“Back to the future” – a retrospective analysis of university business models

Using established strategic management and business model frameworks we map the evolution of universities in the context of their value proposition to students as consumers of their products. We argue that in the main universities over time have transitioned from a value-based business model through to an efficiency-based business model that for numerous reasons, is becoming rapidly unsustainable. We further argue that the future university business models would benefit with a reconfiguration towards a network value based model. This approach requires a revised set of perceived benefits, better aligned to the current and future expectations and an alternate approach to the delivery of those benefits to learner / consumers.

Spectrophotometric analysis of phenols, which involves a hemin-graphene hybrid nanoparticles with peroxidase-like activity

Phenols are well known noxious compounds, which are often found in various water sources. A novel analytical method has been researched and developed based on the properties of hemin–graphene hybrid nanosheets (H–GNs). These nanosheets were synthesized using a wet-chemical method, and they have peroxidase-like activity. Also, in the presence of H2O2, the nanosheets are efficient catalysts for the oxidation of the substrate, 4-aminoantipine (4-AP), and the phenols. The products of such an oxidation reaction are the colored quinone-imines (benzodiazepines). Importantly, these products enabled the differentiation of the three common phenols – pyrocatechol, resorcin and hydroquinone, with the use of a novel, spectroscopic method, which was developed for the simultaneous determination of the above three analytes. This spectroscopic method produced linear calibrations for the pyrocatechol (0.4–4.0 mg L−1), resorcin (0.2–2.0 mg L−1) and hydroquinone (0.8–8.0 mg L−1) analytes. In addition, kinetic and spectral data, obtained from the formation of the colored benzodiazepines, were used to establish multi-variate calibrations for the prediction of the three phenol analytes found in various kinds of water; partial least squares (PLS), principal component regression (PCR) and artificial neural network (ANN) models were used and the PLS model performed best.

A high performance liquid chromatography and electrospray ionization mass spectrometry method for the analysis of the natural medicine, Forsythia suspensa

Samples of Forsythia suspensa from raw (Laoqiao) and ripe (Qingqiao) fruit were analyzed with the use of HPLC-DAD and the EIS-MS techniques. Seventeen peaks were detected, and of these, twelve were identified. Most were related to the glucopyranoside molecular fragment. Samples collected from three geographical areas (Shanxi, Henan and Shandong Provinces), were discriminated with the use of hierarchical clustering analysis (HCA), discriminant analysis (DA), and principal component analysis (PCA) models, but only PCA was able to provide further information about the relationships between objects and loadings; eight peaks were related to the provinces of sample origin. The supervised classification models-K-nearest neighbor (KNN), least squares support vector machines (LS-SVM), and counter propagation artificial neural network (CP-ANN) methods, indicated successful classification but KNN produced 100% classification rate. Thus, the fruit were discriminated on the basis of their places of origin.

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.

Stability analysis and implementation of Power-Hardware-in-the-Loop for power system testing

This project develops the required guidelines to assure stable and accurate operation of Power-Hardware-in-the-Loop implementations. The proposals of this research have been theoretically analyzed and practically examined using a Real-Time Digital Simulator. In this research, the interaction between software simulated power network and the physical power system has been studied. The conditions for different operating regimes have been derived and the corresponding analyses have been presented.

Development and integration of a solar powered unmanned aerial vehicle and a wireless sensor network to monitor greenhouse gases

Measuring gases for environmental monitoring is a demanding task that requires long periods of observation and large numbers of sensors. Wireless Sensor Networks (WSNs) and Unmanned Aerial Vehicles (UAVs) currently represent the best alternative to monitor large, remote, and difficult access areas, as these technologies have the possibility of carrying specialized gas sensing systems. This paper presents the development and integration of a WSN and an UAV powered by solar energy in order to enhance their functionality and broader their applications. A gas sensing system implementing nanostructured metal oxide (MOX) and non-dispersive infrared sensors was developed to measure concentrations of CH4 and CO2. Laboratory, bench and field testing results demonstrate the capability of UAV to capture, analyze and geo-locate a gas sample during flight operations. The field testing integrated ground sensor nodes and the UAV to measure CO2 concentration at ground and low aerial altitudes, simultaneously. Data collected during the mission was transmitted in real time to a central node for analysis and 3D mapping of the target gas. The results highlights the accomplishment of the first flight mission of a solar powered UAV equipped with a CO2 sensing system integrated with a WSN. The system provides an effective 3D monitoring and can be used in a wide range of environmental applications such as agriculture, bushfires, mining studies, zoology and botanical studies using a ubiquitous low cost technology.

