Transnational distance education : cultural and quality considerations

There have now been two decades of rhetoric on the need for culturally and contextually appropriate perspectives in international education. However, the extent to which courses, provision and pedagogy have truly reflected differences in cultural characteristics and learning preferences is still open to question. Little attention has been paid to these matters in quality assurance frameworks. This chapter discusses these issues and draws upon Hofstede’s cultural dimensions framework and studies into Asian pedagogy and uses of educational technology. It proposes a benchmark and performance indicators for assuring cultural, contextual, educational and technological appropriateness in the provision of transnational distance education in Asia by Australian universities.

Semantic spaces : measuring the distance between different subspaces

Semantic Space models, which provide a numerical representation of words’ meaning extracted from corpus of documents, have been formalized in terms of Hermitian operators over real valued Hilbert spaces by Bruza et al. [1]. The collapse of a word into a particular meaning has been investigated applying the notion of quantum collapse of superpositional states [2]. While the semantic association between words in a Semantic Space can be computed by means of the Minkowski distance [3] or the cosine of the angle between the vector representation of each pair of words, a new procedure is needed in order to establish relations between two or more Semantic Spaces. We address the question: how can the distance between different Semantic Spaces be computed? By representing each Semantic Space as a subspace of a more general Hilbert space, the relationship between Semantic Spaces can be computed by means of the subspace distance. Such distance needs to take into account the difference in the dimensions between subspaces. The availability of a distance for comparing different Semantic Subspaces would enable to achieve a deeper understanding about the geometry of Semantic Spaces which would possibly translate into better effectiveness in Information Retrieval tasks.

Opportunities and challenges : combining distance and on-campus students in concurrent courses

Through its Electronic Delivery of Gator Engineering (EDGE) program, University of Florida (UF) offers online master’s degrees from participating departments within the UF College of Engineering. Each master’s degree requires 10 courses (3 credit hours each). One interesting and unique aspect of the EDGE pro-gram is that distance learners are registered concurrently in the same courses with traditional on-campus students. This paper examines the specific challenges involved in integrating distance students into on-campus courses, including communication, interaction, access to resources, and equal grading practices.

Condition monitoring and fault diagnosis of diesel engines using instantaneous angular speed analysis

Continuous monitoring of diesel engine performance is critical for early detection of fault developments in an engine before they materialize into a functional failure. Instantaneous crank angular speed (IAS) analysis is one of a few nonintrusive condition monitoring techniques that can be utilized for such a task. Furthermore, the technique is more suitable for mass industry deployments than other non-intrusive methods such as vibration and acoustic emission techniques due to the low instrumentation cost, smaller data size and robust signal clarity since IAS is not affected by the engine operation noise and noise from the surrounding environment. A combination of IAS and order analysis was employed in this experimental study and the major order component of the IAS spectrum was used for engine loading estimation and fault diagnosis of a four-stroke four-cylinder diesel engine. It was shown that IAS analysis can provide useful information about engine speed variation caused by changing piston momentum and crankshaft acceleration during the engine combustion process. It was also found that the major order component of the IAS spectra directly associated with the engine firing frequency (at twice the mean shaft rotating speed) can be utilized to estimate engine loading condition regardless of whether the engine is operating at healthy condition or with faults. The amplitude of this order component follows a distinctive exponential curve as the loading condition changes. A mathematical relationship was then established in the paper to estimate the engine power output based on the amplitude of this order component of the IAS spectrum. It was further illustrated that IAS technique can be employed for the detection of a simulated exhaust valve fault in this study.

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.

Uniform, dense arrays of vertically aligned, large-diameter single-walled carbon nanotubes

Precisely controlled reactive chemical vapor synthesis of highly uniform, dense arrays of vertically aligned single-walled carbon nanotubes (SWCNTs) using tailored trilayered Fe/Al2O3/SiO2 catalyst is demonstrated. More than 90% population of thick nanotubes (>3 nm in diameter) can be produced by tailoring the thickness and microstructure of the secondary catalyst supporting SiO2 layer, which is commonly overlooked. The proposed model based on the atomic force microanalysis suggests that this tailoring leads to uniform and dense arrays of relatively large Fe catalyst nanoparticles on which the thick SWCNTs nucleate, while small nanotubes and amorphous carbon are effectively etched away. Our results resolve a persistent issue of selective (while avoiding multiwalled nanotubes and other carbon nanostructures) synthesis of thick vertically aligned SWCNTs whose easily switchable thickness-dependent electronic properties enable advanced applications in nanoelectronic, energy, drug delivery, and membrane technologies.

