity (LPA), and moderate-to-vigorous physical activity (MVPA) in toddlers and evaluate their validity in an independent sample. The predictive validity of established preschool cut-points were also evaluated and compared. Twenty-two toddlers (mean age = 2.1 years ± 0.4 years) wore an ActiGraph accelerometer during a videotaped 20-min play period. Videos were subsequently coded for physical activity (PA) intensity using the modified Children's Activity Rating Scale (CARS). Receiver operating characteristic (ROC) curve analyses were conducted to determine cut-points. Predictive validity was assessed in an independent sample of 18 toddlers (mean age = 2.3 ± 0.4 years). From the ROC curve analyses, the 15-s count ranges corresponding to SED, LPA, and MVPA were 0–48, 49–418, and >418 counts/15 s, respectively. Classification accuracy was fair for the SED threshold (ROC-AUC = 0.74, 95% confidence interval = 0.71–0.76) and excellent for MVPA threshold (ROC-AUC = 0.90, 95% confidence interval = 0.88–0.92). In the cross-validation sample, the toddler cut-point and established preschool cut-points significantly overestimated time spent in SED and underestimated time in spent in LPA. For MVPA, mean differences between observed and predicted values for the toddler and Pate cut-points were not significantly different from zero. In summary, the ActiGraph accelerometer can provide useful group-level estimates of MVPA in toddlers. The results support the use of the Pate cut-point of 420 counts/15 s for MVPA.

Methodology for developing real-time motorway traffic risk identification models using individual-vehicle data

Most of existing motorway traffic safety studies using disaggregate traffic flow data aim at developing models for identifying real-time traffic risks by comparing pre-crash and non-crash conditions. One of serious shortcomings in those studies is that non-crash conditions are arbitrarily selected and hence, not representative, i.e. selected non-crash data might not be the right data comparable with pre-crash data; the non-crash/pre-crash ratio is arbitrarily decided and neglects the abundance of non-crash over pre-crash conditions; etc. Here, we present a methodology for developing a real-time MotorwaY Traffic Risk Identification Model (MyTRIM) using individual vehicle data, meteorological data, and crash data. Non-crash data are clustered into groups called traffic regimes. Thereafter, pre-crash data are classified into regimes to match with relevant non-crash data. Among totally eight traffic regimes obtained, four highly risky regimes were identified; three regime-based Risk Identification Models (RIM) with sufficient pre-crash data were developed. MyTRIM memorizes the latest risk evolution identified by RIM to predict near future risks. Traffic practitioners can decide MyTRIM’s memory size based on the trade-off between detection and false alarm rates. Decreasing the memory size from 5 to 1 precipitates the increase of detection rate from 65.0% to 100.0% and of false alarm rate from 0.21% to 3.68%. Moreover, critical factors in differentiating pre-crash and non-crash conditions are recognized and usable for developing preventive measures. MyTRIM can be used by practitioners in real-time as an independent tool to make online decision or integrated with existing traffic management systems.

Two-photon fluorescence spectroscopy for identification of healthy and malignant biological tissues

Two-photon fluorescence spectroscopy has been performed on rat skeletal muscles to investigate the effect of fixation processes on the micro-environments of the endogenous fluorophors in rat skeletal muscles. The two-photon fluorescence spectra measured for different fixation periods show a differential among those samples that were fixed in water, formalin and methanol, respectively. The results imply that two-photon fluorescence spectroscopy can be a potential technique for identification of healthy and malignant biological tissues.

A novel identification method for ultra trace detection of biomolecules using functionalised Surface Enhanced Raman Spectroscopy (SERS)

This thesis developed a new method for measuring extremely low amounts of organic and biological molecules, using Surface enhanced Raman Spectroscopy. This method has many potential applications, e.g. medical diagnosis, public health, food provenance, antidoping, forensics and homeland security. The method development used caffeine as the small molecule example, and erythropoietin (EPO) as the large molecule. This method is much more sensitive and specific than currently used methods; rapid, simple and cost effective. The method can be used to detect target molecules in beverages and biological fluids without the usual preparation steps.

Field validation of controlled Monte Carlo data generation for statistical damage identification employing Mahalanobis squared distance

This article presents the field applications and validations for the controlled Monte Carlo data generation scheme. This scheme was previously derived to assist the Mahalanobis squared distance–based damage identification method to cope with data-shortage problems which often cause inadequate data multinormality and unreliable identification outcome. To do so, real-vibration datasets from two actual civil engineering structures with such data (and identification) problems are selected as the test objects which are then shown to be in need of enhancement to consolidate their conditions. By utilizing the robust probability measures of the data condition indices in controlled Monte Carlo data generation and statistical sensitivity analysis of the Mahalanobis squared distance computational system, well-conditioned synthetic data generated by an optimal controlled Monte Carlo data generation configurations can be unbiasedly evaluated against those generated by other set-ups and against the original data. The analysis results reconfirm that controlled Monte Carlo data generation is able to overcome the shortage of observations, improve the data multinormality and enhance the reliability of the Mahalanobis squared distance–based damage identification method particularly with respect to false-positive errors. The results also highlight the dynamic structure of controlled Monte Carlo data generation that makes this scheme well adaptive to any type of input data with any (original) distributional condition.

