Integrating e-simulations in teaching business information systems

Students’ early exposure to the fundamentals of business and Information and Communications Technologies (ICT), creation of a professional skill base, as well as, the gaining of practical experience in applying such knowledge and skills, are the determinants of success in their study and development as Information Systems (IS) professionals. This chapter argues that e-simulations, or computer-based and online simulations, can be effectively used to engage learners in interactive learning activities and provide them with real world practical experience in the safety of an educational setting. A research project is subsequently described. A suite of e-simulations were developed and deployed across two institutions to support teaching and learning of Information Systems. Using staff discussions and online surveys, quantitative and qualitative data were collected from the staff and students. The collected data were then analysed to evaluate and guide a sequence of curriculum and technology changes with a view to arriving at an optimum support model for students and teachers using the e-simulations. The findings of the study emphasise the usefulness of e-simulations to accommodate the learning styles of generation Y students, to stimulate their interest and creative thinking, and in meeting industry expectations of IS graduates’ ability to fulfil professional roles. Based on these insights, in its concluding remarks, the chapter outlines a conceptual framework for the inclusion of e-simulations in Information Systems curriculum development and teaching delivery.

Interval type-2 fuzzy logic systems for load forecasting : a comparative study

Accurate short term load forecasting (STLF) is essential for a variety of decision-making processes. However, forecasting accuracy can drop due to the presence of uncertainty in the operation of energy systems or unexpected behavior of exogenous variables. This paper proposes the application of Interval Type-2 Fuzzy Logic Systems (IT2 FLSs) for the problem of STLF. IT2 FLSs, with additional degrees of freedom, are an excellent tool for handling uncertainties and improving the prediction accuracy. Experiments conducted with real datasets show that IT2 FLS models precisely approximate future load demands with an acceptable accuracy. Furthermore, they demonstrate an encouraging degree of accuracy superior to feedforward neural networks and traditional type-1 Takagi-Sugeno-Kang (TSK) FLSs.

Optimization of gaussian fuzzy membership functions and evaluation of the monotonicity property of fuzzy inference systems

In this paper, two issues relating to modeling of a monotonicity-preserving Fuzzy Inference System (FIS) are examined. The first is on designing or tuning of Gaussian Membership Functions (MFs) for a monotonic FIS. Designing Gaussian MFs for an FIS is difficult because of its spreading and curvature characteristics. In this study, the sufficient conditions are exploited, and the procedure of designing Gaussian MFs is formulated as a constrained optimization problem. The second issue is on the testing procedure for a monotonic FIS. As such, a testing procedure for a monotonic FIS model is proposed. Applicability of the proposed approach is demonstrated with a real world industrial application, i.e., Failure Mode and Effect Analysis. The results obtained are analysis and discussed. The outcomes show that the proposed approach is useful in designing a monotonicity-preserving FIS model.

Real-time surface formation using a network of interconnected programmable actuators

This research has explored methods for developing a large interactive dynamic 3D surface using an array of interconnected pneumatically actuated cylinders. People can control the surface using body movement, sound or pre-programmed sequences. The main contribution is a method for accurately positioning cylinders without the need for position feedback.

Comparison of advanced waste heat recovery systems with a novel oil heating system

Latest trends in waste heat recovery include systems like Thermo Electric Generation (TEG), Rankine cycle, and active warm up systems. The advantages and disadvantages of different approaches are critically discussed and compared with a novel and effective oil heating system that can deliver between 7% and 12% reductions of CO2 emissions and fuel consumption. The comparison includes the expected CO2 and fuel saving potential related to the legal drive cycle as well as real world driving, effects on regulated exhaust emissions, utilisation of resources, maintenance and service, vehicle performance, comfort, noise, and durability.

An evolutionary-based similarity reasoning scheme for monotonic multi-input fuzzy inference systems

In this paper, an Evolutionary-based Similarity Reasoning (ESR) scheme for preserving the monotonicity property of the multi-input Fuzzy Inference System (FIS) is proposed. Similarity reasoning (SR) is a useful solution for undertaking the incomplete rule base problem in FIS modeling. However, SR may not be a direct solution to designing monotonic multi-input FIS models, owing to the difficulty in getting a set of monotonically-ordered conclusions. The proposed ESR scheme, which is a synthesis of evolutionary computing, sufficient conditions, and SR, provides a useful solution to modeling and preserving the monotonicity property of multi-input FIS models. A case study on Failure Mode and Effect Analysis (FMEA) is used to demonstrate the effectiveness of the proposed ESR scheme in undertaking real world problems that require the monotonicity property of FIS models.

Energy efficiency rating systems for housing : an Australian perspective

Purpose – The purpose of this paper is to examine current trends in energy efficiency ratings and consider their likely impact on the Australian housing market.

Design/methodology/approach – The research is yet to be conducted; however a mixed method is proposed which is grounded on the theoretical model of consumer behaviour within housing markets. This model has been tested and demonstrated to be useful in predicting buyer behaviour.

Findings – Established theories of buyer perception indicate the introduction of energy efficiency rating systems will have an effect on the level of house prices.

Research limitations/implications – This research is focussed on housing in Australia but also has implications for other global housing markets which are addressing sustainability.

Practical implications – Outputs of this research have implications for policy makers, real estate agents and valuers.

Social implications – Increasing energy costs is likely to restructure the way housing markets operate with regard to sustainability and energy efficiency rating systems and also how they are understood.

Originality/value – This research will be in the Australian context utilising international modeling. As mandatory energy efficiency ratings have not been introduced, this research will be original.