Control of ion density distribution by magnetic traps for plasma electrons

The effect of a magnetic field of two magnetic coils on the ion current density distribution in the setup for low-temperature plasma deposition is investigated. The substrate of 400 mm diameter is placed at a distance of 325 mm from the plasma duct exit, with the two magnetic coils mounted symmetrically under the substrate at a distance of 140 mm relative to the substrate centre. A planar probe is used to measure the ion current density distribution along the plasma flux cross-sections at distances of 150, 230, and 325 mm from the plasma duct exit. It is shown that the magnetic field strongly affects the ion current density distribution. Transparent plastic films are used to investigate qualitatively the ion density distribution profiles and the effect of the magnetic field. A theoretical model is developed to describe the interaction of the ion fluxes with the negative space charge regions associated with the magnetic trapping of the plasmaelectrons. Theoretical results are compared with the experimental measurements, and a reasonable agreement is demonstrated.

Angular distribution of carbon ion flux in a nanotube array during the plasma process by the Monte Carlo technique

Angular distribution of microscopic ion fluxes around nanotubes arranged into a dense ordered pattern on the surface of the substrate is studied by means of multiscale numerical simulation. The Monte Carlo technique was used to show that the ion current density is distributed nonuniformly around the carbon nanotubes arranged into a dense rectangular array. The nonuniformity factor of the ion current flux reaches 7 in dense (5× 1018 m-3) plasmas for a nanotube radius of 25 nm, and tends to 1 at plasma densities below 1× 1017 m-3. The results obtained suggest that the local density of carbon adatoms on the nanotube side surface, at areas facing the adjacent nanotubes of the pattern, can be high enough to lead to the additional wall formation and thus cause the single- to multiwall structural transition, and other as yet unexplained nanoscience phenomena.

Dispersion of corona ions downwind from a high-voltage power line

Corona discharge is responsible for the small ions found near overhead power lines, and these are capable of modifying the ambient electrical environment such as the dc electric field at ground level (Fews, Wilding et al. 2002). Once produced, small ions quickly attach to aerosol particles in the air, producing ‘large ions’ which are roughly 1 nm to 1 µm in diameter. However, very few studies have reported measurements of ions produced by power lines and its impact on particle charge concentrations. In this present study, the measurements were conducted as a function of normal downwind distance from a 275kV power line for investigating the effect of corona ions on air ions, aerosol particle charge concentration and dc e-filed.

Air ion concentrations under overhead power lines

Corona discharge is responsible for the flux of small ions from overhead power lines, and is capable of modifying the ambient electrical environment, such as the air ion concentrations at ground level. Once produced, small ions quickly attach to aerosol particles in the air, producing ‘large ions’, approximately 1 nm to 1 µm in diameter. However, very few studies have measured air ion concentrations directly near high voltage transmission lines. The present study involved the simultaneously measurement of small ion concentration and net large ion concentration using air ion counters and an aerosol electrometer at four power line sites. Both positive and negative small ion concentration (<1.6nm), net large ion concentration (2nm-5μm) and particle number concentration (10nm-2μm) were measured using air ion counters and an aerosol electrometer at four power line sites. Measurements at sites 1 and 2 were conducted at both upwind and downwind sides. The results showed that total ion concentrations on the downwind side were 3-5 times higher than on the upwind side, while particle number concentrations did not show a significant difference. This result also shows that a large number of ions were emitted from the power lines at sites 1 and 2. Furthermore, both positive and negative ions were observed at different power line sites. Dominant positive ions were observed at site 1, with a concentration of 4.4 x 103 ions cm-3, which was 10 times higher than on the upwind side. Contrary to site 1, sites 2 to 4 showed negative ion emissions, with concentrations of -1.2 x 103, -460 and -410 ions cm-3, respectively. These values were higher than the background urban negative ion concentration of 400 cm-3. At site 1 and site 2, the net ion concentration and net particle charge concentration on downwind side of the lines showed same polarities. Further investigations were also conducted into the correlation between net ion concentration and net charge particle concentration 20 m downwind of the power lines at site 2. The two parameters showed a correlation coefficient of 0.72, indicating that a substantial number of ions could attach to particles and affect the particle charge status within a short distance from the source.

Long distance calling? Spatial preference patterns in enterprise microblogging in the retail industry

We examine enterprise social network usage data obtained from a community of store managers in a leading Australian retail organization, over a period of fifteen months. Our interest in examining this data is in spatial preferences by the network users, that is, to ascertain who is communicating with whom and where. We offer several contrasting theoretical perspectives for spatial preference patterns and examine these against data collected from over 12,000 messages exchanged between 530 managers in 897 stores. Our findings show that interactions can generally be characterized by individual preferences for local communication but also that two different user communities exist – locals and globals. We develop empirical profiles for these social network user communities and outline implications for theories on spatial influences on communication behaviours on enterprise social networks.

Blended learning : meeting the needs of distance education students

This thesis involved research into the barriers and enablers that existed for a cohort of mature-aged education support students engaging with blended learning through distance education. The findings that emerged from this research indicated that a flexible model of blended learning is possible in this context. The findings shed light on the experiences of novice technology users' participation in blended learning. The study highlighted the significance of factors such as isolation, technology, communication, connectivity, prior learning, and the growth of self-efficacy that influenced learner engagement.