Incorporating complex train paths to an analysis of absolute capacity

Railways are an important mode of transportation. They are however large and complex and their construction, management and operation is time consuming and costly. Evidently planning the current and future activities is vital. Part of that planning process is an analysis of capacity. To determine what volume of traffic can be achieved over time, a variety of railway capacity analysis techniques have been created. A generic analytical approach that incorporates more complex train paths however has yet to be provided. This article provides such an approach. This article extends a mathematical model for determining the theoretical capacity of a railway network. The main contribution of this paper is the modelling of more complex train paths whereby each section can be visited many times in the course of a train’s journey. Three variant models are formulated and then demonstrated in a case study. This article’s numerical investigations have successively shown the applicability of the proposed models and how they may be used to gain insights into system performance.

Improved residential electricity demand management through analysis of the customer perspective

Peak electricity demand requires substantial investment to update transmission, distribution and generation infrastructure. A successful community peak demand reduction project was examined to identify residential consumer motivational and contextual factors involved in their decision to adopt/not adopt interventions. Energy professionals actively worked to achieve community 'peer' membership and by becoming a trusted information source, facilitated voluntary home energy assessment requests from over 80% of the residential community. By combining and tailoring interventions to the specific needs and motivations of individual householders and the community, interventions promoting energy conservation and efficiency can be effective in achieving sustained reduction in peak demand.

Formal modelling and analysis of DNP3 secure authentication

Supervisory Control and Data Acquisition (SCADA) systems are one of the key foundations of smart grids. The Distributed Network Protocol version 3 (DNP3) is a standard SCADA protocol designed to facilitate communications in substations and smart grid nodes. The protocol is embedded with a security mechanism called Secure Authentication (DNP3-SA). This mechanism ensures that end-to-end communication security is provided in substations. This paper presents a formal model for the behavioural analysis of DNP3-SA using Coloured Petri Nets (CPN). Our DNP3-SA CPN model is capable of testing and verifying various attack scenarios: modification, replay and spoofing, combined complex attack and mitigation strategies. Using the model has revealed a previously unidentified flaw in the DNP3-SA protocol that can be exploited by an attacker that has access to the network interconnecting DNP3 devices. An attacker can launch a successful attack on an outstation without possessing the pre-shared keys by replaying a previously authenticated command with arbitrary parameters. We propose an update to the DNP3-SA protocol that removes the flaw and prevents such attacks. The update is validated and verified using our CPN model proving the effectiveness of the model and importance of the formal protocol analysis.

A network DEA quantity and quality-orientated production model: An application to Australian university research services

The motivation for this analysis is the recently developed Excellence in Research for Australia (ERA) program developed to assess the quality of research in Australia. The objective is to develop an appropriate empirical model that better represents the underlying production of higher education research. In general, past studies on university research performance have used standard DEA models with some quantifiable research outputs. However, these suffer from the twin maladies of an inappropriate production specification and a lack of consideration of the quality of output. By including the qualitative attributes of peer-reviewed journals, we develop a procedure that captures both quality and quantity, and apply it using a network DEA model. Our main finding is that standard DEA models tend to overstate the research efficiency of most Australian universities.

