Comparison of ultrafine particle release from hardcopy devices in emission test chambers and office rooms

The release of ultrafine particles (UFP) from laser printers and office equipment was analyzed using a particle counter (FMPS; Fast Mobility Particle Sizer) with a high time resolution, as well as the appropriate mathematical models. Measurements were carried out in a 1 m³ chamber, a 24 m³ chamber and an office. The time-dependent emission rates were calculated for these environments using a deconvolution model, after which the total amount of emitted particles was calculated. The total amounts of released particles were found to be independent of the environmental parameters and therefore, in principle, they were appropriate for the comparison of different printers. On the basis of the time-dependent emission rates, “initial burst” emitters and constant emitters could also be distinguished. In the case of an “initial burst” emitter, the comparison to other devices is generally affected by strong variations between individual measurements. When conducting exposure assessments for UFP in an office, the spatial distribution of the particles also had to be considered. In this work, the spatial distribution was predicted on a case by case basis, using CFD simulation.

Comparison of emission rate values for odour and odorous chemicals derived from two sampling devices

Field and laboratory measurements identified a complex relationship between odour emission rates provided by the US EPA dynamic emission chamber and the University of New South Wales wind tunnel. Using a range of model compounds in an aqueous odour source, we demonstrate that emission rates derived from the wind tunnel and flux chamber are a function of the solubility of the materials being emitted, the concentrations of the materials within the liquid; and the aerodynamic conditions within the device – either velocity in the wind tunnel, or flushing rate for the flux chamber. The ratio of wind tunnel to flux chamber odour emission rates (OU m-2 s) ranged from about 60:1 to 112:1. The emission rates of the model odorants varied from about 40:1 to over 600:1. These results may provide, for the first time, a basis for the development of a model allowing an odour emission rate derived from either device to be used for odour dispersion modelling.

Infrared and infrared emission spectroscopy of gallium oxide α-GaO(OH) nanostructures

Infrared spectroscopy has been used to study nano to micro sized gallium oxyhydroxide α-GaO(OH), prepared using a low temperature hydrothermal route. Rod-like α-GaO(OH) crystals with average length of ~2.5 μm and width of 1.5 μm were prepared when the initial molar ratio of Ga to OH was 1:3. β-Ga2O3 nano and micro-rods were prepared through the calcination of α-GaO(OH) The initial morphology of α-GaO(OH) is retained in the β-Ga2O3 nanorods. The combination of infrared and infrared emission spectroscopy complimented with dynamic thermal analysis were used to characterise the α-GaO(OH) nanotubes and the formation of β-Ga2O3 nanorods. Bands at around 2903 and 2836 cm-1 are assigned to the -OH stretching vibration of α-GaO(OH) nanorods. Infrared bands at around 952 and 1026 cm-1 are assigned to the Ga-OH deformation modes of α-GaO(OH). A significant number of bands are observed in the 620 to 725 cm-1 region and are assigned to GaO stretching vibrations.

Employing the five-factor mentoring instrument : analysing mentoring practices for teaching primary science.

Primary science education is a concern around the world and quality mentoring within schools can develop preservice teachers’ practices. A five-factor model for mentoring has been identified, namely, personal attributes, system requirements, pedagogical knowledge, modelling, and feedback. Final-year preservice teachers (mentees, n=211) from three Turkish universities were administered a previously validated instrument to gather perceptions of their mentoring in primary science teaching. ANOVA indicated that each of these five factors was statistically significant (p<.001) with mean scale scores ranging from 3.36 to 4.12. Although mentees perceived their mentors to provide evaluation feedback (95%), model classroom management (88%), guide their preparation (96%), and outline the science curriculum (92%), the majority of mentors were perceived not to assist their mentees in 10 of the 34 survey items. Professional development programmes that target the specific needs of these mentors may further enhance mentoring practices for advancing primary science teaching.

Combination strategies for a factor analysis phone-conditioned speaker verification system

This work aims to take advantage of recent developments in joint factor analysis (JFA) in the context of a phonetically conditioned GMM speaker verification system. Previous work has shown performance advantages through phonetic conditioning, but this has not been shown to date with the JFA framework. Our focus is particularly on strategies for combining the phone-conditioned systems. We show that the classic fusion of the scores is suboptimal when using multiple GMM systems. We investigate several combination strategies in the model space, and demonstrate improvement over score-level combination as well as over a non-phonetic baseline system. This work was conducted during the 2008 CLSP Workshop at Johns Hopkins University.