Why web-based pseudo relevance feedback systems fail

 We review pseudo-relevance feedback as a mechanism for expanding short texts. Where short texts exhibit evolving concepts, topics and other characteristics, Web-based feedback systems were touted as the most ideal way of enriching the feature space of short texts. However, we note from a recent implementation of a Web-based pseudo-relevance feedback that it would only perform well under clinical situations. Further improvements to address fundamental noise in Web documents did not show significant improvements leading us to conclude that relevance feedback using Web documents directly are unsuitable for real-world conditions. In this paper, we present Eddi, which is a recent system that provides an exemplar of a typical pseudo-relevance feedback system. We first show the conditions in which Eddi will work and then discuss the situations where it would fail. We then present the variations to Eddi from our attempt to improve the robustness of Eddi's algorithm when dealing with complex Web documents. We then present the results from all variations to show the lack of robustness for pseudo-relevance feedback with Web documents.

Comparison of advanced waste heat recovery systems with a novel oil heating system

Latest trends in waste heat recovery include systems like Thermo Electric Generation (TEG), Rankine cycle, and active warm up systems. The advantages and disadvantages of different approaches are critically discussed and compared with a novel and effective oil heating system that can deliver between 7% and 12% reductions of CO2 emissions and fuel consumption. The comparison includes the expected CO2 and fuel saving potential related to the legal drive cycle as well as real world driving, effects on regulated exhaust emissions, utilisation of resources, maintenance and service, vehicle performance, comfort, noise, and durability.

A new framework with similarity reasoning and monotone fuzzy rule relabeling for fuzzy inference systems

A complete and monotonically-ordered fuzzy rule base is necessary to maintain the monotonicity property of a Fuzzy Inference System (FIS). In this paper, a new monotone fuzzy rule relabeling technique to relabel a non-monotone fuzzy rule base provided by domain experts is proposed. Even though the Genetic Algorithm (GA)-based monotone fuzzy rule relabeling technique has been investigated in our previous work [7], the optimality of the approach could not be guaranteed. The new fuzzy rule relabeling technique adopts a simple brute force search, and it can produce an optimal result. We also formulate a new two-stage framework that encompasses a GA-based rule selection scheme, the optimization based-Similarity Reasoning (SR) scheme, and the proposed monotone fuzzy rule relabeling technique for preserving the monotonicity property of the FIS model. Applicability of the two-stage framework to a real world problem, i.e., failure mode and effect analysis, is further demonstrated. The results clearly demonstrate the usefulness of the proposed framework.

Development and delivery of the appropriate assessments items for power systems related PBL subjects

Problem Based Learning (PBL) as a teaching method has been around for many years. It is one of the most effective ways of learning and teaching. It uses real world problems as a learning method and encourages learners to learn independently while a mentoring support is provided by the academic. However, various academic institutions develop the assessments items in many ways; the implementation and delivery become a challenge in ensuring the right assessment and implementation. This paper discusses some of the practical experiences of the development and delivery of the appropriate assessment items for power systems related PBL subjects.

Correlation between extent of osteolytic damage and metastatic burden of human breast cancer metastasis in nude mice: Real-time PCR quantitation

Orthotopic or intracardiac injection of human breast cancer cell lines into immunocompromised mice allows study of the molecular basis of breast cancer metastasis. We have established a quantitative real-time PCR approach to analyze metastatic spread of human breast cancer cells inoculated into nude mice via these routes. We employed MDA-MB-231 human breast cancer cells genetically tagged with a bacterial β-galactosidase (Lac-Z) retroviral vector, enabling their detection by TaqMan® real-time PCR. PCR detection was linear, specific, more sensitive than conventional PCR, and could be used to directly quantitate metastatic burden in bone and soft organs. Attesting to the sensitivity and specificity of the PCR detection strategy, as few as several hundred metastatic MDA-MB-231 cells were detectable in 100 μm segments of paraffin-embedded lung tissue, and only in samples adjacent to sections that scored positive by histological detection. Moreover, the measured real-time PCR metastatic burden in the bone environment (mouse hind-limbs, n=48) displayed a high correlation to the degree of osteolytic damage observed by high resolution X-ray analysis (r 2=0.972). Such a direct linear relationship to tumor burden and bone damage substantiates the so-called `vicious cycle' hypothesis in which metastatic tumor cells promote the release of factors from the bone which continue to stimulate the tumor cells. The technique provides a useful tool for molecular and cellular analysis of human breast cancer metastasis to bone and soft organs, can easily be extended to other cell/marker/organ systems, and should also find application in preclinical assessment of anti-metastatic modalities.

Privacy preserving for tagging recommender systems

Tagging recommender systems allow Internet users to annotate resources with personalized tags. The connection among users, resources and these annotations, often called afolksonomy, permits users the freedom to explore tags, and to obtain recommendations. Releasing these tagging datasets accelerates both commercial and research work on recommender systems. However, adversaries may re-identify a user and her/his sensitivity information from the tagging dataset using a little background information. Recently, several private techniques have been proposed to address the problem, but most of them lack a strict privacy notion, and can hardly resist the number of possible attacks. This paper proposes an private releasing algorithm to perturb users' profile in a strict privacy notion, differential privacy, with the goal of preserving a user's identity in a tagging dataset. The algorithm includes three privacy preserving operations: Private Tag Clustering is used to shrink the randomized domain and Private Tag Selection is then applied to find the most suitable replacement tags for the original tags. To hide the numbers of tags, the third operation, Weight Perturbation, finally adds Lap lace noise to the weight of tags We present extensive experimental results on two real world datasets, Delicious and Bibsonomy. While the personalization algorithmis successful in both cases.

Fuel consumption- and emission analysis of exhaust heat recovery systems

The study examined waste heat recovery systems for combustion engines. Emission tests with a real vehicle showed that fuel consumption and CO2 emissions can be reduced by over 7% for the official certification drive cycle through direct heat transfer from the exhaust gas to the engine oil.