Hierarchical clustering of the genetic connectivity matrix reveals the network topology of gene action on brain microstructure: An N=531 twin study

Genetic correlation (rg) analysis determines how much of the correlation between two measures is due to common genetic influences. In an analysis of 4 Tesla diffusion tensor images (DTI) from 531 healthy young adult twins and their siblings, we generalized the concept of genetic correlation to determine common genetic influences on white matter integrity, measured by fractional anisotropy (FA), at all points of the brain, yielding an NxN genetic correlation matrix rg(x,y) between FA values at all pairs of voxels in the brain. With hierarchical clustering, we identified brain regions with relatively homogeneous genetic determinants, to boost the power to identify causal single nucleotide polymorphisms (SNP). We applied genome-wide association (GWA) to assess associations between 529,497 SNPs and FA in clusters defined by hubs of the clustered genetic correlation matrix. We identified a network of genes, with a scale-free topology, that influences white matter integrity over multiple brain regions.

Brain network efficiency and topology depend on the fiber tracking method: 11 tractography algorithms compared in 536 subjects

As connectivity analyses become more popular, claims are often made about how the brain's anatomical networks depend on age, sex, or disease. It is unclear how results depend on tractography methods used to compute fiber networks. We applied 11 tractography methods to high angular resolution diffusion images of the brain (4-Tesla 105-gradient HARDI) from 536 healthy young adults. We parcellated 70 cortical regions, yielding 70×70 connectivity matrices, encoding fiber density. We computed popular graph theory metrics, including network efficiency, and characteristic path lengths. Both metrics were robust to the number of spherical harmonics used to model diffusion (4th-8th order). Age effects were detected only for networks computed with the probabilistic Hough transform method, which excludes smaller fibers. Sex and total brain volume affected networks measured with deterministic, tensor-based fiber tracking but not with the Hough method. Each tractography method includes different fibers, which affects inferences made about the reconstructed networks.

An analysis of user engagement in student Facebook groups

Analysing the engagement of students in university-based Facebook groups can shed light on the nature of their learning experience and highlight leverage points to build on student success. While post-semester surveys and demographic participation data can highlight who was involved and how they subsequently felt about the experience, these techniques do not necessarily reflect real-time engagement. One way to gain insight into in-situ student experiences is by categorising the original posts and comments into predetermined frameworks of learning. This paper offers a systematic method of coding Facebook contributions within various engagement categories: motivation, discourse, cognition and emotive responses.

Systems modelling of the socio-technical aspects of residential electricity use and network peak demand

Provision of network infrastructure to meet rising network peak demand is increasing the cost of electricity. Addressing this demand is a major imperative for Australian electricity agencies. The network peak demand model reported in this paper provides a quantified decision support tool and a means of understanding the key influences and impacts on network peak demand. An investigation of the system factors impacting residential consumers’ peak demand for electricity was undertaken in Queensland, Australia. Technical factors, such as the customers’ location, housing construction and appliances, were combined with social factors, such as household demographics, culture, trust and knowledge, and Change Management Options (CMOs) such as tariffs, price,managed supply, etc., in a conceptual ‘map’ of the system. A Bayesian network was used to quantify the model and provide insights into the major influential factors and their interactions. The model was also used to examine the reduction in network peak demand with different market-based and government interventions in various customer locations of interest and investigate the relative importance of instituting programs that build trust and knowledge through well designed customer-industry engagement activities. The Bayesian network was implemented via a spreadsheet with a tick box interface. The model combined available data from industry-specific and public sources with relevant expert opinion. The results revealed that the most effective intervention strategies involve combining particular CMOs with associated education and engagement activities. The model demonstrated the importance of designing interventions that take into account the interactions of the various elements of the socio-technical system. The options that provided the greatest impact on peak demand were Off-Peak Tariffs and Managed Supply and increases in the price of electricity. The impact in peak demand reduction differed for each of the locations and highlighted that household numbers, demographics as well as the different climates were significant factors. It presented possible network peak demand reductions which would delay any upgrade of networks, resulting in savings for Queensland utilities and ultimately for households. The use of this systems approach using Bayesian networks to assist the management of peak demand in different modelled locations in Queensland provided insights about the most important elements in the system and the intervention strategies that could be tailored to the targeted customer segments.