Confirmatory factor analysis of the Way of Coping Checklist-Revised (WCCL-R) in the Asian context

Industrial employment growth has been one of the most dynamic areas of expansion in Asia; however, current trends in industrialised working environments have resulted in greater employee stress. Despite research showing that cultural values affect the way people cope with stress, there is a dearth of psychometrically established tools for use in non-Western countries to measure these constructs. Studies of the "Way of Coping Checklist-Revised" (WCCL-R) in the West suggest that the WCCL-R has good psychometric properties, but its applicability in the East is still understudied. A confirmatory factor analysis (CFA) is used to validate the WCCL-R constructs in an Asian population. This study used 1,314 participants from Indonesia, Sri Lanka, Singapore, and Thailand. An initial exploratory factor analysis revealed that original structures were not confirmed; however, a subsequent EFA and CFA showed that a 38-item, five-factor structure model was confirmed. The revised WCCL-R in the Asian sample was also found to have good reliability and sound construct and concurrent validity. The 38-item structure of the WCCL-R has considerable potential in future occupational stress-related research in Asian countries.

Source location of acoustic emission waves for structural health monitoring of bridges

The process of structural health monitoring (SHM) involves monitoring a structure over a period of time using appropriate sensors, extracting damage sensitive features from the measurements made by the sensors and analysing these features to determine the current state of the structure. Various techniques are available for structural health monitoring of structures and acoustic emission (AE) is one technique that is finding an increasing use. Acoustic emission waves are the stress waves generated by the mechanical deformation of materials. AE waves produced inside a structure can be recorded by means of sensors attached on the surface. Analysis of these recorded signals can locate and assess the extent of damage. This paper describes preliminary studies on the application of AE technique for health monitoring of bridge structures. Crack initiation or structural damage will result in wave propagation in solid and this can take place in various forms. Propagation of these waves is likely to be affected by the dimensions, surface properties and shape of the specimen. This, in turn, will affect source localization. Various laboratory test results will be presented on source localization, using pencil lead break tests. The results from the tests can be expected to aid in enhancement of knowledge of acoustic emission process and development of effective bridge structure diagnostics system.

The Children's Depression Scale : assessment of factor structure with data from a normal population

A study investigated the reliability and construct validity of the Children's Depression Scale. The revised subscales were shown to have strong construct and face validity and high reliability.

Structural health monitoring of bridges using acoustic emission technology and signal processing techniques

Structural health monitoring (SHM) is the term applied to the procedure of monitoring a structure’s performance, assessing its condition and carrying out appropriate retrofitting so that it performs reliably, safely and efficiently. Bridges form an important part of a nation’s infrastructure. They deteriorate due to age and changing load patterns and hence early detection of damage helps in prolonging the lives and preventing catastrophic failures. Monitoring of bridges has been traditionally done by means of visual inspection. With recent developments in sensor technology and availability of advanced computing resources, newer techniques have emerged for SHM. Acoustic emission (AE) is one such technology that is attracting attention of engineers and researchers all around the world. This paper discusses the use of AE technology in health monitoring of bridge structures, with a special focus on analysis of recorded data. AE waves are stress waves generated by mechanical deformation of material and can be recorded by means of sensors attached to the surface of the structure. Analysis of the AE signals provides vital information regarding the nature of the source of emission. Signal processing of the AE waveform data can be carried out in several ways and is predominantly based on time and frequency domains. Short time Fourier transform and wavelet analysis have proved to be superior alternatives to traditional frequency based analysis in extracting information from recorded waveform. Some of the preliminary results of the application of these analysis tools in signal processing of recorded AE data will be presented in this paper.