Unbalance identification in large steam turbo-generator unit using a model-based method

Fault identification in industrial machine is a topic of major importance under engineering point of view. In fact, the possibility to identify not only the type, but also the severity and the position of a fault occurred along a shaft-line allows quick maintenance and shorten the downtime. This is really important in the power generation industry where the units are often of several tenths of meters long and where the rotors are enclosed by heavy and pressure-sealed casings. In this paper, an industrial experimental case is presented related to the identification of the unbalance on a large size steam turbine of about 1.3 GW, belonging to a nuclear power plant. The case history is analyzed by considering the vibrations measured by the condition monitoring system of the unit. A model-based method in the frequency domain, developed by the authors, is introduced in detail and it is then used to identify the position of the fault and its severity along the shaft-line. The complete model of the unit (rotor – modeled by means of finite elements, bearings – modeled by linearized damping and stiffness coefficients and foundation – modeled by means of pedestals) is analyzed and discussed before being used for the fault identification. The assessment of the actual fault was done by inspection during a scheduled maintenance and excellent correspondence was found with the identified one by means of authors’ proposed method. Finally a complete discussion is presented about the effectiveness of the method, even in presence of a not fine tuned machine model and considering only few measuring planes for the machine vibration.

Spectroscopic vibrations of austinite (CaZnAsO4⋅OH) and its mineral structure : Implications for identification of secondary arsenic-containing mineral

Austinite (CaZnAsO4⋅OH) is a unique secondary mineral in arsenic-contaminated mine wastes. The infrared and Raman spectroscopies were used to characterize the austenite vibrations. The IR bands at 369, 790 and 416 cm−1 are assigned to the ν2, ν3 and ν4 vibrations of AsO43− unit, respectively. The Raman bands at 814, 779 and 403 cm−1 correspond to the ν1, ν3 and ν4 vibrations of AsO43− unit respectively. The sharp bands at 3265 cm−1 for IR and 3270 cm−1 both reveals that the structural hydroxyl units exist in the austenite structure. The IR and Raman spectra both show that some SO4 units isomorphically replace AsO4 in austinite. X-ray single crystal diffraction provides the arrangement of each atom in the mineral structure, and also confirms that the conclusions made from the vibrational spectra. Micro-powder diffraction was used to confirm our mineral identification due to the small quantity of the austenite crystals.

A gray-box neural network-based model identification and fault estimation scheme for nonlinear dynamic systems

A novel gray-box neural network model (GBNNM), including multi-layer perception (MLP) neural network (NN) and integrators, is proposed for a model identification and fault estimation (MIFE) scheme. With the GBNNM, both the nonlinearity and dynamics of a class of nonlinear dynamic systems can be approximated. Unlike previous NN-based model identification methods, the GBNNM directly inherits system dynamics and separately models system nonlinearities. This model corresponds well with the object system and is easy to build. The GBNNM is embedded online as a normal model reference to obtain the quantitative residual between the object system output and the GBNNM output. This residual can accurately indicate the fault offset value, so it is suitable for differing fault severities. To further estimate the fault parameters (FPs), an improved extended state observer (ESO) using the same NNs (IESONN) from the GBNNM is proposed to avoid requiring the knowledge of ESO nonlinearity. Then, the proposed MIFE scheme is applied for reaction wheels (RW) in a satellite attitude control system (SACS). The scheme using the GBNNM is compared with other NNs in the same fault scenario, and several partial loss of effect (LOE) faults with different severities are considered to validate the effectiveness of the FP estimation and its superiority.

On-road rapid identification of high-polluting transport buses

Exhaust emissions from motor vehicles vary widely and depend on factors such as engine operating conditions, fuel, age, mileage and service history. A method has been devised to rapidly identify high-polluting vehicles as they travel on the road. The method is able to monitor emissions from a large number of vehicles in a short time and avoids the need to conduct expensive and time consuming tests on chassis dynamometers. A sample of the exhaust plume is captured as each vehicle passes a roadside monitoring station and the pollutant emission factors are calculated from the measured concentrations using carbon dioxide as a tracer. Although, similar methods have been used to monitor soot and gaseous mass emissions, to-date it has not been used to monitor particle number emissions from a large fleet of vehicles. This is particularly important as epidemiological studies have shown that particle number concentration is an important parameter in determining adverse health effects. The method was applied to measurements of particle number emissions from individual buses in the Brisbane City Council diesel fleet operating on the South-East Busway. Results indicate that the particle number emission factors are gamma- distributed, with a high proportion of the emissions being emitted by a small percentage of the buses. Although most of the high-emitters are the oldest buses in the fleet, there are clear exceptions, with some newer buses emitting as much. We attribute this to their recent service history, particularly pertaining to improper tuning of the engines. We recommend that a targeted correction program would be a highly effective measure in mitigating urban environmental pollution.

Identification of optimal epitopes for Plasmodium falciparum rapid diagnostic tests that target histidine-rich proteins 2 and 3

Rapid diagnostic tests (RDTs) represent important tools to diagnose malaria infection. To improve understanding of the variable performance of RDTs that detect the major target in Plasmodium falciparum, namely, histidine-rich protein 2 (HRP2), and to inform the design of better tests, we undertook detailed mapping of the epitopes recognized by eight HRP-specific monoclonal antibodies (MAbs). To investigate the geographic skewing of this polymorphic protein, we analyzed the distribution of these epitopes in parasites from geographically diverse areas. To identify an ideal amino acid motif for a MAb to target in HRP2 and in the related protein HRP3, we used a purpose-designed script to perform bioinformatic analysis of 448 distinct gene sequences from pfhrp2 and from 99 sequences from the closely related gene pfhrp3. The frequency and distribution of these motifs were also compared to the MAb epitopes. Heat stability testing of MAbs immobilized on nitrocellulose membranes was also performed. Results of these experiments enabled the identification of MAbs with the most desirable characteristics for inclusion in RDTs, including copy number and coverage of target epitopes, geographic skewing, heat stability, and match with the most abundant amino acid motifs identified. This study therefore informs the selection of MAbs to include in malaria RDTs as well as in the generation of improved MAbs that should improve the performance of HRP-detecting malaria RDTs.

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.