Fingerprinting of complex mixtures with the use of high performance liquid chromatography, inductively coupled plasma atomic emission spectroscopy and chemometrics

The molecular and metal profile fingerprints were obtained from a complex substance, Atractylis chinensis DC—a traditional Chinese medicine (TCM), with the use of the high performance liquid chromatography (HPLC) and inductively coupled plasma atomic emission spectroscopy (ICP-AES) techniques. This substance was used in this work as an example of a complex biological material, which has found application as a TCM. Such TCM samples are traditionally processed by the Bran, Cut, Fried and Swill methods, and were collected from five provinces in China. The data matrices obtained from the two types of analysis produced two principal component biplots, which showed that the HPLC fingerprint data were discriminated on the basis of the methods for processing the raw TCM, while the metal analysis grouped according to the geographical origin. When the two data matrices were combined into a one two-way matrix, the resulting biplot showed a clear separation on the basis of the HPLC fingerprints. Importantly, within each different grouping the objects separated according to their geographical origin, and they ranked approximately in the same order in each group. This result suggested that by using such an approach, it is possible to derive improved characterisation of the complex TCM materials on the basis of the two kinds of analytical data. In addition, two supervised pattern recognition methods, K-nearest neighbors (KNNs) method, and linear discriminant analysis (LDA), were successfully applied to the individual data matrices—thus, supporting the PCA approach.

Particle emission factors during cooking activities

Exposure to particles emitted by cooking activities may be responsible for a variety of respiratory health effects. However, the relationship between these exposures and their subsequent effects on health cannot be evaluated without understanding the properties of the emitted aerosol or the main parameters that influence particle emissions during cooking. Whilst traffic-related emissions, stack emissions and ultrafine particle concentrations (UFP, diameter < 100 nm) in urban ambient air have been widely investigated for many years, indoor exposure to UFPs is a relatively new field and in order to evaluate indoor UFP emissions accurately, it is vital to improve scientific understanding of the main parameters that influence particle number, surface area and mass emissions. The main purpose of this study was to characterise the particle emissions produced during grilling and frying as a function of the food, source, cooking temperature and type of oil. Emission factors, along with particle number concentrations and size distributions were determined in the size range 0.006-20 m using a Scanning Mobility Particle Sizer (SMPS) and an Aerodynamic Particle Sizer (APS). An infrared camera was used to measure the temperature field. Overall, increased emission factors were observed to be a function of increased cooking temperatures. Cooking fatty foods also produced higher particle emission factors than vegetables, mainly in terms of mass concentration, and particle emission factors also varied significantly according to the type of oil used.

Corona discharges on the surfaces of high voltage composite insulators

The degradation of high voltage electrical insulation is a prime factor that can significantly influence the reliability performance and the costs of maintaining high voltage electricity networks. Little information is known about the system of localized degradation from corona discharges on the relatively new silicone rubber sheathed composite insulators that are now being widely used in high voltage applications. This current work focuses on the fundamental principles of electrical corona discharge phenomena to provide further insights to where damaging surface discharges may localize and examines how these discharges may degrade the silicone rubber material. Although water drop corona has been identified by many authors as a major cause of deterioration of silicone rubber high voltage insulation until now no thorough studies have been made of this phenomenon. Results from systematic measurements taken using modern digital instrumentation to simultaneously record the discharge current pulses and visible images associated with corona discharges from between metal electrodes, metal electrodes and water drops, and between waters drops on the surface of silicone rubber insulation, using a range of 50 Hz voltages are inter compared. Visual images of wet electrodes show how water drops can play a part in encouraging flashover, and the first reproducible visual images of water drop corona at the triple junction of water air and silicone rubber insulation are presented. A study of the atomic emission spectra of the corona produced by the discharge from its onset up to and including spark-over, using a high resolution digital spectrometer with a fiber optic probe, provides further understanding of the roles of the active species of atoms and molecules produced by the discharge that may be responsible for not only for chemical changes of insulator surfaces, but may also contribute to the degradation of the metal fittings that support the high voltage insulators. Examples of real insulators and further work specific to the electrical power industry are discussed. A new design concept to prevent/reduce the damaging effects of water drop corona is also presented.

Within-session variability modelling for factor analysis speaker verification

This work presents an extended Joint Factor Analysis model including explicit modelling of unwanted within-session variability. The goals of the proposed extended JFA model are to improve verification performance with short utterances by compensating for the effects of limited or imbalanced phonetic coverage, and to produce a flexible JFA model that is effective over a wide range of utterance lengths without adjusting model parameters such as retraining session subspaces. Experimental results on the 2006 NIST SRE corpus demonstrate the flexibility of the proposed model by providing competitive results over a wide range of utterance lengths without retraining and also yielding modest improvements in a number of conditions over current state-of-the-art.